Systems and methods for microbial biomass production

Abstract

The present disclosure relates to embodiments for addressing microbial biomass production issues. Described herein are systems and methods for increasing the productivity of microbial biomass production by modeling a production process of a specified biomass and selecting parameters of the microbial biomass production process based on the results of the modeling. Systems and methods are also disclosed that use a program model improved by retraining to model the production process of the biomass to reduce resources for producing microbial biomass.

Description

Technical Field

[0001] The embodiments provided herein relate to control systems, and more particularly to systems and methods for producing microbial biomass. Background Art

[0002] The current market forces microbial biomass producers to increase productivity and efficiency in producing microbial biomass, which in turn requires producers to pay attention to production safety and efficiency in consuming the resources used.

[0003] These tasks require enhanced control of the microbial biomass production process, which leads to the partial or complete elimination of human controllers from the microbial biomass production process. The human controllers are replaced by automatic, highly intelligent tools.

[0004] The dynamic development of biotechnology science and the expanded use of devices and apparatuses operating on various types of physicochemical reactions present challenges to the research community. Current research focuses on the need to create unified, trainable digital systems encompassing a set of methods and mathematical models that describe the entire operational cycle of a device throughout its lifecycle, encompassing design, testing, production, and operation. The rapid growth of analytical cloud services and the internet has transformed the need for virtual environments that simulate the complete lifecycle of microbial biomass production processes from a conceptual imperative to a pressing need and a necessary next step in technological development.

[0005] Over the past five years, the demand for the development and implementation of digital replicas of physical objects and procedures has grown significantly. Companies, for example, are interested in improving productivity, optimizing costs, and increasing business efficiency. Virtual prototypes of real-world components enable: Based on a structured requirements matrix of equipment and process characteristics, economic and environmental indicators, technical documentation, outcome standards, and component requirements; through the predictive capabilities of the virtual environment, more specialized equipment design and system validation are achieved; rapid responses to process deviations (including identification of physical and technical issues) are enabled; and ultimately, better products and services are produced at the lowest reasonable cost.

[0006] In parallel with the development of technical hardware solutions and bioreactor devices, the volume of software running on these devices has also grown rapidly. However, they often require excessive computing resources to work or have low efficiency and reliability.

[0007] Effective management of bioreactor operations requires timely prediction of the biomass production process, taking into account the geometrical and fluid dynamic characteristics of the device, and timely proactive analysis using the sensor data and the computing resources of the system provided for this purpose to efficiently detect the prerequisites and violations of the microbial biomass production process.

[0008] For example, patent application CN108681297A discloses a technology for real-time control of a biological fermentation process using a sensor system, wherein the sensor system collects information such as pressure, temperature, pH level, oxygen concentration, etc. during the fermentation process and controls these parameters based on the data obtained to achieve an optimal environment for microbial production.

[0009] The above mentioned techniques are well suited to the task of real-time controlled fermentation processes, but due to real-time operation with weak predictive capabilities, the techniques are only able to respond to changes in monitored parameters. The embodiments disclosed herein address the problem of microbial biomass production with optimal productivity and efficiency by using the results of modeling this process. Summary of the Invention

[0010] This technical solution is aimed at producing microbial biomass.

[0011] The technical result of the claimed technical solution is to improve the productivity of microbial biomass production by modeling the production process of a specified biomass and selecting the optimal parameters of the microbial biomass production process based on the results of the modeling. Another technical result of this technical solution is to reduce the resources used to produce microbial biomass by modeling the production process of the biomass using a model process improved by retraining.

[0012] In some embodiments, these technical results are achieved by using a microbial biomass production system, which includes: a process simulation tool configured to simulate the biomass production process with specified parameters in a virtual environment, wherein the virtual environment is a microbial industrial production model; a selection tool configured to select at least one control tool for the biomass production process from a control list using a trained program model based on the results of the simulation of the biomass production process, wherein the model process is a set of rules for controlling the biomass production process; based on the results of the simulation of the biomass production process, determine the parameters of the selected control tool using the trained model process; and / or a production facility designed to produce biomass.

[0013] Some embodiments provided herein relate to microbial biomass production systems. In some embodiments, the microbial biomass production system includes a methane-oxidizing microorganism.

[0014] In some embodiments, the parameters of the biomass production process include the productivity of biomass production; the proportion of crude protein contained in the biomass; specific energy consumption; and / or specific consumption of resources used to produce the biomass.

[0015] In some embodiments, the results of the simulation of the biomass production process include at least information about resources of a management tool for producing a specified biomass and / or parameters of the biomass production process.

[0016] In some embodiments, the control means include at least means for regulating the flow of a gas mixture configured to control the gas mixture used in the procedure for producing biomass; means for controlling the biomass, intended to control the parameters of the resulting biomass; and / or means for aeration, intended to ensure mass transfer of gas components of the nutrient medium and oxygen of the culture liquid.

[0017] In some embodiments, the gas mixture is controlled by changing at least the composition of the gas mixture by changing the concentrations of components of the gas mixture, changing the temperature of the gas mixture, and / or changing the pressure of the gas mixture.

[0018] In some embodiments, the gas mixture is controlled in a manner that ensures a maximum allowed concentration of oxygen in a predetermined explosion-proof area.

[0019] In some embodiments, at least two components of the gas mixture are selected from nitrogen, oxygen, natural gas, carbon dioxide, and / or air.

[0020] In some embodiments, the characteristic of the increased oxygen flow area according to the Gibbs-Rosebum plot is used as a parameter for the operation of the gas mixture flow control device.

