A chitin substrate catalytic environmental adaptability linkage processing system

Abstract

The application belongs to the technical field of chitin catalytic system, and discloses a chitin substrate catalytic environmental adaptability linkage processing system, which comprises a system core function layer and a core process execution subsystem cluster. The system core function layer comprises a perception layer, a decision layer, an execution layer, and a bidirectional linkage mechanism connecting the perception layer and the decision layer, the decision layer and the execution layer. Through the system core function layer, the perception layer, the decision layer, the execution layer and the bidirectional linkage mechanism, the high-precision sensor array of the perception layer, such as the 0.1 ℃ fiber Bragg grating temperature sensor and the <2 s ion selective electrode pH sensor, realizes accurate parameter acquisition; the decision layer LSTM neural network prediction model and 128 process parameter templates realize rapid decision; and the high-precision execution device of the execution layer realizes accurate parameter regulation, solves the problems of poor environmental adaptability and insufficient hierarchical cooperation of the existing system, improves the stability of the catalytic efficiency, and reduces the waste rate of enzyme preparations.

Description

Technical Field

[0001] This invention belongs to the technical field of chitin catalytic systems, specifically a chitin substrate catalytic environment-adaptive linkage treatment system. Background Technology

[0002] Chitin, as a widely available natural polymer material, has extremely high application value in fields such as biomedicine, food industry, and environmental protection. The core link in its industrial utilization lies in the efficient catalytic treatment of the substrate. Through enzyme catalysis and other methods, chitin is converted into high-value-added products such as low molecular weight oligosaccharides and chitosan. Currently, the industry has gradually formed chitin catalytic systems that include substrate treatment, catalytic reaction, and product purification. Some systems are also attempting to introduce sensors and control modules to improve the level of automation, hoping to optimize the catalytic effect through parameter monitoring and regulation. However, existing chitin catalytic systems consistently face core problems in practical operation, namely poor environmental adaptability and insufficient hierarchical coordination, making it difficult to meet the stability and efficiency requirements of industrial production. On the one hand, the chitin catalytic reaction is extremely sensitive to environmental parameters such as temperature and pH. A temperature deviation of 2°C or a pH fluctuation of 0.3 units can lead to a decrease in enzyme catalytic activity of more than 30%. Existing systems generally have low precision in their sensing modules, such as temperature sensors with an accuracy of only 0.5°C and pH sensors with a response time greater than 5 seconds. Furthermore, there is a lack of efficient linkage mechanisms between the sensing, decision-making, and execution levels, with data transmission bandwidth less than 50Mbps and command delays often exceeding 200ms. This results in the inability to promptly adjust for sudden changes in environmental parameters, leading to significant fluctuations in catalytic efficiency. On the other hand, existing... The decision-making modules of the system mostly rely on fixed process parameter templates, lacking the ability to predict based on reaction trends and the ability to optimize multiple parameters in a coordinated manner. They cannot avoid catalytic imbalance problems caused by batch differences in raw materials, such as different substrate crystallinity and impurity content, in advance. They also cannot initiate targeted compensation measures in extreme environments, such as low temperature <10℃ and high alkalinity pH >8.5. Ultimately, this results in an enzyme preparation waste rate as high as 40%, a product molecular weight distribution standard deviation of more than 800 Da, and a batch qualification rate of only 75%-82%, which seriously restricts the large-scale development of the chitin catalysis industry. Therefore, developing an environmentally adaptable chitin catalysis treatment system with high-precision sensing, rapid decision-making, efficient execution, and multi-level linkage has become a key problem that the industry urgently needs to solve. Summary of the Invention

[0003] The purpose of this invention is to provide a chitin substrate catalytic environmentally adaptable linkage treatment system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A chitin substrate catalytic environmentally adaptable linkage treatment system includes a core functional layer and a core process execution subsystem cluster. The core functional layer includes a sensing layer, a decision-making layer, an execution layer, and a two-way linkage mechanism connecting the sensing layer and the decision-making layer, and the decision-making layer and the execution layer. The sensing layer is equipped with 20 high-precision sensor arrays, including a fiber optic temperature sensor with an accuracy of 0.1℃, an ion-selective electrode pH sensor with a response time of <2s, and a near-infrared substrate concentration detector with a measurement error of <0.2%, with a data sampling frequency of up to 1Hz; The decision-making layer is equipped with an industrial-grade edge computing module and has a built-in prediction model based on LSTM neural network, which can predict the catalytic reaction trend within the next 5 minutes. It also has 128 pre-stored process parameter templates and a decision response time of <100ms. The execution layer includes a multi-channel enzyme addition device with a flow rate accuracy of 0.01 mL / min, a variable frequency temperature control system with a temperature control accuracy of 0.5℃, and an online neutralization module with a pH adjustment rate of 0.1 pH / min; In the bidirectional linkage mechanism, the bandwidth for the linkage between perception and decision-making data is 100Mbps, and the delay of the linkage control signal between decision-making and execution instructions is <50ms.

