Systems and methods for dynamic corrective enzyme selection and formulation in pulp and paper production.

Abstract

The systems and methods disclosed herein automatically provide real-time dosing corrections for industrial processes in which enzyme formulations are applied to natural fibers for pulp / paper production. An initial enzyme formulation (e.g., associated enzyme and supporting formulation components) and respective dosing rates are selected and applied based on expected fiber surface substrate characterization, expected fiber quality characterization, physical conditions of the treated system, and the respective properties of the initially selected enzyme formulation components. Upon application of the initial enzyme formulation, online sensors provide real-time feedback data corresponding to actual measurements of fiber surface substrate characterization and fiber quality characterization. Based on the feedback data, supplemental enzyme formulations (enzymes and supporting formulation components) and respective dosing rates are dynamically selected. The enzyme dosing stage can be optimized substantially in real time in response to product changes and / or variations in fiber source / formulation and / or physical conditions.

Description

Technical Field

[0001] The present invention relates generally to systems and methods for component property evaluation and feedback implementation in industrial processes.

[0002] More particularly, embodiments of the invention disclosed herein relate to systems and methods for optimizing enzyme selection and dosing in pulp and paper production and for more prophylactically and in real-time correcting enzyme dosing based on feedback from online sensors and data analysis. However, alternative embodiments of the systems and methods disclosed herein may relate to other processes such as, for example, biomass production.

Background Art

[0003] Conventional papermaking processes generally involve forming an aqueous suspension of cellulose fibers commonly known as pulp, adding various processing and paper strengthening materials such as strengthening aids, retention aids, drainage aids, and / or sizing materials or other functional additives, sheet-forming and drying the fibers to form the desired cellulose web, and subjecting the web to post-treatment such as surface coating of sizing materials to provide various desired properties to the resulting paper. Thus, various types of enzyme compositions at various enzyme dosing ratios can be applied to treat the fibers to improve the properties of the pulp (e.g., improve the drainage of the fiber suspension slurry) and / or the properties of the finished sheet (e.g., strength, porosity, flexibility).

[0004] Pulp and paper producers purchase and / or produce fibers and engage in grade development activities, and it has been observed that the fiber properties of fibers vary as a result of several reasons, including, but are not limited to, the type of wood used to produce the fibers, the fiber composition used, whether the fibers are virgin or recycled, the growing conditions of the wood, seasonality, the pulping process, and the pulp treatment. This introduces inherent variability into these processes, which can change the type and amount of enzymes that should be administered to the system applying bleaching and / or fiber-modifying enzymes. Incorrect enzyme mixtures or dosages can lead to unnecessary activity, chemical waste, or over-development of the fibers, and consequently, a loss of efficiency.

[0005] To ensure that enzyme administration is optimized for specific fibers / finished paper stocks and systems, it is desirable to generate and utilize a database of fiber surface property evaluation data, fiber quality analysis, system physical conditions (e.g., temperature, pH, flow rate, conductivity, ORP, biocide residue), and appropriate enzyme pumping equipment.

[0006] Furthermore, it would be desirable to apply such optimized dosing techniques to the bleaching process and / or paper machine to enhance the bleaching and / or physical properties (e.g., tensile strength, tear resistance, drainage, porosity, etc.) of the finished sheet.

[0007] Conventional systems and methods are known to be limited in their implementation, including the use of physical conditions and enzyme formation, fiber surface characterization, fiber quality analysis, and manual and online sensors, in order to improve stability and performance.

[0008] However, such conventional techniques are often unreliable in practice and are typically substantially limited in that they utilize techniques that focus on a single enzyme selection and formulation process rather than providing or otherwise enabling the implementation of a more holistic framework. [Overview of the project]

[0009] In general, the systems and methods disclosed herein represent a significant technological advance over the prior art, in that they can provide product selection and application algorithms that can be adjusted substantially in real time using measured values ​​of fiber and physical conditions, at least utilizing a database of information. Such algorithms can be inherently dynamic, based on the observed correlation over time between various combinations of process inputs, such as fiber quality and product effect, and desired results.

[0010] The systems disclosed herein preferably implement visualization graphics, alarms, notifications, etc., accessible via an onboard user interface, mobile computing device, web-based interface, etc., to supplement any automated functions with practical insights into the relevant processes.