[0021] In some embodiments, characteristics of the increased oxygen flow region according to a Gibbs-Rosebum plot include a nitrogen concentration of 81.9%, a methane concentration of 6.0%, and an oxygen concentration of 12.1%.

[0022] In some embodiments, the biomass manipulation includes at least changing the temperature of the biomass and / or changing the pH of the biomass.

[0023] In some embodiments, the aeration control comprises variation in the variable aeration achieved by maximizing the enzyme activity in the aeration zone as an activator of molecular oxygen, followed by oxidation of the organic substrate.

[0024] In some embodiments, the training tool is intended to retrain the model process in at least the following manner: for specified parameters for biomass production, a specified biomass will be produced with less use of management resources; and / or for specified resources of the biomass production management tool, more optimized parameters will be selected for producing a specified biomass.

[0025] These technical results are achieved by using a microbial biomass production method, which is implemented by using a microbial biomass production system and includes at least the following stages: a) simulating the biomass production process with specified parameters in a virtual environment, wherein the virtual environment is a microbial industrial production model; b) based on the results of the simulation of the biomass production process, selecting at least one control tool of the biomass production process from a list of control tools using a trained model process, and the model process is a set of rules for controlling the biomass production process; c) based on the results of the simulation of the biomass production process, determining operating parameters of the selected control tool using a trained program model; d) performing biomass production using the selected control tool operating with certain parameters.

[0026] In some embodiments, the microorganism is a methane-oxidizing microorganism.

[0027] In some embodiments, the parameters of the biomass production process include at least: productivity of biomass production; proportion of crude protein in the biomass; specific energy consumption; and / or specific consumption of resources used to produce the biomass.

[0028] In some embodiments, the results of the simulation of the biomass production process include at least: information about resources of management tools used to produce a specified biomass; and / or parameters of the biomass production process.

[0029] In some embodiments, the control means include at least: gas mixture flow regulating means configured to control the gas mixture used in the procedure for producing biomass; biomass control means intended to control the parameters of the resulting biomass; and / or aeration means intended to ensure mass transfer of gas components of the nutrient medium and oxygen in the culture liquid.

[0030] In some embodiments, the gas mixture is controlled by at least varying the composition of the gas mixture by: varying the concentrations of components of the gas mixture; varying the temperature of the gas mixture; and / or varying the pressure of the gas mixture.

[0031] In some embodiments, at least two components of the gas mixture include nitrogen, oxygen, natural gas, carbon dioxide, and / or air.

[0032] In some embodiments, the gas mixture is controlled in a manner that ensures a maximum allowed concentration of oxygen in a predetermined explosion-proof area.

[0033] In some embodiments, the characteristic of the increased oxygen flow area according to the Gibbs-Rosebum plot is used as a parameter for the operation of the gas mixture flow control device.

[0034] In some embodiments, characteristics of the increased oxygen flow region according to a Gibbs-Rosebum plot include a nitrogen concentration of 81.9%, a methane concentration of 6.0%, and an oxygen concentration of 12.1%.

[0035] In some embodiments, the biomass manipulation includes at least changing the temperature of the biomass and / or changing the pH of the biomass.

[0036] In some embodiments, the aeration control comprises a change in variable aeration achieved by maximizing the enzyme activity in the aeration zone as an activator of molecular oxygen, followed by oxidation of the organic substrate.

[0037] In some embodiments, the training tool is intended to retrain the model process in at least the following manner: for specified parameters for biomass production, a specified biomass will be produced with less use of management resources; and / or for specified resources of the biomass production management tool, more optimized parameters will be selected for producing a specified biomass. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Example of a block diagram representing a microbial biomass production system.

[0039] Figure 2 Example of a block diagram representing a method for producing microbial biomass.

[0040] Figure 3 represents an exemplary Gibbs-Rosebum graph.

[0041] Figure 4 Example of a block diagram representing a simulation process tool for microbial biomass production.

[0042] Figure 5 Represents an example of a general-purpose computer system.

[0043] Although the technical solution may have various modifications and alternative forms, the characteristic features shown as examples in the accompanying drawings will be described in detail. The purpose of this specification is not to be limited to the specific embodiments thereof. The purpose of this specification is to cover all changes and modifications that fall within the scope of the technical solution as defined in the claims. DETAILED DESCRIPTION

[0044] By referring to the exemplary embodiments provided herein, the objects and features of the embodiments described herein (including the manner in which these objects and features are achieved) will become apparent. However, the present disclosure is not limited to the exemplary embodiments disclosed herein, and the disclosure provided herein includes various additional embodiments. The description herein is provided solely to assist a qualified expert in the art in understanding the specific details necessary for the technical solution defined in the claims.

[0045] Definitions and concepts are provided herein and will be used throughout the specification.

[0046] A virtual environment has its ordinary meaning as understood in accordance with the specification and refers to a specially allocated (isolated) environment that may include a set of computing resources or a logical combination thereof that is abstracted from hardware implementation and provides logical isolation from each other in computing processes running on the same physical resources.

[0047] An exemplary purpose of a virtual environment is, for example, to simulate (or model) a physical process. The goal is to reproduce the behavior as accurately as possible, as opposed to the various parameters of a physical process, such as the production of microbial biomass, which involves reproducing the operation of various means of a technological cycle. As used herein, the term "simulation" refers to the ability of a process or device to mimic the operation of another process or device (e.g., mimicking a physical process). A process simulation tool is a tool configured to simulate or mimic a physical process in a virtual environment.