[0005] Preferably, the core process execution subsystem cluster includes a substrate pretreatment subsystem, an intelligent catalysis subsystem, and a product purification subsystem; The substrate pretreatment subsystem integrates an ultrasonic cleaning module with adjustable power of 300-800W, a low-temperature degreasing module with a temperature of 0-10℃, and a gradient decalcification module with adaptive hydrochloric acid concentration of 0.5%-2.0%, which can stably control the substrate purity to above 92%. The intelligent catalytic subsystem adopts an airlift reactor with an adjustable volume of 50-500L, with a built-in adaptive adjustment module for stirring rate of 50-500rpm, and is combined with an enzyme-catalyzed reaction kinetic model. The product purification subsystem is equipped with an ultrafiltration membrane separation device with an adjustable molecular weight cutoff of 1000-10000 Da and a freeze-drying module with a temperature controllable from -50 to -20℃, which can achieve a product purity of over 98%.

[0006] Preferably, the decision layer also incorporates a multi-parameter collaborative optimization model based on the non-dominated sorting genetic algorithm NSGA-II. This model can collaboratively optimize temperature, pH, enzyme concentration, and stirring rate. The execution layer also includes an extreme environment compensation module. The compensation module includes a low-temperature active enzyme candidate library and an alkali-tolerant enzyme modification module. The low-temperature active enzyme candidate library contains chitinases derived from Psychrobactersp. The alkali-tolerant enzyme modification module improves enzyme stability through PEGylation modification. The compensation module can ensure that the catalytic efficiency loss under extreme conditions does not exceed 10%.

[0007] Preferably, the intelligent catalytic subsystem is connected to an online high-performance liquid chromatography (HPLC) detection module. The detection module has a detection cycle of <5 min and can construct a feedback closed loop between product concentration and catalytic parameters. When the product concentration increase rate is <0.5 g / Lh, it triggers an increase in stirring rate and compensation regulation of enzyme addition amount.

[0008] A method for operating a chitin substrate-catalyzed environmentally adaptive integrated treatment system includes the following steps: Step S1: System startup and initialization; Step S2, substrate pretreatment; Step S3, intelligent catalysis; Step S4: Product purification; Step S5: System maintenance and cleanup.

[0009] Preferably, the detailed process of system startup and initialization in step S1 is as follows: S101, the system module starts up, and the core functional layers of the system, including the perception layer, decision layer, execution layer, core process execution subsystem cluster, substrate pretreatment subsystem, intelligent catalysis subsystem, product purification subsystem, and intelligent operation and maintenance subsystem, are activated. The fault tree analysis (FTA) module in the intelligent operation and maintenance subsystem starts up, and the operating status of the sensor array, execution layer equipment and each subsystem module is detected. The digital twin operation and maintenance platform loads the initial operating parameter template. S102, initialization and calibration of the sensing layer. The 20 sets of high-precision sensor arrays in the sensing layer are initialized. Among them, the fiber optic temperature sensor, ion-selective electrode pH sensor, and near-infrared substrate concentration detector are zero-point calibrated respectively. The data sampling frequency is set to 1Hz to verify whether the sensing and decision-making, the data linkage bandwidth of 100Mbps, and the delay of the decision-making and execution command linkage control signal <50ms are normal.

[0010] Preferably, the detailed process of substrate pretreatment in step S2 is as follows: S201, Substrate parameter detection: Chitin raw material is sent into the substrate pretreatment subsystem, the near-infrared spectroscopy analysis module in the subsystem is activated to detect the crystallinity and molecular weight distribution of the substrate, and the detection data is transmitted to the decision layer through the sensing layer. S202, Pretreatment parameter generation and execution: The decision layer calls the historical database and combines the LSTM neural network prediction model to generate pretreatment process parameters 30 minutes in advance. The instruction execution layer controls the substrate pretreatment subsystem and starts the ultrasonic cleaning module, low temperature degreasing module, and gradient decalcification module. S203, Pretreatment effect verification and substrate delivery: The sensing layer monitors the purity of the pretreated substrate in real time. When the purity reaches a stable level of 92% or higher, the decision layer sends an instruction to deliver the pretreated substrate to the intelligent catalysis subsystem.

