Systems, analyzers, controllers, and associated methods to enhance fluid production of refining operations

Abstract

A method includes receiving current operational data of an hydrodeoxygenation process (HDO) and a catalytic dewaxing process. Generating, by a machine learning model, a prediction of a deactivation state of catalysts within an HDO reactor and the catalytic dewaxing reactor based on the current operational data of the HDO and the catalytic dewaxing process. The machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO and catalytic dewaxing historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing process. The method further includes generating, by the machine learning model, an adjustment to a parameter of the HDO or the catalytic dewaxing process to achieve a target property of a product of the HDO or the catalytic dewaxing process.

Description

SYSTEMS, ANALYZERS, CONTROLLERS, AND ASSOCIATED METHODS TO ENHANCE FLUID PRODUCTION OF REFINING OPERATIONSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to, and the benefit of U.S. Provisional Application No. 63 / 786,014, filed April 9, 2025, titled SYSTEMS, ANALYZERS, CONTROLLERS, AND ASSOCIATED METHODS TO ENHANCE FLUID PRODUCTION OF REFINING OPERATIONS, U.S. Provisional Application No. 63 / 778,798, filed March 27, 2025, titled SYSTEMS, ANALYZERS, CONTROLLERS, AND ASSOCIATED METHODS TO ENHANCE FLUID PRODUCTION OF REFINING OPERATIONS, U.S. Patent Application No. 18 / 948,759, filed November 15, 2024, titled “SYSTEMS, ANALYZERS, CONTROLLERS, AND ASSOCIATED METHODS TO ENHANCE FLUID SEPARATION FOR DISTILLATION OPERATIONS,” U.S. Provisional Application No. 63 / 660,196, filed June 14, 2024, titled “SYSTEMS, ANALYZERS, CONTROLLERS, AND ASSOCIATED METHODS TO ENHANCE FLUID PRODUCTION OF REFINING OPERATIONS,” U.S. Provisional Application No. 63 / 658,825, filed June 11, 2024, titled “SYSTEMS, ANALYZERS, CONTROLLERS, AND ASSOCIATED METHODS TO ENHANCE FLUID PRODUCTION OF REFINING OPERATIONS,” and U.S. Provisional Application No. 63 / 655,589, filed June 3, 2024, titled “SYSTEMS, ANALYZERS, CONTROLLERS, AND ASSOCIATED METHODS TO ENHANCE FLUID PRODUCTION OF REFINING OPERATIONS,” the disclosures of which are incorporated herein by reference in their entireties.FIELD OF DISCLOSURE

[0002] The disclosure herein relates to systems, analyzers, controllers, and associated methods to enhance fluid production for refining operations and, more particularly, to systems, analyzers, controllers, and associated methods to enhance fluid production of refining operations and sub-operations using machine learning models during the refining operations and sub-operations.BACKGROUND

[0003] Many and varied operations are executed continuously and simultaneously at a refinery. Each operation affects each subsequent operation or sub-operation. For example,if the product from a first operation is produced based on maximizing a first factor, for example, the research octane number of gasoline, the production of that particular product affects further downstream operations. Additionally, further upstream operations may not be suited for production of that particular product or may, at least, cause other operations to produce that particular product in an inefficient manner. Additionally, a variety of starting feedstock are utilized at a refinery. Even further, different batches or portions of one feedstock may vary over time, for example, different portions of a feedstock may include different properties and / or compositions over time. Optimization (in other words, efficient and accurate production of targeted products) of such operations and feedstock poses a significant challenge when attempting to meet a target product objective, particularly over a period time, as equipment and materials used in the operation change over time. Such problems pose further difficulties since updating one operation affects every other operation at the refinery.

[0004] Controllers and monitoring devices may be utilized at a refinery to attempt to optimize (in other words, efficiently and accurately produce targeted products) those operations. However, those controllers and monitoring devices utilize algorithms that require expert personnel and that take extended amounts of time to execute. For example, first-principle models require expert personnel to ensure that the first-principle model is accurately calculating some formula, in other words, expert personnel are required to maintain the first-principle model. Further still, the equipment utilized at one refinery may experience a different service or maintenance cycle than equipment at another refinery. Such factors further complicate any attempt at uniform optimization at a plurality of refineries.SUMMARY

[0005] Thus, in view of the foregoing, Applicant has recognized these problems and others in the art, and has recognized a need for systems, analyzers, controllers, and associated methods for enhancing fluid production for refinery operations. Particularly, the present disclosure relates to systems, analyzers, controllers, and associated methods to enhance fluid production of refining operations and sub-operations using machine learning models during the refining operations and sub-operations. Such fluids may include hydrocarbons and / or renewable hydrocarbons and fluid production may include, for example, production of transportation fuel, among other products.

[0006] The disclosure herein provides embodiments of systems, analyzers, controllers, and associated methods for enhancing fluid production of ongoing and / or continuous refining operations, as well as refining sub-operations. Such systems, analyzers, controllers, and associated methods may include obtaining data corresponding to a refinery operation from one or more sources, such as sensors, analyzers, refining equipment, devices and / or other sources. The data, along with, in some embodiments, a target product, may then be applied to a machine learning model to produce an output indicative of or including parameters that indicate settings for the devices and / or refining equipment to be set to, to accurately achieve or produce the targeted product.

[0007] Accordingly, an embodiment of the disclosure is directed to a system for enhancing fluid production for a hydrodeoxygenation (HDO) operation. The system may include a HDO unit to receive a feed and produce one or more fluids. The system may include a plurality of sensors to measure a parameter associated with the HDO unit and each positioned at one of (a) proximate the HDO unit or (b) within the HDO unit. The system may include a plurality of refinery operation control devices each positioned proximate and downstream or upstream of the HDO unit and to control aspects of fluid flowing to or from the HDO unit. The system may include one or more sample collection assemblies to collect samples of the fluid associated with the HDO unit. The system may include one or more sample analysis assemblies to analyze each collected sample to provide properties of collected samples. The system may include a HDO controller in signal communication with the HDO unit, the plurality of sensors, the plurality of refinery operation control devices, and the one or more sample analysis assemblies, and storing a trained machine learning model. The HDO controller may be configured to (1) determine an output including predicted properties of the feedstock and parameter settings of the plurality of refinery operation control devices and the HDO unit based on application of one or more of (i) data measured by the plurality of sensors, (ii) data corresponding to analysis from the one or more sample analysis assemblies, or (iii) a target product and corresponding target properties to the trained machine learning model, and (2) adjust one or more of the amount of feed or type of feed and parameters associated with the plurality of refinery operation control devices and HDO unit based on the output to enhance fluid production.

[0008] An embodiment includes a method including receiving product data indicative of a property of a product of an hydrodeoxygenation process (HDO) and receiving feedstock data indicative of a feedstock for processing by the HDO. A machine learning model generates an adjustment to a parameter of the HDO based on the feedstock data and theproduct data to increase product yield from the HDO, wherein the machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO and the adjustment to the parameter of the HDO is implemented.

[0009] In another embodiment, a method includes receiving current operational data of an HDO and generating, by a machine learning model, a prediction of a deactivation state of catalysts within an HDO reactor based on the current operational data of the HDO. The machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO. The method further includes generating, by the machine learning model, an adjustment to a feed rate of a feedstock into the HDO reactor to achieve a target property of an HDO product based on the prediction of the deactivation state of catalysts within the HDO reactor.

[0010] In yet another embodiment, a method includes receiving current operational data of an HDO and HDO product data indicative of a property of an HDO product and generating, by a machine learning model, an adjustment to an operating parameter of an HDO reactor to achieve a target property of the HDO product based on the current operational data of the HDO reactor and the HDO product data. The machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO. The method further includes generating, by the machine learning model, a prediction of effects of the adjustment to the operating parameters of an HDO reactor based on the current operational data of the HDO reactor and the HDO product data.

[0011] Another embodiment includes a method including receiving current operational data of a catalytic dewaxing system and generating, by a machine learning model, a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system. The machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system. The method further includes generating, by the machine learning model, an adjustment to a feed rate of a feedstock into the catalytic dewaxing system to achieve a target property of a catalytic dewaxing product based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system and implementing the adjustment to the feed rate of the feedstock into the catalytic dewaxing system.

[0012] In one embodiment, a method includes receiving current operational data of a catalytic dewaxing system and receiving feedstock data indicative of a property of afeedstock being sent to a catalytic dewaxing reactor. The method further includes receiving catalytic dewaxing product data indicative of a property of a catalytic dewaxing product and generating, by a machine learning model, an adjustment to an operating parameter of the catalytic dewaxing system to achieve a target property of the catalytic dewaxing product based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data. The machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system. The method also includes generating, by a machine learning model, a prediction of effects of the adjustment to the operating parameters of the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data.

[0013] In one other embodiment, a method includes receiving current operational data of an hydrodeoxygenation process (HDO) and generating, by a machine learning model, a prediction of a deactivation state of catalysts within an HDO reactor based on the current operational data of the HDO. The machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO. The method also includes generating, by the machine learning model, an adjustment to a parameter of the HDO to achieve a target property of an HDO product based on the prediction of the deactivation state of catalysts within the HDO reactor.

[0014] In another embodiment, a method includes receiving current operational data of a catalytic dewaxing system and generating, by a machine learning model, a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system. The machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system. The method includes generating, by the machine learning model, an adjustment to a parameter of the catalytic dewaxing system to achieve a target property of a catalytic dewaxing product based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system and implementing the adjustment to the parameter of the catalytic dewaxing system.

[0015] Still other aspects and advantages of these embodiments and other embodiments, are discussed in detail herein. Moreover, it is to be understood that both the foregoing information and the following detailed description provide merely illustrative examples of various aspects and embodiments, and are intended to provide an overview or frameworkfor understanding the nature and character of the claimed aspects and embodiments. Accordingly, these and other objects, along with advantages and features of the present disclosure herein disclosed, will become apparent through reference to the following description and the accompanying drawings. Furthermore, it is to be understood that the features of the various embodiments described herein are not mutually exclusive and may exist in various combinations and permutations.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] These and other features, aspects, and advantages of the disclosure will become better understood with regard to the following descriptions, claims, and accompanying drawings. It is to be noted, however, that the drawings illustrate only several embodiments of the disclosure and, therefore, are not to be considered limiting of the scope of the disclosure.

[0017] FIG. 1A and FIG. IB are simplified diagrams of a refining control system to enhance fluid production at a refinery, according to an embodiment of the disclosure.

[0018] FIG. 2 is a simplified diagram that illustrates an apparatus for enhance fluid production at a refinery, according to an embodiment of the disclosure.

[0019] FIG. 3 is another simplified diagrams of a refining control system to enhance fluid production at a refinery, according to an embodiment of the disclosure.

[0020] FIG. 4 is a simplified diagram that illustrates training of a machine learning model for enhanced fluid production at refinery, according to an embodiment of the disclosure.

[0021] FIG. 5 is a schematic diagram of an enhanced steam methane reformer control system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure.

[0022] FIG. 6 is a schematic diagram of a steam control system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure.

[0023] FIG. 7 is a schematic diagram of hydrogen control system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure.

[0024] FIG. 8 is a schematic diagram of a feed control system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure.

[0025] FIG. 9 is a schematic diagram of a diesel pool control system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure.

[0026] FIG. 10 is a schematic diagram of a process to convert renewable oils and greases into renewable diesel and renewable naphtha.

[0027] FIG. 11 is a schematic diagram of a hydrodeoxygenation control system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure.

[0028] FIG. 12 is a schematic diagram of a catalytic dewaxing system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure.

[0029] FIG. 13 A and FIG. 13B are simplified diagrams of control systems to enhance to enhance fluid production at refinery, according to an embodiment of the disclosure.

[0030] FIG. 14 is a flow chart illustrating enhanced fluid production at a refinery, according to an embodiment of the disclosure.

[0031] FIG. 15 is a schematic diagram of a machine learning model that may be used with the various processes within a refinery.

[0032] FIG. 16 is a flow chart of a method, according to at least one embodiment of the present disclosure.

[0033] FIG. 17 is a flow chart of another method according to one embodiment, according to at least one embodiment of the present disclosure.

[0034] FIG. 18 is flow chart of yet another method, according to at least one embodiment of the present disclosure.

[0035] FIG. 19 is a flow chart of a method, according to at least one embodiment of the present disclosure.

[0036] FIG. 20 is a flow chart of another method according to one embodiment, according to at least one embodiment of the present disclosure.

[0037] FIG. 21 is flow chart of yet another method, according to at least one embodiment of the present disclosure.DETAILED DESCRIPTION

[0038] So that the manner in which the features and advantages of the embodiments of the systems and methods disclosed herein, as well as others that will become apparent, may be understood in more detail, a more particular description of embodiments of systems and methods briefly summarized above may be had by reference to the following detailed description of embodiments thereof, in which one or more are further illustrated in the appended drawings, which form a part of this specification. It is to be noted, however, that the drawings illustrate only various embodiments of the systems and methods disclosed herein and are therefore not to be considered limiting of the scope of the systems and methods disclosed herein as it may include other effective embodiments as well.

[0039] The disclosure herein provides embodiments of systems, analyzers, controllers, and associated methods for enhancing fluid production of ongoing and / or continuous refining operations, as well as refining sub-operations. Such systems, analyzers, controllers, and associated methods may include obtaining data corresponding to a refinery operation from one or more sources, such as sensors, analyzers, refining equipment, refining operation control devices, other devices, and / or other sources. The data, along with, in some embodiments, a target product, may then be applied to a machine learning model to produce an output indicative of or including parameters that indicate settings for the refining operation control devices and / or refining equipment to be set to, to accurately achieve or produce the targeted product. Such an application of data to a trained machine learning model may occur at one or more different layers in the control system of the refinery. Further, a plurality of controllers positioned throughout the refinery may each include a plurality of trained machine learning models that are trained to enhance fluid production of one or more refinery operations or sub-operations.

[0040] In embodiments, the refining operations and / or sub-operations may include the processing, converting, refining, enhancing and / or otherwise altering a fluid via the refining operation or sub-operation. The fluid may be a liquid, vapor, and / or gas and include a hydrocarbon and final product may include a transportation fuel. “Hydrocarbons” or “hydrocarbon fluids” as used herein, may refer to petroleum fluids, renewable fluids, and other hydrocarbon based fluids. “Petroleum fluids” as used herein, may refer to fluid products containing crude oil, petroleum products, natural gas, renewable liquids and / or gasses, and / or distillates or refinery intermediates. For example, crude oil contains a combination of hydrocarbons having different boiling points that exists as a viscous liquid in underground geological formations and at the surface. Petroleum products, for example, may be produced by processing crude oil and other liquids at petroleum refineries, by extracting liquid hydrocarbons at natural gas processing plants, and by producing finished petroleum products at industrial facilities. For example, a petroleum product may include a transportation fuel, among other products. Refinery intermediates, for example, may refer to any refinery hydrocarbon that is not crude oil or a finished petroleum product (such as gasoline), including all refinery output from distillation (for example, distillates or distillation fractions) or from other conversion units. In some non-limiting embodiments of systems and methods, petroleum fluids may include heavy blend crude oil used at a pipeline origination station, natural gas, and / or other types of crude oil, as will be understood by one skilled in the art. Heavy blend crude oil is typically characterized as having anAmerican Petroleum Institute (API) gravity of about 30 degrees or below. In other embodiments, the petroleum fluids may include lighter blend crude oils, for example, having an API gravity of greater than 30 degrees. “Renewable fluids” as used herein, may refer to fluid products containing plant and / or animal derived feedstock. Further, the renewable fluids may be hydrocarbon based. For example, a renewable fluid may be a pyrolysis oil, oleaginous feedstock, biomass derived feedstock, renewable natural gas or other liquids or gasses, as will be understood by those skilled in the art. The API gravity of renewable liquids may vary depending on the type of renewable liquid.

[0041] As used herein “profit” is the potential financial gain from a potential sale of products from a refinery operation less the costs of feedstocks and operational costs of the refinery operation. The potential value of a sale may be based on current market prices for similar products or current prices paid by contracted customers. Costs of feedstocks may include market prices for similar feedstocks as well as the operational costs of upstream processes preparing the feedstocks for use in the refinery operation. Operational costs may include equipment depreciation, employee wages and benefits, and consumables used to process the feedstocks, such as hydrogen, water, catalysts, steam, natural gas, cooling, electricity, and other utilities. For example, a machine learning model may generate adjustments to one or more operational parameters of a refinery process based on the potential profit from the products it produces. A potential profit may be determined from a generated algorithm or generated simulations based on current market pricing and potential ranges of production rates and the corresponding variable production costs. To continue the example, product A may sell for $2 a barrel and may be produced at a rate ranging from 50 barrels a day to 100 barrels a day. Product B may sell for $4 a barrel and be produced at a rate ranging from 10 barrels a day to 20 barrels a day. However, the more product B produced results in less product A being produced, increased utility costs, and accelerated deactivation of the catalysts of the refinery process. All of these variables and more may be managed by the machine learning model to increase the profitability of the refinery process.

[0042] In an embodiment, the systems and methods may include a computing device, apparatus and / or controller (referred to hereafter as a controller) to obtain various data points and / or parameters to train a machine learning model. Such data may include a historical data set and / or a currently generated data set including an outcome. As used in this application, “current” means happening now or recently enough to still be relevant to an ongoing process. For example, a current sample data may have been generated from asample obtained several hours ago from a feedstock that is still being processed by a refinery process. In another embodiment, the data set may include a simulated and / or filled- in data set. For example, a refinery may be modeled based on a first-principle model and synthetic or pseudo-data may be generated for a selected time interval (for example, 1 month, 2 months, 6 months, 1 year, or even longer). For such a data set, random perturbations and / or anomalies are used to simulate a data set. In another embodiment, the data may include a partial data set. In such an embodiment, the partial data set, may be filled in via a first principle model and / or a machine learning model. Each data set may include a series of parameters, properties, spectra, and / or other data points associated with a refining operation or process or sub-operation or sub-process. Each data set may also include target parameters, target properties, and / or an outcome and / or target product. Further, in an embodiment where a supervised machine learning model is utilized, each outcome may be classified as a positive or negative outcome or marked in a manner to indicate desirability of the outcome. In other embodiments, the function generated by a set of data may indicate a desired outcome, based on a maximum or minimum point in that function, thus enabling a machine learning model to determine desired parameters based on that maximum or minimum, or based on some other factor in other embodiments. In yet another embodiment, a trained machine learning model may learn or be trained based on trends included in the data (in other words, the trained machine learning model may comprise a deep learning model). In another embodiment, any of the trained machine learning models described herein may predict and / or optimize target parameters and / or fluids used within a refinery, refinery operation, or refinery sub-operation.

[0043] Once these data sets have been received by the controller, the controller may pre- process the data. For example, the controller may normalize the data (in other words, remove data points that appear to be outliers), remove data corresponding to abnormal events (for example, data generated during start-up, shut-down, turn-arounds, and / or upsets), remove undesired data, remove invalid measurements, and / or segregate the data set into sequences of contiguous data based on selected time intervals (for example, time intervals of 30 minutes, 1 hour, 2 hours, and / or 3 hours, or more or less than the time intervals listed).

[0044] Once the data has been pre-processed, the controller may begin training a model based on a portion of the data set. For example, the controller may utilize an 80 / 20 training and testing process. Other percentages may be utilized in training, testing, and / or validation. As the model is fed data, the model may compare data received to the outcome(in other words, whether the outcome was desired based on some factor, such as an indicated positive / negative flag or classification, based on some maximum or minimum of a function generated based on the data, or based on a trend within the data). Once the training portion of data has been utilized, the controller may test and / or validate the model using the remining portion of the data set. If such testing or validation does not achieve a selected error rate or achieve some other error and / or accuracy based threshold, then the controller may re-train or refine the model using a different and / or randomized portion of the data set and a remaining portion of the data set for testing. Once a model has achieved that threshold, then the controller may output the trained machine learning model for further use.

[0045] Further, a trained machine learning model may be further refined using new data, as such data is generated. Such a refinement may occur while the trained machine learning model is in use. In another embodiment, the trained machine learning model may be refined in an offline environment. In another embodiment, two instances of a trained machine learning model may exist, one stored as an offline copy, while the other is utilized during refining operations. In such an embodiment, the offline copy may be refined and, if testing and / or error rating meets a selected threshold, in addition to other factors, then a controller may replace the version currently utilized to the refined version.