[0048] An artificial neural network, as understood in this specification, has its ordinary meaning and refers to a system of simple processors (artificial neurons) that are interconnected and interact with each other. Such processors are typically quite simple (especially compared to processors used in personal computers). Each processor in such a network processes only the signals it periodically receives and the signals it periodically sends to other processors. However, by connecting to a relatively large network of controlled interactions, these individual simple processors can collectively perform quite complex tasks.

[0049] From a machine learning perspective, the use of neural networks is a special case of, for example, pattern recognition methods, discriminant analysis and / or clustering methods.

[0050] Machine learning (ML), as used in this specification, refers to a class of artificial intelligence methods characterized by learning through the application of a solution to many similar problems rather than direct solution. These methods utilize various techniques for working with digital data, including mathematical statistics, numerical methods, optimization methods, probability theory, and graph theory.

[0051] There are three types of training: case-based learning or inductive learning, which is based on the identification of empirical patterns in data; deductive learning, which involves the formalization of expert knowledge and its transfer to a computer in the form of a knowledge base; and reinforcement learning, which is based on trial and error and encourages taking the right actions in the current situation.

[0052] Figure 1 This is a structural diagram of the microbial biomass production system.

[0053] The block diagram of the microbial biomass production system includes a process simulation tool 110, a virtual environment 111, a selection tool 120, a control tool 130, a gas mixture flow control tool 131, a biomass control tool 132, a water supply control tool 133, a safety tool 134, an aeration tool 135, a production tool 140, and a training tool 150.

[0054] The process simulation tool 110 is configured to perform simulation with specified parameters in the virtual environment 111 of the biomass production process, wherein the virtual environment 111 is a microbial industrial production model.

[0055] In some embodiments, the microorganism is a methane-oxidizing microorganism.

[0056] In some embodiments, the at least two parameters of the biomass production process include: productivity of biomass production; proportion of crude protein contained in the biomass; specific energy consumption; and / or specific consumption of resources used to produce the biomass.

[0057] In some embodiments, the results of the simulation of the biomass production process are at least the following: information about the resources of the management tool 130 used to produce the specified biomass; and information about the parameters of the biomass production process.

[0058] In some embodiments, the use of a preliminary simulation of the biomass production process in a virtual environment 111 (online mode) helps to at least: improve the efficiency, stability and productivity of the biomass production process during the subsequent operation of a set of tools that provide a complete cycle of the procedure (including the stages of substrate preparation, inoculation, cultivation, separation and plasmolysis); ensure the implementation of parameters for biomass production simulated in the physical (offline mode) procedure of biomass production; improve the fault tolerance of the production tool 140; ensure the continuity of the biomass production process by predictive detection of anomalies and elimination of their causes, and reduce the risk of disruption to biomass production.

[0059] This result is achieved by using a single digital platform that connects the virtual environment 111 and the simulation tool 110 with the rest of the control tool 130 and the biomass production tool 140 of the biomass production process, as well as providing a method for controlling the biomass production process, which is a comprehensive software solution containing various sets of models based on the design principles of the main stages of system operation and the biomass production process procedures, including at least the following sets: models in 1D and 3D format, including geometric and fluid dynamic parameters of the devices (bioreactors, etc.) providing a complete cycle of the biomass production process, allowing modeling of main and auxiliary structures and evaluation of the capabilities and characteristics of the design solutions of the production tool 140; a complex of 3D models allowing the performance of full-scale experiments and flow and technical procedures that actually correspond to the operation of the production tool 140 (for example, a bioreactor); testing of the processes; mathematical models of physico-chemical processes, which determine the sequence according to a matrix of efficiency and safety requirements and divide the biomass production process into subroutines characterized by physical processes and chemical reactions and transformations, including mathematical models of mass transfer in heterogeneous cultures of jet fermenters developed for dispersed and continuous phases, including modules describing movement, mass transfer, energy transfer, turbulence; methods for processing data received from sensors of the control tool 130 and the production tool 140; means for storing data on the progress and operating mode of the control tool 130 and the production tool 140, input and output parameters of subroutines (including preparation of the substrate, inoculation, cultivation, separation, plasmolysis); an analysis center for decision-making (algorithms based on comparative analysis of data readings from real sensors of the control tool 130, the production tool 140, and virtual sensors of the virtual environment 111), which allows you to identify anomalies and determine the reasons for their occurrence.

[0060] The selection tool 120 is configured to: based on the results of the simulation of the biomass production process, select at least one control tool (hereinafter referred to as the control tool) for the biomass production process from the list of control tools 130 using the trained model process 121, and the model process 121 is a set of rules for controlling the program for producing biomass; based on the results of the simulation of the biomass production process, determine the operating parameters of the selected control tool using the trained model process 121.

[0061] In some embodiments, the control model 121 is a pre-trained neural network.

[0062] In some embodiments, the control model 121 is pre-trained on a real biomass production process.

[0063] In some embodiments, the control model 121 is pre-trained using a teacher (eg, English supervised learning) under the control of an operator who corrects the control model 121 for errors that occur during the training procedure.

[0064] In some embodiments, the control tool 130 includes at least: a flow of a gas mixture regulating tool 131, which is configured to control the gas mixture used in the biomass production process; a biomass control tool 132, which is configured to control the parameters of the received biomass; a water supply monitoring tool 133, which is configured to control the water supply of the production tool 140; a safety tool 134, which is configured for security, fire alarms, video surveillance, etc.; and / or an aeration tool 135, which is configured to provide mass transfer of gas components of the nutrient culture medium and oxygen of the culture liquid.

[0065] In some embodiments, the control tool 130 performs at least: fully automatic control of the relevant parameters and stages of the biomass production process; and / or automatic monitoring of the relevant parameters and stages of the biomass production process with confirmation of selected actions by the operator.