[0011] Preferably, the detailed process of intelligent catalysis in step S3 is as follows: S301, Initial Catalytic Parameter Setting and Execution: After receiving the substrate, the intelligent catalytic subsystem detects the temperature, pH value, and substrate concentration inside the reactor and transmits them to the decision layer. The decision layer combines 128 pre-stored process parameter templates with the enzyme-catalyzed reaction kinetic model to generate initial catalytic parameters. S302, Product concentration monitoring and parameter feedback adjustment: Start the high-performance liquid chromatography (HPLC) online detection module to detect product concentration in real time and construct a feedback loop between product concentration and catalytic parameters; S303, extreme environment compensation regulation: If the sensing layer detects an extreme environment temperature <10℃ or pH >8.5, the decision layer activates the extreme environment compensation module of the execution layer. When the temperature is <10℃, it calls the low-temperature active enzyme candidate library containing Psychrobactersp, derived from chitinase. When the pH is >8.5, it starts the alkali-tolerant enzyme modification module that improves enzyme stability through PEGylation modification to ensure that the catalytic efficiency loss does not exceed 10%. S304, multi-parameter collaborative optimization, the non-dominated sorting genetic algorithm NSGA-Ⅱ multi-parameter collaborative optimization model built into the decision layer runs in real time, and performs collaborative optimization of temperature, pH value, enzyme concentration and stirring rate, reducing system energy consumption by 22%.

[0012] Preferably, the detailed process for product purification in step S4 is as follows: S401, Catalytic product delivery: When the product in the intelligent catalytic subsystem reaches the target conversion rate, the decision layer sends an instruction to deliver the reaction product to the product purification subsystem. S402, Refining parameter setting and execution: The sensing layer detects the initial concentration and molecular weight distribution of the product, and the decision layer generates the refining parameters: the ultrafiltration membrane separation device is started to separate impurities with an adjustable molecular weight cutoff of 1000-10000 Da, and the freeze-drying module is started to dry the purified product at a controllable temperature of -50 to -20℃. S403, Refined Product Verification and Output: The sensing layer monitors the purity and molecular weight distribution of the dried product. When the product purity reaches 98% or higher and the standard deviation of the molecular weight distribution is <500 Da, the product refining is completed and the finished product is output.

[0013] Preferably, the detailed process for system operation and maintenance and cleanup in step S5 is as follows: S501 provides real-time operation and maintenance monitoring. The intelligent operation and maintenance subsystem runs continuously. The fault tree analysis (FTA) module monitors the status of sensors and actuators in real time. If the pH sensor error is detected to be >0.2pH, it automatically switches to the backup sensor and starts the calibration procedure to ensure that the fault is handled in <5 minutes. The digital twin operation and maintenance platform synchronously simulates the system operation status and optimizes process parameters in advance. S502 supports data storage and access, and the operating parameters of the perception layer, execution layer and each subsystem are stored in the local database in real time, supporting remote terminal access and parameter retrieval. S503, System Cleanup and Report Generation: After a single processing cycle is completed, the system automatically cleans up residual materials in each subsystem; the intelligent operation and maintenance subsystem generates an operation report, and the system enters standby mode, waiting for the next startup command.

[0014] The beneficial effects of this invention are as follows: 1. This invention utilizes a core functional layer consisting of a perception layer, a decision-making layer, an execution layer, and a bidirectional linkage mechanism. The perception layer employs a high-precision sensor array with a fiber optic temperature sensor (accuracy of 0.1℃) and an ion-selective electrode pH sensor with a response time of <2s to achieve precise parameter acquisition. The decision-making layer utilizes an LSTM neural network prediction model and 128 sets of process parameter templates to achieve rapid decision-making. The execution layer employs a high-precision execution device to achieve precise parameter control. This invention addresses the problems of poor environmental adaptability and insufficient hierarchical coordination in existing systems, improves catalytic efficiency and stability, and reduces enzyme waste.

[0015] 2. This invention utilizes a core process execution subsystem cluster consisting of a substrate pretreatment subsystem, an intelligent catalysis subsystem, and a product purification subsystem. The substrate pretreatment subsystem stably controls the substrate purity to above 92%. The intelligent catalysis subsystem combines an enzyme-catalyzed reaction kinetic model with HPLC online detection feedback loop to achieve efficient catalysis. The product purification subsystem ensures product purity of over 98% and a molecular weight distribution standard deviation of <500 Da, thereby improving product quality and batch qualification rate.