[0011] Exemplary techniques for developing predictive models may include supervised and unsupervised learning, hard and soft clustering, classification, prediction, and the like.

[0012] One objective of this disclosure is to provide a database of data points for several key fibers, enzymes, and systems for identifying optimal enzyme formulations and dosages for specific applications. In short, the system and method can provide initial product formulations and dosing rates by correlating fiber surface matrix characterization, fiber quality analysis data (including elements such as fiber length, fiber width, fibrillation, kink, curl, etc.), enzyme activity fingerprints, physical measurements from the process (pH, temperature, flow rate / holding time), and product effect data. The system may be implemented for individual aspects of the entire process, or it may be implemented as part of a pressurized skid that can combine multiple raw materials to obtain an optimal formulation and dosing rate. This skid can be integrated with, for example, online sensors for flow rate, temperature, chemical residue, and pH, as well as system data regarding the strength, filtration efficiency, and quality of the finished sheet, to determine whether the optimal dosing rate has been achieved. Furthermore, data on fiber quality and substrate retention can be collected and uploaded to the system, allowing for adjustment of the balance of existing enzyme raw materials over time.

[0013] The system output can be supplied, for example, into a dosing skid containing enzyme raw materials formulated for direct delivery to pulp or papermaking applications, which may be particularly advantageous with respect to pulp bleaching and tissue / packaging / papermaking applications.

[0014] The systems and methods disclosed herein may further utilize a front-end data capture application that supplies information to the entire database, and such information may further communicate to formulation and administration skids wirelessly or via integrated signals. Various sensors, controllers, online devices, and other intermediate components may be "Internet-of-things" (IoT) compatible, or otherwise include an interconnected network in which relevant outputs can be uploaded in real time to a cloud-based server.

[0015] In view of some or all of the above-mentioned problems and objectives, a first exemplary embodiment of the method disclosed herein provides automatic real-time dosing correction in an industrial process in which one or more enzymes (and supporting compound components) are applied to natural fibers to produce pulp or paper products. Such natural fibers may, of course, include wood fibers, but may also include other cellulose fibers, such as bamboo and grass (e.g., bagasse), and unconventional finished paper stocks. A first step includes selecting an initial enzyme compound to be applied and the dosing rates of each of its one or more components, at least in part on input data including the expected fiber surface matrix characterization of the pulp or paper product, the expected fiber quality characterization of the pulp or paper product, and the characteristics of one or more of the components of the one or more enzyme compound. Real-time feedback data corresponding to the actual measured values ​​of the fiber surface matrix characterization and fiber quality characterization is provided when the initial enzyme compound and the dosing rates of each of its one or more components are applied. Another step includes dynamically selecting a supplemental enzyme compound to be applied and the dosing rates of each of its one or more components, at least in part on the feedback data.

[0016] In the second embodiment, one exemplary aspect relating to the first embodiment described above may include the selection of the initial enzyme formulation and its respective administration rate based on the expected values ​​of one or more industrial process characteristics, and the real-time feedback data may further include measured values ​​of one or more industrial process characteristics.

[0017] In a third embodiment, one exemplary aspect relating to the first or second embodiment described above may further include real-time feedback data further including measurements of industrial process characteristics, including one or more of the following: temperature, system flow rate, pH value, conductivity value, ORP value, biocide residue value, and residence time. Further examples of characteristics under consideration may include pulp stock and pulping methods.

[0018] In the fourth embodiment, one exemplary aspect relating to any one of the first to third embodiments described above, the initial enzyme formulation and their respective dosing rates are selected using a predetermined model related to pulp or paper products resulting from an industrial process, and the method may further include selectively modifying the predetermined model, at least in part, based on provided real-time feedback data.

[0019] In the fifth embodiment, one exemplary embodiment relating to any one of the first to fourth embodiments described above may include formulating one or more components of an initial enzyme formulation according to a first overall dosing rate, and applying the formulated one or more components of the initial enzyme formulation in an industrial process. The exemplary embodiment may be provided via a dosing control stage (e.g., embodied by or otherwise comprising a dosing controller), the dosing control stage may be further configured to, for example, formula one or more components of a selected supplement enzyme formulation according to an overall dosing rate, and apply the formulated one or more components of the selected supplement enzyme formulation in place of one or more components of the initial enzyme formulation.

