Plant process control method and system

Abstract

The invention discloses a factory process control method and system. The system comprises a remote monitoring module, an LCU module, a greenhouse gas emission module and a sewage treatment module. The method comprises the following steps: step 1, comprehensive integration and real-time monitoring; step 2, flow optimization and automatic control; step 3, checking and monitoring greenhouse gas emission; step 4, detecting and removing micro-plastics; step 5, sludge treatment and resource utilization; step 6, performing energy consumption management and system optimization; 7, guaranteeing the safety and stability of the system; the integrated control system is designed on the basis of the LCU control cabinet, the sewage treatment process of a sewage plant can be efficiently integrated, the system operation efficiency is comprehensively improved, the sewage treatment process is optimized, all links are accurately regulated and controlled, the high efficiency and accuracy of the sewage treatment process are ensured, greenhouse gas emission is checked and monitored in real time, and the sewage treatment efficiency is improved. And the green, low-carbon and sustainable development of a sewage treatment plant is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of process control, and in particular to a plant process control method and system. Background Art

[0002] A sewage treatment plant is a facility specially designed to receive, treat and discharge wastewater. Through a series of physical, chemical and biological processes, it removes or transforms harmful substances in wastewater to meet discharge standards, thereby protecting the environment and human health. Its main function is to treat urban and industrial wastewater, remove suspended solids, organic matter, heavy metals, pathogens and other pollutants, and make the treated water meet discharge standards. At the same time, some sewage treatment plants also have the functions of sludge treatment and resource utilization, converting sludge into valuable resources. Sewage treatment plants are important facilities for urban and industrial wastewater treatment, and their functions and roles cannot be ignored. With the continuous advancement of technology and the improvement of environmental protection requirements, sewage treatment plants will continue to develop in a more efficient, intelligent and green direction.

[0003] The existing sewage treatment plant control system has the following defects: First, the lack of a fully integrated control system makes it impossible to efficiently integrate and comprehensively monitor the entire treatment process, thereby limiting the system optimization potential and response speed; Second, greenhouse gases dominated by carbon dioxide, methane, and nitrous oxide will be produced during the sewage treatment process, and the current control system has defects in this key environmental dimension. There is a lack of effective mechanisms for accurate accounting and real-time monitoring of greenhouse gas emissions, which restricts the green and sustainable development of sewage treatment plants; Third, microplastics are easily mixed into the sewage treatment process. Due to the limitations of detection technology, the monitoring and management of these tiny microplastic particles have become extremely difficult, which not only challenges the existing detection methods, but also reduces the overall quality of the sludge, forces the cost of sludge treatment to rise, and weakens its recycling value. These microplastic particles that are difficult to completely remove may be released into the natural environment at the end of the treatment process, causing secondary pollution and posing a long-term threat to the ecosystem. Summary of the invention

[0004] The object of the present invention is to provide a plant process control method and system to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a plant process control system, comprising a remote monitoring module, the remote monitoring module is communicatively connected to an LCU module, the LCU module comprises a PLC control module, an IO module, a communication module, a power module, a protection module, a display module and a data acquisition module, the LCU module is communicatively connected to a field control module, a greenhouse gas emission module and a sewage treatment module, wherein the field control module and the greenhouse gas emission module both establish communication connections with the sewage treatment module, the remote monitoring module is used to remotely view and control the operating status of each process link in the plant, the PLC control module is used to write control logic to realize the automatic control of each process link, the IO module is used to input and output signals, and the field equipment The status information of the equipment is transmitted to the PLC control module. The communication module is used to realize data exchange and command transmission between the LCU module and the remote monitoring module. The power module is used to provide a stable power supply for various devices in the LCU module. The protection module is used to protect the safe operation of the equipment and the system. The display module is used to display the operating status and parameter information of the on-site equipment and perform human-computer interaction operations. The data acquisition module is used to collect and process the data in the sewage treatment process in a centralized manner in real time to ensure comprehensive monitoring of the entire sewage treatment process. The field control module is used to control the start, stop and adjustment of the system control nodes. The greenhouse gas emission module is used to detect and monitor greenhouse gas emissions during sewage treatment. The sewage treatment module is used to systematically treat sewage.

[0006] As a further technical solution of the present invention, the remote monitoring module includes a data management module, a report generation module, a monitoring and alarm module, a system optimization module, an energy consumption management module, an environmental assessment module, a maintenance and upgrade module and a remote control module, wherein the remote control module establishes a communication connection with the communication module, the report generation module is used to generate a comprehensive evaluation report, and according to the evaluation results, provides clear guidance for the optimization of the sewage treatment process, the monitoring and alarm module is used to issue an early warning, the system optimization module is used to automatically adjust the processing parameters through data analysis to optimize the processing flow and improve the processing efficiency, the energy consumption management module collects equipment operation data, analyzes energy consumption distribution, formulates energy-saving control strategies, optimizes equipment operation time, and adjusts power output to reduce energy consumption costs, the data management module backup and recovery module regularly backs up system data to ensure data security and recoverability, monitors the system operation status in real time, and immediately performs automatic diagnosis and recovery operations through the maintenance and upgrade module once a fault is found, thereby improving system stability, wherein the environmental assessment module is used to comprehensively evaluate the water quality, surrounding environment and greenhouse gas emissions before and after treatment to ensure that the impact of the sewage treatment process on the environment is minimized and in compliance with environmental protection, and the remote control module is used for remote control.

[0007] As a further technical solution of the present invention, the field control module includes a sensor module and a control node module, and the sensor module and the control node module are both arranged on the sewage treatment module, the sensor module establishes a communication connection with the data acquisition module, and the control node module establishes an electrical connection with the PLC control module. The sensor module is used to collect node parameters of each node in the process, and the control node module is composed of control nodes in the treatment system, which is used to control the sewage treatment according to the process.