[0016] 3. This invention utilizes the Fault Tree Analysis (FTA) module of the intelligent operation and maintenance subsystem and the digital twin operation and maintenance platform. The FTA module enables rapid fault handling in less than 5 minutes, while the digital twin operation and maintenance platform pre-optimizes process parameters. Combined with the operation data storage and automatic system cleanup functions, it ensures stable system operation, reduces trial and error costs, and improves operation and maintenance efficiency. Attached Figure Description

[0017] Figure 1 This invention relates to the overall system architecture and process flow; Figure 2 This is a diagram of the core functional layers of the system of the present invention; Figure 3 This is a detailed diagram of the sensor array in the sensing layer of the present invention; Figure 4 This is a diagram of the intelligent decision-making module of the present invention; Figure 5 This is a diagram of the execution layer control module of the present invention; Figure 6 This is a cluster diagram of the core process execution subsystem of the present invention; Figure 7 This is a diagram of the substrate pretreatment subsystem of the present invention; Figure 8 This is a diagram of the product refining subsystem of the present invention; Figure 9 This is a diagram of the intelligent operation and maintenance subsystem of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figures 1 to 9 As shown, this embodiment of the invention provides a chitin substrate catalytic environmental adaptability linkage treatment system, including a core functional layer and a core process execution subsystem cluster. The core functional layer includes a sensing layer, a decision layer, an execution layer, and a bidirectional linkage mechanism connecting the sensing layer and the decision layer, and the decision layer and the execution layer. The sensing layer is equipped with 20 high-precision sensor arrays, including a fiber optic temperature sensor with an accuracy of 0.1℃, an ion-selective electrode pH sensor with a response time of <2s, and a near-infrared substrate concentration detector with a measurement error of <0.2%, with a data sampling frequency of up to 1Hz; The decision-making layer is equipped with an industrial-grade edge computing module and has a built-in prediction model based on LSTM neural network, which can predict the catalytic reaction trend within the next 5 minutes. It also has 128 pre-stored process parameter templates and a decision response time of <100ms. The execution layer includes a multi-channel enzyme addition device with a flow rate accuracy of 0.01 mL / min, a variable frequency temperature control system with a temperature control accuracy of 0.5℃, and an online neutralization module with a pH adjustment rate of 0.1 pH / min; In the bidirectional linkage mechanism, the bandwidth for the linkage between perception and decision-making data is 100Mbps, and the delay of the linkage control signal between decision-making and execution instructions is <50ms.

[0020] Through the perception layer, decision-making layer, execution layer, and two-way linkage mechanism of the system's core functional layer, accurate parameter acquisition, rapid decision-making, and precise control are achieved, providing a foundation for system environmental adaptability and hierarchical collaboration.

[0021] The core process execution subsystem cluster includes a substrate pretreatment subsystem, an intelligent catalysis subsystem, and a product purification subsystem. The substrate pretreatment subsystem integrates an ultrasonic cleaning module with adjustable power of 300-800W, a low-temperature degreasing module with a temperature of 0-10℃, and a gradient decalcification module with adaptive hydrochloric acid concentration of 0.5%-2.0%, which can stably control the substrate purity to above 92%. The intelligent catalytic subsystem adopts an airlift reactor with an adjustable volume of 50-500L, with a built-in adaptive adjustment module for stirring rate of 50-500rpm, and is combined with an enzyme-catalyzed reaction kinetic model. The product purification subsystem is equipped with an ultrafiltration membrane separation device with an adjustable molecular weight cutoff of 1000-10000 Da and a freeze-drying module with a temperature controllable from -50 to -20℃, which can achieve a product purity of over 98%.

[0022] The core process execution subsystem cluster comprises a substrate pretreatment subsystem, an intelligent catalysis subsystem, and a product purification subsystem, which respectively achieve substrate purity enhancement, efficient catalytic reaction, and control of product purity and molecular weight distribution, ensuring the effectiveness of the entire chitin catalytic treatment process.

[0023] The decision layer also incorporates a multi-parameter collaborative optimization model based on the non-dominated sorting genetic algorithm NSGA-II. This model can collaboratively optimize temperature, pH, enzyme concentration, and stirring rate. The execution layer also includes an extreme environment compensation module, which includes a low-temperature active enzyme candidate library and an alkali-tolerant enzyme modification module. The low-temperature active enzyme candidate library contains chitinases derived from Psychrobactersp, and the alkali-tolerant enzyme modification module improves enzyme stability through PEGylation modification. The compensation module can ensure that the catalytic efficiency loss under extreme conditions does not exceed 10%.