[0046] It will be understood that such systems and methods described herein may utilize a number of trained machine learning models. For example, a model may be trained for each specific operation or process, as well as each particular piece of equipment, at a refinery, such as fluid catalytic cracking (FCC) operations or processes, hydrocracking operations or processes, reforming operations or processes, alkylation operations or processes, isomerization operations or processes, hydrotreating operations or processes, distillation operations or processes, blending operations or processes, hydrodeoxygenation operations or processes, steam management, hydrogen coordination or management, absorption, propylene splitting operations or processes, aromatic recovery, sulfur recovery, coker unit operations, feed optimization, IMO blending, hydrodeoxygenation, hydrocracker operations, other blending operations, Residuum Oil Supercritical Extraction operation, solvent deasphalting (SDA) operation, operations or processes for formation of specific fuels, and / or the refining operation or process overall (which may, in an embodiment, utilize outputs from models associated with each sub-operation or sub-process). The use of terms operation and process refers to the steps taken to produce a particular product from a selected feedstock (and, in some embodiments, other inputs). As such, when referring to aparticular refining operation or process, the terms “operation” and “process” may be used interchangeably. Further, such models may be trained specifically for equipment at a particular plant or refinery. For example, a FCC unit at a first plant may exhibit different characteristics than that of a FCC unit at a second plant. Thus, a model trained for one may not work for the other and training a model for either FCC unit may include utilization of historical data corresponding to that FCC unit. Various aspects of one model may be utilized to train other models for other similar equipment though.

[0047] Once a model is available, the controller, one or more sub-operation controllers or sub-controllers, and / or one or more operation controllers including a local enhancement or optimization module or circuitry, predictive controls, and / or equipment and device controls may begin optimizing, enhancing, and / or adjusting an operation and / or parameters associated with that operation, in real-time, near real-time, and / or continuously or substantially continuously, at a refinery. In such embodiments, the controller may obtain data from a plurality of sensors, a plurality of refining operation control devices (such as flow control devices, temperature control devices, pressure control devices, and / or other device configured to control an aspect of a refining operation), equipment at the refinery (in other words, refining equipment), and / or one or more sample analyzers and / or, in some embodiments, one or more sub-operation controllers or sub-controllers. In another embodiment, one or more operation controllers may obtain such data, as well as target products and / or other factors or parameters from a refinery controller or platform.

[0048] As noted, one input to any of the models described herein may include spectra or properties of feedstock, intermediaries, products or outputs, and / or other fluids or materials utilized in a refinery, determined via one or more of a spectrographic analyzer or a chromatographic analyzer. The controller or controllers may work in conjunction with such an analyzer to further enhance fluid production (for example, transportation fuel, hydrocarbon based fluid products, and / or other fluids produced during a refining operation) of the refining operation or sub-operation. As such, spectrographic analyzers may be calibrated or standardized and results may be obtained in a faster than typical timeframe, thus enabling prompt acquisition of fluid properties. For example, for any operation described herein, the controller may obtain spectrographic or chromatographic analysis of any feedstock utilized, any intermediaries produced, and / or any products produced by first initiating sample collection. Once a sample has been obtained, the controller and / or an analyzer may initiate analysis of the sample.

[0049] Once the controller or controllers has / have obtained data related to each operation and / or analysis of one or more fluids associated with the operation, then the controller may apply such data and analysis to a corresponding machine learning model. The output of the model may indicate adjustment of one or more devices or refining operation control devices and / or refining equipment and / or adjustment of a feedstock or intermediary used in the operation or sub-operation. In some embodiments, the output may include targets and / or properties for a feedstock and / or blend of feedstock. In another embodiment, the output may be in the form of a vector, each component of the vector corresponding to a value associated with a parameter of equipment or a device or refining operation control device. In an embodiment, a refining operation control device may comprise or include a temperature control device (such as a furnace, heat exchanger, condenser, boiler, induction coil, fans, a cooling device, and / or other device capable of adjusting the temperature of a fluid and / or the temperature within refining equipment), a flow control device (such as a pump, valve, control valve, and / or other device capable of adjusting the flow rate of a fluid), a pressure control device (such as a compressor, a pump, a let-down station or valve, and / or another device configured to adjust the pressure of a fluid), and / or other devices configured to adjust some aspect of a fluid and / or aspect of refining equipment.

[0050] Once the controller has the output of the model, the controller may adjust the relevant aspects of the refining operation. For example, the controller may adjust components of a blend utilized in a feedstock, settings for various refining operation control devices (such as temperature, pressure, flow rate, and / or another aspect associated with a fluid and / or device), use of hydrogen, recovery of selected fluids or materials, and / or use of other fluids or materials (for example, a catalyst), among other adjustments.

[0051] In another embodiment, the controller may optimize an operation based on the current demand for selected products. For example, for a particular targeted product, selected amounts of feed and / or intermediaries may be utilized, increasing the demand for that feed and / or intermediaries. In other embodiments, demand may be a factor utilized in training a model. For example, a selected product may experience increased demand at varying times or a particular feedstock, used to produce a particular product, may be in high demand. Data indicating such demand may be utilized in the described trained learning models.

[0052] In yet another embodiment, the controller may compare the output of the model to the current properties for a selected operation. Based on that difference of such a comparison, the controller may adjust various aspects of that operation.

[0053] By utilizing the trained machine learning models, the systems and methods described herein may determine specific adjustments to a plurality of operations and parameters specific to equipment at a refinery to accurately and more frequently (as compared to typical adjustment times) achieve a target product. Further, such adjustments may increase efficiency of the refinery equipment and / or reduce energy utilized by the refinery equipment, thus reducing cost of the refinery operation. The target product may be based on a number of factors, such as demand and / or price or cost for the product, cost of the product and / or feedstock, and / or based on a target product provided by a refinery controller or platform. Such adjustments may be determined in real-time or near real-time using data from continuous and / or ongoing refinery operations.

[0054] Thus, rather than attempting to adjust operations at a significant delay, a refinery’s operations may be adjusted in-real time, near real-time, or at time intervals shorter than in typical optimization operations (such typical optimization operations including operations by operating personnel to efficiently and accurately produce a target product). Further, such adjustments may be determined faster than typical adjustments to operations, leading to relevant and timely adjustments. Further, such analysis and adjustment utilizes complex non-linear equations which typically take longer to analyze, however with the use of machine learning, such analysis occurs significantly faster and with comparable accuracy.

[0055] FIG. 1 A and FIG. IB simplified diagrams of a refining control system to enhance fluid production at a refinery, according to an embodiment of the disclosure. As illustrated in FIG. 1A, a refinery 100 may include various refining control operation devices and refining equipment. While selected equipment are illustrated in FIG. 1A, it will be understood by those skilled in the art that additional and / or different equipment may be included in or at a refinery 100, particularly based on the type of feedstock processed at the refinery. For example, the refinery 100 may include a desalter, blending tanks, storage tanks, and / or wastewater treatment units, among other equipment. Further, each unit or equipment at the refinery 100 may be optimized using the machine learning models disclosed herein, using data specific to the equipment from the refinery 100. In other words, the equipment may be operated such that the corresponding refinery operations produce an accurate and / or on-specification target product.

[0056] As illustrated in FIG. 1A, a refinery 100 may include a refinery controller 101 and / or a plurality of operation controllers 102. As will be illustrated in subsequent drawings, additional components may be included, such as a refining enhancer, other circuitry, various other controllers, and / or other computing devices. In an embodiment, therefinery controller 101 and / or the plurality of operation controllers 102 may include, for example, a trained machine learning model, as well as other instructions to adjust various devices and / or operations or processes within the refinery 100. The refinery controller 101 and / or the plurality of operation controllers 102 may connect to or be in signal communication with (a) one or more sensors, meters, transducers, and / or other measurement devices positioned throughout the refinery 100 and / or (b) to the equipment (for example, connected to some control aspect or device associated with the equipment) positioned at the refinery 100. The refinery controller 101 may be configured to receive data via such a connection. Further, the refinery controller 101 may receive such data in real-time or near real-time. In an embodiment, the refinery controller 101 may determine a target product and / or other parameters for a selected period of time. The refinery controller 101 may provide such data to each of the operation controllers 102. In other embodiments, the refinery controller 101 may utilize outputs from each of the operation controllers 102 to determine parameters for a target product. In other embodiments, the refinery controller 101 may apply those outputs, as well as other data, to a machine learning model.

[0057] In another embodiment, each of the operation controllers 102 may include a local enhancement circuitry 184, predictive controls circuitry 194, 191, and / or 195, and / or equipment and device controls 199. In embodiments, the circuitry may be a module or instructions. In embodiments, the operation controller 102 may include one or more varying or different predictive controls. For example, as illustrated, one or more of the predictive controls circuitry 191 may include a trained machine learning model 193. The trained machine learning model 193 may be trained for a specific operation and / or piece of equipment and, in some embodiments, may be trained to recognize an adjustment, maximization, and / or optimization for specified factors of the specific operation and / or piece of equipment. As such, the operation controller 102 may include a plurality of predictive control circuitry 191. The operation controls may also include predictive control circuitry 194, which includes a trained machine learning model 196 and / or a first-principle model (and / or, in some embodiments, another type of model). The trained machine learning model 196 may be trained to fill missing data for the first-principle model 198. The operation controller may also include predictive control circuitry 195, which may include a first-principle model 197. The first-principle model 197 may be a model using a known, physics based equation or formulation.

[0058] In an embodiment, the operation controller 102 may include a local enhancement circuitry 184. The local enhancement circuitry 184 may include a trained machine learningmodel 190 and target setpoint instructions 192. The trained machine learning model 190 may utilize data associated with a specific refining operation and / or the output from each predictive controls circuitry to produce an output. The target setpoint instructions may utilize the output of the trained machine learning model 190 to determine a set of parameters that equipment and / or devices associated with a specific refining operation should be set to, to achieve a target product. The operation controller may also include the equipment and device controls 199. The equipment and device controls 199 may cause equipment and / or devices to adjust to the target setpoints.

[0059] As illustrated in Fig. IB, the operation controllers 102 may further be connected to or in signal communication with a sample collection assembly 186 and / or a sample analysis assembly or sample analyzer 188. The sample analyzers 188 may include spectrographic analyzers, standardized spectrographic analyzers, and / or chromatographic analyzers. The type of spectrographic analyzers utilized may include one or more of near-infrared spectroscopic analyzer, a mid-infrared spectroscopic analyzer, a combination of a nearinfrared spectroscopic analyzer and a mid-infrared spectroscopic analyzer, a Raman spectroscopic analyzer, or a nuclear magnetic resonance spectroscopic analyzer. The sample analyzer 188 may analyze received samples and provide corresponding spectra indicating properties or other analysis indicating components and / or properties of the sample. The operation controllers 102 may also be connected to one or more sub-controllers or sub-operation controllers that are positioned or configured to manage selected aspects or operations of the refinery 100.

[0060] The refinery controller 101 and / or operation controllers 102 may include a processor and a memory or non-transitory machine-readable storage medium storing instructions executable by the processor (as illustrated in subsequent drawings). In some examples, the refinery controller 101 and / or the operation controller 102 may be a computing device. The term “computing device” is used herein to refer to any one or all of programmable logic controllers (PLCs), distributed control systems (DCSs), a proportional integral derivative (PID) controller, a DCS-PID controller, programmable automation controllers (PACs), industrial computers, servers, virtual computing device or environment, desktop computers, personal data assistants (PDAs), laptop computers, tablet computers, smart books, palm-top computers, personal computers, smartphones, virtual computing devices, cloud based computing devices, and similar electronic devices equipped with at least a processor and any other physical components necessarily toperform the various operations described herein. Devices such as smartphones, laptop computers, and tablet computers are generally collectively referred to as mobile devices.

[0061] The term “server” or “server device” is used to refer to any computing device capable of functioning as a server, such as a master exchange server, web server, mail server, document server, or any other type of server. A server may be a dedicated computing device or a server module (e.g., an application) hosted by a computing device that causes the computing device to operate as a server. A server module (e.g., server application) may be a full function server module, or a light or secondary server module (e.g., light or secondary server application) that is configured to provide synchronization services among the dynamic databases on computing devices. A light server or secondary server may be a slimmed-down version of server type functionality that can be implemented on a computing device, such as a smart phone, thereby enabling it to function as an Internet server (e.g., an enterprise e-mail server) only to the extent necessary to provide the functionality described herein.

[0062] As used herein, a “non-transitory machine-readable storage medium” or “memory” may be any electronic, magnetic, optical, or other physical storage apparatus to contain or store information such as executable instructions, data, and the like. For example, any machine-readable storage medium described herein may be any of random access memory (RAM), volatile memory, non-volatile memory, flash memory, a storage drive (e.g., hard drive), a solid state drive, any type of storage disc, and the like, or a combination thereof. The memory may store or include instructions executable by the processor.

[0063] As used herein, a “processor” or “processing circuitry” may include, for example one processor or multiple processors included in a single device or distributed across multiple computing devices. The processor (such as, processing circuitry 202 shown in FIG. 2 and / or a processor included in, for example, refinery controller 101 and / or the operation controllers 102 (not illustrated)) may be at least one of a central processing unit (CPU), a semiconductor-based microprocessor, a graphics processing unit (GPU), a field- programmable gate array (FPGA) to retrieve and execute instructions, a real time processor (RTP), other electronic circuitry suitable for the retrieval and execution instructions stored on a machine-readable storage medium, or a combination thereof.

[0064] Turning to the equipment positioned at the refinery 100, the refinery 100 may include a reactor 104 and, in some embodiments, a regenerator 120. The reactor 104 may be a catalytic reactor and / or a fluid catalytic cracking unit or reactor. The reactor 104 may include one or more sensors or meters positioned within the reactor 104 (such as sensor ormeter 108) and / or proximate the reactor (such as sensors or meters 112, 114, 110, and 144). These sensor or meters may measure some aspect of fluid or material where the sensor or meter is positioned. Further, the reactor 104 may receive feedstock and / or an amount of water / steam at one or more locations of the reactor 104. The reactor 104 may be positioned or configured to convert heavy gas oil, residua, and / or other gas oil blends to an effluent or cracked fluid including smaller molecules, the effluent, in some embodiments, being further separated downstream into different products via distillation or fractionation. The reactor 104 may be operated at or it may be beneficial to operate the reactor 104 at a selected temperature range and / or pressure range to reduce over-cracking, which may cause a loss in valuable products, as well as to reduce over-all energy usage. Further, the amount of catalyst within and / or being fed to (for example, from the regenerator 120 and / or as fresh catalyst) the reactor 104 may impact the value of product produced by the reactor 104. Further, as a catalyst is regenerated within the regenerator 120, degradation may occur and / or coke deposited on the catalyst may not be completely burned off, particularly after multiple uses, thus further impacting product from the reactor 104. The feedstock fed to the reactor 104 may also affect parameters, such as temperature and residence time, among other parameters. Thus, several factors and / or parameters may impact the product produced by a reactor 104, those factors and / or parameters including temperature, pressure, type of catalyst, quality of catalyst, amount of catalyst, flow rate of feed or feed stock and / or catalyst, amount and temperature of steam injected, and / or properties of feed or feedstock, in addition to the product desired or targeted. The refinery controller 101 and / or operation controllers 102 may gather data related to such factors over time and apply that data, along with, in some embodiments, spectra provided from the sample analyzer 188, to trained machine learning models to produce parameters that enable production of a target product via a minimal amount of energy and / or lowest cost. Such application of data to a model may occur within various modules or circuits of one or more of the operation controllers 102 and / or, in addition to other data generated within the refinery 100, within the refinery controller 101. For example, one trained machine learning model within the predictive controls module 191 may be trained to maximize or be utilized for maximizing operating temperature in relation feedstock and a threshold temperature that may cause overcracking. In another example, another trained machine learning model may be trained to adjust or be utilized for adjusting heater temperature and / or temperature within the reactor in relation to feedstock and process parameters, composition, and / or other aspects to maximize yield or economically optimize yield from the reactor 104. In anotherembodiment, the trained machine learning model may be trained to adjust or be utilized for adjusting one or more refinery operation control devices to produce a selected yield of a product that also maximizes profit. Such an application of data to such a model may generate a vector that includes parameters or parameter settings corresponding to devices and / or equipment associated with the reactor 104 and, in some embodiments, the regenerator 120. That vector may be utilized by the local enhancement model to further determine, based on the outputs from other models as well as gathered data, parameters or parameter settings that enhance production of effluent from the reactor 104, such parameters or parameter settings being applied to actual equipment and / or devices via the equipment and device controller. Other models may be trained to determine parameters based on other relationships associated with the reactor 104 and / or other equipment.

[0065] In an embodiment, a trained machine learning model may be utilized by predictive controls (which may also be referred to as a prediction model) to optimize targets and / or properties and / or the predictive controls may be utilized by a an online optimization algorithm (which may also be referred to as the local enhancement module) to generate or determine targets.

[0066] The refinery 100 may include a regenerator 120. While a reactor 104 with a side- by-side configuration is illustrated in FIG. 1A, it will be understood that other configurations may be utilized, such as a stacked configuration. In an embodiment, in addition to reactor data and corresponding fluid properties, refinery controller 101 and / or operation controllers 102 may obtain data and fluid properties corresponding to the regenerator 120. For example, the refinery controller 101 and / or operation controllers 102 may obtain data from sensors or meters 116, 124, 126, 128, 132, 134, and 138, flow control devices associated with the regenerator 120 (such as valves 118, 130, 136, and 142), and / or the regenerator 120, as well as properties or spectra associated with spent catalyst, regenerated catalyst, a feed or feedstock (for example, to aid in catalyst regeneration), and / or air (which may include pure oxygen or some combination of oxygen and other elements). Data may be obtained from other devices, such as flow control devices (such as, valves and / or pumps, among other devices configured to control flow of a fluid) and / or temperature control devices (such as boilers, heat exchangers, heating coils, condensers, and / or other heating or cooling devices). In an embodiment, the regenerator 120 may be positioned or configured to burn coke off of spent catalyst, the coke being deposited onto the catalyst in the reactor 104. The regenerator 120 may then provide the regenerated catalyst back to the reactor 104. In embodiments, the refinery controller 101 and / oroperation controllers 102 may apply the data from the regenerator 120 to produce parameters or parameter settings to adjust corresponding equipment or devices to. The trained machine learning models may be trained or be utilized to determine parameters to maximize the amount of carbon build up burned from catalyst, to increase temperature within the reactor 104 (for example, via heat from regenerated catalyst), and / or to minimize the amount of resources utilized by the regenerator 120.

[0067] Other equipment may be positioned throughout the refinery 100 and the refinery controller 101 and / or operation controllers 102 may connect to such equipment. The refinery controller 101 and / or operation controllers 102 may obtain or gather data related to that equipment during the refining operation. For example, the refinery controller 101 and / or operation controllers 102 may obtain data from and / or related to a fractionation column 148 or distillation column. Further, the refinery 100 may include and the refinery controller 101 and / or operation controllers 102 and / or one or more of the sub-controllers 190 may obtain data from a hydrotreater (such as hydrotreater 166 and hydrotreater 174) and / or an alkylation unit 158. The refinery controller 101 and / or operation controllers 102 may obtain data from the valve 146, sensors or meters 150, 154, 160, 164, 168, 172, 178, and 180, as well as the properties associated with the products from the fractionation column 148 (for example, off gas 152, LPG 156, alkylate 162, gasoline 170, diesel 176, slurry 182, and / or other products), the hydrotreater 166 (for example, gasoline or high- octane gasoline), the hydrotreater 174 (for example, diesel, low-sulfur diesel, and / or higher purity diesel), and / or the alkylation unit 158 (for example, alkylate).

[0068] In an embodiment, the refinery controller 101 and / or operation controllers 102 may obtain data in real time, near real-time, and / or continuously or substantially continuously. In another embodiment, the refinery controller 101 and / or operation controllers 102 may obtain data periodically. In another embodiment, the refinery controller 101 and / or operation controllers 102 may apply data to a trained machine learning model at a selected time interval. Such an interval may be based on the time for samples from various positions within the refinery 100 to be collected and then analyzed. In yet another embodiment, each of the operation controllers 102 may obtain data related to a selected section of the refinery 100. Each of the operation controllers 102 may also obtain properties and / or spectra from the sample analyzer 188. After the operation controllers 102 obtain the properties and / or spectra and data, then the operation controllers 102 may apply the properties and / or spectra and data to a corresponding predictive controls module to produce parameters to produce a target product. The local enhancement module of the operation controller may then apply,to a trained machine learning model of the local enhancement module, the output of each of the predictive controls module, the data obtained throughout the refinery 100, and / or the properties and / or each spectra associated with a collected sample. Such an application may produce an enhanced or optimized set of parameters, which may then be applied to equipment or devices via the equipment and devices controls.

[0069] In yet another embodiment, the refinery controller 101 may first obtain data and the properties and / or spectra and then apply the data and the properties and / or spectra to a trained machine learning model. The refinery controller 101 may transmit the output of the trained machine learning model to each operation controller 102. In another embodiment, the refinery controller 101 may provide target products and corresponding parameters to each of the operation controllers 102, based on user input, previously utilized parameters, current cost of a target product and / or feedstock, and / or other factors. In another embodiment, the refinery controller 102 may facilitate communication between each of the operation controllers 102, facilitate data acquisition for the operation controllers 102, and / or facilitate parameter prediction and / or adjustment among the plurality of operation controllers 102. For example, if one operation controller adjusts a process to meet a selected target, that adjustment may affect upstream and / or downstream processes. The refinery controller 101 may facilitate communication and / or perform additional predictions to ensure that such parameter adjustments enable the upstream and / or downstream processes to continue to produce target products.