[0066] In some embodiments of the system, the control tool 130 includes various sensors that allow for the collection of data about the biomass production process. Based on the collected data, the control tool 130 allows for at least: controlling the biomass production process based on preset operating parameters of a given tool; adjusting the preset parameters of a given tool to match the parameters of the biomass production process; and / or transmitting the collected data to the training tool 150 to retrain the process model 121.

[0067] In some embodiments, the gas mixture is controlled in a manner that ensures a maximum permissible oxygen concentration within a predetermined explosion proof area.

[0068] In some embodiments, at least one of the gas mixture control functions includes: changing the composition of the gas mixture by changing the concentration of components of the gas mixture; changing the temperature of the gas mixture; and / or changing the pressure of the gas mixture and dissolved gas.

[0069] For example, temperature is an important parameter for fermentation, because during the cultivation of many microorganisms, a temperature deviation of a few degrees can lead to a significant reduction in biomass growth productivity. Maintain the culture temperature with an accuracy of not less than ±0.5°C; In another example, the partial pressure of dissolved oxygen is set as a percentage of saturation. The set point has a lower limit and an upper limit with a difference of 10% to 20%.

[0070] In another example, several principles for regulating the partial pressure of dissolved oxygen are used, including: a change in the rotational speed of the agitator that is proportional to the change in the partial pressure of dissolved oxygen by regulating the flow of the gas mixture regulating tool 131; a change in the rotational speed of the agitator that is combined with the amount of compressed air delivered to the production tool 140 by regulating the flow of the gas mixture regulating tool 131; and the addition of a substrate or some of its components by controlling the biomass tool 132 (it is believed that the partial pressure of dissolved oxygen is inversely proportional to the intensity of fertilization, and a controlled peristaltic pump is usually used for fertilization).

[0071] Any of the above methods can be combined with each other in any of various combinations.

[0072] In some embodiments, at least two components of the gas mixture serve as components of the gas mixture and include: nitrogen; oxygen; natural gas; carbon dioxide; and / or air.

[0073] In some embodiments, the characteristic of the increased oxygen flow area according to the Gibbs-Rosebum diagram is used as an operating parameter of the gas mixture flow control means 131 .

[0074] In some embodiments, the increased oxygen flow region according to the Gibbs-Rosebum plot is characterized by a nitrogen concentration of 81.9%, a methane concentration of 6.0%, and an oxygen concentration of 12.1%.

[0075] like Figure 3 As shown, in some embodiments, the Gibbs-Rosebum diagram identifies a non-explosive region 320 for oxygen concentrations up to 20%-25% in the gas mixture, which allows the process to increase productivity by up to 2-2.5 times.

[0076] In some embodiments, the stream of gas mixture conditioning means 131 includes sensors for analyzing the gas mixture content of methane, oxygen, carbon dioxide, and nitrogen.

[0077] In some embodiments, the flow of the gas mixture conditioning means 131 provides an oxygen concentration in the gas mixture of no more than 8% during biomass production, which in turn significantly reduces the area of ​​the safe working zone (e.g., Figure 3 : 330) (about 2-2.5 times) and hinder the increase in productivity of the microbial growth process.

[0078] In some embodiments, the parameters of operation of the biomass control tool 132 are at least: parameters of biomass mixing (rotation mode); and / or parameters of mass transfer of the culture fluid.

[0079] In some embodiments, at least one of the following serves as biomass management: a change in the temperature of the biomass; and / or a change in the pH of the biomass.

[0080] For example, the pH change is based on a comparison of specified upper and lower pH limits with the actual pH value. Automatic pH adjustment is provided by a peristaltic pump, adding acid or base, respectively. The pH measurement must be accurate (±0.02 pH units), as pH changes carry important information about the process's dynamics.

[0081] In some embodiments, the aeration control is a change in the aeration variable at which a maximum is reached for the enzyme NAD·H2 (nicotinamide adenine dinucleotide), which is an activator of molecular oxygen in the aeration zone and subsequently oxidizes the organic substrate.

[0082] In some embodiments, the control means 130 comprises a single control means (eg, gas mixture adjustment means 131, flow of biomass control means 132). In this case, the selection means only determines the parameters of the specified (only) control means.

[0083] In some embodiments, the production means 140 is configured to produce biomass using one or more of the selected control means 130 operating at certain parameters.

[0084] In some embodiments, the production tool 140 is a bioreactor.

[0085] In some embodiments, at least the following stages of the technical process for biomass production are performed in the production tool 140: dry and liquid chemical reception and storage; solution preparation of nutrient salts of micro- and macronutrients; inoculated biomass cultivation; bioprotein production; biomass thickening; biomass inactivation; bioprotein drying; pelleting and packaging; bioprotein storage; and / or sterilization and biological treatment of wastewater.

[0086] In some embodiments, specified stages of the technical biomass production process are controlled by the control means 130 , which includes collecting data from the sensors of the control means 130 , analyzing them and changing parameters of those processes controlled by the corresponding control means 130 .

[0087] In some embodiments, the control means 130 and the production means 140 are interconnected at least directly: using any hardware and software means known from the prior art; a local computer network; a global computer network (eg, the Internet).

[0088] In some embodiments, if the control tool 130 and the production tool 140 are interconnected by a global computer network, they form a cloud that is managed by cloud technologies known from the prior art.

[0089] For example, the separation of seed biomass (as a component of the production tool 140) may comprise a laboratory fermenter, FKER-1M, MPU UFS32, MPU emk. XZB, a server of a plant dispatch console, connected via the global computer network Internet to the control tool 130 controlled by the operator of the seed biomass separation.