[0024] By using a multi-parameter collaborative optimization model at the decision-making level and an extreme environment compensation module at the execution level, the collaborative optimization of catalytic parameters and the stabilization of catalytic efficiency under extreme conditions are achieved, thereby improving the system's adaptability to different environments.

[0025] The intelligent catalytic subsystem is connected to an online high-performance liquid chromatography (HPLC) detection module. The detection module has a detection cycle of <5 min and can construct a feedback closed loop between product concentration and catalytic parameters. When the product concentration increase rate is <0.5 g / Lh, it triggers an increase in stirring rate and compensation regulation of enzyme addition.

[0026] By connecting the intelligent catalytic subsystem to the HPLC online detection module, a feedback loop between product concentration and catalytic parameters is constructed, enabling dynamic adjustment of the catalytic process and ensuring catalytic efficiency.

[0027] A method for operating a chitin substrate-catalyzed environmentally adaptive integrated treatment system includes the following steps: Step S1: System startup and initialization; Step S2, substrate pretreatment; Step S3, intelligent catalysis; Step S4: Product purification; Step S5: System maintenance and cleanup.

[0028] The chitin substrate catalysis environmentally adaptive linkage treatment operation method achieves complete operation and control of the system from startup to maintenance completion through steps S1 to S5.

[0029] The detailed process of system startup and initialization in step S1 is as follows: S101, the system module starts up, and the core functional layers of the system, including the perception layer, decision layer, execution layer, core process execution subsystem cluster, substrate pretreatment subsystem, intelligent catalysis subsystem, product purification subsystem, and intelligent operation and maintenance subsystem, are activated. The fault tree analysis (FTA) module in the intelligent operation and maintenance subsystem starts up, and the operating status of the sensor array, execution layer equipment and each subsystem module is detected. The digital twin operation and maintenance platform loads the initial operating parameter template. S102, initialization and calibration of the sensing layer. The 20 sets of high-precision sensor arrays in the sensing layer are initialized. Among them, the fiber optic temperature sensor, ion-selective electrode pH sensor, and near-infrared substrate concentration detector are zero-point calibrated respectively. The data sampling frequency is set to 1Hz to verify whether the sensing and decision-making, the data linkage bandwidth of 100Mbps, and the delay of the decision-making and execution command linkage control signal <50ms are normal.

[0030] By starting the system modules and initializing and calibrating the sensing layer in step S1, we can ensure the normal operation of each module of the system and the accuracy of the acquisition of sensing layer parameters.

[0031] The detailed process of substrate pretreatment in step S2 is as follows: S201, Substrate parameter detection: Chitin raw material is sent into the substrate pretreatment subsystem, the near-infrared spectroscopy analysis module in the subsystem is activated to detect the crystallinity and molecular weight distribution of the substrate, and the detection data is transmitted to the decision layer through the sensing layer. S202, Pretreatment parameter generation and execution: The decision layer calls the historical database and combines the LSTM neural network prediction model to generate pretreatment process parameters 30 minutes in advance. The instruction execution layer controls the substrate pretreatment subsystem and starts the ultrasonic cleaning module, low temperature degreasing module, and gradient decalcification module. S203, Pretreatment effect verification and substrate delivery: The sensing layer monitors the purity of the pretreated substrate in real time. When the purity reaches a stable level of 92% or higher, the decision layer sends an instruction to deliver the pretreated substrate to the intelligent catalysis subsystem.

[0032] By detecting substrate parameters, generating and executing pretreatment parameters, and verifying and delivering pretreatment effects in step S2, the orderly connection between substrate pretreatment and the intelligent catalytic subsystem is achieved, ensuring the quality of catalytic feedstock.

[0033] The detailed process of intelligent catalysis described in step S3 is as follows: S301, Initial Catalytic Parameter Setting and Execution: After receiving the substrate, the intelligent catalytic subsystem detects the temperature, pH value, and substrate concentration inside the reactor and transmits them to the decision layer. The decision layer combines 128 pre-stored process parameter templates with the enzyme-catalyzed reaction kinetic model to generate initial catalytic parameters. S302, Product concentration monitoring and parameter feedback adjustment: Start the high-performance liquid chromatography (HPLC) online detection module to detect product concentration in real time and construct a feedback loop between product concentration and catalytic parameters; S303, extreme environment compensation regulation: If the sensing layer detects an extreme environment temperature <10℃ or pH >8.5, the decision layer activates the extreme environment compensation module of the execution layer. When the temperature is <10℃, it calls the low-temperature active enzyme candidate library containing Psychrobactersp, derived from chitinase. When the pH is >8.5, it starts the alkali-tolerant enzyme modification module that improves enzyme stability through PEGylation modification to ensure that the catalytic efficiency loss does not exceed 10%. S304, multi-parameter collaborative optimization, the non-dominated sorting genetic algorithm NSGA-Ⅱ multi-parameter collaborative optimization model built into the decision layer runs in real time, and performs collaborative optimization of temperature, pH value, enzyme concentration and stirring rate, reducing system energy consumption by 22%.