[0008] As a further technical solution of the present invention, the greenhouse gas emission module includes an emission accounting module and an emission monitoring module, wherein the emission monitoring module is arranged on the sewage treatment module and establishes a communication connection with the data acquisition module, and the emission accounting module and the emission monitoring module are used to calculate gas emissions and monitor emissions in real time.

[0009] As a further technical solution of the present invention, the sewage treatment module includes a pretreatment module, a primary treatment module, a secondary treatment module, a deep treatment module, a microplastic monitoring module and a sludge treatment module, the sludge treatment module includes a sludge monitoring module, a sludge detection module and a sludge quality assessment module, wherein the sludge monitoring module and the sludge detection module are both connected to the data acquisition module to establish a communication connection, the pretreatment module, the primary treatment module and the secondary treatment module are used to systematically treat the sewage, the deep treatment module is used to remove microplastics in the sewage, the microplastic monitoring module is used to monitor the microplastic components in the sewage, and the sludge treatment module The sludge monitoring module is used to connect to the remote monitoring module to realize remote visual monitoring, ensuring comprehensive control of the sludge treatment process. The sludge detection module uses near-infrared spectroscopy analysis and moisture meter to detect sludge, and conducts real-time and rapid detection of sludge moisture content, organic matter content, and heavy metal content indicators. Combined with regular laboratory calibration, the accuracy of the test results is ensured. The sludge quality assessment module builds a sludge quality assessment model based on the data of the sludge detection module, combined with industry standards and environmental protection requirements, and automatically calculates the sludge treatment effect, stability and resource potential indicators through algorithms, and forms a comprehensive evaluation report through the report generation module.

[0010] A plant process control method, comprising the following steps: step 1, comprehensive integration and real-time monitoring; step 2, process optimization and automatic control; step 3, greenhouse gas emission accounting and monitoring; step 4, microplastic detection and removal; step 5, sludge treatment and resource utilization; step 6, energy consumption management and system optimization; step 7, system safety and stability assurance; Step 1 includes: through the LCU module, the data of each subsystem is collected and processed in real time to ensure comprehensive monitoring of the entire sewage treatment process. At the same time, the remote monitoring module continuously monitors key parameters and immediately triggers an alarm once an abnormality is found, thereby improving the response speed; Step 2 includes: the PLC control module accurately controls the start, stop and adjustment of the field control module to achieve automatic control, while the system optimization module automatically adjusts the treatment parameters of aeration volume and residence time through data analysis to optimize the treatment process and improve treatment efficiency; Step three includes: real-time monitoring of greenhouse gases generated during sewage treatment through a greenhouse gas emission module and timely warning; Step 4 includes: removing and real-time monitoring of microplastics in sewage through a deep treatment module and a microplastic monitoring module; Step five includes: real-time monitoring of sludge treatment through the sludge treatment module, and monitoring and evaluating its quality; Step six includes: the energy consumption management module collects equipment operation data, analyzes energy consumption distribution, formulates energy-saving control strategies, optimizes equipment operation time, and adjusts power output to reduce energy consumption costs; Step seven includes: the data management module backup and recovery module regularly backs up system data to ensure data security and recoverability, monitors the system operation status in real time, and once a fault is found, automatically diagnoses and recovers the fault through the maintenance and upgrade module to improve system stability.

[0011] As a further technical solution of the present invention, in the step 3, the emission accounting module in the greenhouse gas emission module constructs a dynamic model based on the process simulation software based on the data in the processing process, inputs real-time processing parameters and operating conditions, simulates the generation and emission process of greenhouse gases in the entire processing flow, and outputs an accurate emission prediction value. At the same time, the greenhouse gas emission monitoring module monitors the emission situation in real time and compares it with the standard value to ensure that the emission meets the environmental protection requirements. In order to ensure that the greenhouse gas emissions are always controlled within the environmental protection standard range, the emission monitoring module collects greenhouse gas concentration and flow rate parameter data in real time through the high-precision sensor deployed at the emission point in the sensor module to ensure the timeliness and accuracy of the data. According to the real-time collected data, combined with the characteristics of the emission source and environmental conditions, the continuous emission monitoring technology and related calculation models are used to dynamically calculate the total amount of greenhouse gas emissions, and the real-time calculated emissions are compared with the preset environmental protection standard value. Once it is found that the emissions exceed the standard range, the alarm mechanism is immediately triggered to notify the management personnel to take corresponding measures to make adjustments through the monitoring alarm module, and the historical emission data is recorded and analyzed through the data management module to identify emission trends and periodic changes, so as to provide data support for optimizing the processing process and formulating emission reduction strategies.

[0012] As a further technical solution of the present invention, in the step four, microplastics in the sewage are removed through the deep processing module, and the microplastics in the sewage treatment process are detected and monitored through the microplastic monitoring module. Spectral analysis technology is adopted, and the advanced spectroscopy method of Fourier transform infrared spectroscopy is used to analyze the unique chemical bond vibration mode of microplastics to quickly and accurately identify the types and components of microplastics. Even at extremely low concentrations, effective detection can be achieved, and microplastics in water samples can be accurately identified. When the microplastic content exceeds the standard, an early warning will be immediately issued through the monitoring alarm module.

