A method for automatic control of decanediamine production line

Abstract

The present invention belongs to the field of industrial automation control, and in particular to a method for automatic control of a decanediamine production line. First, an automatic control based on PLC and DCS is established, and an improved composite PID control algorithm and multi-stage dynamic feedback compensation are introduced to improve the system response speed and control accuracy. The composite PID control formula contains multiple coefficients and adjustment amounts to achieve adaptive adjustment. Secondly, multi-parameter data is collected, and the process parameters are optimized in real time using an improved ant colony algorithm. The objective function considers multi-factor optimization synergy. Furthermore, edge computing nodes and IoT gateways are deployed in key equipment, and communication delays are reduced using multi-layer optimization functions through Internet of Things interconnected monitoring. Finally, multi-layer energy consumption management is configured, and energy is saved based on dynamic compensation load and real-time monitoring of energy consumption, and energy consumption optimization models. The present invention effectively solves many problems of existing control methods and improves the overall efficiency of the production line.

Description

Technical Field

[0001] The invention belongs to the field of industrial automation control, and in particular relates to a method for automatic control of a decanediamine production line. Background Art

[0002] As an important chemical raw material, decanediamine is widely used in many fields. The automation control level of its production line is crucial to product quality, production efficiency and cost control. The existing decanediamine production control methods have many limitations. In terms of control accuracy, due to the lack of advanced algorithms and control systems, it is difficult to maintain accurate and stable parameters in complex processes such as raw material feeding, reaction synthesis, purification separation and product collection, which can easily lead to product quality fluctuations, extended production cycles and waste of raw materials. Process parameter optimization usually relies on experience, lacks real-time and scientific nature, cannot effectively respond to dynamic changes in the production process, and is difficult to achieve coordinated optimization among various parameters. Communication between devices is often not efficient enough, with high latency, which affects data synchronization and real-time control, and thus restricts the overall coordinated operation of the production line. In addition, energy consumption management lacks a dynamic optimization mechanism, and energy cannot be reasonably allocated according to load and environmental changes, resulting in high energy consumption and increased production costs. With the continuous development of industrial automation technology, higher requirements are put forward for the automation control of decanediamine production lines. An innovative control method is urgently needed to solve the above problems and improve the overall efficiency of production. Summary of the invention

[0003] The present invention aims at the control technology problems existing in the above-mentioned background technology and proposes an automatic control method for a decanediamine production line.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: comprising the following steps:

[0005] S1. First, establish automatic control based on PLC and DCS, and perform multi-level control on raw material feeding, reaction synthesis, purification separation and product collection processes to ensure accurate and stable parameters at different production stages. Introduce improved composite PID control algorithm and multi-level dynamic feedback compensation to improve the response speed and control accuracy of the system. The composite PID control formula is: Where u(t) is the control output, which is used to accurately adjust the operation of the production equipment, K p , K i , K d are proportional, integral and differential gain coefficients respectively, e(t) is the current error value, K c1 , K c2 is a multi-level compensation coefficient, Δf1(t) and Δf2(t) are dynamic feedback adjustment quantities, which are used for deviation compensation at different process nodes to achieve adaptive adjustment.

[0006] S2. Then, the reaction temperature, feed flow rate, stirring speed, reaction time, cooling temperature, and reactant ratio parameter data are collected, and the improved ant colony algorithm is used to achieve real-time optimization of process parameters. The optimization objective function is: Where T react is the reaction temperature, F in is the feed flow rate, R is the stirring speed, R ratio is the reactant ratio, T cool is the cooling temperature, T target is the target value of the reaction temperature, F set is the set value of feed flow rate, is the product quality deviation item, It is a production cycle control item. is the variable interaction term to optimize the synergy between process parameters;

[0007] S3. In addition, edge computing nodes and IoT gateways are deployed on key devices to achieve localized data processing and real-time control. Devices are interconnected and remotely monitored through the Internet of Things to ensure data synchronization and control optimization of multiple nodes. The multi-layer optimization function of communication delay is: Where D comm is the total communication delay time, L k is the packet size of the kth device, B k is the bandwidth of the kth device, T proc,j is the data processing time of the jth device, η is the power factor penalty coefficient, P s is the power consumption of the sth node, C max To maximize the bandwidth of the nodes, communication delay is reduced through multi-layer optimization;

[0008] S4. Finally, multi-layer energy consumption management is configured to dynamically optimize the energy consumption in the production process based on dynamic load compensation and real-time monitoring of energy consumption. The energy consumption optimization model is: Where E total is the total energy consumption, P(t) is the equipment power, R(t) is the dynamic load compensation, α and β are compensation coefficients used to adjust the load and ambient temperature fluctuations, and φ(t) is the ambient temperature compensation factor. Energy saving is achieved by optimizing energy consumption distribution.