[0090] In another example, the separation of storage and preparation of solutions of nutrient salts and trace elements (as components of the production tool 140) can include counters, terminals, USOs, MPUs, and servers of a central plant control panel, which are connected via a local Ethernet computer network to a control tool 130 controlled by an operator of the storage department and prepare solutions of nutrient salts and trace elements.

[0091] In some embodiments, the training tool 150 is used to retrain the model process 121 in at least the following manner: for specified parameters of biomass production, a specified biomass will be produced with less management resource usage; for specified resources of the biomass production management tool, more optimized parameters will be selected for producing the specified biomass.

[0092] In some embodiments, the retraining of the model process 121 is based on data received from selected control tools 130 during the biomass production process.

[0093] In another variation of system implementation, the model process 121 is retrained based on responses from an operator to correct the operation of the control tool 130 .

[0094] Figure 2 A block diagram showing an exemplary microbial biomass production process.

[0095] The block diagram of the microbial biomass production method (hereinafter referred to as biomass) includes: stage 210, simulating the biomass production process; stage 220, selecting a process control tool; stage 230, determining operating parameters; stage 240, producing biomass; stage 241, regulating the flow of the gas mixture; and stage 250, training the model process.

[0096] At step 210 , the simulation process tool 110 is used to simulate the biomass production process with specified parameters in a virtual environment 111 , wherein the virtual environment 111 is a microbial industrial production model.

[0097] At step 220 , the selection tool 120 is used to select at least one tool for controlling the biomass production process (hereinafter referred to as a control tool) from a control list 130 using a trained model process 121 based on the results of the simulation of the biomass production process, and the model process 121 is a set of rules for controlling the biomass production process.

[0098] At step 230 , the selection tool 120 is used to determine operating parameters of the selected control tool 130 using the modeling process 121 based on the results of the simulation of the biomass production process.

[0099] At step 240, biomass is produced using the production means 140 using the selected control means 130 operating at specific parameters.

[0100] In step 241 , the gas mixture flow control device 131 is used to control the gas mixture used in the biomass production process.

[0101] At step 250, after producing the biomass, the model process 121 is retrained using the training tool 150 in at least the following manner: for specified parameters of biomass production, the specified biomass will be produced with less management resource usage; for specified resources of the biomass production management tool, more optimized parameters will be selected for producing the specified biomass.

[0102] Figure 3 represents an exemplary Gibbs-Rosebum graph.

[0103] In some embodiments, natural gas and air are supplied to the apparatus as carbon sources during the cultivation of methane-oxidizing microorganisms. During microbial growth, carbon dioxide is released. In this case, the gas phase comprises a mixture of methane as fuel, oxygen as an oxidant, and air, nitrogen, and carbon dioxide as inert fillers. At certain ratios, this gas phase can be explosive (region 310).

[0104] The triangular Gibbs-Rosebum diagram ( Figure 3 ) shows the change in the explosion limit of the fuel-oxidizer-inert component system. As the inert component content increases, the range of the combustible composition between the upper and lower concentration limits decreases. When the inert component content is determined, the two branches of the critical composition curve close at point 340, which is called the corner of the explosive region.

[0105] In some embodiments, point 340 depicts the composition of the explosive zone near the corner: 81.9% nitrogen, 6.0% methane, and 12.1% oxygen.

[0106] In another embodiment, if the concentration of the inert component increases with a fixed ratio of the fuel and oxidant contents in their mixture, the temperature and flame speed values ​​decrease, since the energy of the chemical conversion is expended on heating the additional components of the mixture of combustion products. This determines the dependence of the explosive limits on the inert component content.

[0107] The productivity of an aerobic microbial growth process is proportional to the amount of dissolved oxygen in the culture medium, which is determined by the fermenter's mass exchange parameters and the dissolved oxygen concentration. The dissolved oxygen concentration in the culture medium is in turn determined by Henry's law and is proportional to the concentration of oxygen in the gas phase.

[0108] exist Figure 1 The system described in provides for regulation of the concentrations of the components of the gas mixture during fermentation, operating in the previously required explosion proof area 310 and at the same time providing the maximum permissible oxygen concentration.

[0109] This objective is achieved by the control means 130 (particularly the flow of the gas mixture regulation means 131 ) receiving data from the gas mixture concentration sensors (methane, oxygen, carbon dioxide, and nitrogen), processing and determining a safe mode at the maximum permissible oxygen concentration. Regulation of the gas phase is performed by varying (increasing or decreasing) the natural gas supply, varying (increasing or decreasing) the air supply, or varying (increasing or decreasing) the nitrogen supply.

[0110] This control system for safe operation of the fermenter allows the use of concentration data of all components of the gas culture medium in both the decreasing and increasing directions, which extends the working range within the safe zone of the working components of the gas phase, unlike existing schemes that only provide oxygen concentration limitations.

[0111] Figure 4 An example of a block diagram representing a process for simulating microbial biomass production.

[0112] The block diagram for simulating the microbial biomass production process (hereinafter referred to as biomass) includes a virtual environment 111 , a control tool 130 , a production tool 140 , an analysis center 410 , a virtual management tool 430 , and a virtual production tool 440 .

[0113] The biomass production process control tool 130 and the biomass production tool 140 provide a complete cycle of biomass production (including the stages of substrate preparation, inoculation, cultivation, separation, and plasmolysis).

[0114] The analysis center 410 is configured to analyze data collected by the process simulation tool 110 during simulation of the biomass production process in the virtual environment 111 and adjust the operation of the control tool 130 and the production tool 140 , including through training and retraining of the model process 121 .