[0013] As a further technical solution of the present invention, in the step five, sludge treatment is carried out through a sludge treatment module, and the Internet of Things technology is adopted to deploy a sensor network in key areas of sludge treatment to collect sludge volume, treatment progress, and equipment operation status data in real time. In combination with a video monitoring system, remote visual monitoring is achieved through a remote monitoring module to ensure comprehensive control of the sludge treatment process. Sludge detection is carried out through a sludge detection module using near-infrared spectroscopy analysis and a moisture meter, and real-time and rapid detection of the moisture content, organic matter content, and heavy metal content indicators of the sludge is performed. Combined with regular laboratory calibration, the accuracy of the test results is ensured.

[0014] As a further technical solution of the present invention, in the step 5, the sludge quality assessment module constructs a sludge quality assessment model based on the data of the sludge detection module, combined with industry standards and environmental protection requirements, automatically calculates the treatment effect, stability and resource potential indicators of the sludge through an algorithm, and forms a comprehensive assessment report through a report generation module, and provides clear guidance for the optimization of the sewage treatment process according to the assessment results; The calculation formula for calculating the sludge treatment efficiency is as follows: E = (C1 × W1 + C2 × W2 + ... +Cn × Wn) / (W1 + W2 + ... + Wn), E represents the treatment effect of the factory sewage, C1, C2, ..., Cn represent the indicators of each component detected after sludge treatment: water content, organic matter content, heavy metal content... The treatment efficiency of other contents, W1, W2, ..., Wn represent the weight of each component index; Cn(%)=(C n -C n+1 ) / C n × 100%, C n is the pollutant concentration before treatment; C n+1is the concentration of pollutants after treatment. Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention is based on the LCU control cabinet design with an integrated integrated control system, which can efficiently integrate the sewage treatment process of the sewage plant, comprehensively improve the system operation efficiency, optimize the sewage treatment process, accurately control each link, ensure the efficiency and accuracy of the sewage treatment process, and through the accounting and real-time monitoring of greenhouse gas emissions, ensure the green, low-carbon and sustainable development of the sewage treatment plant, and effectively remove and detect microplastics in sewage, ensuring that microplastics are effectively intercepted in the treatment process, and preventing their potential release to the natural environment, thereby effectively preventing the risk of secondary pollution, protecting the health and safety of the ecosystem, and through monitoring and analysis methods, ensuring the standardization of sludge treatment and resource utilization, promoting the transformation of sludge from waste to valuable resources, and further improving the environmental protection and economic benefits of sewage treatment plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system structure diagram of the present invention; Figure 2 A module architecture diagram of a remote monitoring module of the present invention; Figure 3 is a module architecture diagram of the LCU module of the present invention; Figure 4 It is a module architecture diagram of the field control module of the present invention; Figure 5 A module architecture diagram of the greenhouse gas emission module of the present invention; Figure 6 It is a module architecture diagram of the sewage treatment module of the present invention; Figure 7 It is a module architecture diagram of the sludge treatment module of the present invention; Figure 8 is a flow chart of the method of the present invention; Fig. 9 It is a system flow chart of the present invention.