[0070] FIG. 2 is a simplified diagram that illustrates an apparatus for enhance fluid production at a refinery, according to an embodiment of the disclosure. Such an apparatus 200 may be comprised of a processing circuitry 202, a memory 204, a communications circuitry 206, a modeling circuitry 208, a fluid adjustment circuitry 210, and an equipment and device adjustment circuitry 212, each of which will be described in greater detail below. While the various components are illustrated in FIG. 2 as being connected with processing circuitry 202, it will be understood that the apparatus 200 may further comprise a bus (not expressly shown in FIG. 2) for passing information amongst any combination of the various components of the apparatus 200. The apparatus 200 may be configured to execute various operations described herein, such as those described above in connection with FIGS. 1A-1B and below in connection with FIGS. 3-15.

[0071] The processing circuitry 202 (and / or co-processor or any other processor assisting or otherwise associated with the processor) may be in communication with the memory 204 via a bus for passing information amongst components of the apparatus. The processingcircuitry 202 may be embodied in a number of unusual ways and may, for example, include one or more processing devices configured to perform independently. Furthermore, the processor may include one or more processors configured in tandem via a bus to enable independent execution of software instructions, pipelining, and / or multithreading.

[0072] The processing circuitry 202 may be configured to execute software instructions stored in the memory 204 or otherwise accessible to the processing circuitry 202 (e.g., software instructions stored on a separate storage device). In some cases, the processing circuitry 202 may be configured to execute hard-coded functionality. As such, whether configured by hardware or software methods, or by a combination of hardware with software, the processing circuitry 202 represents an entity (for example, physically embodied in circuitry) capable of performing operations according to various embodiments of the present disclosure while configured accordingly. Alternatively, as another example, when the processing circuitry 202 is embodied as an executor of software instructions, the software instructions may specifically configure the processing circuitry 202 to perform the algorithms and / or operations described herein when the software instructions are executed.

[0073] Memory 204 is non-transitory and may include, for example, one or more volatile and / or non-volatile memories. In other words, for example, the memory 204 may be an electronic storage device (for example, a computer readable storage medium). The memory 204 may be configured to store information, data, content, applications, software instructions, or the like, for enabling the apparatus 200 to carry out various functions in accordance with example embodiments contemplated herein.

[0074] The communications circuitry 206 may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and / or transmit data from / to a network and / or any other device, circuitry, or module in communication with the apparatus 200. In this regard, the communications circuitry 206 may include, for example, a network interface for enabling communications with a wired or wireless communication network. For example, the communications circuitry 206 may include one or more network interface cards, antennas, buses, switches, routers, modems, and supporting hardware and / or software, or any other device suitable for enabling communications via a network. Furthermore, the communications circuitry 206 may include the processing circuitry for causing transmission of such signals to a network or for handling receipt of signals received from a network. The communications circuitry 206, in an embodiment, may enable reception of parameters from various components, devices, and / or sensors (for example, flow control devices, analyzers, sensors, equipment,and / or other components), as well as communication of instructions and / or signals indicative of adjustment to those components and / or devices.

[0075] The apparatus 200 may include a modeling circuitry 208 configured to obtain parameters from one or more components, equipment, devices, sensors, and / or analyzers and / or apply those parameters to a trained machine learning model to obtain parameters that enable equipment to produce a target product. In other embodiments, the modeling circuitry 208 may apply, in addition to the parameters described herein, the output of other similar circuitry (in other words, an additional plurality of modeling circuitry that each correspond to one of a plurality of sub-operations). Obtaining the parameters from the one or more components, equipment, devices, sensors, and / or analyzers may occur periodically, at selected times, continuously, or substantially continuously. In an example, the modeling circuitry 208 may obtain parameters for sub-operations first, generating an output for each sub-operation. Upon generation of an output for each sub-operation, the modeling circuitry 208 may obtain each output and a current data set. The modeling circuitry 208 may poll the components, devices, sensors, and / or analyzers to obtain such parameters or, in an embodiment, receive the parameters without polling. The modeling circuitry 208 may obtain the parameters via the communications circuitry 206. Application of the parameters to the trained machine learning model may determine, generate, or cause generation of an output. The output may be indicative of an adjustment to equipment, fluids, devices, and / or operations to meet or accurately meet a target product and / or to operate the equipment at higher than typical efficiency, for example, utilizing less power or resources such as in a heater or boiler or utilizing a heat exchanger to reduce power usage.

[0076] In another embodiment, the modeling circuitry 208 may train the trained machine learning model prior to use. In such embodiments, the modeling circuitry 208 may obtain historical data, preprocess the historical data, and then train and test the machine learning model. In yet another embodiment, after a refining operation (in other words, after a selected product has been generated via the refining operation), the modeling circuitry 208 may re-train or refine the trained machine learning model, based on the results of the refining operation (in other words, the accuracy of the parameters in achieving the target product’s properties).

[0077] In another, the modeling circuitry 208 may train and / or include a plurality of machine learning models. Each of the plurality of machine learning models may correspond to a selected refinery operation and / or sub-operation.

[0078] The modeling circuitry 208 may utilize processing circuitry 202, memory 204, or any other hardware component included in the apparatus 200 to perform these operations, as described above in connection with FIGS. 1A-1B and below in connection with FIGS. 3-10. The output of the modeling circuitry 208 may be transmitted to other circuitry of the apparatus 200 (such as the fluid adjustment circuitry 210 and / or equipment and device adjustment circuitry 212).

[0079] In addition, the apparatus 200 further comprises the fluid adjustment circuitry 210 that may cause adjustment of feedstock and / or other fluids utilized in a refining operation. In an embodiment the output from the modeling circuitry 208 may be a matrix, a series of parameters, and / or some indicator. In an embodiment, the fluid adjustment circuitry 210 may utilize that output to adjust a blend of feedstock and / or the fluid used in other inputs (for example, an amount of hydrogen, butane, other alkanes, and / or other fluids). The fluid adjustment circuitry 210 may utilize processing circuitry 202, memory 204, or any other hardware component included in the apparatus 200 to perform these operations, as described above in connection with FIGS. 1A-1B and below in connection with FIGS. 3- 15. The fluid adjustment circuitry 210 may further utilize communications circuitry 206 to transmit signals to adjust the type and / or amount of feedstock to utilize.

[0080] In addition, the apparatus 200 further comprises the equipment and device adjustment circuitry 212 that may cause adjustment of equipment and / or devices utilized in a refining operation. In an embodiment, the equipment and device adjustment circuitry 212 may utilize that output to adjust temperature, pressure, flow rate, and / or other parameters corresponding to the equipment and / or devices positioned within the refinery (e.g., by setting valve positions, pump speeds, etc.). The equipment and device adjustment circuitry 212 may utilize processing circuitry 202, memory 204, or any other hardware component included in the apparatus 200 to perform these operations, as described above in connection with FIGS. 1A and IB and below in connection with FIGS. 3-10. The equipment and device adjustment circuitry 212 may further utilize communications circuitry 206 to transmit signals to adjust equipment and / or devices utilized.

[0081] Although components 202-212 are described in part using functional language, it will be understood that the particular implementations necessarily include the use of particular hardware. It should also be understood that certain of these components 202-212 may include similar or common hardware. For example, the modeling circuitry 208, the fluid adjustment circuitry 210, and the equipment and device adjustment circuitry 212 may, in some embodiments, each at times utilize the processing circuitry 202, memory 204, orcommunications circuitry 206, such that duplicate hardware is not required to facilitate operation of these physical elements of the apparatus 200 (although dedicated hardware elements may be used for any of these components in some embodiments, such as those in which enhanced parallelism may be desired). Use of the terms “circuitry,” with respect to elements of the apparatus therefore shall be interpreted as necessarily including the particular hardware configured to perform the functions associated with the particular element being described. Of course, while the terms “circuitry” should be understood broadly to include hardware, in some embodiments, the terms “circuitry” may in addition refer to software instructions that configure the hardware components of the apparatus 200 to perform the various functions described herein.

[0082] Although the modeling circuitry 208, the fluid adjustment circuitry 210, and the equipment and device adjustment circuitry 212 may utilize processing circuitry 202, memory 204, or communications circuitry 206 as described above, it will be understood that any of these elements of apparatus 200 may include one or more dedicated processors, specially configured field programmable gate arrays (FPGA), or application specific interface circuits (ASIC) to perform its corresponding functions, and may accordingly utilize processing circuitry 202 executing software stored in a memory or memory 204, communications circuitry 206 for enabling any functions not performed by special-purpose hardware elements. In all embodiments, however, it will be understood that the modeling circuitry 208, the fluid adjustment circuitry 210, and the equipment and device adjustment circuitry 212 are implemented via particular machinery designed for performing the functions described herein in connection with such elements of apparatus 200.

[0083] In some embodiments, various components of the apparatus 200 may be hosted remotely (e.g., by one or more cloud servers) and thus need not physically reside on the corresponding apparatus 200. Thus, some or all of the functionality described herein may be provided by third party circuitry. For example, a given apparatus 200 may access one or more third party circuitries via any sort of networked connection that facilitates transmission of data and electronic information between the apparatus 200 and the third party circuitries. In turn, that apparatus 200 may be in remote communication with one or more of the other components describe above as comprising the apparatus 200.

[0084] As will be appreciated based on this disclosure, example embodiments contemplated herein may be implemented by an apparatus 200 (or by a refinery controller). Furthermore, some example embodiments (such as the embodiments described for FIGS. 1A-1B and 3-10) may be a computer program product comprising software instructionsstored on at least one non-transitory computer-readable storage medium (such as memory 204). Any suitable non-transitory computer-readable storage medium may be utilized in such embodiments, some examples of which are non-transitory hard disks, CD-ROMs, flash memory, optical storage devices, and magnetic storage devices. It should be appreciated, with respect to certain devices embodied by apparatus 200 as described in FIG. 2, that loading the software instructions onto a computing device or apparatus produces a special-purpose machine comprising the means for implementing various functions described herein.

[0085] FIG. 3 a simplified diagram that illustrates example refining controllers and an example refining enhancer to enhance control of a refining process at a refinery, according to an embodiment of the disclosure. As illustrated in FIG. 3, a refinery may include one or more operation controllers 302. The operation controllers 302 may connect to, for example, a number of feeds and / or processing units (refinery equipment configured to process a feedstock or other input). As illustrated, the operation controllers 302 may connect to and receive data from feed A 303 A, feed B 303B, and up to feed 303N, sensors or other devices associated with each feed (such as sensor 304A, 304B, and up to 304N), and various flow control devices (such as valve 306A, valve 306B, and up to valve 306N). In such embodiments, each of feed A 303 A, feed B 303B, and up to feed 303N may flow to a first processing unit 308. A processing unit, for example, in this case, the first processing unit 308, may include one or more refining devices or equipment positioned at a refinery (for example, a FCC unit, a distillation column, and other equipment as described herein). The first processing unit 308 may convert, process, and / or transform a feed into a unit material (such as unit material A 310A, unit material B 310B, and up to unit material N 310 N). Additional processing units may be positioned throughout the refinery. As illustrated though, the unit materials may flow to a “Nth” processing unit 314. Sensors (such as sensor 312A, 312B, and up to 312N) and valves (such as valve 313 A, valve 313B, and up to valve 313N) may be positioned between the feed and “Nth” processing unit 314. The final processing device (in other words, the “Nth” processing unit 314) may produce one or more end materials (such as end materials A 316A, end materials 316B, and up to end materials 316N). Sensors (such as sensor 318A, 318B, and up to 318N) and valves (such as valve 320A, valve 320B, and up to valve 320N) may be positioned between the end materials and the material destination 322.

[0086] In an embodiment, as each feed is fed to the next processing unit, the operation controller 302 may determine various characteristics and / or properties of the feed. Forexample, the operation controller 302 may determine temperature, pressure, and / or flow rate, in addition to the composition of the feed and the spectra or properties determined via spectrographic analysis of the feed. For example, as illustrated, the operation controller 302 may determine or obtain feed information 324 (including, at least feed composition 326 and / or feed properties 328, among other data), unit material information 330 (including, at least unit material composition 332 and / or unit material properties 334, among other data), and / or end material information 336 (including, at least end material composition 338 and / or end material properties 340, among other data). Thus, the operation controller 302 may obtain data related to each feed / material in real-time or near real-time, during a refinery operation, and / or directly or indirectly (for example, spectra may be obtained via a sample or spectrographic analyzer).

[0087] Once all the data has been obtained, the operation controller 302 may apply, to the machine learning model 360 of a local enhancer 358, the data including processing unit constraints 350, a target product 352 (including a target composition 354 and target properties 356), and / or material differences 344 (including composition differences 346 and properties difference 348) as determined via a comparator 342 (the comparator positioned or configured to compare composition and properties of different materials). The machine learning model 360 may produce material targets 362 which may be utilized to produce target feed ratios 364 and target operation unit parameters 366. In another embodiment, these values may be fed to the comparator and then, after obtaining differences related to another material, reapplied to the machine learning model 360. The machine learning model 360 may then produce adjusted targets 370 (including adjusted target feed ratios 372 and adjusted target operation unit parameters 374).

[0088] In another embodiment, the output of the machine learning model (for example, a vector comprising a plurality of components, each component being a parameter setting for a selected or specific device or equipment) may be compared to current parameter settings for the selected or specific devices or equipment in the comparator 342. In such embodiments, if the comparator 342 determines that there is a difference between an output of the machine learning model, then the parameter settings of the equipment or devices at the refinery may be adjusted.

[0089] In embodiments, the controller 302 may drive the materials to the target by adjusting the valves and / or feed (for example, the blend of different feeds or materials used in the subsequent operation) at one or more points in the overall refining operation.

[0090] The machine learning model 360 may include neural networks, supervised learning models, semi -supervised learning models, unsupervised learning models, or some combination thereof, as will be readily understood by one having ordinary skill in the art. In another embodiment, different types of machine learning algorithms may be utilized for different refinery operations. In further embodiments, some refining operations may use, rather than or in addition to a neural network, decision trees, support vector machines, hidden Markov models, Bayesian networks, linear regression, k-means, and / or tabular reinforcement learning. Specific neural networks that may be utilized include a recurrent neural network, such as a long short-term memory network. Such neural networks may utilize a fixed horizon of historical data to predict future behavior. Additionally, such neural networks may utilize standard active functions, for example a rectified linear unit or a hyperbolic tangent. As noted, in embodiments, different models may be utilized for different operations. The determination for which model to use for each operation may be determined based on error rates associated with a selected model, the R2value, SHAP plots and / or values, gain directions and / or magnitude, and / or gain distributions, among other factors.

[0091] In an embodiment, the operation controller 302, local enhancer 358, and / or comparator 342 may be included in a single controller, a plurality of controllers, one or more computing devices, and / or as one or more modules or as instructions. In other embodiments, a plurality of controllers or computing devices may each include a specific model corresponding to one of the processing units. In another embodiment, the operation controller 302 may include or may be a supervisory controller that considers the predictions of other processing unit specific controllers when generating adjusted targets via the supervisory controller’s machine learning model.

[0092] As noted, data may be obtained in real time or near real-time. In some embodiments, the application of data to a machine learning model may be delayed by the time taken to obtain spectra or properties for a feed or material. Thus, in an embodiment where the operation controller 302 is a supervisory controller, the supervisory controller may generate adjusted targets after each sub-controller generates a target for a specific processing unit. Thus, the overall adjustment targets may be determined at a second time interval, greater than the first time interval, while each sub-adjustment target may be determined at a first time interval.

[0093] In another embodiment, a refinery may include a plurality of operation controllers. Each operation controller 302 may include a plurality of trained machine learning models.Each trained machine learning model may be trained to recognize a specific or selected trend in a set of data. Thus, each operation may be adjusted based on the outputs of a plurality of models, ensuring the operation as a whole produces an accurate target product. Further, each operation controller may interact with each other operation controllers. For example, adjusted parameters from all operation controllers may be provided to each operation controller. Thus, as one operation is adjusted, a downstream and / or upstream operation may be further adjusted based on the adjustment of the one operation.

[0094] FIG. 4 is a simplified diagram that illustrates training of a machine learning model for enhanced fluid production at refinery, according to an embodiment of the disclosure. Each model described herein may be trained prior to use. Such training may be performed prior to use with a set of historical data specific to a refinery. In a further embodiment, a plurality of machine learning models may be trained, each based on data specific to an operation and selected equipment at the refinery.

[0095] As noted, the machine learning models described herein may be trained using data. As illustrated in FIG. 4, the data may include historical, equipment specific data 402. In other embodiments, the training data may include data related to the entire operation of a refinery, as well as outputs from equipment specific models. In another embodiment, a machine learning model may be re-trained and / or refined via current and marked up equipment specific data 404. The historical equipment specific data 402 and current and marked up equipment specific data 404 may include feed composition, feed properties, material composition, material properties, a target product or products, target composition, target properties, temperatures in equipment, pressure in equipment, flow rates associated with feed and / or materials, and / or equipment parameters. In embodiments, the historical equipment specific data 402 may include or may be utilized to generate a non-linear concave function. In such embodiments, the desired outcome may be determined based on the maximum of such a function. In another embodiment, the desired outcome may be included or added to the data set. In yet another embodiment, training may include the machine learning model learning particular patterns that indicate what the desired outcome may be based on trends within the data. In another embodiment, physics-based data may be provided along with the historical data set to ensure that outputs from a trained machine learning model remain consistent and / or emulate real process / actual possibilities. In yet another embodiment, a plurality of machine learning models may be trained for the same operation. Each of the plurality of machine learning models may utilize different portions of historical data and / or other inputs to cause the model to maximize a specific attribute orparameter. Another model may be trained to utilize the outputs of each of those plurality of models, in addition to data.

[0096] Once the historical data, and any other current data, is available, that data may be pre-processed 406. In such embodiments, the data may be normalized. In other words, outlying data points that are anomalies may be removed from the data set. Further, data corresponding to abnormal events may be removed, such as data generating during startup, shut-down, turn-arounds, maintenance, and / or upsets. Further, undesired data and invalid measurements may be removed. Finally, data may be segregated or separated into sequences based on time. The sequences may comprise data obtained over a consecutive time period, such as time intervals of 30 minutes, 1 hour, 2 hours, and / or 3 hours, or more or less than the time intervals listed. In another embodiment, other factors may be utilized to segregate or separate the data, such as feed used and / or target product being produced.

[0097] Once the data set has been pre-processed, a model may be trained 408. In embodiments, a portion of the data set (for example, 70%, 80%, or 90%) may be fed to the machine learning model. The machine learning model may utilize the inputs versus the known desired outcome (such as target product composition and properties) and / or known undesired outcome to “learn” what parameters can be utilized to achieve the known desired outcome and what parameters lead to the known undesired outcome. Once the data has been used to train the machine learning model, then the remaining portion of the data set may be utilized to test 410 the trained machine learning model. If the trained machine learning model does not meet or achieve a selected error rate, then trained machine learning model may be re-trained or refined with a different randomized portion of the data set, and the re-training repeated as necessary, until the selected error rate is met or achieved. In another embodiment, other training schema may be utilized. In another embodiment, readiness of the trained machine learning model may be determined based on how close the trained machine learning model comes to an expected outcome, based on the test data set.

[0098] Once the trained machine learning model 412 meets a selected error rate, then the trained machine learning model may be released for further use. In another embodiment, a separate step may include selection of a type of machine learning model prior to training of the machine learning model. In other embodiments, various types of models may be trained, then tested. The most accurate models, determined by an error rate for each model, may be utilized.

[0099] FIG. 5 is a schematic diagram of an enhanced steam methane reformer control system 700 to enhance fluid production (for example, such as hydrogen production) at a portion of a refinery, according to an embodiment of the disclosure. A section of the refinery corresponding to a steam methane reformer may include a steam methane reformer controller 726. Feedstocks 702 of natural gas composed primarily of methane are fed into a desulfurization 732 process to remove contaminants and then fed into a pre-heater 734 that heats the natural gas in preparation to be fed into a reformer reactor 736. The reformer reactor 736 may include nickel convection tubes that act as a catalyst to split the methane in the feedstocks 702 into hydrogen gas, water, and carbon monoxide. The products of the reformer reactor 736 are then passed into a shift reactor 738 that converts the carbon monoxide and water into carbon dioxide and hydrogen gas. The products of the shift reactor 738 are then passed into a separator 740 that separates the hydrogen gas 704 from the byproducts of water, carbon dioxide, and other contaminants. The hydrogen gas 704 may then be distributed to hydrogen gas consuming processes within the refinery or transported and sold.