[0115] In some embodiments, the analysis center 410 is not a single tool, but rather a group of separate tools combined using cloud technology.

[0116] In some embodiments, the analysis center 410 corrects the operation of the virtual management tool 430 and the virtual production tool 440 based on analysis of data collected by the process simulation tool 110 during simulation of the biomass production process in the virtual environment 111, including through training and retraining of the model process 121.

[0117] In some embodiments, validation of certain adjustments to the operation of the control tool 130 and the production tool 140 is performed by an operator of the analysis center 410 .

[0118] The virtual environment 111 includes a virtual management tool 430 that issues operations of the control tool 130 and a virtual production tool 440 that issues operations of the production tool 140 .

[0119] Figure 5 The figure shows an example of a general-purpose computer system, a personal computer or server 20, including a central processor 21, a system memory 22, and a system bus 23 containing various system components, including the memory associated with the central processor 21. The system bus 23 can be implemented as any bus structure known in the art, which in turn includes a bus memory or bus memory controller, a peripheral bus, and a local bus capable of interoperating with any other bus architecture. The system memory includes a permanent storage device (ROM) 24 and a random access memory (RAM) 25. The main input / output system (BIOS) 26 contains the basic routines that ensure the transfer of information between the components of the personal computer 20, for example, when the operating system is loaded using ROM 24.

[0120] The personal computer 20 further includes a hard disk 27 for reading and writing data, a magnetic disk drive 28 for reading and writing a removable magnetic disk 29, and an optical disk drive 30 for reading and writing a removable optical disk 31, such as a CD-ROM, DVD-ROM, or other optical media. The hard disk 27, magnetic disk drive 28, and optical disk drive 30 are connected to the system bus 23 via a hard disk interface 32, a magnetic disk interface 33, and an optical disk drive interface 34, respectively. The drives and corresponding computer data carriers are non-volatile devices that store computer instructions, data structures, program modules, and other data for the personal computer 20.

[0121] This specification discloses embodiments of the system using a hard disk 27, a removable magnetic disk 29, and a removable optical disk 31, but it should be understood that other types of computer media 56 capable of storing data in a computer-readable form (solid-state drives, flash memory cards, digital disks, random access memory (RAM), etc.) may be used and connected to the system bus 23 via the controller 55.

[0122] The computer 20 has a file system 36, which stores the recorded operating system 35, as well as additional software applications 37, other software modules 38, and program data 39. A user can enter commands and information into the personal computer 20 via input devices (keyboard 40, mouse controller 42). Other input devices (not shown) may be used, including, for example, a microphone, joystick, game console, or scanner. Such input devices are typically connected to the computer system 20 via a serial port 46, which in turn is connected to the system bus, but may be connected in another manner, such as using a parallel port, game port, or universal serial bus (USB). A monitor 47 or other type of display device is also connected to the system bus 23 via an interface, such as a video adapter 48. In addition to the monitor 47, the personal computer may be equipped with other peripheral output devices (not shown), such as speakers or a printer.

[0123] The personal computer 20 can work in a network environment, using a network connection with one or more remote computers 49. The remote computer(s) 49 are the same personal computers or servers described in the description. Figure 5 The basic configuration of the personal computer 20 shown has most or all of the elements mentioned above. Other devices may also be present in the computer network, such as routers, network stations, peer devices or other network nodes.

[0124] Network connections can form local area networks (LANs) 50 and global area networks (WANs). Such networks are used for corporate computer networks, intranets, and often provide access to the Internet. In a LAN or WAN network, personal computer 20 is connected to local network 50 via a network adapter or network interface 51. When using a network, personal computer 20 may use a modem 54 or other device that provides communication with a global computer network, such as the Internet. Modem 54, either an internal or external device, is connected to system bus 23 via serial port 46. It should be understood that the network connections are only approximate and do not necessarily illustrate an exact network configuration; in reality, other ways of establishing a connection exist through technical means of communication from one computer to another.

[0125] Some embodiments are provided, as outlined in the following enumerated alternatives.

[0126] 1. A microbial biomass production system, comprising: a) a process simulation tool, the process simulation tool being configured to perform simulation with specified parameters in a virtual environment of a biomass production process, wherein the virtual environment is a microbial industrial production model; b) a selection tool, the selection tool being configured to: based on the results of the simulation of the biomass production process, select at least one control tool of the biomass production process from a list of control tools using a trained model process, wherein the model process includes a set of rules for controlling the biomass production process; based on the results of the simulation of the biomass production process, determine operating parameters of the selected control tool using the trained model process; and c) a production tool, the production tool being configured to perform biomass production using the selected control tool operating with certain parameters.

[0127] 2. The microbial biomass production system of alternative 1, wherein the microorganism is a methane-oxidizing microorganism.

[0128] 3. The microbial biomass production system of any one of alternatives 1 to 2, wherein the parameters of the biomass production process include: the productivity of biomass production; the proportion of crude protein contained in the biomass; specific energy consumption; and specific consumption of resources used to produce the biomass.

[0129] 4. The microbial biomass production system of any one of alternatives 1 to 3, wherein the results of the simulation of the biomass production process include: information about the resources of the management tool used to produce a specified biomass; or parameters of the biomass production process.

[0130] 5. A microbial biomass production system according to any one of alternatives 1 to 4, wherein the control means comprises: a gas mixture flow regulation means configured to control the gas mixture used in the biomass production process; a biomass control means configured to control the parameters of the resulting biomass; or an aeration means configured to ensure mass transfer of nutrient medium gas components and culture liquid oxygen.