[0020] In the sixth embodiment, one exemplary aspect relating to any one of the first to fifth embodiments described above may include determining the fiber quality characteristics with respect to one or more of the following: fiber length, width, fibrillation, cell wall thickness, fine fiber density / distribution, fiber kink, and fiber curl.

[0021] In the seventh embodiment, one exemplary aspect relating to any one of the first to sixth embodiments described above may further include system performance data relating to one or more of the fiber strength, porosity, caliper, flexibility, crepe count, drainability, and drainage of the pulp or paper product.

[0022] In the eighth embodiment, one exemplary aspect relating to any one of the first to seventh embodiments described above may include the provision of a selected initial enzyme formulation and a dynamically selected supplemental enzyme formulation, as well as their respective administration rates, to a pulp bleaching process controller.

[0023] In the ninth embodiment, one exemplary aspect relating to any one of the first to seventh embodiments described above may include the provision of a selected initial enzyme formulation and a dynamically selected supplemental enzyme formulation to be applied, as well as their respective administration rates, to a paper manufacturing controller.

[0024] In a tenth exemplary embodiment, the system disclosed herein automatically provides real-time dosing corrections for an industrial process in which one or more components of an enzyme formulation are applied to natural fibers to produce pulp or paper products. A data storage unit includes a model that correlates one or more pulp or paper products with their respective expected fiber surface matrix characterizations and expected fiber quality characterizations, and further includes data corresponding to enzyme properties. One or more online sensors are configured to generate output signals representing actual measured values ​​of the fiber surface matrix characterizations and fiber quality characterizations. A production stage may comprise a plurality of containers, each configured to store and selectively deliver the respective raw materials corresponding to the selected enzyme formulation components. A dosing control stage may comprise one or more computing devices functionally linked to the data storage unit and one or more online sensors and configured to direct the execution of steps in a method corresponding to any one of the first to ninth embodiments.

[0025] One or more computing devices may include, for example, a dosage controller according to the fifth exemplary embodiment described above.

[0026] In another alternative embodiment, the production stage in the tenth exemplary embodiment can comprise a pulp bleaching process controller configured to receive and apply an initial set of one or more selected enzymes, a supplemental set of one or more dynamically selected enzymes, and their respective dosing rates.

[0027] In another alternative embodiment, the production stage in the tenth exemplary embodiment can comprise a paper manufacturing controller configured to receive and apply an initial set of one or more selected enzymes, a supplemental set of one or more dynamically selected enzymes, and their respective dosing rates.

[0028] The computing device, dosing controller, pulp bleaching process controller, and / or paper manufacturing controller described with respect to any one of the first to tenth exemplary embodiments may be integrated into the same device or provided as separate components, within the scope of the present disclosure.

[0029] The numerous objects, features, and advantages of the embodiments described herein will be readily apparent to those skilled in the art upon reading the following disclosure in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0030] [Figure 1] A block diagram representing an exemplary embodiment of the system disclosed herein. [Figure 2] Figures and simplified flowcharts representing exemplary embodiments of the methods disclosed herein for the selection and subsequent adjustment of initial products and dosages based on fiber parameters and system parameters.

Modes for Carrying Out the Invention

[0031] In short, the systems and methods disclosed herein can be implemented to provide adjusted or otherwise optimized dosing regimens of enzyme formulations as needed to maintain the ideal properties of the finished pulp or paper product by correlating the properties of the finished pulp, system, and enzyme. The following description of embodiments of the systems and methods disclosed herein focuses on the selection and formulation of one or more enzymes for illustrative purposes, but those skilled in the art will understand the suitability of such methods in the corresponding selection and / or formulation of supporting components of the enzyme technology, such as nonionic surfactants and polymers, as these may contribute to the optimization of the system with respect to enzyme activity. Accordingly, the enzymes and corresponding auxiliary agents such as polymer surfactants used in the systems and methods disclosed herein may be supplied separately or together as an enzyme formulation, and their selection, formulation, and dynamic adaptation can be improved by various embodiments of this disclosure.

