Energy consumption optimization method and system based on VOCs thermal conversion parameter sensitivity analysis

Abstract

The present invention discloses an energy consumption optimization method and system based on VOCs thermal conversion parameter sensitivity analysis, and relates to the field of energy consumption optimization technology. The method includes: determining the basic fuel and air volume ratio information and the key parameters of the conversion process; mapping the fuel ratio information and the key parameters to simulate the thermal conversion process; performing parameter control correlation analysis to establish a parameter correlation function; interactive hardware configuration and system configuration to establish a target mathematical model; taking the parameter correlation function and the production line environment and working conditions as constraints, optimizing the control parameters and determining the pre-processing parameter group; transmitting to the production management system to optimize production management. The method solves the technical problems of poor accuracy of real-time parameter acquisition, inaccurate sensitivity analysis results, and low parameter matching in the existing thermal conversion energy consumption optimization, which leads to high energy consumption and poor energy consumption optimization effect, and achieves the technical effect of accurately analyzing parameter sensitivity, reducing energy consumption, and improving energy consumption optimization effect.

Description

Technical Field

[0001] The present application relates to the field related to energy consumption optimization technology, and specifically to an energy consumption optimization method and system based on VOCs thermal conversion parameter sensitivity analysis. Background Art

[0002] Volatile organic compounds (VOCs) are one of the main sources of air pollution. They are easily volatilized into the atmosphere at room temperature and are commonly found in various fields such as industrial production, transportation, home decoration, paints and coatings, detergents, printing, and inks, which have serious impacts on human health and the environment. Therefore, VOCs control projects have become a key measure to improve air quality and protect the ecological environment. In these control projects, thermal conversion technology is widely used due to its efficient processing capabilities. VOCs thermal conversion equipment uses the reaction mechanisms of catalytic oxidation, pyrolysis or reduction of VOCs under high temperature conditions to convert VOCs into harmless substances, such as carbon dioxide and water vapor, or into valuable compounds that can be recycled. By controlling various parameters of VOCs thermal conversion equipment, efficient treatment of VOCs can be achieved. However, the energy consumption problem of thermal conversion technology has always been a bottleneck restricting its further promotion. The sensitivity analysis of traditional VOCs thermal conversion to parameters is not accurate enough, and the key factors affecting energy consumption cannot be obtained. The parameters of VOCs thermal conversion equipment are balanced, resulting in high energy consumption in the entire process and low energy utilization efficiency.

[0003] Therefore, in the current technologies related to VOCs thermal conversion energy consumption optimization, there are technical problems such as poor accuracy in obtaining real-time parameters of VOCs thermal conversion, inaccurate sensitivity analysis results, and low parameter matching, which in turn lead to poor energy consumption optimization effects and high energy consumption. Summary of the invention

[0004] The present application provides an energy consumption optimization method and system based on VOCs thermal conversion parameter sensitivity analysis, and adopts technical means such as variance decomposition, principal component analysis or regression analysis, and establishment of mathematical models to solve the technical problems existing in the existing thermal conversion energy consumption optimization, such as poor accuracy in obtaining real-time parameters of VOCs thermal conversion, inaccurate sensitivity analysis results, and low parameter matching, which in turn lead to poor energy consumption optimization effect and high energy consumption. The application achieves the technical effect of accurately analyzing parameter sensitivity, reducing energy consumption, and improving energy consumption optimization effect.

[0005] The present application provides an energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis, the method comprising: determining basic fuel and air volume ratio information of pre-treated materials and key parameters of conversion processing, wherein the key parameters include control parameters and auxiliary element parameters of VOCs thermal conversion equipment; mapping the basic fuel and air volume ratio information and the key parameters, assisting Aspen-Plus in simulating the thermal conversion process flow, and reading a simulation data set; based on the simulation data set, performing parameter control correlation analysis based on multiple sensitive dimensions, and establishing a parameter correlation function, wherein the correlation is the impact characteristic on VOCs emissions and energy consumption; interactively combining the hardware configuration and system configuration of the color aluminum spraying production line, and establishing a target mathematical model; using the parameter correlation function and the production line environment and working conditions as constraints, combining the target mathematical model to optimize the control parameters, and determining a pre-processing parameter group, wherein the pre-processing parameter group is a parameter combination with the lowest VOCs emissions and energy consumption; transmitting the pre-processing parameter group to the production management system for optimized production management.