[0009] Preferably, the specific process of implementing adaptive adjustment of the composite PID control formula in step S1 is as follows:

[0010] S11, first monitor the error value e(t) of the current process node in real time, and use the error value as the input of proportional, integral and differential control;

[0011] S12. According to the changing trend of real-time error, proportional, integral and differential gain coefficients K are adjusted. p , Ki , K d Perform dynamic adjustment, and automatically increase K when the error increases. p To speed up the system response; when the error gradually decreases, appropriately reduce K p To reduce overshoot;

[0012] S13, then includes dynamic feedback adjustment amounts Δf1(t), Δf2(t), which are used to perform feedback compensation according to the actual deviations of different process nodes, and automatically adjust the compensation amounts Δf1(t), Δf2(t) to optimize the control output by detecting the real-time status of each node;

[0013] S14, then set different control gains and compensation parameters according to the process status, and the control parameters of each production stage automatically adapt to the current process requirements. c1 , K c2 Compensation coefficient;

[0014] S15. Finally, adaptive learning is performed based on historical error data to continuously optimize the proportion, integration, differentiation and compensation coefficients to achieve the best control effect.

[0015] Preferably, the specific implementation of the improved ant colony algorithm optimization in step S2 is:

[0016] S21, firstly, the pheromone concentration representing the combined path of each process parameter is initialized to guide the ants to select the path between different variables, and the initialization is performed according to the influence of each process variable on the objective function;

[0017] S22, decompose the objective function into sub-objectives, set weight vectors, and dynamically adjust the weights after each round of iteration;

[0018] S23, using adaptive increments to update pheromones, so that the increment of pheromones is dynamically adjusted to increase the probability of selecting a high-quality path;

[0019] S24. Finally, the iteration is repeated until the change in the objective function value is less than 1%, the optimal solution is reached and the process parameter combination is output.

[0020] Preferably, the update formula for the dynamic adjustment of the pheromone increment in step S23 is: ij =(1-ρ)T ij +ΔT ij , where T ij is the pheromone matrix, ρ is the volatility factor that controls the decay rate of the pheromone, where f current , f best are the current and optimal objective function values ​​respectively. When the current solution is close to the global optimum, the pheromone increment is increased, and when it is far away, the pheromone increment is reduced.

[0021] Compared with the prior art, the advantages and positive effects of the present invention are that, in terms of control accuracy, the automatic control based on PLC and DCS is combined with the improved composite PID algorithm and multi-level dynamic feedback compensation to monitor and adjust the process node parameters in real time and reduce product quality fluctuations. In terms of process parameter optimization, the improved ant colony algorithm is optimized in real time to improve synergy and production efficiency. In terms of communication, edge computing nodes, IoT gateways and Internet of Things applications cooperate with communication delay optimization functions to ensure data synchronization and efficient control. In terms of energy consumption management, the multi-layer energy consumption management system relies on dynamic compensation and real-time monitoring, and reasonably allocates energy according to the energy consumption optimization model to reduce consumption and cost. Compared with the prior art, the present invention comprehensively improves the level of automatic control of the production line, improves product quality and production efficiency, reduces costs and energy consumption, and enhances the comprehensive benefits and competitiveness of decanediamine production. DETAILED DESCRIPTION

[0022] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments of the following disclosure.

[0024] Embodiment: In order to improve the level of automated control of the production process in a decanediamine production line and solve the problems of low control accuracy, unscientific process parameter optimization, high equipment communication delay, and lack of dynamic optimization of energy consumption management in the existing control method, the present invention proposes an automated control method for a decanediamine production line.