[0016] In the figure: 1. Remote monitoring module; 11. Data management module; 12. Report generation module; 13. Monitoring and alarm module; 14. System optimization module; 15. Energy consumption management module; 16. Environmental assessment module; 17. Maintenance and upgrade module; 18. Remote control module; 2. LCU module; 21. PLC control module; 22. IO module; 23. Communication module; 24. Power module; 25. Protection module; 26. Display module; 27. Data acquisition module; 3. Field control module; 31. Sensor module; 32. Control node module; 4. Greenhouse gas emission module; 41. Emission accounting module; 42. Emission monitoring module; 5. Sewage treatment module; 51. Pretreatment module; 52. Primary treatment module; 53. Secondary treatment module; 54. Deep treatment module; 55. Microplastic monitoring module; 56. Sludge treatment module; 561. Sludge monitoring module; 562. Sludge detection module; 563. Sludge quality assessment module. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Please see attached Figure 1 -Attached Figure 7, an embodiment provided by the present invention: a plant process control system, comprising a remote monitoring module 1, the remote monitoring module 1 is communicatively connected with an LCU module 2, the LCU module 2 comprises a PLC control module 21, an IO module 22, a communication module 23, a power module 24, a protection module 25, a display module 26 and a data acquisition module 27, the LCU module 2 is communicatively connected with a field control module 3, a greenhouse gas emission module 4 and a sewage treatment module 5, wherein the field control module 3 and the greenhouse gas emission module 4 are both communicatively connected with the sewage treatment module 5, the remote monitoring module 1 is used to remotely view and control the operating status of each process link in the plant, the PLC control module 21 is used to write control logic to realize the automatic control of each process link, the IO module 22 is used to input and output signals, and the field control module 3 and the greenhouse gas emission module 4 are both communicatively connected with the sewage treatment module 5, the remote monitoring module 1 is used to remotely view and control the operating status of each process link in the plant, the PLC control module 21 is used to write control logic to realize the automatic control of each process link, and the IO module 22 is used to input and output signals. The status information of the field equipment is transmitted to the PLC control module 21, the communication module 23 is used to realize the data exchange and command transmission between the LCU module 2 and the remote monitoring module 1, the power module 24 is used to provide a stable power supply for various devices in the LCU module 2, the protection module 25 is used to protect the safe operation of the equipment and the system, the display module 26 is used to display the operating status and parameter information of the field equipment and perform human-computer interaction operations, the data acquisition module 27 is used to centrally collect and process the data in the sewage treatment process in real time to ensure comprehensive monitoring of the entire sewage treatment process, the field control module 3 is used to control the start, stop and adjustment of the system control node, the greenhouse gas emission module 4 is used to detect and monitor the greenhouse gas emissions in the sewage treatment process, and the sewage treatment module 5 is used to systematically treat the sewage; The remote monitoring module 1 includes a data management module 11, a report generation module 12, a monitoring and alarm module 13, a system optimization module 14, an energy consumption management module 15, an environmental assessment module 16, a maintenance and upgrade module 17 and a remote control module 18, wherein the remote control module 18 establishes a communication connection with the communication module 23, the report generation module 12 is used to generate a comprehensive evaluation report, and according to the evaluation results, provides clear guidance for the optimization of the sewage treatment process, the monitoring and alarm module 13 is used to issue an early warning, the system optimization module 14 is used to automatically adjust the processing parameters through data analysis to optimize the processing flow and improve the processing efficiency, and the energy consumption management module 15 is used to automatically adjust the processing parameters through data analysis to optimize the processing flow and improve the processing efficiency. Collect equipment operation data, analyze energy consumption distribution, formulate energy-saving control strategies, optimize equipment operation time, adjust power output, so as to reduce energy consumption costs. The data management module 11 backup and recovery module regularly backs up system data to ensure data security and recoverability, monitors system operation status in real time, and immediately performs automatic diagnosis and recovery operations through the maintenance and upgrade module 17 once a fault is found to improve system stability. Among them, the environmental assessment module 16 is used to comprehensively evaluate the water quality, surrounding environment and greenhouse gas emissions before and after treatment to ensure that the impact of the sewage treatment process on the environment is minimized and in line with environmental protection. The remote control module 18 is used for remote control; The field control module 3 includes a sensor module 31 and a control node module 32, and the sensor module 31 and the control node module 32 are both arranged on the sewage treatment module 5, the sensor module 31 establishes a communication connection with the data acquisition module 27, and the control node module 32 establishes an electrical connection with the PLC control module 21, the sensor module 31 is used to collect node parameters of each node in the process, and the control node module 32 is composed of control nodes in the treatment system, and is used to control the sewage treatment according to the process; The greenhouse gas emission module 4 includes an emission accounting module 41 and an emission monitoring module 42, wherein the emission monitoring module 42 is arranged on the sewage treatment module 5 and establishes a communication connection with the data acquisition module 27, and the emission accounting module 41 and the emission monitoring module 42 are used to calculate gas emissions and monitor emissions in real time; The sewage treatment module 5 includes a pretreatment module 51, a primary treatment module 52, a secondary treatment module 53, a deep treatment module 54, a microplastic monitoring module 55 and a sludge treatment module 56. The sludge treatment module 56 includes a sludge monitoring module 561, a sludge detection module 562 and a sludge quality assessment module 563, wherein the sludge monitoring module 561 and the sludge detection module 562 both establish communication connections with the data acquisition module 27. The pretreatment module 51, the primary treatment module 52 and the secondary treatment module 53 are used to systematically treat the sewage, the deep treatment module 54 is used to remove microplastics in the sewage, the microplastic monitoring module 55 is used to monitor the microplastic components in the sewage, and the sludge treatment module 56 includes a sludge monitoring module 561, a sludge detection module 562 and a sludge quality assessment module 563. The sludge monitoring module 561 and the sludge detection module 562 both establish communication connections with the data acquisition module 27. The pretreatment module 51, the primary treatment module 52 and the secondary treatment module 53 are used to systematically treat the sewage, the deep treatment module 54 is used to remove microplastics in the sewage, the microplastic monitoring module 55 is used to monitor the microplastic components in the sewage, and the sludge treatment module 56 includes a sludge monitoring module 561, a sludge detection module 562 and a sludge quality assessment module 563. The sludge monitoring module 561 in module 56 is used to connect to the remote monitoring module 1 to realize remote visual monitoring, ensure comprehensive control of the sludge treatment process, and use near-infrared spectroscopy analysis and moisture meter to perform sludge detection through the sludge detection module 562. The moisture content, organic matter content, and heavy metal content of the sludge are detected in real time and quickly. Combined with regular laboratory calibration, the accuracy of the test results is ensured. The sludge quality assessment module 563 is based on the data of the sludge detection module 562, combined with industry standards and environmental protection requirements, to construct a sludge quality assessment model, automatically calculate the sludge treatment effect, stability and resource potential indicators through an algorithm, and form a comprehensive assessment report through the report generation module 12.