[0032] Referring first to Figure 1, the system 100 disclosed herein may comprise a plurality of dosing control stages 106a, 106b, ... 106x, which are shown to functionally coordinate with a production stage 110, for example, a pulp or paper production stage, and each dosing control stage may be provided for each enzyme applied to the prepared composition. Alternatively, within the scope of this disclosure, the selection and dosing operations may be performed by a single dosing control stage for a plurality of enzymes that are mixed together to form an enzyme product.

[0033] For example, an array of sensors 102 comprising an online sensor 102 is linked to a dosing control stage 106, along with a data storage 104 containing one or more databases 104 having models, algorithms, and data for implementing the methods and operations disclosed herein. The output from the dosing control stage may include dosing information 108 provided to a pulp or paper production stage 110, which further provides feedback information 112 to the dosing control stage. Although the feedback 112 from the production stage 110 is shown to be independent with respect to the array of sensors 102, it can be understood that the feedback 112 may include signals from the array of sensors. The dosing control stage 106 may further provide feedback information 114(112) to the data storage 104, for example, in relation to observation and model improvement via machine learning.

[0034] The term “sensor” may include, but is not limited to, physical level sensors, relays, and equivalent monitoring devices that may be provided to directly measure values ​​or variables of a relevant process component or element, or to measure appropriate derivative values ​​that can be measured or calculated of a process component or element. As used herein, the term “online” may generally refer to the use of a device, sensor, or corresponding element that is positioned in close proximity to a container, machine, or relevant process element and generates an output signal corresponding to a desired process element in substantially real time, distinct from manual or automated sample collection and “offline” analysis in a laboratory or through visual observation by one or more operators.

[0035] Online sensors 102 are known in the art for detecting or calculating properties such as temperature, flow rate, ORP, conductivity, biocide residue, and pH, and exemplary such sensors are considered to be fully compatible with the scope of the systems and methods disclosed herein. Individual sensors may be mounted and configured separately, or the system 100 may provide a modular housing including, for example, multiple sensors or sensing elements. Sensors or sensing elements may be permanently or portablely mounted at specific locations for each production stage 110, or their position may be dynamically adjustable to collect data from multiple locations during operation.

[0036] The online sensor 102 disclosed herein may provide substantially continuous measurements of various process components and elements in substantially real time. The terms “continuous” and “real time” as used herein do not require an explicit degree of continuity, at least with respect to the disclosed sensor output, but rather can generally describe a series of measurements corresponding to the physical and technical capabilities of the sensor, the physical and technical capabilities of the transmission medium, the physical and technical capabilities of any intervening local controller, communication device, and / or interface configured to receive the sensor output signal, etc. For example, measurements may be measured and provided periodically and at a rate slower than the maximum possible rate, based on the relevant hardware components or based on a communication network configuration that smooths the input values ​​over time, and may still be considered “continuous.”

[0037] A user interface (not shown) can further enable users, such as operators and administrators, to provide periodic inputs regarding the conditions or states of additional components related to models, algorithms, etc., as further discussed herein. The user interface can functionally communicate with the dosing control stage 106, a distributed control system (not shown) associated with the industrial facility, and / or a remote host server (not shown) to receive and provide process-related information, or to provide other forms of feedback regarding the control process, for example, as further discussed herein. The term “user interface” as used herein is not limited unless otherwise noted, and may include any input / output modules relating to the controller and / or hosted data server, including fixed operator panels having key-based data input, touch screens, buttons, or dials, web portals such as those that collectively define individual web pages or hosted websites, and mobile device applications.

[0038] With respect to data communication between two or more system components, or otherwise between communication network interfaces related to two or more system components, the term “communication network” as used herein may refer to one or more of the following: telecommunication networks (whether wired, wireless, or cellular), global networks such as the Internet, local networks, network links, Internet service providers (ISPs), and intermediate communication interfaces. However, one or more conventionally recognized interface standards, including but not limited to Bluetooth, RF, and Ethernet, may be implemented.

[0039] An embodiment of Method 200 will now be described with reference to Figure 2. The illustrated steps are illustrative and are not intended to expressly limit the scope of this disclosure unless otherwise specified.

[0040] Various inputs 211-215 are provided for the initial product selection 220, which may refer to the selection of each enzyme to be applied, one of several enzymes to be applied, or an enzyme formulation further incorporating one or more auxiliary agents, such as a nonionic surfactant.