[0025] First, in order to solve the problem of difficulty in multi-stage control and poor precision in the production process of decanediamine, an automated control architecture based on PLC and DCS was established to perform multi-level control of processes such as raw material feeding, reaction synthesis, purification separation and product collection. An improved composite PID control algorithm was introduced, and the composite PID control formula is: Where u(t) is the control output, which is used to accurately adjust the operation of the production equipment, K p , K i , K d are proportional, integral and differential gain coefficients respectively, e(t) is the current error value, K c1 , K c2is a multi-stage compensation coefficient, Δf1(t) and Δf2(t) are dynamic feedback adjustment amounts, which are used for deviation compensation of different process nodes to achieve adaptive adjustment; through the composite PID control algorithm, the error value of each process node is monitored in real time, such as the difference between the reaction temperature and the set temperature, the difference between the feed flow rate and the set flow rate, etc., and it is used as the input of proportional, integral, and differential control. According to the error change trend, the proportional, integral, and differential gain coefficients are dynamically adjusted. When the reaction temperature error increases, the corresponding gain coefficient is automatically increased to make the heating or cooling device respond quickly and accelerate the system's temperature adjustment; when the error gradually decreases, the gain coefficient is appropriately reduced to avoid excessive temperature adjustment. Using multi-stage dynamic feedback compensation, by detecting the real-time status of each node, such as the pressure in the reactor and the current of the agitator, the dynamic feedback adjustment amount is obtained, the compensation amount is automatically adjusted, the control output is optimized, and the parameters of each process are ensured to be accurate and stable. For example, in the purification and separation process, the discharge flow rate is adjusted in time according to the changes in pressure and liquid level in the separation tower to ensure product purity. At the same time, different control gains and compensation parameters are set according to the process status, and the control parameters of each production stage can automatically adapt to the needs. And based on historical error data, adaptive learning is performed to continuously optimize the proportion, integration, differentiation and compensation coefficients, ultimately achieving the effect of improving system response speed and control accuracy, effectively reducing product quality fluctuations, shortening production cycles, and reducing raw material waste.

[0026] In order to reduce the mutual interference between process parameters and solve the problem of insufficient parameter coordination, the improved ant colony algorithm is used to optimize the process parameters. First, the pheromone concentration representing the combined path of each process parameter is initialized according to the influence of each process variable on the objective function, and the ants are guided to choose the path between different variables. If the reaction temperature has a greater impact on the product quality, a higher pheromone concentration is given during initialization. The objective function is: Where T react is the reaction temperature, F in is the feed flow rate, R is the stirring speed, R ratio is the reactant ratio, T cool is the cooling temperature, T target is the target value of the reaction temperature, F set is the set value of feed flow rate, is the product quality deviation item, It is a production cycle control item. The variable interaction term is used to optimize the synergy between process parameters. The objective function is decomposed into sub-objectives, and the weight vector is set. The weight is dynamically adjusted after each iteration to make the optimization process more targeted. If it is found in a certain iteration that the product quality deviation term has a greater impact on the overall objective function, its weight is increased to guide the algorithm to prioritize the optimization of process parameters related to product quality.

[0027] Use adaptive increments to update pheromones, so that the increment of pheromones can be dynamically adjusted to increase the probability of selecting high-quality paths. The update formula is: T ij =(1-ρ)T ij +ΔT ij , where T ij is the pheromone matrix, ρ is the volatility factor that controls the decay rate of the pheromone, where f current , f best are the current and optimal objective function values, respectively. When the current solution is close to the global optimum, the pheromone increment is increased, and when it is far away, the pheromone is reduced. Finally, the iteration is repeated until the change in the objective function value is less than 1%, and the optimal process parameter combination is output. In this way, the real-time optimization of process parameters is achieved, the synergy between the parameters is improved, and the production process can be dynamically adjusted according to the actual situation, further improving product quality and production efficiency.

[0028] Then, in view of the current situation of high communication delay and difficult data synchronization between production line equipment, the present invention deploys edge computing nodes and IoT gateways on each key equipment. The equipment is interconnected and remotely monitored through the Internet of Things to ensure data synchronization and control optimization of multiple nodes. The multi-layer optimization function of communication delay is: Where D comm is the total communication delay time, L k is the packet size of the kth device, B k is the bandwidth of the kth device, T proc,j is the data processing time of the jth device, η is the power factor penalty coefficient, P s is the power consumption of the sth node, C max The maximum bandwidth of the node is obtained by multi-layer optimization to reduce communication delay. This method uses a multi-layer optimization model of communication delay to reduce the delay of data transmission between devices. The deployment of edge computing nodes makes localized data processing more efficient.

[0029] Finally, in order to reduce production energy consumption and improve energy utilization efficiency, the energy consumption in the production process is dynamically optimized based on dynamic load compensation and real-time monitoring of energy consumption. The energy consumption optimization model is: Where E total is the total energy consumption, P(t) is the equipment power, R(t) is the dynamic load compensation, α and β are compensation coefficients for adjusting the load and ambient temperature fluctuations, and φ(t) is the ambient temperature compensation factor. Energy saving is achieved by optimizing the energy consumption distribution. Energy consumption can be adjusted in real time with load fluctuations and environmental changes, and energy saving effects are achieved through load compensation and ambient temperature compensation. Through the above scheme, the present invention not only achieves the high efficiency and stability of the automatic control of the decanediamine production line, but also significantly reduces production energy consumption and resource waste, and improves product quality and consistency.