[0019] Please see attached Figure 8 -Attached Fig. 9 , an embodiment provided by the present invention: a plant process control method: It includes step 1, comprehensive integration and real-time monitoring; step 2, process optimization and automated control; step 3, greenhouse gas emission accounting and monitoring; step 4, microplastic detection and removal; step 5, sludge treatment and resource utilization; step 6, energy consumption management and system optimization; step 7, system safety and stability assurance; among which: Step 1 includes: through the LCU module 2, the data of each subsystem is collected and processed in real time to ensure comprehensive monitoring of the entire sewage treatment process. At the same time, the remote monitoring module 1 continuously monitors the key parameters and immediately triggers an alarm once an abnormality is found to improve the response speed; Step 2 includes: the PLC control module 21 accurately controls the start, stop and regulation of the field control module 3 to achieve automatic control, and the system optimization module 14 automatically adjusts the treatment parameters such as aeration volume and residence time through data analysis to optimize the treatment process and improve the treatment efficiency; Step three includes: using the emission accounting module 41 in the greenhouse gas emission module 4 to construct a dynamic model based on process simulation software based on the data in the processing process, inputting real-time processing parameters and operating conditions, simulating the generation and emission process of greenhouse gases in the entire processing flow, and outputting accurate emission prediction values. At the same time, the greenhouse gas emission monitoring module 42 monitors the emission situation in real time and compares it with the standard value to ensure that the emission meets environmental protection requirements. In order to ensure that the greenhouse gas emissions are always controlled within the environmental protection standard range, the emission monitoring module 42 collects greenhouse gas concentration and flow rate parameter data in real time through the high-precision sensor deployed at the emission point in the sensor module 31 to ensure the timeliness and accuracy of the data. According to the real-time collected data, combined with the characteristics of the emission source and environmental conditions, the continuous emission monitoring CEMS technology and related calculation models are used to dynamically calculate the total amount of greenhouse gas emissions, and the real-time calculated emissions are compared with the preset environmental protection standard values. Once it is found that the emissions exceed the standard range, the alarm mechanism is immediately triggered to notify the management personnel to take corresponding measures to make adjustments through the monitoring alarm module 13, and the historical emission data is recorded and analyzed through the data management module 11 to identify emission trends and periodic changes, so as to provide data support for optimizing the processing process and formulating emission reduction strategies; Step 4 includes: removing microplastics from sewage through the deep treatment module 54, detecting and monitoring microplastics in the sewage treatment process through the microplastic monitoring module 55, using spectral analysis technology, using Fourier transform infrared spectroscopy FTIR advanced spectroscopy method, by analyzing the chemical bond vibration mode unique to microplastics, quickly and accurately identifying the types and components of microplastics, and achieving effective detection even at extremely low concentrations, accurately identifying microplastics in water samples, and immediately issuing an early warning through the monitoring alarm module 13 when the microplastic content exceeds the standard; Step five includes: treating sludge through the sludge treatment module 56, using the Internet of Things technology, deploying a sensor network in key areas of sludge treatment, collecting sludge volume, treatment progress, and equipment operation status data in real time, combining with the video monitoring system, and realizing remote visual monitoring through the remote monitoring module 1 to ensure comprehensive control of the sludge treatment process, using the sludge detection module 562 to use near-infrared spectroscopy analysis and moisture meter to perform sludge detection, and real-time and rapid detection of the moisture content, organic matter content, and heavy metal content of the sludge, combined with regular laboratory calibration to ensure the accuracy of the test results, the sludge quality assessment module 563, based on the data of the sludge detection module 562, combined with industry standards and environmental protection requirements, constructs a sludge quality assessment model, automatically calculates the treatment effect, stability, and resource potential indicators of the sludge through an algorithm, and forms a comprehensive assessment report through the report generation module 12, and provides clear guidance for the optimization of the sewage treatment process based on the assessment results; The calculation formula for calculating the sludge treatment efficiency is as follows: E = (C1 × W1 + C2 × W2 + ... +Cn × Wn) / (W1 + W2 + ... + Wn), E represents the treatment effect of the factory sewage, C1, C2, ..., Cn represent the treatment efficiency of each component index detected after sludge treatment: water content, organic matter content, heavy metal content... other content, W1, W2, ..., Wn represent the weight of each component index; Cn (%) = (C n -C n+1 ) / C n × 100%, C n is the pollutant concentration before treatment; C n+1 is the pollutant concentration after treatment; Each component index can be tested after the sewage treatment is completed and the test results can be uploaded to the system. The weight or concentration changes before and after can also be analyzed and tested by biochemical experiments and calculated. The component indexes and treatment efficiency calculated after each sampling can be written into the pre-configured evaluation report in an orderly manner, so that enterprises can view the control process parameters and corresponding treatment efficiency of each sewage treatment process, and find the control process parameters corresponding to the best treatment efficiency.

[0020] These sewage treatment big data, which are constructed by several groups of component indicators, control process parameters and corresponding treatment efficiencies (one-to-one correspondence), can further use deep learning models such as RNN to learn the sequence features corresponding to different treatment efficiencies (such as the efficiency values ​​corresponding to the characteristic values ​​of different control process parameters, which are used as corresponding characteristic parameters for model training), and train and construct prediction models that can identify the control process parameters corresponding to different treatment efficiencies, or identify the possible treatment efficiencies of different component indicators. It can provide users with prediction management services. By inputting the required treatment efficiency, the model can predict and output the corresponding control process parameters. The subsequent system performs automatic control operations according to the control process parameters output by the model to achieve intelligent regulation.

[0021] Step six includes: the energy consumption management module 15 collects equipment operation data, analyzes energy consumption distribution, formulates energy-saving control strategies, optimizes equipment operation time, and adjusts power output to reduce energy consumption costs; Step seven includes: the data management module 11 backup and recovery module regularly backs up system data to ensure data security and recoverability, monitors the system operation status in real time, and once a fault is found, automatically diagnoses and recovers the fault immediately through the maintenance and upgrade module 17 to improve system stability.