[0041] The fiber surface substrate characterization data 211 can be selectively extracted from a database communicatively linked to the dosing controller in various embodiments. Numerous conventional techniques are known for characterizing fiber surface substrates in a manner that assists enzyme selection and formulation, and such techniques may be considered within the scope of this disclosure in combination with one or more other inputs further described herein. Conventionally, numerous techniques for fiber surface characterization are known, including, for example, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS), and Fourier transform infrared (FTIR). However, in connection with this disclosure, it may be preferable to utilize techniques for rapid characterization of fiber surface polymers that allow for better prediction of the effects of various treatments on pulp or paper. Exemplary sensors (detection probes) and methods of use thereof, such as those disclosed in U.S. Patent No. 10,788,477, are incorporated herein by reference and can therefore be implemented within the scope of this disclosure for such characterization, or, otherwise, the data obtained therefrom can be selectively accessed in a database for various enzyme selection and compounding steps or operations according to other inputs as described below.

[0042] Fiber quality data 212 can be collected from one or more sensors in substantially real time, as is known in the art, and transmitted to or uploaded to a dosing controller, the data relating to, for example, conventional fiber quality variables such as fiber length, fiber width, fiber roughness, fiber kink angle, fine fiber content / density, fiber curl, external fibrillation, cell wall thickness, etc. Such sensors may, within the scope of this disclosure, be online measurement devices and / or offline fiber image analyzers that can be operated automatically or manually. Relevant outputs to the dosing control stage may further include the raw detection signal, its conversion and / or derivative values, machine learning classification of the detection signal, etc.

[0043] Enzyme function characterization data 213 may, for example, relate to an activity profile or fingerprint measured in relation to a given enzyme or otherwise extracted from data storage.

[0044] The application result data 214 may generally relate to observations from system performance feedback for the purpose of optimizing future product formulations and relative dosing rates, for example, with respect to machine learning, but may also include user input from the user interface that further defines, confirms, or otherwise overturns the insights generated by the system.

[0045] Physical condition data 215 can be collected from one or more sensors in substantially real time, as is known in the art, and transmitted to or uploaded to a dosing controller, and this data relates, for example, to conventional variables such as temperature, pH value, flow rate, residence time, etc. Such sensors providing physical condition data may be online sensors and / or manual sensors, within the scope of this disclosure.

[0046] The product identification information and initial dosing rate setting stage 230 can generally be configured to use the aforementioned inputs, such as relevant fiber, enzyme, and system data points, to identify the optimal enzyme formulation and initial dosing rate for the selected product application.

[0047] The next stage 240 and related substeps collectively refer to the application of the selected enzyme(s) in the process, along with the newly specified administration rate 250 and product formulation 260. Feedback of measured physical conditions from the process includes, for example, measured retention time or system flow rate 251, measured pH 252, measured temperature 253, etc. Additional data affecting the product formulation may further include new fiber surface matrix characterization data 261, new fiber quality analysis data 262, and enzyme characterization data 263.

[0048] The newly designated product and associated current dosing rate 270, along with any other information that may be determined to be relevant by the dosing controller, can be provided to the field compounding and pumping controller and associated equipment 280. In one embodiment described herein, the dosing control stage 106 (or the respective dosing control stages 106a, 106b for different enzymes) may be integrated with the production stage 110, for example, in relation to a dosing skid having appropriate enzyme pumping equipment. In other embodiments, the dosing control stage 106 (or the respective dosing control stages 106a, 106b for different enzymes) may be a separate product or component of the entire system and may be configured to transmit relevant information for downstream implementation (i.e., enzyme compounding and pumping) via a communication network, for example, wirelessly or via an integrated signal.

[0049] Feedback data, including system performance data 290, may relate to the bleaching and / or physical quality of the finished product (e.g., a sheet), preferably acquired in real time or as a reasonable approximation thereof, and may include, for example, strength data, porosity, caliper, crepe count, flexibility, filtration efficiency, drainage, etc. Such feedback can be provided to the dosing controller to determine whether the optimal dosing has been achieved, and then steps 240-280 can be repeated as needed to dynamically adjust the selection and / or balance of the enzyme raw materials present over time. System performance data can be acquired by offline or online methods and may be directly accessible from an existing process data repository, e.g., a distributed control system (DCS).