[0131] 6. The microbial biomass production system of Alternative 5, wherein the gas mixture is controlled by at least: changing the composition of the gas mixture by changing the concentrations of components of the gas mixture; changing the temperature of the gas mixture; or changing the pressure of the gas mixture.

[0132] 7. The microbial biomass production system of Alternative 6, wherein at least two components of the gas mixture are selected from nitrogen, oxygen, natural gas, carbon dioxide, or air.

[0133] 8. The microbial biomass production system according to alternative 7, wherein the gas mixture is controlled in a predetermined explosion-proof area in such a way as to ensure a maximum permissible concentration of oxygen.

[0134] 9. The microbial biomass production system of alternative 8 wherein the characteristic of the increased oxygen flow region according to the Gibbs-Rosebum diagram is used as a parameter for the operation of the gas mixture flow control device.

[0135] 10. The microbial biomass production system of Alternative 9 wherein the characteristics of the increased oxygen flow region according to a Gibbs-Rosebum plot include 81.9% nitrogen, 6.0% methane, and 12.1% oxygen.

[0136] 11. The microbial biomass production system of any one of Alternatives 5 to 10 wherein the biomass control comprises: changing the temperature of the biomass; changing the pH of the biomass.

[0137] 12. The microbial biomass production system of any one of Alternatives 5 to 11, wherein the aeration control is a change in variable aeration, the change in variable aeration being achieved by maximizing the enzyme acting as a molecular oxygen activator in the aeration zone, followed by oxidation of the organic substrate.

[0138] 13. The microbial biomass production system of any one of Alternatives 1 to 12, further comprising a training tool configured to retrain the model process in at least the following manner: for specified parameters for biomass production, a specified biomass will be produced with less use of management resources; or for specified resources of the biomass production management tool, more optimized parameters will be selected for producing a specified biomass.

[0139] 14. A method for microbial biomass production, comprising: a) simulating the biomass production process with specified parameters in a virtual environment, wherein the virtual environment is a microbial industrial production model; b) based on the results of the simulation of the biomass production process, selecting at least one control tool for the biomass production process from a list of control tools using a trained model process, wherein the model process is a set of rules for controlling the biomass production process; c) based on the results of the simulation of the biomass production process, determining operating parameters of the selected control tool using the trained model process; d) performing biomass production using the selected control tool operating with certain parameters.

[0140] 15. The microbial biomass production method of Alternative 14, wherein the microorganism is a methane-oxidizing microorganism.

[0141] 16. The microbial biomass production method of any one of Alternatives 14 to 15, wherein the parameters of the biomass production process include: productivity of biomass production; proportion of crude protein contained in the biomass; specific energy consumption; or specific consumption of resources used to produce the biomass.

[0142] 17. The microbial biomass production method of any one of Alternatives 14 to 16, wherein the results of the simulation of the biomass production process include: information about the resources of the management tool used to produce a specified biomass; or parameters of the biomass production process.

[0143] 18. The microbial biomass production method of any one of Alternatives 14 to 17, wherein the control means comprises: a gas mixture flow regulation means configured to control the gas mixture used in the biomass production process; a biomass control means configured to control parameters of the produced biomass; or an aeration means configured to ensure mass transfer of gas components of the nutrient medium and oxygen in the culture liquid.

[0144] 19. The microbial biomass production method of Alternative 18, wherein the gas mixture is controlled by at least: changing the composition of the gas mixture by changing the concentrations of components of the gas mixture; changing the temperature of the gas mixture; or changing the pressure of the gas mixture.

[0145] 20. The microbial biomass production method of Alternative 19, wherein at least two components of the gas mixture are selected from nitrogen, oxygen, natural gas, carbon dioxide, or air.

[0146] 21. The microbial biomass production method of alternative 20, wherein the gas mixture is controlled in a predetermined explosion-proof area to ensure a maximum permissible concentration of oxygen.

[0147] 22. The microbial biomass production method of alternative 21 wherein the characteristic of the increased oxygen flow region according to the Gibbs-Rosebum diagram is used as a parameter for the operation of the gas mixture flow control device.

[0148] 23. The microbial biomass production method of alternative 22, wherein the characteristics of the increased oxygen flow region according to the Gibbs-Rosebum plot include a nitrogen concentration of 81.9%, a methane concentration of 6.0%, and an oxygen concentration of 12.1%.

[0149] 24. The microbial biomass production method of any one of Alternatives 18 to 23, wherein the biomass control comprises: changing the temperature of the biomass; or changing the pH of the biomass.

[0150] 25. The microbial biomass production method of any one of Alternatives 18 to 24, wherein the aeration control is a change in variable aeration, the change in variable aeration being achieved by maximizing the enzyme acting as a molecular oxygen activator in the aeration zone, followed by oxidation of the organic substrate.

[0151] 26. The microbial biomass production method of any one of Alternatives 14 to 25, further comprising a training tool configured to retrain the model process in at least the following manner: for specified parameters for biomass production, a specified biomass will be produced with less use of management resources; or for specified resources of the biomass production management tool, more optimized parameters will be selected for producing a specified biomass.

[0152] In summary, it should be noted that the information provided in this specification is exemplary and does not limit the scope of the present invention as defined in the claims.

Claims

1. A microbial biomass production system comprising: a) a process simulation tool configured to simulate a biomass production process in a virtual environment with specified parameters, wherein: The virtual environment is a microbial industrial production model; b) Select a tool configured to: selecting at least one control tool for the biomass production process from a list of control tools using a trained model process based on results of the simulation of the biomass production process, wherein the model process includes a set of rules for controlling the biomass production process; determining operating parameters of a selected control tool using the trained model process based on the simulation results of the biomass production process; and c) A production tool configured to produce biomass using the selected control tool operated at certain parameters.