[0022] Based on the above, the advantages of the present invention are: when the present invention is used to control the process of a sewage treatment plant, first, the sewage treatment module 5 treats the sewage through the pretreatment module 51, the primary treatment module 52 and the secondary treatment module 53. During the process, the data of each subsystem is collected and processed in real time through the data acquisition module 27 in the LCU module 2 to ensure comprehensive monitoring of the entire sewage treatment process. At the same time, the remote monitoring module 1 continuously monitors the key parameters, and immediately triggers an alarm once an abnormality is found to improve the response speed. The PLC control module 21 accurately controls the field control module 3 to start and stop and adjust the system through the control node module 32 to achieve automatic control, and the system optimization module 14 automatically adjusts the processing parameters, such as aeration, through data analysis. The amount, residence time, etc. are used to optimize the processing flow and improve the processing efficiency. The emission accounting module 41 in the greenhouse gas emission module 4 builds a dynamic model based on the process simulation software based on the data in the processing process, inputs real-time processing parameters and operating conditions, simulates the generation and emission process of greenhouse gases in the entire processing flow, and outputs accurate emission prediction values. At the same time, the greenhouse gas emission monitoring module 42 monitors the emission situation in real time and compares it with the standard value to ensure that the emission meets the environmental protection requirements. In order to ensure that the greenhouse gas emissions are always controlled within the environmental protection standards, the emission monitoring module 42 collects key parameter data such as greenhouse gas concentration and flow rate in real time through the high-precision sensors deployed at the emission point in the sensor module 31 to ensure the timeliness and accuracy of the data. According to the real-time collected data, combined with the characteristics of the emission source and environmental conditions, the continuous emission monitoring CEMS technology and related calculation models are used to dynamically calculate the total amount of greenhouse gas emissions, and compare the real-time calculated emissions with the preset environmental protection standard values. Specifically, the CEMS system uses the high-precision sensors deployed at the emission point through the sensor module 31 to collect real-time key parameter data such as greenhouse gas concentration, flow rate, temperature, pressure, etc. Based on the dynamic model of the process simulation software, according to the data in the processing process and the real-time processing parameters and operating conditions, the generation and emission process of greenhouse gases in the entire processing flow are simulated, and the generation and emission of greenhouse gases are predicted through model calculation. Combined with the real-time collected data and the prediction results of the emission model, dynamic calculation is performed. The total amount of greenhouse gas emissions, taking into account the characteristics of the emission source and environmental conditions, the calculation results are corrected and calibrated, and the real-time calculated greenhouse gas emissions are compared with the preset environmental protection standard values. The environmental protection standard values ​​are usually formulated by regional environmental protection departments to regulate the emission behavior of enterprises. If the real-time emissions do not exceed the preset environmental protection standard values, the system continues to operate normally. Once it is found that the emissions exceed the standard range, the alarm mechanism is immediately triggered to notify the management personnel to take corresponding measures to make adjustments through the monitoring and alarm module 13, and the historical emission data is recorded and analyzed through the data management module 11 to identify emission trends and periodic changes, so as to provide data support for optimizing the treatment process and formulating emission reduction strategies, and remove microplastics from sewage through the deep processing module 54.During the process, the microplastic monitoring module 55 is used to detect and monitor microplastics in the sewage treatment process. Spectral analysis technology is used, and Fourier transform infrared spectroscopy FTIR advanced spectroscopy method is used to analyze the chemical bond vibration mode unique to microplastics to quickly and accurately identify the type and composition of microplastics. Even at extremely low concentrations, effective detection can be achieved, and microplastics in water samples can be accurately identified. When the microplastic content exceeds the standard, an early warning is immediately issued through the monitoring and alarm module 13. Sludge is treated through the sludge treatment module 56. The Internet of Things technology is used to deploy a sensor network in the key area of ​​sludge treatment to collect key data such as sludge volume, treatment progress, and equipment operation status in real time. Combined with the video monitoring system, the sludge monitoring module 561 The remote monitoring module 1 is connected to realize remote visual monitoring to ensure comprehensive control of the sludge treatment process. The sludge detection module 562 uses near-infrared spectroscopy analysis and a moisture meter to detect sludge, and performs real-time and rapid detection of key indicators such as sludge moisture content, organic matter content, and heavy metal content. Combined with regular laboratory calibration, the accuracy of the test results is ensured. The sludge quality assessment module 563 builds a sludge quality assessment model based on the data of the sludge detection module 562, combined with industry standards and environmental protection requirements, and automatically calculates indicators such as sludge treatment effect, stability, and resource utilization potential through algorithms. Specifically, the sludge detection module 562 will provide a series of sludge quality detection data, including but not limited to the sludge moisture content, organic matter content, heavy metal content, Nutrient composition, particle size distribution, microbial community structure, etc. The collected data are cleaned, denoised, standardized and other pre-processing operations are carried out to ensure the quality and consistency of the data. According to the evaluation indicators and data characteristics, a multivariate linear regression model is selected for modeling, and the model is trained using historical data. The model parameters are adjusted to optimize the evaluation results. The accuracy and generalization ability of the model are evaluated through cross-validation, leave-one-out validation and other methods. According to industry standards and environmental protection requirements, specific evaluation standards and weights are set for each evaluation indicator. The weight distribution reflects the importance of each indicator in sludge quality assessment, and a comprehensive evaluation report is formed through the report generation module 12. The evaluation results are intuitively presented in the form of charts, tables, etc. to form a comprehensive evaluation report. The report contains the sludge quality The current status, analysis of evaluation results, existing problems and suggestions for improvement are included. According to the evaluation results, clear guidance is provided for the optimization of the sewage treatment process. The energy consumption management module 15 collects equipment operation data, analyzes energy consumption distribution, and formulates energy-saving control strategies, such as optimizing equipment operation time, adjusting power output, etc., to reduce energy consumption costs. The data management module 11 backup and recovery module regularly backs up system data to ensure data security and recoverability, monitors the system operation status in real time, and immediately performs automatic diagnosis and recovery operations through the maintenance and upgrade module 17 once a fault is found, thereby improving system stability. Among them, the environmental assessment module 16 is used to conduct a comprehensive assessment of the water quality, surrounding environment and greenhouse gas emissions before and after treatment to ensure that the impact of the sewage treatment process on the environment is minimized.It is environmentally friendly. The remote control module 18 is used for remote control. The IO module 22 is used to input and output signals, transmit the status information of the field equipment to the PLC control module 21, and receive the control instructions of the PLC control module 21. The communication module 23 is used to realize the data exchange and instruction transmission between the LCU module 2 and the remote monitoring module 1. The power module 24 is used to provide a stable power supply for various devices in the LCU module 2. The protection module 25 is used to protect the safe operation of the equipment and system. The display module 26 is used to display the operating status and parameter information of the field equipment and perform human-computer interaction operations.