[0050] Throughout this specification and the claims, the following terms have the meanings expressly associated herein, unless otherwise indicated by context. The meanings identified below are not necessarily limiting and are merely illustrative examples of the terms. The meanings of “a, an” and “the” may include multiple references, and the meaning of “in” may include “in” and “on.” As used herein, the phrase “in one embodiment” may not necessarily refer to the same embodiment. As used herein, the phrase “one or more of” means, when used with a list of items, that one or more different combinations of those items may be used, and that only one of each item in the list may be required. For example, “one or more of” items A, B, and C may include, for example, item A, or items A and B, for example, but not limited to these. This example may also include items A, B, and C, or items B and C.

[0051] Various exemplary logic blocks, modules, and algorithmic steps described in relation to the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly demonstrate this interchangeability between hardware and software, various exemplary components, blocks, modules, and steps have been described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The described functionality may be implemented in various ways for each specific application, but such implementation decisions should not be construed as causing a departure from the scope of this disclosure.

[0052] Various exemplary logic blocks and modules described in connection with the embodiments disclosed herein may be implemented or executed by machines such as general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic elements, discrete gate or transistor logic circuits, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternative examples, the processor may be a controller, microcontroller, or state machine, or a combination thereof. The processor may be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0053] Steps of methods, processes, or algorithms described in relation to embodiments disclosed herein may be directly embodied in hardware, in software modules executed by a processor, or in a combination of both. Software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of computer-readable medium known in the art. An exemplary computer-readable medium may be coupled to a processor so that the processor can read information from and write information to the memory / storage medium. Alternatively, the medium may be integrated with the processor. The processor and medium may reside within an ASIC. The ASIC may reside within a user terminal. Alternatively, the processor and medium may reside as separate components within a user terminal.

[0054] In particular, conditional language used herein, such as “can,” “might,” “may,” and “e.g.,” is generally intended to convey that a particular embodiment includes a particular feature, element, and / or state, but other embodiments do not, unless otherwise stated or understood in the context in which they are used. Accordingly, such conditional language is generally not intended to suggest that a feature, element, and / or state is required in any way in one or more embodiments, or that one or more embodiments necessarily include logic for determining whether such features, elements, and / or states are included in or should be performed in any particular embodiment, with or without input or inducement by the author.

[0055] The detailed description above is provided for illustrative and explanatory purposes. While it describes specific embodiments of the novel and useful invention, such references are not intended to be construed as limitations on the scope of the invention, except as provided in the appended claims. [Explanation of Symbols]

[0056] Drawing translation Figure 1 102a, 102b Online Sensors 104a, 104b Data Storage 106a, 106b Dosing Control Stage 108a, 108b Dosing Information 110 Production Stage 112a, 112b Feedback 114a, 114b Learning Figure 2 211 Fiber Surface Substrate Characterization Data 212 Fiber Quality Analysis 213, 263 Enzyme Characterization Data 214 Application Outcomes Data 215 Physical Condition Data (Temp, pH, Flow Rate, or Residence Time) 220 Product Selection Software 230 Product ID and Initial Dose Rate 240 Application of Enzymes on Systems 250 Dose Rate 251 Residence Time or Flow Rate Measure 252 pH Measure pH scale 253 Temperature Measure Temperature scale 260 Product Blend 261 New Fiber Surface Substrate Characterization Data 262 New Fiber Quality Analysis 270 Product ID and Ongoing Dose Rate 280 Information to Onsite Blending and Pumping Equipment 290 System Performance Data

Claims

1. A method (200) for automatically providing real-time dosage corrections for an industrial process in which one or more enzyme compositions are applied to natural fibers for the production of pulp or paper products, The initial enzyme formulation to be applied (220) and the respective administration rates (dosage per unit time) (230) of one or more of its components are selected at least in part on input data (211-215) including expected fiber surface matrix characterization of the pulp or paper product based on measurements of one or more of the following: fiber length, width, fibrillation, cell wall thickness, fine fiber density / distribution, fiber kink, and fiber curl, and characteristics of one or more of the components of the one or more enzyme formulations. When the administration rates of the initial enzyme formulation and one or more of its components are applied (240), real-time feedback data (261-263) corresponding to the actual measured values ​​of the fiber surface matrix characteristics evaluation and fiber quality characteristics evaluation are provided. Dynamically select the applicable replacement enzyme formulation (220) and the administration rate (230) of one or more of its components, based at least in part on the aforementioned feedback data. Methods that include...