[0034] By setting and executing initial catalytic parameters, monitoring and adjusting product concentration, compensating for extreme environments, and optimizing multiple parameters in step S3, precise control and efficiency optimization of the intelligent catalytic process are achieved, ensuring stable and efficient catalytic reactions.

[0035] The detailed process for product purification in step S4 is as follows: S401, Catalytic product delivery: When the product in the intelligent catalytic subsystem reaches the target conversion rate, the decision layer sends an instruction to deliver the reaction product to the product purification subsystem. S402, Refining parameter setting and execution: The sensing layer detects the initial concentration and molecular weight distribution of the product, and the decision layer generates the refining parameters: the ultrafiltration membrane separation device is started to separate impurities with an adjustable molecular weight cutoff of 1000-10000 Da, and the freeze-drying module is started to dry the purified product at a controllable temperature of -50 to -20℃. S403, Refined Product Verification and Output: The sensing layer monitors the purity and molecular weight distribution of the dried product. When the product purity reaches 98% or higher and the standard deviation of the molecular weight distribution is <500 Da, the product refining is completed and the finished product is output.

[0036] By delivering the catalytic product, setting and executing the purification parameters, and verifying and outputting the purified product in step S4, the purification of the catalytic product and the output of qualified finished products are achieved, ensuring product quality.

[0037] The detailed process for system operation and maintenance and cleanup in step S5 is as follows: S501 provides real-time operation and maintenance monitoring. The intelligent operation and maintenance subsystem runs continuously. The fault tree analysis (FTA) module monitors the status of sensors and actuators in real time. If the pH sensor error is detected to be >0.2pH, it automatically switches to the backup sensor and starts the calibration procedure to ensure that the fault is handled in <5 minutes. The digital twin operation and maintenance platform synchronously simulates the system operation status and optimizes process parameters in advance. S502 supports data storage and access, and the operating parameters of the perception layer, execution layer and each subsystem are stored in the local database in real time, supporting remote terminal access and parameter retrieval. S503, System Cleanup and Report Generation: After a single processing cycle is completed, the system automatically cleans up residual materials in each subsystem, the intelligent operation and maintenance subsystem generates an operation report, and the system enters standby mode, waiting for the next startup command.

[0038] Through real-time operation and maintenance monitoring, running data storage and access support, and system cleanup and report generation in step S5, the system's stable operation, data traceability, and preparation for subsequent operation are ensured, thereby improving system operation and maintenance efficiency and sustainable operation capabilities.

[0039] Working principle and usage process: After system startup, each functional layer and subsystem is activated first. The sensor array in the sensing layer is initialized and calibrated to verify the status of the two-way linkage mechanism. Simultaneously, the intelligent operation and maintenance subsystem detects the equipment status and loads the initial parameter template. Then, the chitin raw material is sent to the substrate pretreatment subsystem. The substrate parameters are detected by the near-infrared spectroscopy analysis module and transmitted to the decision layer. The decision layer generates pretreatment parameters based on the prediction model, and the command execution layer regulates the operation of the pretreatment module. After the substrate purity meets the standard, it is delivered to the intelligent catalysis subsystem. After receiving the substrate, the intelligent catalysis subsystem detects the reaction environment parameters in the sensing layer and transmits them to the decision layer. The decision layer generates initial catalytic parameters based on the process parameter template and the kinetic model. The execution layer controls parameters and simultaneously activates the HPLC online detection module to build a feedback loop, adjusting parameters according to the product concentration increase. In case of extreme environments, the compensation module is activated. The decision layer also optimizes catalytic parameters through a collaborative optimization model. When the catalytic product meets the standards, the decision layer instructs the product to be transported to the product purification subsystem. The sensing layer detects product parameters, the decision layer generates purification parameters, and the execution layer activates the separation and drying module. After the product purity and molecular weight distribution meet the standards, the finished product is output. Throughout the process, the intelligent operation and maintenance subsystem monitors the equipment status in real time, handles faults, and optimizes process parameters. After the process is completed, the system automatically cleans up residual materials, stores operating data, and generates a report.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A chitin substrate catalytic environmentally adaptable linkage treatment system, characterized in that: It includes a core functional layer and a core process execution subsystem cluster. The core functional layer includes a perception layer, a decision layer, an execution layer, and a two-way linkage mechanism connecting the perception layer and the decision layer, and the decision layer and the execution layer. The sensing layer is equipped with 20 high-precision sensor arrays, including a fiber optic temperature sensor with an accuracy of 0.1℃, an ion-selective electrode pH sensor with a response time of <2s, and a near-infrared substrate concentration detector with a measurement error of <0.2%, with a data sampling frequency of up to 1Hz; The decision-making layer is equipped with an industrial-grade edge computing module and has a built-in prediction model based on LSTM neural network, which can predict the catalytic reaction trend within the next 5 minutes. It also has 128 pre-stored process parameter templates and a decision response time of <100ms. The execution layer includes a multi-channel enzyme addition device with a flow rate accuracy of 0.01 mL / min, a variable frequency temperature control system with a temperature control accuracy of 0.5℃, and an online neutralization module with a pH adjustment rate of 0.1 pH / min; In the bidirectional linkage mechanism, the bandwidth for the linkage between perception and decision-making data is 100Mbps, and the delay of the linkage control signal between decision-making and execution instructions is <50ms.