[0100] The steam methane reformer controller 726 may obtain data associated with the equipment of the steam methane reformer unit, such as desulfurization 732, pre-heater 734, reformer reactor 736, shift reactor 738, and separator 740. The steam methane reformer controller 726 may also initiate capture of samples of fluids associated with the steam methane reformer from sensor packages 742, 744, 746, 748, 750, 752 disposed throughout the steam methane reformer system 700 to measure, analyze, and sample the feedstocks 702 as they move through the steam methane reformer system 700 and the hydrogen gas 704. The sensor packages 742, 744, 746, 748, 750, 752 may measure the temperature and pressure within the steam methane reformer system 700, as well as the composition and flow rate of the feedstocks 702 and hydrogen gas 704. The sensor packages 742, 744, 746, 748, 750, 752 may include one or more temperature sensors, pressure sensors, flow rate sensors, composition sensors such as spectrometers and gas chromatographs, and sampling units.

[0101] The sample collection and analysis assembly 708 may then analyze the samples, produce properties and / or a spectra for each sample, and receive lab and sensor data. The steam methane reformer controller 726 may apply the data, properties, and / or spectra to one or more machine learning models of the local enhancement module 728, predictive controls module 730, or other machine learning model to produce an output indicative of predictions and adjustments to parameters of the steam methane reformer system 700 andupstream processes. The steam methane reformer controller 726 or machine learning model may then utilize the output to adjust various parameters and / or feed rates associated with the steam methane reformer system 700 via the local enhancement module 728, and, for example, further via a PID controller, DCS controller, PLC controller, or a DCS-PID controller. In an example, the steam methane reformer controller 726 may optimize the reformer process based on a number of factors, for example, the hydrogen output may be maximized.

[0102] Based on the data gathered by a machine learning model, the machine learning model may alter the temperature, pressure, and flow rates of the steam 706 and the feedstocks 702 of the reformer reactor 736 and the shift reactor 738 in order to optimize the steam methane reformer system 700. Further, the machine learning model may recommend regeneration or replacement of the catalysts in the reformer reactor 736 and the shift reactor 738. Additionally, the machine learning model may optimize the steam methane reforming system 700 process to increase hydrogen gas production or decrease the consumption of steam 706 based on the data gathered from sensor packages 742, 744, 746, 748, 750, 752 and from desulfurization 732, pre-heater 734, reformer reactor 736, shift reactor 738, and separator 740.

[0103] FIG. 6 is a schematic diagram of a steam control system 1700 to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure. Many sections or portions of the refinery may utilize steam. For example, steam may be used in a FCC unit for stripping, while, in another unit, steam may be utilized as a heat source. As such, the sections or portions of a refinery that utilize steam may include a steam controller 1702. Similar to previously described controllers, the steam controller 1702 may obtain data associated with the equipment of the refinery, such as from one or more steam drums 1708, one or more boilers 1710, one or more waste heat sources 1712, one or more heat exchangers 1714, one or more condensers 1716, and one or more refinery equipment 1718 (in particular, refinery equipment 1718 that utilizes steam to some degree). The steam controller 1702 may apply the data and / or properties to one or more machine learning models of the local enhancement module 1704 and / or predictive controls module 1706 to produce an output indicative of adjustment to parameters. The steam controller 1702 may then utilize the output to adjust various parameters associated with refinery equipment that utilizes steam via the local enhancement module 1706.

[0104] The steam controller 1702 may prioritize steam to units or portions of the refinery that are generating high value products and / or products or intermediaries that are utilizedto produce high value products. As such, at least one machine learning model for steam management may maximize steam production for one or more different refinery operations, while providing sufficient steam to other refinery operations that utilize steam.

[0105] FIG. 7 is a schematic diagram of hydrogen control system 1800 to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure. Similar to steam, several refinery operations may utilize hydrogen for various purposes. Further, a refinery may also produce some amount of hydrogen, either as a main product and / or as a by-product. Thus, hydrogen management may occur at the refinery level, rather than at a sub-level. Similar to previously described controllers, the hydrogen controller 1802 may obtain data associated with the equipment that produces hydrogen and / or utilizes hydrogen, such as from a steam methane reformer 1808, a catalytic reformer 1809, a hydrolysis unit 1810, an external hydrogen sources 1812 (such data including an amount of hydrogen available at a selected times), and / or refinery equipment 1814 that utilizes hydrogen. The hydrogen controller 1802 may also initiate capture of samples of fluids associated with the devices and / or equipment that produce and / or utilize hydrogen. The sample collection and analysis assembly (not illustrated) may then analyze the samples and produce properties and / or a spectra for each sample. The hydrogen controller 1802 may apply the data, properties, and / or spectra to one or more machine learning models of the local enhancement module 1804 and / or predictive controls module 1806 to produce an output indicative of adjustment to parameters and / or feed. The hydrogen controller 1802 may then utilize the output to adjust various parameters and / or feed associated with the hydrogen production units and / or equipment or devices utilizing hydrogen via the local enhancement module 1806.

[0106] The hydrogen controller 1802 may prioritize hydrogen to units or portions of the refinery that are generating high value products and / or products or intermediaries that are utilized to produce high value products, as well as maximize purity of hydrogen, such as via methane reforming and / or hydrolysis. As such, at least one machine learning model for hydrogen management may maximize hydrogen production or acquisition for one or more different refinery operations, while providing sufficient hydrogen to other refinery operations that utilize hydrogen.

[0107] FIG. 8 is a schematic diagram of a feed control system 1900 to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure. Each refinery operation utilizes a feed stream or feedstock. Such a feed stream or feedstock may, in some examples, be adjusted, to optimize or enhance fluid production of a correspondingrefinery operation. Similar to previously described controllers, the feed controller 1902 may obtain data associated with the equipment that produces a feed or intermediary, blends a feed or intermediary, and / or utilizes a feed and / or intermediary, such as from one or more blend tanks 1908, one or more in-line blend pipes 1910, one or more feedstock sources 1912, one or more intermediary sources 1914, one or more sample collection and analysis assemblies 1916, and / or refinery equipment 1918. The feed controller 1902 may also initiate capture of samples of fluids associated with the devices and / or equipment that produce, blend, and / or utilize feed. The sample collection and analysis assembly (not illustrated) may then analyze the samples and produce properties and / or a spectra for each sample. The feed controller 1902 may apply the data, properties, and / or spectra to one or more machine learning models of the local enhancement module 1904 and / or predictive controls module 1906 to produce an output indicative of adjustment to parameters and / or feed. The feed controller 1902 may then utilize the output to adjust various parameters and / or blends of a feed or intermediary associated with the refinery equipment 1918 via the local enhancement module 1906. The trained machine learning model utilized for the feed controller 1902 may be trained to or utilized to predict feed, blend, and / or intermediary properties and / or components that produce a maximum or greater than typical yield for a particular refinery operation.

[0108] FIG. 9 is a schematic diagram of a diesel pool control system 2100 to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure. After processing various feeds via one or more different operations or sub-operations, various products from those operations or sub-operations may be combined to produce a selected diesel fuel or to form a selected diesel pool. Such a diesel pool may include a specification, the specification including one or more of a cetane number, a density, a color, a flash point, a pour point, an amount and / or type of lubricant, an amount and / or type of detergents, an amount and / or type of anti-rust agents, an amount and / or type of anti-icing agents, and / or an amount of sulfur or other contaminants, among other properties. To achieve the properties specified, a refinery controller and / or a diesel pool controller 2102, for example may obtain analysis of and / or other data associated with various products within a refinery and select a percentage, e.g. the percentage in relation to the whole final diesel product, of each product for combination or blending. As such, the refinery controller and / or diesel pool controller 2102 may obtain data from a variety of sources. For example, the diesel pool controller 2102 may obtain data from a FCC unit 2108, a fractionation / distillation column 2110, a hydrocracker 2112, a hydrotreater 2114, a hydrodeoxygenation (HDO) unit2115, and / or a coker unit 2116. Further, the diesel pool controller 2102 may obtain data from the sample collection and analysis assembly 2118 and / or other refinery equipment 2120. In other embodiments, the diesel pool controller 2102 may connect to a refinery controller and / or other controllers or sub-operation controllers within the refinery to obtain data related to that operation or sub-operation. Further, the diesel pool controller 2102 may obtain data and / or properties from the collection and analysis assembly 2118 related to other fluids produced within the refinery. Once such data and / or properties has been collected, the diesel pool controller 2102 may apply the data to one or more trained machine learning models stored in the local enhancement module 2104 and / or the predictive controls module 2106 to produce amounts of each varying fluid and / or properties associated with a fluid that causes production of the fluid to achieve selected properties. In an embodiment, the local enhancement module 2104 may apply the output of a plurality of trained machine learning models each stored in one or more of a plurality of predictive controls modules 2106 to a trained machine learning model of the local enhancement module 2104.

[0109] Once a prediction is determined by the diesel pool controller 2102, the diesel pool controller 2102 may adjust one or more devices (for example, refinery operation control devices) within the refinery to cause those one or more devices to provide fluids for and / or adjust parameters to produce the selected or target diesel. The selected components of the selected or targeted diesel may then be blended.

[0110] In an embodiment, the trained machine learning models within the diesel pool controller 2102 may be trained to or utilized for predicting parameters and / or properties for one or more of the units described in relation to the diesel pool controller 2102. For example, if a specific property of a diesel pool controller 2102 is known or input into the trained machine learning model, along with other related data, then the diesel pool controller 2102 may determine, based on application of that specific property and / or data to the trained machine learning model, process parameters and / or feed and / or intermediary properties for that particular process. In an embodiment, another controller specific for that operation may utilize the output to drive that operation to produce a fluid that accurately exhibits that specific parameter. In further embodiments, the diesel pool controller 2102 may determine such parameters and / or properties for a plurality of other operations. In other embodiments, the diesel pool controller 2102 may work in conjunction with other controllers to produce selected fluids. Thus, the diesel pool controller 2102, in embodiments, may coordinate targeted outputs, by adjusting or causing adjustment of one or more refinery operations or sub-operations and / or by blending selected components fromthe refinery operations or sub-operations, from a plurality of operations to meet a selected diesel pool specification[OHl] FIG. 10 is a schematic diagram of a process 1100 to produce renewable naphtha 1102, renewable liquid petroleum gas 1104, and renewable diesel 1106. Fuel gas 1108 is produced as a byproduct of the process 1100. As shown, this process includes a source of natural gas 1108 and a feedstock of a source of oils and greases 1110, 1178. The source of renewable fats, oils, and greases 1110, 1178 may include tallow, soy, corn, canola, camelina, peanut, sunflower, palm, palm kernel oils, lard, used cooking oil, white grease, brown grease, sorghum oil, and other biological sources of oil. More specifically, the source of renewable fats, oils, and greases 1110, 1178 may include triglycerides, diglycerides, monoglycerides, esters of fatty acids, and free fatty acids. Triglycerides have a higher molecular weight than diesel, a higher boiling point than diesel, and is relatively non- corrosive. Free fatty acids have a molecular weight like diesel and similar boiling point. Both triglycerides, diglycerides, monoglycerides, esters of fatty acids, and free fatty acids may be converted into diesel.

[0112] The process 1100 consumes significant amounts of hydrogen gas 1113. Hydrogen gas 1113 may be purchased from third parties, produced through catalytic reforming, electrolytic hydrogen production, or steam methane reforming. As shown, two methane steam reformers 1111, 1112 operate and produce hydrogen gas 1113 as discussed and shown in FIG. 5 of this disclosure. Steam methane reforming produces hydrogen gas from methane in natural gas from a natural gas source 1108. The hydrogen gas 1113 is distributed to one or more hydrodeoxygenation processes (“HDO”). In this embodiment, a first HDO 1114, a second HDO 1116, and a third HDO 1118 may be used to process renewable fats, oils, and greases 1110, 1178 into renewable naphtha 1102, renewable liquid petroleum gas 1104, sustainable paraffinic kerosene (“SPK”) 1190, and renewable diesel 1106.

[0113] As the renewable fats, oils, and greases 1110, 1178 exit the storage tanks 1121, 1180, the renewable fats, oils, and greases 1110, 1178 may optionally pass through a flow control valve 1126 that depending on the composition of the renewable fats, oils, and greases 1110, 1178 may divert the renewable fats, oils, and greases 1110, 1178 directly to the HDOs 1114, 1116, 1118 or to a feed treatment process 1128, 1183. Where the renewable fats, oils, and greases 1110, 1178 have been pretreated and are ready to be sent to the hydrodeoxygenation processes, the flow control valve 1126 bypasses the feed treatment process 1128. Sensor packages 1124, 1181 may include one or more flow rate sensors, temperature sensors, pressure sensors, and composition sensors that may be usedto provide data about the renewable fats, oils, and greases 1110, 1178. Based on the data collected by sensor package 1124, the flow control valve 1126 may divert the flow of oils and greases 1110 to the feed treatment process 1128.

[0114] The feed treatment processes 1128, 1183 filters, cleans, and purifies the renewable fats, oils, and greases 1110, 1178 to a state where they may be sent to an HDO. The feed treatment process 1128 may include a sensor package 1130 disposed to gather data about the incoming oils and greases 1110, a sensor package 1132 disposed to gather data about the feed treatment process 1128, and a sensor package 1132 disposed to gather data about the renewable fats, oils, and greases 1110 exiting the feed treatment process 1128. Similarly, The feed treatment process 1183 may include a sensor package 1182 disposed to gather data about the incoming oils and greases 1178, a sensor package 1184 disposed to gather data about the feed treatment process 1183, and a sensor package 1185 disposed to gather data about the renewable fats, oils, and greases 1178 exiting the feed treatment process 1183. The sensor packages 1134, 1185 may direct a recycle valve 1135, 1186 that may be used to divert the renewable fats, oils, and greases 1110, 1178 exiting the feed treatment processes 1128, 1183 to the beginning of the feed treatment process 1128, 1183 in the event that the renewable fats, oils, and greases 1110, 1178 fail to meet desired pretreatment targets. In some embodiments, the feed treatment process 1128 may include one or more of filtering the renewable fats, oils, and greases 1110, 1178 to remove solid contaminants, degumming to remove phospholipids, waxes, and metals, drying to remove water, adsorption with bleaching clay or activated carbon to remove metals, phosphorus, sulfur, and other impurities. Once the renewable fats, oils, and greases 1110, 1178 meet desired pretreatment targets, the renewable fats, oils, and greases 1110, 1178 are fed into the first HDO 1114, second HDO 1116, and the third HDO 1118.

[0115] The renewable fats, oils, and greases 1110, 1178 may be specifically selected for a specific HDO. For example, the metallurgy of the first HDO 1114 may better tolerate processing distiller’s corn oil, while the metallurgy of the second HDO 1116 may be better suited to process soybean oil. Consequently, a machine learning model may be used to optimize feed recipes of different types of renewable fats, oils, and greases provided to an individual HDO units to take advantage of each HDO’s combination of equipment and metallurgy.

[0116] Certain attributes and properties of the products of the HDO and catalytic dewaxing may be controlled through a feedstock recipe or blend of renewable fats, oils, and greases 1110, 1178. For example, the corrosiveness or carbon intensity of the resulting products,including renewable diesel and SPK, may be determined by the feedstock recipe. Distiller’s corn oil is more corrosive than soybean oil, but has a lower carbon intensity than soybean oil, because the carbon foot print of distiller’s corn oil is shared with the produced ethanol produced from the com. In contrast, soybean oil has a higher carbon intensity, but is relatively noncorrosive.

[0117] The first HDO 1114, second HDO 1116, and the third HDO 1118 may each include one or more of a sensor package 1136 disposed to measure data from the incoming oils and greases 1110, 1178, a sensor package 1138 disposed to measure data from the incoming hydrogen gas 1113, a sensor package 1140 disposed to measure data from within the first HDO 1114, second HDO 1116, and the third HDO 1118, a sensor package 1142 disposed to measure data from lighter fluids 1146 exiting the first HDO 1114, second HDO 1116, and the third HDO 1118, and a sensor package 1144 disposed to measure data from the heavier molecules 1148 exiting the first HDO 1114, second HDO 1116, and the third HDO 1118.

[0118] The hydrodeoxygenation reaction and decarboxylation reaction within the first HDO 1114, second HDO 1116, and the third HDO 1118 removes oxygen from the renewable fats, oils, and greases 1110, 1178 and to break up the triglycerides in the renewable fats, oils, and greases 1110, 1178 into carbon chain lengths suitable for use as diesel fuel and smaller hydrocarbons such as propane, water, and carbon dioxide. The hydrodeoxygenation reaction and decarboxylation reaction are an exothermic reactions so temperatures within the first HDO 1114, second HDO 1116, and the third HDO 1118 may be tracked and adjusted by a machine learning model. Temperature excursions are a risk.

[0119] The first HDO 1114, second HDO 1116, and the third HDO 1118 may also include a plurality of subprocesses including hydrotreating processes, filtering processes, stripping processes, stabilizing processes, isomerization processes, and separating processes to remove phosphorus, sulfur, oxygen, nitrogen, and other contaminants, as well as separate the heavier molecules 1148 from lighter fluids 1146.

[0120] Each of the first HDO 1114, second HDO 1116, and the third HDO 1118 include catalysts that facilitate the removal of oxygen from the renewable fats, oils, and greases 1110, 1178. The catalysts 1172 may include molybdenum, nickel and molybdenum, cobalt and molybdenum based catalysts. The metals on the catalysts 1172 may be converted to sulfides to increase durability and sulfur may be injected into the first HDO 1114, second HDO 1116, and the third HDO 1118 to protect the catalysts 1172 because of the low sulfur content of the renewable fats, oils, and greases 1110, 1178. Further, phosphorus, cations,metals, silicon, and other contaminants may deactivate the catalysts 1172 used in the hydrodeoxygenation reaction. Carbon deposits may also cover the active sites of the catalysts 1172. Adequate flow of hydrogen gas 1113 helps to protect the catalysts 1172 from the buildup of carbon deposits. High temperatures within the first HDO 1114, second HDO 1116, and the third HDO 1118 may over time result in the agglomeration of the crystals of molybdenum, nickel and molybdenum, cobalt and molybdenum in the catalysts reducing the useful surface area of the catalysts 1172.

[0121] In some applications, the current operational data of an HDO reactor may include a temperature differential across a catalyst bed disposed closest to an exit of the HDO reactor. When a temperature differential is greater than a predetermined threshold, a the machine learning model may be triggered to generate an adjustment to an operating parameter of the HDO to reduce the temperature differential below the predetermined threshold. The predetermined threshold may be determined by a machine learning model. For example, the machine learning model may receive HDO product data indicative of a property of an HDO product and then generate the predetermined threshold based on the property of the HDO product. In some applications, the property of the HDO product may be a measured oxygen concentration or an end point of the HDO product. The predetermined threshold may also be set by an operator and may be less than 30°F / 16.67°C, less than 20°F / l 1.11°C, less than 10°F / 5.56°C, or smaller ranges may be used. The threshold may vary between each HDO and depend upon the specific design and equipment of each HDO. For example, one HDO may have a threshold of 10°F / 5.56°C, while another may have a threshold of 20°F / l 1.11°C.

[0122] The heavier molecules 1148 and hydrogen gas 1113 are fed into catalytic dewaxing 1120 that adjusts the shape and size of the molecules to provide acceptable cold temperature performance. As shown, catalytic dewaxing 1120 may include a one or more catalytic dewaxing units. Catalytic dewaxing 1120 may include isomerization catalysts 1174 that results in branching isomerization of the hydrocarbons. As higher temperatures are used, more hydrocracking may also occur during catalytic dewaxing resulting the formation of lighter hydrocarbons. Isomerization does not consume hydrogen, while hydrocracking does consume hydrogen.

[0123] The processed hydrocarbons 1169 exit catalytic dewaxing 1120 and are sent to separations 1150. A machine learning model may be applied to catalytic dewaxing 1120 to carefully control the temperature within catalytic dewaxing 1120 to reduce hydrocracking of the heavier molecules 1148.

[0124] The lighter fluids 1146 from the HDO units 1114, 1116, 1118 and processed hydrocarbons 1169 from catalytic dewaxing 1120 are sent to separations 1150 to be separated into different products. Separations 1150 may include one or more distillation columns, separators, splitters, stabilizers, and feed drums. Hydrogen and methane 1152 may be sent back to the methane steam reformer 1111. The methane 1152 may be used to generate hydrogen gas 1113. The lighter fluids 1146 and processed hydrocarbons 1169 may be further separated into fuel gas 1108, and renewable liquid petroleum gas 1104, renewable naphtha 1102, SPK 1190, and renewable diesel 1106. Separations 1150 may include one or more of a sensor package 1154 disposed to measure data from the lighter fluids 1146 entering separations 1150, a sensor package 1156 disposed to measure data from within separations 1150, a sensor package 1158 disposed to measure data from the hydrogen and methane 1152, a sensor package 1160 disposed to measure data from fuel gas 1108 exiting separations 1150, a sensor package 1162 disposed to measure data from renewable liquid petroleum gas 1104 exiting separations 1150, a sensor package 1164 disposed to measure data from renewable naphtha 1102 exiting separations 1150, a sensor package 1188 disposed to measure data from SPK 1190 exiting separations 1150, and a sensor package 1170 disposed to measure data from the renewable diesel 1106 exiting separations 1150.