2. The microbial biomass production system according to claim 1, wherein: The microorganism is a methane-oxidizing microorganism.

3. The microbial biomass production system according to claim 1, wherein: The parameters of the biomass production process include: productivity of biomass production; the proportion of crude protein contained in the biomass; specific energy consumption; and Unit consumption of resources used to produce biomass.

4. The microbial biomass production system according to claim 1, wherein: The results of the simulation of the biomass production process include: Resource information on management tools used to produce designated biomass; or Parameters of the biomass production process.

5. The microbial biomass production system according to claim 1, wherein: The control tools include: gas mixture flow regulating means configured to control said gas mixture used in said biomass production process; a biomass control tool configured to control parameters of the resulting biomass; Aeration means configured to ensure mass transfer of gaseous components of the nutrient medium and oxygen in the culture liquid.

6. The microbial biomass production system according to claim 5, wherein: The gas mixture is controlled by at least the following means: changing the composition of the gas mixture by changing the concentrations of components of the gas mixture; changing the temperature of the gas mixture; or The pressure of the gas mixture is varied.

7. The microbial biomass production system according to claim 6, wherein: At least two components of the gas mixture are selected from nitrogen, oxygen, natural gas, carbon dioxide, or air.

8. The microbial biomass production system according to claim 7, wherein: The gas mixture is controlled in a predetermined explosion proof area to ensure a maximum permissible concentration of oxygen.

9. The microbial biomass production system according to claim 8, wherein: The characteristic of the increased oxygen flow area according to the Gibbs-Rosebum diagram is used as a parameter for the operation of the gas mixture flow control device.

10. The microbial biomass production system according to claim 9, wherein: Characteristics of the increased oxygen flow region according to the Gibbs-Rosebum plot include 81.9% nitrogen, 6.0% methane, and 12.1% oxygen.

11. The microbial biomass production system according to claim 5, wherein: The biomass control includes: changing the biomass temperature; The pH of the biomass is altered.

12. The microbial biomass production system according to claim 5, wherein: Aeration control is the variation of variable aeration achieved by maximizing the enzymes in the aeration zone as activators of molecular oxygen, followed by oxidation of the organic substrate.

13. The microbial biomass production system of claim 1 , further comprising a training tool configured to retrain the model process in at least the following manner: For specified parameters of biomass production, the specified biomass will be produced with less use of management resources; or For a given resource of the biomass production management tool, more optimized parameters for producing said given biomass will be selected.

14. A method for producing microbial biomass, comprising: a) performing simulation with specified parameters in a virtual environment of a biomass production process, wherein the virtual environment is a microbial industrial production model; b) selecting at least one control tool for the biomass production process from a list of control tools using a trained model process based on a result of the simulation of the biomass production process, wherein the model process is a set of rules for controlling the biomass production process; c) determining operating parameters of a selected control tool using the trained model process based on the results of the simulation of the biomass production process; d) Producing biomass using selected control tools that work together with certain parameters.

15. The microbial biomass production method according to claim 14, wherein: The microorganism is a methane-oxidizing microorganism.

16. The method for producing microbial biomass according to claim 14, wherein: The parameters of the biomass production process include: productivity of biomass production; the proportion of crude protein contained in the biomass; specific energy consumption; or Unit consumption of resources used to produce biomass.

17. The microbial biomass production method according to claim 14, wherein: The results of the simulation of the biomass production process include: Resource information on management tools used to produce designated biomass; or Parameters of the biomass production process.

18. The method for producing microbial biomass according to claim 14, wherein: The control tools include: gas mixture flow regulating means configured to control said gas mixture used in said biomass production process; a biomass control tool configured to control parameters of the produced biomass; or, Aeration means configured to ensure mass transfer of gaseous components of the nutrient medium and oxygen in the culture liquid.

19. The method for producing microbial biomass according to claim 18, wherein: The gas mixture is controlled by at least the following means: changing the composition of the gas mixture by changing the concentrations of components of the gas mixture; changing the temperature of the gas mixture; or The pressure of the gas mixture is varied.

20. The microbial biomass production method according to claim 19, wherein: At least two components of the gas mixture are selected from nitrogen, oxygen, natural gas, carbon dioxide, or air.

21. The microbial biomass production method according to claim 20, wherein: The gas mixture is controlled in a predetermined explosion proof area to ensure a maximum permissible concentration of oxygen.

22. The microbial biomass production method according to claim 21, wherein The characteristics of the increased oxygen flow area according to the Gibbs-Rosebum diagram are used as parameters for the operation of the gas mixture flow control device.

23. The microbial biomass production method according to claim 22, wherein: Characteristics of the increased oxygen flow region according to the Gibbs-Rosebum plot include a nitrogen concentration of 81.9%, a methane concentration of 6.0%, and an oxygen concentration of 12.1%.

24. The microbial biomass production method according to claim 18, wherein: The biomass control includes: Changing the temperature of the biomass; or The pH of the biomass is altered.

25. The microbial biomass production method according to claim 18, wherein: The aeration control is a change in variable aeration that is achieved by maximizing the enzymes in the aeration zone as molecular oxygen activators, followed by oxidation of the organic substrate.

26. The microbial biomass production method of claim 14, further comprising a training tool configured to retrain the model process in at least the following manner: For specified parameters of biomass production, the specified biomass will be produced with less use of management resources; or For a given resource of the biomass production management tool, more optimized parameters for producing said given biomass will be selected.

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