[0023] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A plant process control system, comprising a remote monitoring module (1), characterized in that: The remote monitoring module (1) is communicatively connected to an LCU module (2), the LCU module (2) comprising a PLC control module (21), an IO module (22), a communication module (23), a power module (24), a protection module (25), a display module (26) and a data acquisition module (27), and the LCU module (2) is communicatively connected to a field control module (3), a greenhouse gas emission module (4) and a sewage treatment module (5), wherein the field control module (3) and the greenhouse gas emission module (4) are both communicatively connected to the sewage treatment module (5), wherein: The remote monitoring module (1) is used to remotely view and control the operating status of each process link in the plant area. The PLC control module (21) is used to write control logic to realize the automatic control of each process link. The IO module (22) is used to input and output signals and transmit the status information of the field equipment to the PLC control module (21). The communication module (23) is used to realize data exchange and command transmission between the LCU module (2) and the remote monitoring module (1). The power supply module (24) is used to provide a stable power supply for various devices in the LCU module (2). The protection module (25) is used to protect the safe operation of the equipment and system. The display module (26) is used to display the operating status and parameter information of the field equipment and perform human-computer interaction operations. The data acquisition module (27) is used to collect and process the data in the sewage treatment process in a centralized manner in real time to ensure comprehensive monitoring of the entire sewage treatment process. The field control module (3) is used to control the start, stop and adjustment of the control node of the system. The greenhouse gas emission module (4) is used to detect and monitor the greenhouse gas emission in the sewage treatment process. The sewage treatment module (5) is used to perform systematic treatment on the sewage.

2. A plant process control system according to claim 1, characterized in that: The remote monitoring module (1) comprises a data management module (11), a report generation module (12), a monitoring and alarm module (13), a system optimization module (14), an energy consumption management module (15), an environmental assessment module (16), a maintenance and upgrade module (17) and a remote control module (18), wherein the remote control module (18) establishes a communication connection with the communication module (23), the report generation module (12) is used to generate a comprehensive assessment report, and according to the assessment results, provides clear guidance for the optimization of the sewage treatment process, the monitoring and alarm module (13) is used to issue an early warning, and the system optimization module (14) is used to automatically adjust the processing parameters through data analysis to optimize the processing flow and improve the processing efficiency. The energy consumption management module (15) collects equipment operation data, analyzes energy consumption distribution, formulates energy-saving control strategies, optimizes equipment operation time, and adjusts power output to reduce energy consumption costs. The data management module (11) backup and recovery module regularly backs up system data to ensure data security and recoverability, monitors system operation status in real time, and immediately performs automatic diagnosis and recovery operations through the maintenance and upgrade module (17) once a fault is found, thereby improving system stability. Among them, the environmental assessment module (16) is used to conduct a comprehensive assessment of water quality, surrounding environment and greenhouse gas emissions before and after treatment to ensure that the impact of the sewage treatment process on the environment is minimized and in compliance with environmental protection. The remote control module (18) is used for remote control.

3. A plant process control system according to claim 1, characterized in that: The field control module (3) comprises a sensor module (31) and a control node module (32), and the sensor module (31) and the control node module (32) are both arranged on the sewage treatment module (5), the sensor module (31) establishes a communication connection with the data acquisition module (27), and the control node module (32) establishes an electrical connection with the PLC control module (21), the sensor module (31) is used to collect node parameters of each node in the process, and the control node module (32) is composed of control nodes in the treatment system and is used to control the sewage treatment to be carried out according to the process.

4. A plant process control system according to claim 1, characterized in that: The greenhouse gas emission module (4) comprises an emission accounting module (41) and an emission monitoring module (42), wherein the emission monitoring module (42) is arranged on the sewage treatment module (5) and establishes a communication connection with the data acquisition module (27), and the emission accounting module (41) and the emission monitoring module (42) are used to calculate gas emissions and monitor emissions in real time.

5. A plant process control system according to claim 4, characterized in that: The sewage treatment module (5) comprises a pretreatment module (51), a primary treatment module (52), a secondary treatment module (53), a deep treatment module (54), a microplastic monitoring module (55) and a sludge treatment module (56); the sludge treatment module (56) comprises a sludge monitoring module (561), a sludge detection module (562) and a sludge quality assessment module (563); the sludge monitoring module (561) and the sludge detection module (562) are both connected to a data acquisition module (27) for communication; the pretreatment module (51), the primary treatment module (52) and the secondary treatment module (53) are used to perform systematic treatment on the sewage; the deep treatment module (54) is used to remove microplastics in the sewage; the microplastic monitoring module (55) is used to monitor the sewage quality; The microplastic components in the water, the sludge monitoring module (561) in the sludge treatment module (56) is used to connect to the remote monitoring module (1) to realize remote visual monitoring, to ensure the comprehensive control of the sludge treatment process, and to detect the sludge through the sludge detection module (562) using near-infrared spectroscopy analysis and moisture meter, to conduct real-time and rapid detection of the moisture content, organic matter content and heavy metal content of the sludge, combined with regular laboratory calibration to ensure the accuracy of the test results, the sludge quality assessment module (563) is based on the data of the sludge detection module (562), combined with industry standards and environmental protection requirements, to construct a sludge quality assessment model, to automatically calculate the treatment effect, stability and resource potential indicators of the sludge through an algorithm, and to form a comprehensive assessment report through the report generation module (12).