[0125] Catalytic dewaxing 1120 may include one or more of a sensor package 1166 disposed to measure data from heavier molecules 1148 as they are input into catalytic dewaxing 1120 and a sensor package 1168 disposed to measure data from within the catalytic dewaxing 1120 process. ,

[0126] Sensor packages 1122, 1124, 1130, 1132, 1134, 1136, 1138, 1140, 1142, 1144, 1154, 1156, 1158, 1160, 1162, 1164, 1166, 1168, 1170, 1179, 1182, 1184, 1185, 1188 may include temperature sensors; such as resistance temperature detectors, thermocouples, and infrared sensors; pressure sensors, such as hydrostatic pressure sensors and differential pressure sensors; flow rate sensors, such as coriolis flow meters, ultrasonic flow meters, and turbine flow meters for metering flow; density and composition analyzers, such as sulfur analyzers, cloud point analyzer, flash point analyzers, refractometers, spectrometers, including near infrared spectrometry, and gas chromatographs; and sampling units for analyzing the composition of a sample through testing, distillation, or obtaining physical samples from the feedstocks 2602 to be analyzed in a lab or by a sample collection and analysis assembly. A machine learning model may use the data from sensor packages 1122, 1124, 1130, 1132, 1134, 1136, 1138, 1140, 1142, 1144, 1154, 1156, 1158, 1160, 1162,1164, 1166, 1168, and 1170 to adjust and control the feed rates of hydrogen gas to adjust temperature and pressure within the first HDO 1114, second HDO 1116, the third HDO 1118, and catalytic dewaxing 1120. The machine learning model may also adjust the feed rate of renewable fats, oils, and greases 1110, 1178 into the first HDO 1114, second HDO 1116, the third HDO 1118 to reduce the exothermic generation of heat from the reactions within the first HDO 1114, second HDO 1116, the third HDO 1118. The machine learning model may also adjust the temperature and pressure within the first HDO 1114, second HDO 1116, the third HDO 1118, and catalytic dewaxing 1120 using external sources of heat or cooling and recycled product. Additional cooling may be designed into the first HDO 1114, second HDO 1116, the third HDO 1118, and catalytic dewaxing 1120 to assist in controlling the temperatures of these processes. A machine learning model may be configured to adjust these process control points to improve the processes to reduce hydrocracking, increase isomerization, lower costs, and potentially manage the process to minimize the consumption of hydrogen.

[0127] SPK 1190 and renewable diesel 1106 have attributes that may be measured and specified through testing including aromatic content, olefin content, benzene content, sulfur content, density, pour point, cloud point, cetane number, water content, haze, flash point, flash point variability, freeze point, smoke point, cold filter plugging point, particulate emissions, fuel burn quality, oxidative stability, diesel lubricity, NOx emission factor, viscosity, energy content, conductivity, ash content, red dye content (if applicable), Ramsbottom carbon residue, and corrosion inhibition. The machine learning model may control and improve the process to improve one or more of these attributes. For example, the cloud point may be improved through the isomerization process and catalytic dewaxing process to meet or exceed a target specification cloud point for the renewable diesel 1106 by adjusting the feed rates of the hydrogen gas 1113 and renewable fats, oils, and greases 1110, 1178 into and the operating pressures and temperatures of the first HDO 1114, second HDO 1116, and the third HDO 1118 and the feed rates of the heavier molecules 1148 and hydrogen gas 1113 into and the operating pressures and temperatures of catalytic dewaxing 1120. In some embodiments, particular feed rates and operating temperatures and pressures may be altered to favor one or more of hydrodeoxygenation, decarboxylation, isomerization, and hydrocracking to adjust the outputs from these processes. The cloud point of the renewable diesel 1106 may also be adjusted by adjusting a final boiling point of the renewable diesel 1106 by using a fractionator or separator in separations 1150 by selecting the cut points of the hydrocarbons included in the renewable diesel 1106. Cloudpoint may also be improved by better management of the catalysts 1172 and 1174 to reduce deactivation and poisoning of the catalysts 1172 and 1174 through better control of feed rates of the feedstocks through the first HDO 1114, second HDO 1116, and the third HDO 1118 and catalytic dewaxing 1120, as well as by better management of feed treatment process 1128 to remove contaminants.

[0128] FIG. 11 is a schematic diagram of a hydrodeoxygenation (HDO) control system to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure. A section of the refinery corresponding to an HDO unit 2500, representative of first HDO 1114, second HDO 1116, and the third HDO 1118 of FIG. 10, may include a HDO controller 2502. A HDO unit may be utilized, in an embodiment, for treatment and / or processing of a renewable feedstock. Typically, a renewable feedstock may include oxygen or a greater than typical amount of oxygen. To process such a feedstock, the oxygen is first, or at some point in the process, removed. The HDO unit may process such feedstock to remove that oxygen, enabling conversion of the feedstock to a transportation fuel, such as renewable diesel, renewable naphtha, and / or renewable LPG.

[0129] As shown, a feedstock 2501 may be passed through a filter 2510 to remove large particles and contaminants from the feedstock 2501. From the filter 2510, the feedstock 2501 may be passed into a surge drum 2512 that helps provide a more consistent feed rate of feedstock 2501 into the HDO 2500. The feedstock 2501 may be pressurized by pump 2514 and mixed with hydrogen gas 2503.

[0130] The mixture of feedstock 2501 and hydrogen gas 2503 passes through the catalysts 2521 in the reactor 2520. The catalysts 2521 facilitate the removal of oxygen from the feedstock 2501 and breakup of triglycerides in the feedstock 2501 into carbon chain lengths of suitable for use as diesel fuel and smaller hydrocarbons such as propane, water, and carbon dioxide. Deoxygenation and decarboxylation are generally exothermic reactions and consume significant amounts of hydrogen gas 2503. Quench flows 2523 of hydrogen gas 2503 positioned within the reactor 2520 provide additional cooling and hydrogen gas within the reactor 2520 to control the operating temperatures within the reactor 2520, as well as assist in controlling pressure drops across the catalysts with the additional partial pressure of provided by the added hydrogen gas 2503. The added hydrogen gas 2503 from the quench flows 2523 further facilitate the deoxygenation and decarboxylation of the feedstock 2501.

[0131] If temperatures within the reactor 2520 are too high, a lower yield of HDO diesel results. In addition, as the catalysts 2521 deactivate, higher temperatures are required tosupport the deoxygenation and decarboxylation reactions. Further, the deoxygenation reaction is reduced with deactivation of the catalysts and the decarboxylation reaction becomes more prevalent reducing HDO diesel yield.

[0132] The products of the reactor 2520 may be passed into a condenser 2522 and then into a separator 2524. The separator 2524 separates the unconsumed hydrogen gas 2525 and adds the unconsumed hydrogen gas 2525 to fresh hydrogen gas 2503. From the separator 2524, the products of the reactor 2520 are then passed into a fractionator 2526 or other distillation equipment to separate the fuel gas 2528, naphtha 2530, sustainable paraffinic kerosene “SPK” 2560 and HDO diesel 2532. In some configurations, the stripper may separate light ends composed of fuel gas 2528 and naphtha 2530 from the HDO diesel 2532. The fuel gas 2528 may include hydrogen sulfide, other light contaminants, methane, and propane. The naphtha 2530 is generally a low value naphtha and may be recycled back into the fractionator 2526 to facilitate the separation of the products of the reactor 2520.

[0133] The bottoms of the fractionator 2526 including the HDO diesel 2532 or the liquid product 2535 of the separator 2524 may be split with a portion being sent to catalytic dewaxing and a recycle portion 2534 being mixed with the feedstock 2501 and fed back into the reactor 2520 to assist in controlling temperature within the reactor 2520 by acting as a heat sink within the reactor 2520.

[0134] The HDO controller 2502 may obtain data associated with the equipment of the HDO unit, such as one or more a filter 2510, a surge drum 2512, a pump 2514, a heat exchanger, a feed heater, a reactor 2520, a condenser 2522, a separator 2524, and / or a fractionator 2526. The HDO controller 2502 may also initiate capture of samples of fluids associated with the HDO unit. The sample collection and analysis assembly 2508 may also receive data from sensor packages 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554. The sensor packages 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554 may measure, analyze, and sample the feedstock 2501, hydrogen gas 2503, and the products of the reactor 2520, including fuel gas 2528, naphtha 2530, and HDO diesel 2532. The sensor packages 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554 may be used to analyze the samples and produce properties and / or a spectra for each sample. The sensor packages 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554 may include temperature sensors; such as resistance temperature detectors, thermocouples, and infrared sensors; pressure sensors, such as hydrostatic pressure sensors and differential pressure sensors; flow rate sensors, such as coriolis flow meters, ultrasonic flow meters, and turbine flow meters for metering flow; density and composition analyzers, such as sulfur analyzers, refractometers, spectrometers, includingnear infrared spectrometry, and gas chromatographs; and sampling units for analyzing the composition of a sample through testing, distillation, or obtaining physical samples from the feedstocks 2501 to be analyzed in a lab or by the sample collection and analysis assembly 2508.

[0135] The HDO controller 2502 may apply the data, properties, and / or spectra to one or more machine learning models of the local enhancement module 2504 and / or predictive controls module 2506 to produce an output indicative of adjustment to parameters and / or feed. The HDO controller 2502 may then utilize the output to adjust various parameters and / or feed associated with the HDO unit via the local enhancement module 2504. For an HDO operation, the HDO controller 2502 may optimize or maximize the renewable fuel production by manipulating a reactor and a heater outlet temperature, quench flows, liquid recycle streams, system pressure, hydrogen to hydrocarbon ratio, and / or other operation conditions (such additionally described components also being included, in embodiments, in the HDO unit). The HDO controller may further drive the HDO unit to produce the maximum product yield.

[0136] A machine learning model may be applied to the HDO and / or the catalytic dewaxing system to improve each process, to improve the coordination between the products and processes of the HDO and the catalytic dewaxing system, to optimize each process to produce a product with a target property, to protect the catalysts and useful life of the catalysts of the reactors of the HDO and the catalytic dewaxing system, as well as provide other improvements. The machine learning model may receive and use the current data and historical data of the operational parameters, operational constraints, feedstock properties and composition, and product properties and composition to generate an adjustments to an operating parameter of the HDO or catalytic dewaxing systems and predictions of the effects of those adjustments. The machine learning model may also generate predictions of the deactivation state of the catalysts of the HDO and catalytic dewaxing systems. The machine learning model or a user may implement the adjustments.

[0137] The machine learning model may manage the HDO and catalytic dewaxing systems to produce products with target properties. The target properties may include oxygen concentration, product end point, a target product yield, a cloud point, a flash point, a pour point, a freeze point, a cold filter plugging point, or a desired distribution of hydrocarbon chain lengths of the product.

[0138] In some applications, the machine learning model may use a temperature differential across a catalyst bed disposed closest to an exit of the reactor as a trigger togenerate an adjustment to an operating parameter to increase the temperature differential above the predetermined threshold when the temperature differential is less than a predetermined threshold. For some reactors, the predetermined threshold is less than 20°F / l 1.11°C. For other reactors, the predetermined threshold is less than 10°F / 5.56°C. In some applications, the machine learning model may generate the predetermined threshold based on one or more of the historical data, the operational data, or the product data. In some applications, the product data used may be a measured oxygen concentration in a product or an endpoint of the product. Specifically, the product data may indicate insufficient processing and thus, an operating temperature is too low for the current deactivation state of the catalysts. The threshold may also be used by the machine learning model to indicate insufficient hydrogen feed or fouling of equipment.

[0139] The machine learning model may also use a temperature differential across a catalyst bed disposed closest to an exit of the reactor as a trigger to generate an adjustment to an operating parameter to decrease the temperature differential below the predetermined threshold when the temperature differential is greater than a predetermined threshold. A high temperature differential may indicate a thermal excursion risk, a more reactive feed composition, inadequate quench, too much fresh feedstock, or inadequate recycle flow of processed fluid. Consequently, the adjustment may be adjusting a flow of a quench flow of hydrogen gas into the reactor or adjusting a heater outlet temperature. The adjustment may be an adjustment to a liquid recycle stream or a pressure within the reactor. Alternatively, the adjustment may be to a hydrogen to hydrocarbon ratio or to a feed rate of renewable fats, oils, and greases. The adjustment may also be to the feedstock recipe or an operating temperature of the reactor.

[0140] The machine learning model may be programmed to maximize product yield of renewable diesel or SPK through control of a temperature of the reactor so that its recommendations are weighted in favor product yield when other targets are met. The machine learning model may generate and periodically update a predictive function of diesel yield versus a temperature of the reactor.

[0141] The machine learning model may use the current data and historical data to generate a prediction of a deactivation state of the catalysts. In particular, the machine learning model may generate a prediction of a coking rate of catalysts within the reactor or a prediction of an oxygen poisoning rate of catalysts within a catalytic dewaxing reactor. The predictions may be used to determine a regeneration window of the catalysts or make an adjustment to operational parameters to compensate for the deactivation state of thecatalysts. In some applications, an amount of ammonia may be injected into a reactor based on the prediction of the deactivation state of catalysts within the reactor. The ammonia may help increase catalyst selectivity by evening out the deactivation states of the different functions of the catalysts. The prediction of the deactivation state of catalysts may be used by the machine learning model to adjust the operating temperature to achieve a target property of a product of the reactor.

[0142] The machine learning model may also access unique operational constraints of a reactor from the historical data to use in generating adjustments and making predictions. Unique operational constraints include one or more of feedstock flow rate constraints, hydrogen flow rate constraints, heating constraints, cooling constraints, and equipment condition.

[0143] In some applications, a plurality of machine learning models may be distributed throughout the HDO and catalytic dewaxing process and may be installed on individual operation controllers. Each machine learning model may communicate with and share predictions, data, and recommendations with other machine learning models and users to improve control and operation of the HDO and catalytic dewaxing systems.

[0144] FIG. 12 is a schematic diagram of a catalytic dewaxing system 2600 to enhance fluid production at a portion of a refinery, according to an embodiment of the disclosure. The catalytic dewaxing system 2600 may be used to selectively remove or convert long- chain paraffinic molecules, such as waxy, high-melting-point components, into products with improved cold flow properties. In particular, SPK, renewable diesel, may be processed in the catalytic dewaxing system 2600 to improve low-temperature properties including pour point, freeze point, and cloud point. In some applications, feedstock 2602 may include HDO diesel from an HDO unit.

[0145] In an embodiment of a catalytic dewaxing system 2600, the feedstock 2602 may be passed through an optional distribution unit 2606 where the feedstock 2602 may be selectively distributed between one or more reactors 2608, 2610, 2612. The catalytic dewaxing system 2600 may include a single reactor or multiple reactors arranged in parallel or series. Thus, reactors 2610 and 2612 are optional. With multiple reactors, a machine learning model may distribute feedstock 2602 at different feed rates that may depend on one or more of the state of catalysts within each reactor or the operational constraints unique to each reactor.

[0146] The distribution unit 2606 may include a surge drum to assist in providing constant feed rates of feedstock 2602 and may include feed heaters 2660, 2662, 2664 to provide thefeedstock 2602 at a desired temperature to the catalytic dewaxing reactors 2608, 2610, 2612. The distribution unit 2606 may include a charge pump to pressurize the feedstock 2602 to a desired pressure as it is being fed into the catalytic dewaxing reactors 2608, 2610, 2612

[0147] Hydrogen gas 2604 is also fed into each reactor 2608, 2610, 2612. As the feedstock 2602 and hydrogen gas 2604 is mixed and passes through the catalysts 2614, 2616, 2618 of each reactor 2608, 2610, 2612, the feedstock may isomerize from straight chain hydrocarbons into branched hydrocarbons. As catalyst life decreases through deactivation and the buildup of contaminants and coke on the catalysts, operating temperature may be raised to compensate and achieve one or more target properties of the resulting products of the each reactor 2608, 2610, 2612. As operating temperature increases, hydrocracking may occur more frequently, which may reduce the yield of diesel, but increase the hydrocarbon fraction suitable for use as sustainable paraffinic kerosene (“SPK”) 2626.

[0148] Once processed in the reactors 2608, 2610, 2612, the processed feedstock 2602 is passed to a separator 2620 that separates the unconsumed hydrogen from the processed feedstock 2602 and recycles the unconsumed hydrogen gas 2604 to be mixed with fresh feedstock 2602 or used in a quench flow in one or more of the reactors 2608, 2610, 2612.

[0149] From the separator 2620, the processed feedstock 2602 may be separated in a stabilizer 2622 or other distillation system into products. The products include light hydrocarbons 2624 and renewable diesel 2628. In some configurations, a third cut may be taken as SPK 2626. In operating the stabilizer 2622, the cutoff may be set to allow some naphtha to be part of the renewable diesel 2628 as along as target properties are met. Consequently, greater yield may be achieved through optimized control of the stabilizer 2622.

[0150] Sensor packages 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2644, 2646, 2648, 2650, 2652, 2654, 2656 may be positioned throughout the catalytic dewaxing system 2600 to measure, analyze, and sample the feedstock 2602, hydrogen gas 2604, operating parameters of equipment of the catalytic dewaxing system 2600, and the products, including light hydrocarbons 2624, SPK 2626, and renewable diesel 2628. The sensor packages 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2644, 2646, 2648, 2650, 2652, 2654, 2656 may include temperature sensors; such as resistance temperature detectors, thermocouples, and infrared sensors; pressure sensors, such as hydrostatic pressure sensors and differential pressure sensors; flow rate sensors, such as coriolis flow meters, ultrasonic flow meters, and turbine flow meters for metering flow; density and composition analyzers,such as sulfur analyzers, refractometers, spectrometers, including near infrared spectrometry, and gas chromatographs; and sampling units for analyzing the composition of a sample through testing, distillation, or obtaining physical samples from the feedstocks 2602 to be analyzed in a lab or by a sample collection and analysis assembly.

[0151] In particular, sensor package 2630 may be gather data from upstream processes of the feedstock 2602 that may be used by a machine learning model to infer properties of the feedstock 2602. Additionally, the sensor package may generate feedstock data that may be used to predict or measure the composition of the feedstock 2602.

[0152] Sensor packages 2646, 2648, 2650, 2652, 2654, 2656 may be used to determine the temperature and pressure differential between different points in the catalysts 2614, 2616, 2618 and the change in pressure and temperature differentials over time. The catalysts 2614, 2616, 2618 may include one or more catalyst beds. Sensor packages 2646, 2648, 2650, 2652, 2654, 2656 may also be used to determine the weighted average bed temperature of the catalysts 2614, 2616, 2618 as well as the inlet and outlet temperatures of the reactors 2608, 2610, 2612. This reactor data may be used by a machine learning model to predict a state of catalyst deactivation or predict the remaining life and recommended operating temperatures and pressures of the reactors 2608, 2610, 2612 to optimize isomerization, achieve a target property of a product, optimize product yield distribution, or maximize yield of a specific product of the catalytic dewaxing system 2600. For example, for a period of time, it may be more valuable to facilitate hydrocracking to produce more SPK 1190, while at other times, it may be better to minimize hydrocracking to produce more renewable diesel 1106.

[0153] Sensor packages 2632, 2634, 2636 may be disposed to analyze, sample, and measure the properties of the products, including composition of the products, of each of the reactors 2608, 2610, 2612. This data may also be used to further verify predictions about the state of catalyst deactivation or predict the remaining life of the catalysts 2614, 2616, 2618, as well as the effect of adjustments to the operating parameters of a reactor. Each reactor 2608, 2610, 2612 may unique design characteristics and equipment that respond differently than predicted to adjustments to operating parameters. Consequently, a machine learning model may be trained on the specific historical data of a reactor, as well as general data for managing types of processes. The historical data may include feedstock data, operational data, and product data.

[0154] Sensor package 2638 may be disposed to measure the purity of the recycled hydrogen gas 2604 as well as the composition of the other constituent parts of the recycledhydrogen gas 2604. Sensor packages 2640, 2642, 2644 may be used to determine yields, specific properties, and composition of the products, including light hydrocarbons 2624, the SPK 2626, and the renewable diesel 2628. For example, sensor packages 2642, 2644 may include cloud point, freeze point analyzers, density analyzers, and flash point analyzers. This product data may facilitate faster and more accurate adjustments by a machine learning model to the operating parameters of the reactors 2608, 2610, 2612 to achieve desired target product properties and yields. Each of the products of the catalytic dewaxing system 2600 may be sent for further processing and separation into higher purity products, sent to blending pools to create blended products, or offered for sale.