2. The chitin substrate catalytic environmentally adaptable linkage treatment system according to claim 1, characterized in that: The core process execution subsystem cluster includes a substrate pretreatment subsystem, an intelligent catalysis subsystem, and a product purification subsystem; The substrate pretreatment subsystem integrates an ultrasonic cleaning module with adjustable power of 300-800W, a low-temperature degreasing module with a temperature of 0-10℃, and a gradient decalcification module with adaptive hydrochloric acid concentration of 0.5%-2.0%, which can stably control the substrate purity to above 92%. The intelligent catalytic subsystem adopts an airlift reactor with an adjustable volume of 50-500L, with a built-in adaptive adjustment module for stirring rate of 50-500rpm, and is combined with an enzyme-catalyzed reaction kinetic model. The product purification subsystem is equipped with an ultrafiltration membrane separation device with an adjustable molecular weight cutoff of 1000-10000 Da and a freeze-drying module with a temperature controllable from -50 to -20℃, which can achieve a product purity of over 98%.

3. The chitin substrate catalytic environmentally adaptable linkage treatment system according to claim 2, characterized in that: The decision layer also incorporates a multi-parameter collaborative optimization model based on the non-dominated sorting genetic algorithm NSGA-II. This model can collaboratively optimize temperature, pH, enzyme concentration, and stirring rate. The execution layer also includes an extreme environment compensation module. This compensation module includes a low-temperature active enzyme candidate library and an alkali-tolerant enzyme modification module. The low-temperature active enzyme candidate library contains chitinases derived from Psychrobactersp. The alkali-tolerant enzyme modification module improves enzyme stability through PEGylation modification. The compensation module can ensure that the catalytic efficiency loss under extreme conditions does not exceed 10%.

4. The chitin substrate catalytic environmentally adaptable linkage treatment system according to claim 1, characterized in that: The intelligent catalytic subsystem is connected to an online high-performance liquid chromatography (HPLC) detection module. The detection module has a detection cycle of <5 min and can construct a feedback loop between product concentration and catalytic parameters. When the product concentration increase rate is <0.5 g / Lh, it triggers an increase in stirring rate and compensation regulation of enzyme addition.

5. A method for the operation of a chitin substrate catalytic environmentally adaptive linkage treatment, characterized in that: Includes the following steps: Step S1: System startup and initialization; Step S2, substrate pretreatment; Step S3, intelligent catalysis; Step S4: Product purification; Step S5: System maintenance and cleanup.

6. The chitin substrate catalytic environmentally adaptive linkage treatment operation method according to claim 5, characterized in that: The detailed process of system startup and initialization described in step S1 is as follows: S101, the system module starts up, and the core functional layers of the system, including the perception layer, decision layer, execution layer, core process execution subsystem cluster, substrate pretreatment subsystem, intelligent catalysis subsystem, product purification subsystem, and intelligent operation and maintenance subsystem, are activated. The fault tree analysis (FTA) module in the intelligent operation and maintenance subsystem starts up, and the operating status of the sensor array, execution layer equipment and each subsystem module is detected. The digital twin operation and maintenance platform loads the initial operating parameter template. S102, initialization and calibration of the sensing layer. The 20 sets of high-precision sensor arrays in the sensing layer are initialized. Among them, the fiber optic temperature sensor, ion-selective electrode pH sensor, and near-infrared substrate concentration detector are zero-point calibrated respectively. The data sampling frequency is set to 1Hz to verify whether the sensing and decision-making, the data linkage bandwidth of 100Mbps, and the delay of the decision-making and execution command linkage control signal <50ms are normal.