[0030] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for automated control of a decanediamine production line, characterized in that: The following steps are involved: S1. First, establish automatic control based on PLC and DCS, and perform multi-level control on raw material feeding, reaction synthesis, purification separation and product collection processes to ensure accurate and stable parameters at different production stages. Introduce improved composite PID control algorithm and multi-level dynamic feedback compensation to improve the response speed and control accuracy of the system. The composite PID control formula is: Where u(t) is the control output, which is used to accurately adjust the operation of the production equipment, K p , K i , K d are proportional, integral and differential gain coefficients respectively, e(t) is the current error value, K c1 , K c2 is a multi-level compensation coefficient, Δf1(t) and Δf2(t) are dynamic feedback adjustment quantities, which are used for deviation compensation at different process nodes to achieve adaptive adjustment. S2. Then, the reaction temperature, feed flow rate, stirring speed, reaction time, cooling temperature, and reactant ratio parameter data are collected, and the improved ant colony algorithm is used to achieve real-time optimization of process parameters. The optimization objective function is: Where T react is the reaction temperature, F in is the feed flow rate, R is the stirring speed, R ratio is the reactant ratio, T cool is the cooling temperature, T target is the target value of the reaction temperature, F set is the set value of feed flow rate, is the product quality deviation item, It is a production cycle control item. is the variable interaction term to optimize the synergy between process parameters; S3. In addition, edge computing nodes and IoT gateways are deployed on key devices to achieve localized data processing and real-time control. Devices are interconnected and remotely monitored through the Internet of Things to ensure data synchronization and control optimization of multiple nodes. The multi-layer optimization function of communication delay is: Where D comm is the total communication delay time, L k is the packet size of the kth device, B k is the bandwidth of the kth device, T proc,j is the data processing time of the jth device, η is the power factor penalty coefficient, P s is the power consumption of the sth node, C max To maximize the bandwidth of the nodes, communication delay is reduced through multi-layer optimization; S4. Finally, multi-layer energy consumption management is configured to dynamically optimize the energy consumption in the production process based on dynamic load compensation and real-time monitoring of energy consumption. The energy consumption optimization model is: Where E total is the total energy consumption, P(t) is the equipment power, R(t) is the dynamic load compensation, α and β are compensation coefficients used to adjust the load and ambient temperature fluctuations, and φ(t) is the ambient temperature compensation factor. Energy saving is achieved by optimizing energy consumption distribution.

2. The method for automatic control of a decanediamine production line according to claim 1, characterized in that: The specific process of implementing adaptive adjustment of the composite PID control formula in step S1 is as follows: S11, first monitor the error value e(t) of the current process node in real time, and use the error value as the input of proportional, integral and differential control; S12. According to the changing trend of real-time error, proportional, integral and differential gain coefficients K are adjusted. p , K i , K d Perform dynamic adjustment, and automatically increase K when the error increases. p To speed up the system response; when the error gradually decreases, appropriately reduce K p To reduce overshoot; S13, then includes dynamic feedback adjustment amounts Δf1(t), Δf2(t), which are used to perform feedback compensation according to the actual deviations of different process nodes, and automatically adjust the compensation amounts Δf1(t), Δf2(t) to optimize the control output by detecting the real-time status of each node; S14, then set different control gains and compensation parameters according to the process status, and the control parameters of each production stage automatically adapt to the current process requirements. c1 , K c2 Compensation coefficient; S15. Finally, adaptive learning is performed based on historical error data to continuously optimize the proportion, integration, differentiation and compensation coefficients to achieve the best control effect.

3. The method for automatic control of a decanediamine production line according to claim 1, characterized in that: The specific implementation of the improved ant colony algorithm optimization in step S2 is: S21, firstly, the pheromone concentration representing the combined path of each process parameter is initialized to guide the ants to select the path between different variables, and the initialization is performed according to the influence of each process variable on the objective function; S22, decompose the objective function into sub-objectives, set weight vectors, and dynamically adjust the weights after each round of iteration; S23, using adaptive increments to update pheromones, so that the increment of pheromones is dynamically adjusted to increase the probability of selecting a high-quality path; S24. Finally, the iteration is repeated until the change in the objective function value is less than 1%, the optimal solution is reached and the process parameter combination is output.

4. The method for automatic control of a decanediamine production line according to claim 3, characterized in that: The update formula for the dynamic adjustment of the pheromone increment in step S23 is: ij =(1-ρ)T ij +ΔT ij , where T ij is the pheromone matrix, ρ is the volatility factor that controls the decay rate of the pheromone, where f current , f best are the current and optimal objective function values ​​respectively. When the current solution is close to the global optimum, the pheromone increment is increased, and when it is far away, the pheromone increment is reduced.

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