[0155] The catalytic dewaxing system 2600 may include a number of process constraints for each equipment unit. For example, available hydrogen gas may be constrained. The distribution unit 2606 may be constrained in capacity to hold and distribute feedstock 2602 to any specific reactor. The reactors may be constrained in the heat available to heat the feedstocks to a desired temperature, and conversely, cooling may be constrained in the amount of excess heat that may be removed from a process to control the operating temperatures within each of the reactors. The separator 2620 may be limited in its ability to purify, recycle, and compress hydrogen gas 2604. The stabilizer 2622 may be constrained in maintaining specific product cutoffs.

[0156] FIG. 13 A and FIG. 13B are simplified diagrams of control systems to enhance to enhance fluid production at refinery, according to an embodiment of the disclosure. As noted, control system 2700 may include an operation controller 2701. Further, the operation controller 2701 may connect to one or more sensors 2716A, 2716B, and up to 2716N, one or more devices 2718 A, 2718B, and up to 2718N (such as flow control devices and / or temperature control devices), one or more equipment 2720A, 2720B, and up to 2720N, one or more analyzers 2722A, 2722B, and up to 2722N, and one or more predictive controls 2714A, 2714B, and up to 2714N. The operation controller 2701 may include memory 2704 and one or more processors 2702. The memory 2704 may store instructions executable by one or more processors 2702. In an example, the memory 2704 may be a non-transitory machine-readable storage medium. As noted, the memory 2704 may store or include instructions executable by the processor 2702.

[0157] As used herein, “signal communication” refers to electric communication such as hardwiring two components together or wireless communication, as understood by those skilled in the art. For example, wireless communication may be Wi-Fi®, Bluetooth®, ZigBee, or other near-field communications. In addition, signal communication mayinclude one or more intermediate controllers or relays disposed between elements in signal communication.

[0158] The memory 2704 may include or store sample and data collection and instructions 2706. Upon execution of such instructions, the operation controller 2701 may obtain samples associated with each equipment 2720A, 2720B, and up to 2720N. Further, the operation controller 2701 may obtain data from the one or more sensors 2716A, 2716B, and up to 2716N and / or one or more flow control devices 2718 A, 2718B, and up to 2718N. Upon collection of the samples, the operation controller 2701 may send the sample to one of the one or more analyzers 2722A, 2722B, and up to 2722N. The one of the one or more analyzers 2722A, 2722B, and up to 2722N may then analyze the sample and generate properties and / or a spectra.

[0159] The operation controller 2701 may connect to and receive data from the one or more predictive controls 2714A, 2714B, and up to 2714N. In an embodiment, the operation controller 2701 may receive the output from each trained machine learning model of each the predictive controls 2714A, 2714B, and up to 2714N. In an embodiment, the output may comprise a vector or, in other embodiments, a value indicative of a parameter adjustment.

[0160] The memory 2704 may include or store trained machine learning models 2708. The trained machine learning models 2708 may include at least one trained machine learning model to generate an output indicative of parameter and / or feed adjustment. The operation controller 2701 may apply the data, properties, spectra, and / or the output of each trained machine learning model from one or more predictive controls 2714A, 2714B, and up to 2714N to the trained machine learning models 2708 to generate an output indicative of parameter adjustments and / or feed adjustment.

[0161] The memory 2704 may include or store parameter adjustment instructions 2710. Upon generation of the output, the operation controller 2701 may adjust parameters associated with equipment at the refinery. Further the memory 2704 may include or store feed adjustment instructions 2712 to adjust feed based on the output.

[0162] In FIG. 13B, predictive controls 2714 may connect to subsets of each of the components described in FIG. 13A. For example, the predictive controls 2714 may connect to a subset of the sensors 2736A, 2736B, and up to 2736N, a subset of the devices 2738A, 2738B, and up to 2738N, a subset of the equipment 2740A, 2740B, and up to 2740N, and / or a subset of the analyzers 2742A, 2742B, and up to 2742N. The predictive controls 2714 may include a trained machine learning model 2728 and instructions stored in a memory 2726 and executable by a processor 2724.

[0163] The instructions may include sample and data collection instructions 2730, which when executed cause the predictive controls 2714 to collect various data points and / or properties. Based on application of the data received to the trained machine learning model 2728 and, in some embodiments, an output from the operation controller 2701, the predictive controls 2714 may supply or provide the output to the operation controller 2701.

[0164] FIG. 14 is a flow chart illustrating enhanced fluid production at a refinery, according to an embodiment of the disclosure. Unless otherwise specified, the actions of method 2800 may be completed within operation controller 2701 and / or predictive controls 2714 of FIG. 13 A and 13B. Specifically, method 2800 may be included in one or more programs, protocols, or instructions loaded into the memory 2704 of operation controller 2701 and executed on the processor or one or more processors of the operation controller 2701. In other embodiments, method 2800 may be implemented in or included in components of FIGS. 1A-21. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order and / or in parallel to implement the methods.

[0165] At block 2802, the one or more predictive controls may each obtain data from corresponding sources. In such an example, each of the predictive controls may poll corresponding sensors, flow control devices, equipment, temperature control devices, and / or other data generating sources related to a corresponding refinery unit to obtain data therefrom. Further, the predictive controls may initiate sample collection for inputs or feedstock, as well as intermediaries and / or products produced by the corresponding source. Such a sample may be analyzed by one or more spectroscopic analyzers, which may subsequently produce properties and / or spectra of the samples.

[0166] At block 2804, once each sub-controller has obtained data, each sub-controller may apply that data, which may also include one or more different properties and / or spectra, to a corresponding machine leaning model stored therein. In another embodiment, the targeted product and / or targeted properties may be applied, in addition to the data described above, to the machine learning model of the predictive controls. The machine learning model may produce vectors, indicators, and / or other values indicative of one or more parameters that may cause the corresponding source to produce a targeted product. Each of the parameters output from the trained machine learning model may correspond to some aspect of the source. For example, the parameters may include temperature, amounts of other fluids used in the operation (for example, hydrogen or alkanes, among others), pressure, flow rate, residence time, feedstock used, and / or intermediaries used.

[0167] At block 2806, each of the predictive controls may determine whether the subparameters are different than currently set parameters. If the parameters are different, then, in some embodiments and at block 2808, each of the predictive controls may adjust one or more of the equipment, devices, or fluids. In such embodiments, the predictive controls may provide the adjustments to an equipment and device controller. In another embodiment, the predictive controls may provide the adjustments to the operation controller 2701 (FIG. 12A) to perform such adjustments. In another embodiment, rather than or in addition to adjusting the equipment or devices, the predictive controls may provide the trained machine learning model output to the operation controller 2701.

[0168] At block 2810, the operation controller 2701 (FIG. 12A) may determine updated parameters and / or fluid compositions and / or ratios based on application of the obtained data, as well as the outputs from each of the predictive controls, to a trained machine learning model. The operation controller 2701, in some embodiments, may first obtain data from the all, substantially all, a portion of the refinery, or from a corresponding operation. Data, as noted above, may include data from sensors, meters, equipment, and / or other devices, as well as properties and / or spectra obtained from samples taken from each unit within the refinery. Further, the operation controller 2701 may obtain the output of each model of each predictive controls. Once the operation controller 2701 obtains all relevant data, the operation controller 2701 may determine the updated parameters based on application of that data to a machine learning model.

[0169] At block 2812, the operation controller 2701 (FIG. 12A) may determine whether the output of the model indicates updates to the parameters or whether the determined parameters are different than the current parameters. For example, the operation controller 2701 may compare the values of the updated parameters to the currently set parameters. If the parameters are different, then at block 2814, the operation controller 2701 may adjust the devices, equipment, or fluid within the refinery to the updated parameters.

[0170] In an embodiment, refinery operations may occur continuously or substantially continuously. As such, method 2800 may be an iterative and continuous process that occurs in real-time or near real-time. As target products change and / or other aspects of the refinery change, parameters may continue to be adjusted via method 2800.

[0171] FIG. 15 is a schematic diagram of a machine learning model 3800 according to one embodiment. As used herein, a “machine learning model” refers to a computer algorithm or model, such as a classification model, a regression model, a language model, an object detection model, a multi-modal model, or an artificial intelligence, that can be trained andtuned based on training input to approximate unknown functions. For example, a machine learning model may refer to a neural network or other machine learning algorithm or architecture that learns and approximates complex functions and generates outputs based on a plurality of inputs provided to the machine learning model. Further, a “machine learning model” may refer to one or multiple machine learning models that cooperatively generate one or more outputs based on corresponding inputs. For example, a machine learning model may refer to any system architecture having multiple discrete machine learning components that consider different kinds of information or inputs.

[0172] The machine learning model 3800 is connected to a historical database 3802. The historical database 3802 may include multiple separate databases of information including process data, incident information and analysis, business information, demand planning, historical forecast information, historical pricing and purchasing data, and process training information. In some embodiments, the historical database 3802 may be a copy of historical databases that are maintained and updated by the machine learning model 3800. The machine learning model 3800 is also connected to a training database 3803 that includes the process data and historical information that is used for training of the machine learning model 3800.

[0173] As discussed in relation to other figures of this disclosure, the machine learning model 3800 may be used to analyze and improve specific processes, process controllers, and process interactions. The machine learning model 3800 may also be used as a refinery controller.

[0174] In this embodiment, the machine learning model 3800 may receive feedstock sensor, sample, and process data 3804 from a feedstock process controller 3814 or directly from the sensors, sampling and testing systems, and labs obtaining data from the feedstock process. A feedstock process is any process preceding the targeted process under review by the machine learning model 3800. For example, the feedstock process may include one or more of atmospheric distillation, vacuum distillation, filtering, hydrotreater, mercaptan treater, splitter, stripper, and separator.

[0175] Further, the machine learning model 3800 may receive targeted process sensor, sample, and process data 3806 from a targeted process controller 3816 or directly from the sensors, sampling and testing systems, and labs obtaining data from the targeted process. The machine learning model 3800 may also receive subsequent process sensor, sample, and process data 3808 from a targeted process controller 3816 or directly from the sensors, sampling and testing systems, and labs obtaining data from the targeted process. Thefeedstock sensor, sample, and process data 3804, the targeted process sensor, sample, and process data 3806, and the subsequent process sensor, sample, and process data 3808 may include operating pressure and temperature data for the components of each process and sub-process. The feedstock sensor, sample, and process data 3804, the targeted process sensor, sample, and process data 3806, and the subsequent process sensor, sample, and process data 3808 may include material composition data describing the material moving through the process, such as feed rates, and quantities and percentages of contaminants, reactants, and the hydrocarbons moving through the process.

[0176] The machine learning model 3800 may also access business data 3810. Business data 3810 may include demand planning information, forecasting information for various products, current pricing information, product distribution information, and information regarding the current and anticipated regulatory landscape. Business data 3810 may also include information regarding the costs, availability, and location of storage, disposal, and in some cases carbon capture options of waste products from each process. Business data 3810 may also include historical and current costs of each process and the refinery products, as well as current and historical market pricing for each product. Business data 3810 may also include the inventory levels of various products that may be stored in the storage tanks of the various blending pools.

[0177] The machine learning model 3800 may also access lab data 3811 that is obtained from laboratory analysis of samples collected from processes within a refinery and products produced by those processes. Lab data 3811 may include chemical composition and component distribution data about each sample, as well as information about when and where the sample was taken, tests performed on the sample, and sensor data from the process at the time the sample was taken.

[0178] The machine learning model 3800 may also access weather data 3813 as the ambient temperature, pressure, and solar radiation may also affect the temperature and pressure of feedstocks, products, processes, and storage tanks within a refinery. The machine learning model may build algorithms to predict the effect of weather on process operating parameters, feedstocks, the resulting products from those processes, and the effect of weather on the storage of products within the blending pools.

[0179] The machine learning model 3800 may publish to and receive instructions from an engineering gateway 3812. The engineering gateway 3812 may act as a user interface for engineers to review process and machine learning model 3800 data, recommendations, warnings, and requests. A user may use the engineering gateway 3812 to assist the machinelearning model 3800 in refining and tuning its algorithms to better predict and adjust each process in response to changes in ambient weather, demand seasonality, and composition and quality of feedstocks including the crude oil received for processing into refined hydrocarbons. The engineering gateway may also be used to facilitate active learning by the machine learning model 3800.

[0180] The machine learning model 3800 may access, receive data from, and provide instructions to a feedstock process controller 3814, a targeted process controller 3816, and a subsequent process controller 3818. In some embodiments, the machine learning model 3800 may adjust an algorithm used by the targeted process controller 3816 based on changes identified from the adjusted algorithm. Once an adjusted algorithm has been selected by the machine learning model 3800, the machine learning model 3800 requests approval to implement the adjusted algorithm through the engineering gateway 3812. Upon receipt of approval, the machine learning model 3800 saves a copy of the approval information in the historical data 3802 and sends the approved adjusted algorithm to the targeted process controller 3816.

[0181] The machine learning model 3800 includes a data analysis module 3822. The data analysis module 3822 may be used by the machine learning model 3800 to review data and identify data that may be considered outliers and disregarded. Once the data set has been reviewed and amended to remove outlier data, the machine learning model 3800 may save the revised data set in either the training data 3803 or the historical data 3802 for retraining of the machine learning model 3800, for analysis and adjustment of a targeted process, or for later use.

[0182] The machine learning model 3800 includes an interpolation module 3824. The interpolation module 3824 may be used to analyze a data set and interpolate missing data from the data set. The interpolation module 3824 may compare the historical data 3802 with feedstock sensor, sample, and process data 3804, the targeted process sensor, sample, and process data 3806, and the subsequent process sensor, sample, and process data 3808 to identify process changes. The interpolation module 3824 may be able to identify and flag small deviations for further investigation. The interpolation module 3824 works with the communication module 3828 to publish the data regarding the flagged deviation for user review and guidance. For example, the interpolation module 3824 may be used to identify miscalibrated sensors or test data that may be inaccurate. The machine learning model 3800 may accomplish this by identifying and labeling data as outliers. Labeled data may be associated with a sensor or data and then reported to a user through the engineering gateway3812. The machine learning model 3800 may use the interpolation module 3824 to identify components that may be wearing out and catalysts that may be deactivated or poisoned. The interpolation module 3824 may also communicate through the communication module 3828 with the engineering gateway 3812 to identify these potential process concerns to a user for further investigation.

[0183] The machine learning model 3800 includes a prediction module 3826. The prediction module 3826 may analyze the feedstock sensor, sample, and process data 3804, the targeted process sensor, sample, and process data 3806, and the subsequent process sensor, sample, and process data 3808 to predict changes in a process. For example, temperature excursions may be predicted in a process and the prediction module 3826 may communicate through the communication module 3828 with the engineering gateway 3812 to notify a user of a predicted temperature excursion and recommending process changes to avoid the temperature excursion. A user may accept the recommended process changes through the engineering gateway 3812 or provide different instructions for the machine learning model 3800 to implement. In predicting changes in a process, prediction module 3826 may recommend regeneration or replacement of a catalyst or repair or replacement of a component of a process as part of a maintenance procedure. The prediction module 3826 may provide a recommended maintenance window for the maintenance procedure to occur.

[0184] The machine learning model 3800 includes a communication module 3828. The communication module 3828 may translate recommendations, instructions, and data from other modules from machine code into a human language or convert data into graphs and other visual communication elements. The communication module 3828 may send or publish communications to the engineering gateway 3812. The communication module 3828 may also communicate with the various process controllers and other models used by the refinery.

[0185] In some embodiments, the machine learning model 3800 includes a confidence module 3830. The confidence module 3830 may review the analysis and recommendations of the different modules of the machine learning model 3800 and assign a confidence level to the analysis and recommendations. For example, the confidence module 3830 may produce SHAP values, or use LIME or anchors to analyze each algorithm that is adjusted or created by the machine learning model 3800. The results may be published by the communication module 3828 to the engineering gateway 3812 to assist a user in reviewing each algorithm and the changes recommended by the machine learning model 3800.

[0186] In some embodiments, the machine learning model 3800 includes a regulatory module 3832. The regulatory module 3832 may access the business data 3810 to assist in regulatory compliance. For example, the regulatory module 3832 may flag a process that may be predicted by the prediction module 3826 to move out of compliance. The regulatory module 3832 may issue a warning through the communication module 3828 to the engineering gateway 3812. The regulatory module 3832 may also identify windows of time when regulations are lessened and recommend changes to process parameters to reduce process costs. The regulatory module 3832 may make recommendations to gasoline blending pool controllers, diesel blending pool controllers, and jet fuel blending pool controllers to adjust blending parameters in line with upcoming regulatory changes. Further, the regulatory module 3832 may make recommendations for various process controllers to adjust their parameters to promote the production of blend components inline with demand that may accompany regulatory changes.

[0187] In some embodiments, the machine learning model 3800 includes a forecasting module 3834. The forecasting module 3834 may analyze business data 3810 and recommend that various process controllers adjust their parameters to promote the production of components that may be in greater demand. The forecasting module 3834 may also recommend maintenance windows for equipment producing products that may be in low demand during specific time frames. The forecasting module 3834 may forecast pricing for various components based on the historical data 3802 and the business data 3810 and publish the forecasted pricing via the communication module 3828 to the engineering gateway 3812.

[0188] In some embodiments, the machine learning model 3800 may include an authority module 3836 and additional modules 3838, such as a display module for converting data into graphical representations of a data set or a translation module for converting data between different languages, formats, or units. The authority module 3836 may track user instructions and approvals for various instructions and changes to be made by the machine learning model 3800 to the various controllers throughout the refinery. In some applications, the authority module 3836 may consider a recommendation from a module of the machine learning model 3800 and automatically authorize the machine learning model 3800 to issue an instruction to the targeted process controller 3816 to make a change to an associated process. In other applications, the authority module 3836 may direct the communication module 3828 to request approval through the engineering gateway 3812before a recommendation may be implemented and instructions sent to the targeted process controller 3816.

[0189] The machine learning model 3800 provides better process control and faster changes to processes within a refinery. The machine learning model 3800 also provides better data and clarity to changes and predicted changes within the nonlinear processes found within a refinery.

[0190] FIG. 16 is a flow chart of a method, according to at least one embodiment of the present disclosure. The method includes receiving current operational data of an hydrodeoxygenation process (HDO) at 4010 and generating, by a machine learning model, a prediction of a deactivation state of catalysts within an HDO reactor based on the current operational data of the HDO at 4020. The machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO. The method further includes generating, by the machine learning model, an adjustment to a parameter of the HDO to achieve a target property of an HDO product based on the prediction of the deactivation state of catalysts within the HDO reactor at 4030. The machine learning model may perform all of the steps of the method of Fig. 16.

[0191] FIG. 17 is a flow chart of another method according to one embodiment, according to at least one embodiment of the present disclosure. A method includes receiving current operational data of an hydrodeoxygenation process (HDO) and HDO product data indicative of a property of an HDO product at 4110 and generating, by a machine learning model, an adjustment to an operating parameter of an HDO reactor to achieve a target property of the HDO product based on the current operational data of the HDO reactor and the HDO product data at 4120. The machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO. The method includes generating, by a machine learning model, a prediction of effects of the adjustment to the operating parameters of an HDO reactor based on the current operational data of the HDO reactor and the HDO product data at 4130. The machine learning model may perform all of the steps of the method of Fig. 17.

[0192] FIG. 18 is flow chart of yet another method, according to at least one embodiment of the present disclosure. A method includes receiving current operational data of a catalytic dewaxing system 4210 and generating, by a machine learning model, a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system 4220. The machine learning model istrained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system. The method includes generating, by the machine learning model, an adjustment to a feed rate of a feedstock into the catalytic dewaxing system to achieve a target property of a catalytic dewaxing product based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system at 4230 and implementing the adjustment to the feed rate of the feedstock into the catalytic dewaxing system at 4240. The machine learning model may perform all of the steps of the method of Fig. 18.

[0193] FIG. 19 is a flow chart of a method, according to at least one embodiment of the present disclosure. The method includes receiving current operational data of a catalytic dewaxing system at 4310 and receiving feedstock data indicative of a property of a feedstock being sent to a catalytic dewaxing reactor at 4320. The method includes receiving catalytic dewaxing product data indicative of a property of a catalytic dewaxing product at 4330. A machine learning model generates an adjustment to an operating parameter of the catalytic dewaxing system to achieve a target property of the catalytic dewaxing product based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data at 4340. The machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system and generates a prediction of effects of the adjustment to the operating parameters of the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data at 4350. The machine learning model may perform all of the steps of the method of Fig. 19.

[0194] FIG. 20 is a flow chart of another method according to one embodiment, according to at least one embodiment of the present disclosure. The method includes receiving current operational data of an hydrodeoxygenation process (HDO) at 4410. A machine learning model generates a prediction of a deactivation state of catalysts within an HDO reactor based on the current operational data of the HDO at 4420. The machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO at and generates an adjustment to a parameter of the HDO to achieve a target property of an HDO product based on the prediction of the deactivation state of catalysts within the HDO reactor at 4430. The machine learning model may perform all of the steps of the method of Fig. 20.