2. The method according to claim 1, wherein the applied initial enzyme formulation and the respective administration rates are further selected based on expected values ​​of one or more industrial process characteristics, and the real-time feedback data further includes measured values ​​of the one or more industrial process characteristics (290).

3. The method according to claim 2, wherein the real-time feedback data further includes measured values ​​of industrial process characteristics (251-253, 290) including one or more of temperature, system flow rate, pH value, conductivity value, ORP value, biocide residue value, and residence time.

4. The method according to claim 1, wherein the initial enzyme formulation and the respective administration rates are selected using a predetermined model relating to a pulp or paper product resulting from the industrial process, and the method further comprises selectively modifying the predetermined model based at least in part on the provided real-time feedback data.

5. The initial enzyme formulation is formulated by combining one or more components of the initial enzyme formulation according to the first overall administration rate, In the aforementioned industrial process, one or more of the components of the initial enzyme formulation are applied, The method according to claim 1, further comprising:

6. The selection of the replacement enzyme formulation includes one or more components according to the overall administration rate, Substituting one or more components of the initial enzyme formulation with one or more components of the selected supplemental enzyme formulation, The method according to claim 5, further comprising:

7. The method according to claim 1, wherein the real-time feedback data further includes system performance data relating to one or more of the fiber strength, porosity, caliper, flexibility, crepe count, filtration efficiency, and drainage of the pulp or paper product.

8. The method according to claim 1, wherein the selected initial enzyme formulation to be applied, the dynamically selected supplemental enzyme formulation, and the respective administration rates thereof are provided to a pulp bleaching process controller.

9. The method according to claim 1, wherein the selected initial enzyme formulation and the dynamically selected supplemental enzyme formulation to be applied, and the respective administration rates thereof, are provided to a paper manufacturing controller.

10. A system (100) that automatically provides real-time dosage corrections for industrial processes in which one or more components of an enzyme formulation are applied to natural fibers for the production of pulp or paper products, A data storage unit (104) includes a model that correlates one or more pulp or paper products with fiber surface matrix characterization determined based on one or more expected measurements of X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS), and Fourier transform infrared (FTIR), and with fiber quality characterization determined with one or more expected fiber length, width, fibrillation, cell wall thickness, fine fiber density / distribution, fiber kink, and fiber curl, and further includes data corresponding to enzyme properties, One or more online sensors (102) configured to generate output signals representing actual measured values ​​for the fiber surface matrix characteristic evaluation and the fiber quality characteristic evaluation, A production stage (110) comprising multiple containers, each configured to store and selectively deliver the respective raw materials corresponding to the selected enzyme formulation components, A dosing control stage (106) comprising one or more computing devices functionally linked to the data storage unit and one or more online sensors and configured to instruct the execution of steps in a method corresponding to any one of claims 1 to 9, A system equipped with these features.

11. The aforementioned administration control stage is: The initial enzyme formulation is formulated by combining one or more components of the initial enzyme formulation according to the first overall administration rate, Applying the initial enzyme formulation in the aforementioned industrial process, The system according to claim 10, configured to perform the following:

12. The aforementioned administration control stage is: The selection of the replacement enzyme formulation includes one or more components according to the overall administration rate, The aforementioned supplemental enzyme formulation is applied instead of the aforementioned initial enzyme formulation, The system according to claim 11, further configured to perform the following:

13. The system according to claim 10, wherein the production stage further comprises a pulp bleaching process controller configured to receive and apply an initial set of one or more selected enzymes and a replenishment set of one or more dynamically selected enzymes, and the respective administration rates thereof.

14. The system according to claim 10, wherein the production stage further comprises a paper manufacturing controller configured to receive and apply an initial set of one or more selected enzymes and a replenishment set of one or more dynamically selected enzymes, and the respective administration rates thereof.

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