7. The chitin substrate catalytic environmentally adaptive linkage treatment operation method according to claim 5, characterized in that: The detailed process of substrate pretreatment in step S2 is as follows: S201, Substrate parameter detection: Chitin raw material is sent into the substrate pretreatment subsystem, the near-infrared spectroscopy analysis module in the subsystem is activated to detect the crystallinity and molecular weight distribution of the substrate, and the detection data is transmitted to the decision layer through the sensing layer. S202, Pretreatment parameter generation and execution: The decision layer calls the historical database and combines the LSTM neural network prediction model to generate pretreatment process parameters 30 minutes in advance. The instruction execution layer controls the substrate pretreatment subsystem and starts the ultrasonic cleaning module, low temperature degreasing module, and gradient decalcification module. S203, Pretreatment effect verification and substrate delivery: The sensing layer monitors the purity of the pretreated substrate in real time. When the purity reaches a stable level of 92% or higher, the decision layer sends an instruction to deliver the pretreated substrate to the intelligent catalysis subsystem.

8. The chitin substrate catalytic environmentally adaptive linkage treatment operation method according to claim 5, characterized in that: The detailed process of intelligent catalysis described in step S3 is as follows: S301, Initial Catalytic Parameter Setting and Execution: After receiving the substrate, the intelligent catalytic subsystem detects the temperature, pH value, and substrate concentration inside the reactor and transmits them to the decision layer. The decision layer combines 128 pre-stored process parameter templates with the enzyme-catalyzed reaction kinetic model to generate initial catalytic parameters. S302, Product concentration monitoring and parameter feedback adjustment: Start the high performance liquid chromatography (HPLC) online detection module to detect product concentration in real time and construct a feedback loop between product concentration and catalytic parameters; S303, extreme environment compensation regulation: If the sensing layer detects an extreme environment temperature <10℃ or pH >8.5, the decision layer activates the extreme environment compensation module of the execution layer. When the temperature is <10℃, it calls the low-temperature active enzyme candidate library containing Psychrobactersp, derived from chitinase. When the pH is >8.5, it starts the alkali-tolerant enzyme modification module that improves enzyme stability through PEGylation modification to ensure that the catalytic efficiency loss does not exceed 10%. S304, multi-parameter collaborative optimization, the non-dominated sorting genetic algorithm NSGA-Ⅱ multi-parameter collaborative optimization model built into the decision layer runs in real time, and performs collaborative optimization of temperature, pH value, enzyme concentration and stirring rate, reducing system energy consumption by 22%.

9. The chitin substrate catalytic environmentally adaptive linkage treatment operation method according to claim 5, characterized in that: The detailed process for product purification in step S4 is as follows: S401, Catalytic product delivery: When the product in the intelligent catalytic subsystem reaches the target conversion rate, the decision layer sends an instruction to deliver the reaction product to the product purification subsystem. S402, Refining parameter setting and execution: The sensing layer detects the initial concentration and molecular weight distribution of the product, and the decision layer generates the refining parameters: the ultrafiltration membrane separation device is started to separate impurities with an adjustable molecular weight cutoff of 1000-10000 Da, and the freeze-drying module is started to dry the purified product at a controllable temperature of -50 to -20℃. S403, Refined Product Verification and Output: The sensing layer monitors the purity and molecular weight distribution of the dried product. When the product purity reaches 98% or higher and the standard deviation of the molecular weight distribution is <500 Da, the product refining is completed and the finished product is output.

10. The chitin substrate catalytic environmentally adaptive linkage treatment operation method according to claim 5, characterized in that: The detailed system operation and maintenance and cleanup process described in step S5 is as follows: S501 provides real-time operation and maintenance monitoring. The intelligent operation and maintenance subsystem runs continuously. The fault tree analysis (FTA) module monitors the status of sensors and actuators in real time. If the pH sensor error is detected to be >0.2pH, it automatically switches to the backup sensor and starts the calibration procedure to ensure that the fault is handled in <5 minutes. The digital twin operation and maintenance platform synchronously simulates the system operation status and optimizes process parameters in advance. S502 supports data storage and access, and the operating parameters of the perception layer, execution layer and each subsystem are stored in the local database in real time, supporting remote terminal access and parameter retrieval. S503, System Cleanup and Report Generation: After a single processing cycle is completed, the system automatically cleans up residual materials in each subsystem, the intelligent operation and maintenance subsystem generates an operation report, and the system enters standby mode, waiting for the next startup command.

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