[0195] FIG. 21 is flow chart of yet another method, according to at least one embodiment of the present disclosure. The method includes receiving current operational data of a catalytic dewaxing system at 4510. A machine learning model generates a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system at 4520 and is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system. The machine learning model generates an adjustment to a parameter of the catalytic dewaxing system to achieve a target property of a catalytic dewaxing product based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system at 4530. The method further includes implementing the adjustment to the parameter of the catalytic dewaxing system at 4540. The machine learning model may perform all of the steps of the method of Fig. 21.

[0196] In the drawings and specification, several embodiments of systems and methods to provide in-line mixing of hydrocarbon liquids have been disclosed, and although specific terms are employed, the terms are used in a descriptive sense only and not for purposes of limitation. Embodiments of systems and methods have been described in considerable detail with specific reference to the illustrated embodiments. However, it will be apparent that various modifications and changes may be made within the spirit and scope of the embodiments of systems and methods as described in the foregoing specification, and such modifications and changes are to be considered equivalents and part of this disclosure.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A method compri sing : receiving current operational data of an hydrodeoxygenation process (HDO) and HDO product data indicative of a property of an HDO product; generating, by a machine learning model, an adjustment to an operating parameter of an HDO reactor to achieve a target property of the HDO product based on the current operational data of the HDO reactor and the HDO product data, wherein the machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO; and generating, by a machine learning model, a prediction of effects of the adjustment to the operating parameters of an HDO reactor based on the current operational data of the HDO reactor and the HDO product data.

2. The method of claim 1, implementing the adjustment to the operating parameter of the HDO reactor.

3. The method of any of claims 1 to 2, wherein a feedstock for the HDO is a recycled product of the HDO, wherein the adjustment to the operating parameter of the HDO reactor reduces a temperature of the HDO reactor by adjusting a feed rate of the feedstock.

4. The method of any of claims 1 to 3, wherein the target property of the HDO product is an oxygen concentration in the HDO product.

5. The method of any of claims 1 to 4, wherein the current operational data includes a temperature differential across a catalyst bed disposed closest to an exit of the HDO, wherein when the temperature differential is less than a predetermined threshold the machine learning model is triggered to generate an adjustment to an operating parameter of the HDO to increase the temperature differential above the predetermined threshold.

6. The method of claim 5, wherein the predetermined threshold is less than 20°F / l 1.11°C.

7. The method of claim 6, wherein the predetermined threshold is less than 10°F / 5.56°C.

8. The method of claim 5, further comprising: receiving HDO product data indicative of a property of an HDO product; and generating the predetermined threshold based on the property of the HDO product.

9. The method of claim 8, wherein the property of the HDO product is a measured oxygen concentration.

10. The method of claim 8, wherein the property of the HDO product is an endpoint of the HDO product.

11. The method of any of claims 1 to 10, wherein the current operational data includes a temperature differential across a catalyst bed disposed closest to an exit of the HDO, wherein when the temperature differential is greater than a predetermined threshold the machine learning model is triggered to generate an adjustment to an operating parameter of the HDO to decrease the temperature differential below the predetermined threshold.

12. The method of any of claims 1 to 11, wherein the feedstock is a renewable fluid.

13. The method of any of claims 1 to 12, wherein the machine learning model is programmed to maximize product yield of HDO diesel through control of a temperature of the HDO reactor.

14. The method of any of claims 1 to 13, further comprising generating, by the machine learning model, a predictive function of HDO diesel yield versus a temperature of the HDO reactor.

15. The method of any of claims 1 to 14, wherein the adjustment to the parameter of the HDO is an adjustment to a temperature of the HDO reactor by adjusting a flow of a quench flow of hydrogen gas into the HDO reactor.

16. The method of any of claims 1 to 15, wherein the adjustment to the parameter of the HDO is an adjustment to a heater outlet temperature.

17. The method of any of claims 1 to 16, wherein the adjustment to the parameter of the HDO is an adjustment to a plurality of quench flows.

18. The method of any of claims 1 to 17, wherein the adjustment to the parameter of the HDO is an adjustment to a liquid recycle stream.

19. The method of any of claims 1 to 18, wherein the adjustment to the parameter of the HDO is an adjustment to a pressure within the HDO.

20. The method of any of claims 1 to 19, wherein the adjustment to the parameter of the HDO is an adjustment to a hydrogen to hydrocarbon ratio.

21. The method of any of claims 1 to 20, further comprising: generating, by the machine learning model, a demand for hydrogen gas within the HDO; and adjusting a feed rate of hydrogen gas into one or more of an inlet of the HDO reactor or a quench flow into the HDO reactor.

22. The method of any of claims 1 to 21, further comprising generating, by the machine learning model, an adjustment to a feed rate of the feedstock into the HDO reactor based on cooling constraints of the HDO reactor.

23. The method of any of claims 1 to 21, further comprising generating, by the machine learning model, an adjustment to a feed rate of the feedstock into the HDO reactor is based on hydrogen gas availability.

24. The method of claim 23, further comprising receiving data indicative of hydrogen gas availability to the HDO.

25. The method of any of claims 1 to 24, receiving HDO product data indicative of a property of an HDO product being sent to a catalytic dewaxing reactor.

26. The method of claim 25, wherein generating, by the machine learning model, the adjustment to the parameter of the HDO based on the HDO product data.

27. The method of claim 26, wherein the parameter is one or more of a temperature or a pressure of the HDO reactor.

28. The method of any of claims 1 to 27, further comprising generating, by the machine learning model, a prediction of a deactivation state of catalysts within the HDO reactor based on the current operational data of the HDO, wherein generating, by the machine learning model, the adjustment to the parameter of the HDO is further based on the prediction of the deactivation state of catalysts within the HDO reactor.

29. The method of any of claims 1 to 28, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the HDO reactor, wherein the adjustment to the parameter of the HDO is further based on the prediction of the coking rate of the catalysts of the HDO.

30. The method of any of claims 28 to 29, further comprising generating, by the machine learning model, an amount of ammonia to be injected into the HDO reactor based on the prediction of the deactivation state of catalysts within the HDO reactor.

31. The method of claim 30, further comprising injecting the amount of ammonia into the HDO reactor.

32. The method of claim 31, generating, by the machine learning model, an adjustment to an operating temperature of the HDO reactor based on the prediction of the deactivation state of catalysts within the HDO reactor and the amount of ammonia injected into the HDO reactor.

33. The method of any of claims 1 to 32, further comprising: receiving current operational data of a catalytic dewaxing system; receiving HDO product data indicative of a property of an HDO product being sent to a catalytic dewaxing reactor;receiving catalytic dewaxing product data indicative of a property of a catalytic dewaxing product; generating, by a machine learning model, an adjustment to an operating parameter of the catalytic dewaxing system to achieve a target property of the catalytic dewaxing product based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data, wherein the machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system; and generating, by a machine learning model, a prediction of effects of the adjustment to the operating parameters of the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data.

34. The method of claim 33, wherein the adjustment to operating parameter of the catalytic dewaxing system is further based on unique operational constraints of a catalytic dewaxing reactor.

35. The method of claim 34, wherein the unique operational constraints of the catalytic dewaxing reactor include one or more of feedstock flow rate constraints, hydrogen flow rate constraints, heating constraints, cooling constraints, and equipment condition.

36. The method of any of claims 33 to 35, wherein the machine learning model is programmed to optimize product yield of renewable diesel through control of a temperature of the catalytic dewaxing reactor.

37. The method of any of claims 33 to 36, further comprising generating, by the machine learning model, a predictive function of renewable diesel yield versus a temperature of the catalytic dewaxing reactor.

38. The method of any of claims 33 to 37, further comprising implementing the adjustment to the parameter of the catalytic dewaxing reactor.

39. The method of any of claims 33 to 38, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a temperature of the catalytic dewaxing reactor by adjusting a flow of hydrogen gas into the catalytic dewaxing reactor.

40. The method of any of claims 33 to 39, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a heater outlet temperature.

41. The method of any of claims 33 to 40, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a pressure within the catalytic dewaxing reactor.

42. The method of any of claims 33 to 41, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a hydrogen to hydrocarbon ratio.

43. The method of any of claims 33 to 42, further comprising generating, by the machine learning model, a demand for hydrogen gas within the catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a feed rate of hydrogen gas based on the generated demand for hydrogen gas within the catalytic dewaxing reactor.

44. The method of any of claims 33 to 43, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the coking rate of the catalysts.

45. The method of any of claims 33 to 44, further comprising generating, by the machine learning model, a prediction of an oxygen poisoning rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the oxygen poisoning rate of the catalysts.

46. The method of any of claims 33 to 45, wherein the target property of the catalytic dewaxing product includes a cloud point of the catalytic dewaxing product.

47. The method of any of claims 33 to 46, wherein the target property of the catalytic dewaxing product includes a flash point of the catalytic dewaxing product.

48. The method of any of claims 33 to 47, wherein the target property of the catalytic dewaxing product includes a pour point of the catalytic dewaxing product.

49. The method of any of claims 33 to 48, wherein the target property of the catalytic dewaxing product includes desired distribution of hydrocarbon chain lengths in the catalytic dewaxing product.

50. The method of any of claims 33 to 49, wherein the target property of the catalytic dewaxing product includes a freeze point of the catalytic dewaxing product.

51. The method of any of claims 33 to 50, wherein the target property of the catalytic dewaxing product includes a cold filter plugging point of the catalytic dewaxing product.

52. A memory including instructions that causes a processor to perform the instructions including the method of any of claims 1 to 51.

53. A computing system including a processor and a memory including instructions that cause the processor to perform the instructions, wherein the instructions include the method of any of claims 1 to 51.

54. A system for enhancing fluid production for an hydrodeoxygenation (HDO) operation, the system comprising: an HDO unit to receive a feed and produce one or more fluids; a plurality of sensors to measure a parameter associated with the HDO unit and each positioned at one of (a) proximate the HDO unit or (b) within the HDO unit; a plurality of refinery operation control devices each positioned proximate and downstream or upstream of the HDO unit and to control aspects of fluid flowing to or from the HDO unit; one or more sample collection assemblies to collect samples of the fluid associated with the HDO unit; one or more sample analysis assemblies to analyze each collected sample to provide properties of collected samples; anda HDO controller in signal communication with the HDO unit, the plurality of sensors, the plurality of refinery operation control devices, and the one or more sample analysis assemblies, and storing a trained machine learning model, the HDO controller configured to: determine an output including predicted properties of the feed and the parameters of the refinery operation control device and the HDO unit based on application of one or more of (i) data measured by the plurality of sensors, (ii) data corresponding to analysis from the one or more sample analysis assemblies, or (iii) a target product and corresponding target properties to the trained machine learning model, and adjust one or more of an amount of feed or type of feed and parameters associated with the refinery operation control device and HDO unit based on the output to enhance fluid production.

55. A method compri sing : receiving product data indicative of a property of a product of an hydrodeoxygenation process (HDO); receiving feedstock data indicative of a feedstock for processing by the HDO; generating, by a machine learning model, an adjustment to a parameter of the HDO based on the feedstock data and the product data to improve product yield from the HDO, wherein the machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO; and implementing the adjustment to the parameter of the HDO.

56. The method of claim 55, wherein the machine learning model is programmed to maximize product yield of HDO diesel through control of a reactor temperature.

57. The method of any of claims 55 to 56, further comprising generating, by the machine learning model, a predictive function of HDO diesel yield versus reactor temperature.

58. The method of any of claims 55 to 57, wherein the adjustment to the parameter of the HDO is an adjustment to reactor temperature by adjusting a flow of a quench flow of hydrogen gas into a reactor of the HDO.

59. The method of any of claims 55 to 58, wherein the adjustment to the parameter of the HDO is an adjustment to a heater outlet temperature.

60. The method of any of claims 55 to 59, wherein the adjustment to the parameter of the HDO is an adjustment to a plurality of quench flows.

61. The method of any of claims 55 to 60, wherein the adjustment to the parameter of the HDO is an adjustment to a liquid recycle stream.

62. The method of any of claims 55 to 61, wherein the adjustment to the parameter of the HDO is an adjustment to a pressure within the HDO.

63. The method of any of claims 55 to 62, wherein the adjustment to the parameter of the HDO is an adjustment to a hydrogen to hydrocarbon ratio.

64. A method comprising: receiving current operational data of an hydrodeoxygenation process (HDO); generating, by a machine learning model, a prediction of a deactivation state of catalysts within an HDO reactor based on the current operational data of the HDO, wherein the machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO; and generating, by the machine learning model, an adjustment to a feed rate of a feedstock into the HDO reactor to achieve a target property of an HDO product based on the prediction of the deactivation state of catalysts within the HDO reactor.

65. The method of claim 64, further comprising implementing the adjustment to the feed rate of the feedstock into the HDO reactor.

66. The method of any of claims 64 to 65, wherein the feedstock is a recycled product of the HDO, wherein the adjustment to a feed rate of the feedstock into the HDO reactor reduces a temperature of the HDO reactor.

67. The method of any of claims 64 to 66, wherein the target property is an oxygen concentration in the HDO product.

68. The method of any of claims 64 to 67, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the HDO reactor, wherein the adjustment to a feed rate of a feedstock into the HDO reactor is further based on the prediction of the coking rate of the catalysts of the HDO.

69. The method of any of claims 64 to 68, further comprising generating, by the machine learning model, an amount of ammonia to be injected into the HDO reactor based on the prediction of the deactivation state of catalysts within the HDO reactor.

70. The method of claim 69, further comprising injecting the amount of ammonia into the HDO reactor.

71. The method of claim 70, generating, by the machine learning model, an adjustment to an operating temperature of the HDO reactor based on the prediction of the deactivation state of catalysts within the HDO reactor and the amount of ammonia injected into the HDO reactor.

72. The method of any of claims 64 to 71, wherein the current operational data includes a temperature differential across a catalyst bed disposed closest to an exit of the HDO, wherein when the temperature differential is less than a predetermined threshold the machine learning model is triggered to generate an adjustment to an operating parameter of the HDO to increase the temperature differential above the predetermined threshold.

73. The method of claim 72, wherein the predetermined threshold is less than 20°F / l 1.11°C.

74. The method of claim 73, wherein the predetermined threshold is less than 10°F / 5.56°C.

75. The method of claim 72, further comprising: receiving HDO product data indicative of a property of an HDO product; andgenerating the predetermined threshold based on the property of the HDO product.

76. The method of claim 75, wherein the property of the HDO product is a measured oxygen concentration.

77. The method of claim 75, wherein the property of the HDO product is an end point of the HDO product.

78. The method of any of claims 64 to 77, wherein the current operational data includes a temperature differential across a catalyst bed disposed closest to an exit of the HDO, wherein when the temperature differential is greater than a predetermined threshold the machine learning model is triggered to generate an adjustment to an operating parameter of the HDO to decrease the temperature differential below the predetermined threshold.

79. The method of any of claims 64 to 78, wherein the feedstock is a renewable fluid.

80. The method of any of claims 64 to 79, wherein the machine learning model is programmed to maximize product yield of HDO diesel through control of a temperature of the HDO reactor.

81. The method of any of claims 64 to 80, further comprising generating, by the machine learning model, a predictive function of HDO diesel yield versus a temperature of the HDO reactor.

82. The method of any of claims 64 to 81, further comprising generating, by a machine learning model, an adjustment to a parameter of the HDO.

83. The method of claim 82, further comprising implementing the adjustment to the parameter of the HDO.

84. The method of any of claims 82 to 83, wherein the adjustment to the parameter of the HDO is an adjustment to a temperature of the HDO reactor by adjusting a flow of a quench flow of hydrogen gas into the HDO reactor.

85. The method of any of claims 82 to 84, wherein the adjustment to the parameter of the HDO is an adjustment to a heater outlet temperature.

86. The method of any of claims 82 to 85, wherein the adjustment to the parameter of the HDO is an adjustment to a plurality of quench flows.

87. The method of any of claims 82 to 86, wherein the adjustment to the parameter of the HDO is an adjustment to a liquid recycle stream.

88. The method of any of claims 82 to 87, wherein the adjustment to the parameter of the HDO is an adjustment to a pressure within the HDO.

89. The method of any of claims 82 to 88, wherein the adjustment to the parameter of the HDO is an adjustment to a hydrogen to hydrocarbon ratio.

90. The method of any of claims 64 to 89, further comprising: generating, by the machine learning model, a demand for hydrogen gas within the HDO; and adjusting a feed rate of hydrogen gas into one or more of an inlet of the HDO reactor or a quench flow into the HDO reactor.

91. The method of any of claims 64 to 90, wherein generating, by the machine learning model, the adjustment to the feed rate of the feedstock into the HDO reactor is based on cooling constraints of the HDO reactor.

92. The method of any of claims 64 to 91, wherein generating, by the machine learning model, the adjustment to the feed rate of the feedstock into the HDO reactor is based on hydrogen gas availability.

93. The method of claim 92, further comprising receiving data indicative of hydrogen gas availability to the HDO.

94. The method of any of claims 64 to 93, receiving HDO product data indicative of a property of an HDO product being sent to a catalytic dewaxing reactor.

95. The method of claim 94, wherein generating, by the machine learning model, the adjustment to the parameter of the HDO based on the HDO product data.

96. The method of claim 95, wherein the parameter is one or more of a temperature or a pressure of the HDO reactor.

97. The method of any of claims 95 to 96, wherein generating, by the machine learning model, the adjustment to the parameter of the HDO is further based on the prediction of the deactivation state of catalysts within the HDO reactor.

98. The method of any of claims 95 to 97, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the HDO reactor, wherein the adjustment to the parameter of the HDO is further based on the prediction of the coking rate of the catalysts within the HDO reactor.

99. The method of any of claims 64 to 98, further comprising: receiving current operational data of a catalytic dewaxing system; generating, by the machine learning model, a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system, wherein the machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system; generating, by the machine learning model, an adjustment to a feed rate of an HDO product into the catalytic dewaxing system to achieve a target property of a catalytic dewaxing product based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system; and implementing the adjustment to the feed rate of the HDO product into the catalytic dewaxing system.

100. The method of claim 99, wherein the adjustment to the feed rate of the HDO product into the catalytic dewaxing system is further based on unique operational constraints of a catalytic dewaxing reactor.

101. The method of claim 100, wherein the unique operational constraints of the catalytic dewaxing reactor include one or more of feedstock flow rate constraints, hydrogen flow rate constraints, cooling constraints, heating constraints, and equipment condition.

102. The method of any of claims 99 to 101, wherein the machine learning model is programmed to optimize product yield of renewable diesel through control of a temperature of the catalytic dewaxing system.

103. The method of any of claims 99 to 102, wherein generating, by a machine learning model, a prediction of the deactivation state of catalysts within the catalytic dewaxing system includes a prediction of the deactivation state of catalysts within a catalytic dewaxing reactor.

104. The method of any of claims 99 to 103, further comprising generating, by the machine learning model, a predictive function of renewable diesel yield versus a temperature of the catalytic dewaxing system.

105. The method of any of claims 99 to 104, further comprising generating, by the machine learning model, an adjustment to a parameter of the catalytic dewaxing system.

106. The method of claim 105, further comprising implementing the adjustment to the parameter of the catalytic dewaxing system.

107. The method of any of claims 105 to 106, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a temperature of a catalytic dewaxing reactor by adjusting a flow of hydrogen gas into the catalytic dewaxing reactor.

108. The method of any of claims 105 to 107, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a heater outlet temperature.

109. The method of any of claims 105 to 108, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a pressure within the catalytic dewaxing system.

110. The method of any of claims 105 to 109, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a hydrogen to hydrocarbon ratio.

111. The method of any of claims 99 to 110, further comprising generating, by the machine learning model, a demand for hydrogen gas within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a feed rate of hydrogen gas based on the generated demand for hydrogen gas within the catalytic dewaxing system.

112. The method of any of claims 99 to 111, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the coking rate of the catalysts.

113. The method of any of claims 99 to 112, further comprising generating, by the machine learning model, a prediction of an oxygen poisoning rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the oxygen poisoning rate of the catalysts.

114. The method of any of claims 99 to 113, wherein the target property of the catalytic dewaxing product includes a cloud point of the catalytic dewaxing product.

115. The method of any of claims 99 to 114, wherein the target property of the catalytic dewaxing product includes a flash point of the catalytic dewaxing product.

116. The method of any of claims 99 to 115, further comprising generating, by the machine learning model, an amount of ammonia to be injected into a catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system.

117. The method of claim 116, further comprising injecting the amount of ammonia into the catalytic dewaxing reactor.

118. The method of claim 117, generating, by the machine learning model, an adjustment to an operating temperature of the catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing reactor and the amount of ammonia injected into the catalytic dewaxing reactor.

119. The method of any of claims 99 to 118, wherein the target property of the catalytic dewaxing product includes a pour point of the catalytic dewaxing product.