6. A plant process control method comprising the following steps: Step 1: comprehensive integration and real-time monitoring; Step 2: process optimization and automated control; Step 3: greenhouse gas emission accounting and monitoring; Step 4: microplastic detection and removal; Step 5: sludge treatment and resource utilization; Step 6: energy consumption management and system optimization; Step 7: system safety and stability assurance; It is characterized by: Step 1 includes: using the LCU module (2), centrally collecting and processing the data of each subsystem in real time to ensure comprehensive monitoring of the entire sewage treatment process; at the same time, the remote monitoring module (1) continuously monitors key parameters and immediately triggers an alarm once an abnormality is found, thereby improving the response speed; Step 2 includes: the PLC control module (21) accurately controls the start, stop and adjustment of the field control module (3) to achieve automatic control, and the system optimization module (14) automatically adjusts the treatment parameters such as aeration volume and residence time through data analysis to optimize the treatment process and improve the treatment efficiency; Step three includes: real-time monitoring of greenhouse gases generated during sewage treatment through a greenhouse gas emission module (4) and timely warning; Step 4 includes: removing microplastics in sewage and real-time monitoring through the deep treatment module (54) and the microplastic monitoring module (55); Step 5 includes: real-time monitoring of sludge treatment through the sludge treatment module (56), and monitoring and evaluating its quality; Step six includes: the energy consumption management module (15) collects equipment operation data, analyzes energy consumption distribution, formulates energy-saving control strategies, optimizes equipment operation time, and adjusts power output to reduce energy consumption costs; Step seven includes: the data management module (11) and the backup and recovery module regularly back up system data to ensure data security and recoverability, monitor the system operation status in real time, and once a fault is found, automatically diagnose and recover the fault immediately through the maintenance and upgrade module (17) to improve system stability.

7. A plant process control method according to claim 6, characterized in that: In the step 3, the emission accounting module (41) in the greenhouse gas emission module (4) constructs a dynamic model based on the process simulation software based on the data in the processing process, inputs real-time processing parameters and operating conditions, simulates the generation and emission process of greenhouse gases in the entire processing flow, and outputs an accurate emission prediction value. At the same time, the greenhouse gas emission monitoring module (42) monitors the emission situation in real time and compares it with the standard value to ensure that the emission meets the environmental protection requirements. In order to ensure that the greenhouse gas emissions are always controlled within the environmental protection standard range, the emission monitoring module (42) collects greenhouse gas emissions in real time through the high-precision sensors deployed at the emission point in the sensor module (31). The data of volume concentration and flow rate parameters are collected in real time to ensure the timeliness and accuracy of the data. Based on the real-time collected data, combined with the characteristics of the emission source and environmental conditions, the continuous emission monitoring (CEMS) technology and related calculation models are used to dynamically calculate the total amount of greenhouse gas emissions. The real-time calculated emissions are compared with the preset environmental protection standard values. Once it is found that the emissions exceed the standard range, the alarm mechanism is immediately triggered to notify the management personnel to take corresponding measures to make adjustments through the monitoring alarm module (13). The historical emission data is recorded and analyzed through the data management module (11), and emission trends and periodic changes are identified, providing data support for optimizing the treatment process and formulating emission reduction strategies.

8. A plant process control method according to claim 6, characterized in that: In the step 4, the microplastics in the sewage are removed by the deep treatment module (54), and the microplastics in the sewage treatment process are detected and monitored by the microplastics monitoring module (55). The spectral analysis technology and the advanced spectroscopy method of Fourier transform infrared spectroscopy (FTIR) are used to analyze the chemical bond vibration mode unique to microplastics, so as to quickly and accurately identify the types and components of microplastics. Even at extremely low concentrations, effective detection can be achieved, and microplastics in water samples can be accurately identified. When the microplastic content exceeds the standard, an early warning is immediately issued through the monitoring alarm module (13).

9. A plant process control method according to claim 6, characterized in that: In the step 5, sludge treatment is performed through the sludge treatment module (56), and the Internet of Things technology is used to deploy a sensor network in the key area of ​​sludge treatment to collect sludge volume, treatment progress, and equipment operation status data in real time. In combination with the video monitoring system, remote visual monitoring is achieved through the remote monitoring module (1) to ensure comprehensive control of the sludge treatment process. The sludge detection module (562) uses near-infrared spectroscopy analysis and a moisture meter to perform sludge detection, and the moisture content, organic matter content, and heavy metal content of the sludge are detected in real time and quickly. In combination with regular laboratory calibration, the accuracy of the detection results is ensured.

10. A plant process control method according to claim 9, characterized in that: In the step 5, the sludge quality assessment module (563) constructs a sludge quality assessment model based on the data of the sludge detection module (562) in combination with industry standards and environmental protection requirements, automatically calculates the treatment effect, stability and resource utilization potential indicators of the sludge through an algorithm, and forms a comprehensive assessment report through a report generation module (12). According to the assessment results, clear guidance is provided for the optimization of the sewage treatment process; wherein, the calculation formula for calculating the sludge treatment efficiency is as follows: E = (C1 × W1 + C2 × W2 + ... + Cn × Wn) / (W1 + W2 + ... + Wn), where E represents the treatment effect of the factory sewage, C1, C2, ..., Cn represent the treatment efficiency of each component indicator detected after the sludge is treated: water content, organic matter content, heavy metal content... other content, and W1, W2, ..., Wn represent the weight of each component indicator; Cn (%) = (C1 × W1 + C2 × W2 + ... + Cn × Wn) / (W1 + W2 + ... + Wn), where E represents the treatment effect of the factory sewage, C1, C2, ..., Cn represent the treatment efficiency of each component indicator detected after the sludge is treated: water content, organic matter content, heavy metal content... other content, and W1, W2, ..., Wn represent the weight of each component indicator; Cn (%) = (C1 × W1 + C2 × W2 + ... + Cn × Wn) / (W1 + W2 + ... + Wn) n -C n+1 ) / C n × 100%, C n is the pollutant concentration before treatment; C n+1 is the pollutant concentration after treatment.

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