120. The method of any of claims 99 to 119, wherein the target property of the catalytic dewaxing product includes desired distribution of hydrocarbon chain lengths in the catalytic dewaxing product.

121. The method of any of claims 99 to 120, wherein the target property of the catalytic dewaxing product includes a freeze point of the catalytic dewaxing product.

122. The method of any of claims 99 to 121, wherein the target property of the catalytic dewaxing product includes a cold filter plugging point of the catalytic dewaxing product.

123. A memory including instructions that causes a processor to perform the instructions including the method of any of claims 64 to 122.

124. A computing system including a processor and a memory including instructions that cause the processor to perform the instructions, wherein the instructions include the method of any of claims 64 to 122.

125. A method comprising: receiving current operational data of a catalytic dewaxing system; generating, by a machine learning model, a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system, wherein the machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system; generating, by the machine learning model, an adjustment to a feed rate of a feedstock into the catalytic dewaxing system to achieve a target property of a catalyticdewaxing product based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system; and implementing the adjustment to the feed rate of the feedstock into the catalytic dewaxing system.

126. The method of claim 125, wherein the adjustment to the feed rate of the feedstock into the catalytic dewaxing system is further based on unique operational constraints of a catalytic dewaxing reactor.

127. The method of claim 126, wherein the unique operational constraints of the catalytic dewaxing reactor include one or more of feedstock flow rate constraints, hydrogen flow rate constraints, heating constraints, cooling constraints, and equipment condition.

128. The method of any of claims 125 to 127, wherein the machine learning model is programmed to maximize product yield of renewable diesel through control of a temperature of a catalytic dewaxing reactor.

129. The method of any of claims 125 to 128, further comprising generating, by the machine learning model, a predictive function of renewable diesel yield versus a temperature of a catalytic dewaxing reactor.

130. The method of any of claims 125 to 129, further comprising generating, by the machine learning model, an adjustment to a parameter of a catalytic dewaxing reactor.

131. The method of claim 130, further comprising implementing the adjustment to the parameter of the catalytic dewaxing reactor.

132. The method of any of claims 130 to 131, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a temperature of the catalytic dewaxing reactor by adjusting a flow of hydrogen gas into the catalytic dewaxing reactor.

133. The method of any of claims 130 to 132, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a heater outlet temperature.

134. The method of any of claims 125 to 133, wherein generating, by a machine learning model, a prediction of the deactivation state of catalysts within the catalytic dewaxing system includes a prediction of the deactivation state of catalysts within a catalytic dewaxing reactor.

135. The method of any of claims 125 to 134, further comprising generating, by the machine learning model, an amount of ammonia to be injected into a catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system.

136. The method of claim 135, further comprising injecting the amount of ammonia into the catalytic dewaxing reactor.

137. The method of claim 136, generating, by the machine learning model, an adjustment to an operating temperature of the catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing reactor and the amount of ammonia injected into the catalytic dewaxing reactor.

138. The method of any of claims 130 to 137, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a pressure within the catalytic dewaxing reactor.

139. The method of any of claims 130 to 138, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a hydrogen to hydrocarbon ratio.

140. The method of any of claims 125 to 139, further comprising generating, by the machine learning model, a demand for hydrogen gas within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a feed rate of hydrogen gas based on the generated demand for hydrogen gas within the catalytic dewaxing reactor.

141. The method of any of claims 132 to 140, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the catalyticdewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the coking rate of the catalysts.

142. The method of any of claims 132 to 141, further comprising generating, by the machine learning model, a prediction of an oxygen poisoning rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the oxygen poisoning rate of the catalysts.

143. The method of any of claims 125 to 142, wherein the target property of the catalytic dewaxing product includes a cloud point of the catalytic dewaxing product.

144. The method of any of claims 125 to 143, wherein the target property of the catalytic dewaxing product includes a flash point of the catalytic dewaxing product.

145. The method of any of claims 125 to 144, wherein the target property of the catalytic dewaxing product includes a pour point of the catalytic dewaxing product.

146. The method of any of claims 125 to 145, wherein the target property of the catalytic dewaxing product includes desired distribution of hydrocarbon chain lengths in the catalytic dewaxing product.

147. The method of any of claims 125 to 146, wherein the target property of the catalytic dewaxing product includes a freeze point of the catalytic dewaxing product.

148. The method of any of claims 125 to 147, wherein the target property of the catalytic dewaxing product includes a cold filter plugging point of the catalytic dewaxing product.

149. A memory including instructions that causes a processor to perform the instructions including the method of any of claims 125 to 148.

150. A computing system including a processor and a memory including instructions that cause the processor to perform the instructions, wherein the instructions include the method of any of claims 125 to 148.

151. A method compri sing : receiving current operational data of a catalytic dewaxing system; receiving feedstock data indicative of a property of a feedstock being sent to a catalytic dewaxing reactor; receiving catalytic dewaxing product data indicative of a property of a catalytic dewaxing product; generating, by a machine learning model, an adjustment to an operating parameter of the catalytic dewaxing system to achieve a target property of the catalytic dewaxing product based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data, wherein the machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system; and generating, by a machine learning model, a prediction of effects of the adjustment to the operating parameters of the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system and the catalytic dewaxing product data.

152. The method of claim 151, wherein the adjustment to the operating parameter of the catalytic dewaxing system is further based on unique operational constraints of a catalytic dewaxing reactor.

153. The method of claim 152, wherein the unique operational constraints of the catalytic dewaxing reactor include one or more of feedstock flow rate constraints, hydrogen flow rate constraints, heating constraints, cooling constraints, and equipment condition.

154. The method of any of claims 151 to 153, wherein the machine learning model is programmed to maximize product yield of renewable diesel through control of a temperature of the catalytic dewaxing reactor.

155. The method of any of claims 151 to 154, further comprising generating, by the machine learning model, a predictive function of renewable diesel yield versus a temperature of the catalytic dewaxing reactor.

156. The method of any of claims 151 to 155, further comprising implementing the adjustment to the parameter of the catalytic dewaxing reactor.

157. The method of any of claims 151 to 156, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a temperature of the catalytic dewaxing reactor by adjusting a flow of hydrogen gas into the catalytic dewaxing reactor.

158. The method of any of claims 151 to 157, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a heater outlet temperature.

159. The method of any of claims 151 to 158, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a pressure within the catalytic dewaxing reactor.

160. The method of any of claims 151 to 159, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a hydrogen to hydrocarbon ratio.

161. The method of any of claims 151 to 160, further comprising generating, by the machine learning model, a demand for hydrogen gas within the catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a feed rate of hydrogen gas based on the generated demand for hydrogen gas within the catalytic dewaxing reactor.

162. The method of any of claims 151 to 161, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the coking rate of the catalysts.

163. The method of any of claims 151 to 162, further comprising generating, by the machine learning model, a prediction of an oxygen poisoning rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the oxygen poisoning rate of the catalysts.

164. The method of any of claims 151 to 163, wherein the target property of the catalytic dewaxing product includes a cloud point of the catalytic dewaxing product.

165. The method of any of claims 151 to 164, wherein the target property of the catalytic dewaxing product includes a flash point of the catalytic dewaxing product.

166. The method of any of claims 151 to 165, wherein the target property of the catalytic dewaxing product includes a pour point of the catalytic dewaxing product.

167. The method of any of claims 151 to 166, wherein the target property of the catalytic dewaxing product includes desired distribution of hydrocarbon chain lengths in the catalytic dewaxing product.

168. The method of any of claims 151 to 167, wherein the target property of the catalytic dewaxing product includes a freeze point of the catalytic dewaxing product.

169. The method of any of claims 151 to 168, wherein the target property of the catalytic dewaxing product includes a cold filter plugging point of the catalytic dewaxing product.

170. A memory including instructions that causes a processor to perform the instructions including the method of any of claims 151 to 169.

171. A computing system including a processor and a memory including instructions that cause the processor to perform the instructions, wherein the instructions include the method of any of claims 151 to 169.

172. A method comprising: receiving current operational data of an hydrodeoxygenation process (HDO); generating, by a machine learning model, a prediction of a deactivation state of catalysts within an HDO reactor based on the current operational data of the HDO, wherein the machine learning model is trained with HDO historical data indicative of operating parameters, operational constraints, feedstocks, and products of the HDO; andgenerating, by the machine learning model, an adjustment to a parameter of the HDO to achieve a target property of an HDO product based on the prediction of the deactivation state of catalysts within the HDO reactor.

173. The method of claim 172, further comprising implementing the adjustment to the parameter of the HDO.

174. The method of any of claims 172 to 173, wherein the feedstock is a recycled product of the HDO, wherein the adjustment to a feed rate of the feedstock into the HDO reactor reduces a temperature of the HDO reactor.

175. The method of any of claims 172 to 174, wherein the target property is an oxygen concentration in the HDO product.

176. The method of any of claims 172 to 175, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the HDO reactor, wherein the adjustment to the parameter of the HDO is further based on the prediction of the coking rate of the catalysts of the HDO.

177. The method of any of claims 172 to 176, further comprising generating, by the machine learning model, an amount of ammonia to be injected into the HDO reactor based on the prediction of the deactivation state of catalysts within the HDO reactor.

178. The method of claim 177, further comprising injecting the amount of ammonia into the HDO reactor.

179. The method of claim 178, generating, by the machine learning model, an adjustment to an operating temperature of the HDO reactor based on the prediction of the deactivation state of catalysts within the HDO reactor and the amount of ammonia injected into the HDO reactor.

180. The method of any of claims 172 to 179, wherein the current operational data includes a temperature differential across a catalyst bed disposed closest to an exit of the HDO, wherein when the temperature differential is less than a predetermined threshold themachine learning model is triggered to generate an adjustment to an operating parameter of the HDO to increase the temperature differential above the predetermined threshold.

181. The method of claim 180, wherein the predetermined threshold is less than 20°F / l 1.11°C.

182. The method of claim 181, wherein the predetermined threshold is less than 10°F / 5.56°C.

183. The method of claim 180, further comprising: receiving HDO product data indicative of a property of an HDO product; and generating the predetermined threshold based on the property of the HDO product.

184. The method of claim 183, wherein the property of the HDO product is a measured oxygen concentration.

185. The method of claim 183, wherein the property of the HDO product is an endpoint of the HDO product.

186. The method of any of claims 172 to 185, wherein the current operational data includes a temperature differential across a catalyst bed disposed closest to an exit of the HDO, wherein when the temperature differential is greater than a predetermined threshold the machine learning model is triggered to generate an adjustment to an operating parameter of the HDO to decrease the temperature differential below the predetermined threshold.

187. The method of any of claims 172 to 186, wherein the feedstock is a renewable fluid.

188. The method of any of claims 172 to 187, wherein the machine learning model is programmed to maximize product yield of HDO diesel through control of a temperature of the HDO reactor.

189. The method of any of claims 172 to 188, further comprising generating, by the machine learning model, a predictive function of HDO diesel yield versus a temperature of the HDO reactor.

190. The method of any of claims 172 to 189, wherein the adjustment to the parameter of the HDO is an adjustment to a temperature of the HDO reactor by adjusting a flow of a quench flow of hydrogen gas into the HDO reactor.

191. The method of any of claims 172 to 190, wherein the adjustment to the parameter of the HDO is an adjustment to a heater outlet temperature.

192. The method of any of claims 172 to 191, wherein the adjustment to the parameter of the HDO is an adjustment to a plurality of quench flows.

193. The method of any of claims 172 to 192, wherein the adjustment to the parameter of the HDO is an adjustment to a liquid recycle stream.

194. The method of any of claims 172 to 193, wherein the adjustment to the parameter of the HDO is an adjustment to a pressure within the HDO.

195. The method of any of claims 172 to 194, wherein the adjustment to the parameter of the HDO is an adjustment to a hydrogen to hydrocarbon ratio.

196. The method of any of claims 172 to 195, further comprising: generating, by the machine learning model, a demand for hydrogen gas within the HDO; and adjusting a feed rate of hydrogen gas into one or more of an inlet of the HDO reactor or a quench flow into the HDO reactor.

197. The method of any of claims 172 to 196, wherein generating, by the machine learning model, the adjustment to the parameter of the HDO is based on cooling constraints of the HDO reactor.

198. The method of any of claims 172 to 197, wherein generating, by the machine learning model, the adjustment to the parameter the HDO reactor is based on hydrogen gas availability.

199. The method of claim 198, further comprising receiving data indicative of hydrogen gas availability to the HDO.

200. The method of any of claims 172 to 199, receiving HDO product data indicative of a property of an HDO product being sent to a catalytic dewaxing reactor.

201. The method of claim 200, wherein generating, by the machine learning model, the adjustment to the parameter of the HDO based on the HDO product data.

202. The method of claim 201, wherein the parameter is one or more of a temperature or a pressure of the HDO reactor.

203. The method of any of claims 201 to 202, wherein generating, by the machine learning model, the adjustment to the parameter of the HDO is further based on the prediction of the deactivation state of catalysts within the HDO reactor.

204. The method of any of claims 201 to 203, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within the HDO reactor, wherein the adjustment to the parameter of the HDO is further based on the prediction of the coking rate of the catalysts of the HDO.

205. The method of any of claims 172 to 204, further comprising: receiving current operational data of a catalytic dewaxing system; generating, by the machine learning model, a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system, wherein the machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system; generating, by the machine learning model, an adjustment to a parameter of the catalytic dewaxing system to achieve a target property of a catalytic dewaxing product based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system; and implementing the adjustment to the parameter of the catalytic dewaxing system.

206. The method of claim 205, wherein the adjustment to the parameter of the catalytic dewaxing system is further based on unique operational constraints of a catalytic dewaxing reactor.

207. The method of claim 206, wherein the unique operational constraints of the catalytic dewaxing reactor include one or more of feedstock flow rate constraints, hydrogen flow rate constraints, heating constraints, cooling constraints, and equipment condition.

208. The method of any of claims 205 to 207, wherein generating, by a machine learning model, a prediction of the deactivation state of catalysts within the catalytic dewaxing system includes a prediction of the deactivation state of catalysts within a catalytic dewaxing reactor.

209. The method of any of claims 205 to 208, wherein the machine learning model is programmed to optimize product yield of renewable diesel through control of a temperature of a catalytic dewaxing reactor.

210. The method of any of claims 205 to 209, further comprising generating, by the machine learning model, a predictive function of renewable diesel yield versus a temperature of a catalytic dewaxing reactor.

211. The method of any of claims 205 to 210, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a temperature of a catalytic dewaxing reactor by adjusting a flow of hydrogen gas into the catalytic dewaxing reactor.

212. The method of any of claims 205 to 211, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a heater outlet temperature.

213. The method of any of claims 205 to 212, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a pressure within a catalytic dewaxing reactor.

214. The method of any of claims 205 to 213, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a hydrogen to hydrocarbon ratio.

215. The method of any of claims 205 to 214, further comprising generating, by the machine learning model, a demand for hydrogen gas within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is an adjustment to a feed rate of hydrogen gas based on the generated demand for hydrogen gas within the catalytic dewaxing reactor.

216. The method of any of claims 205 to 215, further comprising generating, by the machine learning model, an amount of ammonia to be injected into a catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing system.

217. The method of claim 216, further comprising injecting the amount of ammonia into the catalytic dewaxing reactor.

218. The method of claim 217, generating, by the machine learning model, an adjustment to an operating temperature of the catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing reactor and the amount of ammonia injected into the catalytic dewaxing reactor.

219. The method of any of claims 205 to 218, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the coking rate of the catalysts.

220. The method of any of claims 205 to 219, further comprising generating, by the machine learning model, a prediction of an oxygen poisoning rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the oxygen poisoning rate of the catalysts.

221. The method of any of claims 205 to 220, wherein the target property of the catalytic dewaxing product includes a cloud point of the catalytic dewaxing product.

222. The method of any of claims 205 to 221, wherein the target property of the catalytic dewaxing product includes a flash point of the catalytic dewaxing product.

223. The method of any of claims 205 to 222, wherein the target property of the catalytic dewaxing product includes a pour point of the catalytic dewaxing product.

224. The method of any of claims 205 to 223, wherein the target property of the catalytic dewaxing product includes desired distribution of hydrocarbon chain lengths in the catalytic dewaxing product.

225. The method of any of claims 205 to 224, wherein the target property of the catalytic dewaxing product includes a freeze point of the catalytic dewaxing product.

226. The method of any of claims 205 to 225, wherein the target property of the catalytic dewaxing product includes a cold filter plugging point of the catalytic dewaxing product.

227. A memory including instructions that causes a processor to perform the instructions including the method of any of claims 172 to 226.

228. A computing system including a processor and a memory including instructions that cause the processor to perform the instructions, wherein the instructions include the method of any of claims 172 to 226.

229. A method comprising: receiving current operational data of a catalytic dewaxing system; generating, by a machine learning model, a prediction of a deactivation state of catalysts within the catalytic dewaxing system based on the current operational data of the catalytic dewaxing system, wherein the machine learning model is trained with catalytic dewaxing system historical data indicative of operating parameters, operational constraints, feedstocks, and products of the catalytic dewaxing system; generating, by the machine learning model, an adjustment to a parameter of the catalytic dewaxing system to achieve a target property of a catalytic dewaxing productbased on the prediction of the deactivation state of catalysts within the catalytic dewaxing system; and implementing the adjustment to the parameter of the catalytic dewaxing system.

230. The method of claim 229, wherein the adjustment to the parameter of the catalytic dewaxing system is further based on unique operational constraints of a catalytic dewaxing reactor.

231. The method of claim 230, wherein the unique operational constraints of the catalytic dewaxing reactor include one or more of feedstock flow rate constraints, hydrogen flow rate constraints, heating constraints, cooling constraints, and equipment condition.

232. The method of any of claims 229 to 231, wherein generating, by a machine learning model, a prediction of the deactivation state of catalysts within the catalytic dewaxing system includes a prediction of the deactivation state of catalysts within a catalytic dewaxing reactor.

233. The method of any of claims 229 to 232, wherein the machine learning model is programmed to optimize product yield of renewable diesel through control of a temperature of the catalytic dewaxing system.

234. The method of any of claims 229 to 233, further comprising generating, by the machine learning model, a predictive function of renewable diesel yield versus a temperature of the catalytic dewaxing system.

235. The method of any of claims 229 to 234, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a temperature of a catalytic dewaxing reactor by adjusting a flow of hydrogen gas into the catalytic dewaxing reactor.

236. The method of any of claims 229 to 235, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a heater outlet temperature.

237. The method of any of claims 229 to 236, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a pressure within a catalytic dewaxing reactor.

238. The method of any of claims 229 to 237, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a hydrogen to hydrocarbon ratio.

239. The method of any of claims 229 to 238, further comprising generating, by the machine learning model, a demand for hydrogen gas within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing system is an adjustment to a feed rate of hydrogen gas based on the generated demand for hydrogen gas within the catalytic dewaxing reactor.

240. The method of any of claims 229 to 239, further comprising generating, by the machine learning model, an amount of ammonia to be injected into a catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing reactor.

241. The method of claim 240, further comprising injecting the amount of ammonia into the catalytic dewaxing reactor.

242. The method of claim 241 , generating, by the machine learning model, an adjustment to an operating temperature of the catalytic dewaxing reactor based on the prediction of the deactivation state of catalysts within the catalytic dewaxing reactor and the amount of ammonia injected into the catalytic dewaxing reactor.

243. The method of any of claims 229 to 242, further comprising generating, by the machine learning model, a prediction of a coking rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalytic dewaxing reactor is further based on the prediction of the coking rate of the catalysts.

244. The method of any of claims 229 to 243, further comprising generating, by the machine learning model, a prediction of an oxygen poisoning rate of catalysts within a catalytic dewaxing reactor, wherein the adjustment to the parameter of the catalyticdewaxing reactor is further based on the prediction of the oxygen poisoning rate of the catalysts.

245. The method of any of claims 229 to 244, wherein the target property of the catalytic dewaxing product includes a cloud point of the catalytic dewaxing product.

246. The method of any of claims 229 to 245, wherein the target property of the catalytic dewaxing product includes a flash point of the catalytic dewaxing product.

247. The method of any of claims 229 to 246, wherein the target property of the catalytic dewaxing product includes a pour point of the catalytic dewaxing product.

248. The method of any of claims 229 to 247, wherein the target property of the catalytic dewaxing product includes desired distribution of hydrocarbon chain lengths in the catalytic dewaxing product.

249. The method of any of claims 229 to 248, wherein the target property of the catalytic dewaxing product includes a freeze point of the catalytic dewaxing product.

250. The method of any of claims 229 to 249, wherein the target property of the catalytic dewaxing product includes a cold filter plugging point of the catalytic dewaxing product.

251. A memory including instructions that causes a processor to perform the instructions including the method of any of claims 229 to 250.

252. A computing system including a processor and a memory including instructions that cause the processor to perform the instructions, wherein the instructions include the method of any of claims 229 to 250.

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