[0006] In a possible implementation, the thermal conversion process flow simulation performs the following processing: determining a random variable based on the key parameter, the random variable being at least one of the key parameters; performing parallel detection of the thermal conversion process flow simulation based on the random variable to determine first simulation data; adjusting the random variable, performing multiple parallel detections of the thermal conversion process flow simulation to obtain the Nth simulation data; and adding the first simulation data to the Nth simulation data into the simulation data set.

[0007] In a possible implementation, based on the simulation data set, parameter control correlation analysis is performed based on multiple sensitive dimensions, a parameter association function is established, and the following processing is performed: the system analyzes key parameters, sets parameter variables and parameter quantification, wherein the parameter variables include at least one; identifies the basic fuel and air volume ratio information, and determines related indicators based on VOCs emissions and energy consumption; determines independent variable parameters based on the parameter variables, and performs indicator change analysis based on the related indicators in combination with the simulation data set to determine multiple variable sequences; based on the multiple variable sequences, performs parameter control correlation analysis in multiple sensitive dimensions to determine the parameter association function.

[0008] In a possible implementation, based on the multiple variable sequences, a parameter control correlation analysis of multiple sensitive dimensions is performed to determine the parameter association function, and the following processing is performed: the multiple variable sequences are traversed, and a fluctuation analysis based on the relevant indicators is performed in combination with regression analysis to determine the indicator sensitivity order; based on the multiple variable sequences, the correlation and influence relationship between the unit parameter variation and the indicator variation is analyzed as a first association function; based on the indicator sensitivity order and the first association function, the parameter association function is added; wherein the first association function includes a positive correlation function of the influence of the unit parameter variation on the associated indicator, and a negative correlation function of the influence of the unit indicator variation on the parameter.

[0009] In a possible implementation, the control parameters are optimized in combination with the target mathematical model, and the following processing is also performed: the VOCs type of the pretreated material is determined; if there are multiple VOCs types, priority is sorted based on the hazard degree and content ratio, and the VOCs avoidance principle is determined, among which specific hazards have a high priority; based on the VOCs avoidance principle, the overall balance constraint of the control parameter optimization is performed.

[0010] In a possible implementation, the target mathematical model includes an inversion calculation layer, a global balance layer and a verification and analysis layer. The control parameter optimization is performed in combination with the target mathematical model, and the following processing is also performed: the thermal conversion quality standard is read, and the single control parameter that meets the thermal conversion quality standard is determined in combination with the inversion calculation layer, and the thermal conversion quality standard corresponds to the VOCs type one by one; the single control parameter is transferred to the global balance layer, and the parameter balance control is performed in combination with the VOCs avoidance principle to determine the pre-processing parameter group; based on the verification and analysis layer, the pre-processing parameter group is subjected to production line simulation verification.

[0011] In a possible implementation, if the simulation verification result does not meet the standard, the following processing is also performed: identify the simulation data set and determine the VOCs change trend, wherein the change characteristics include the proportion of each VOCs type and the thermal conversion rate; identify the VOCs change trend, locate the segmentation node based on the trend decline threshold, partition the entire parameter control cycle, and determine the processing partition, wherein the processing partition includes the front zone, the middle zone and the back zone; based on the processing partition, adjust the parameters of the pre-processing parameter group.

[0012] This application also provides an energy consumption optimization system based on VOCs thermal conversion parameter sensitivity analysis, including:

[0013] A thermal conversion process simulation module, which is used to determine the basic fuel and air volume ratio information of the pre-treated material and the key parameters of the conversion process, wherein the key parameters include the control parameters and auxiliary element parameters of the VOCs thermal conversion equipment, map the basic fuel and air volume ratio information and the key parameters, assist Aspen-Plus in simulating the thermal conversion process, and read the simulation data set;

[0014] A parameter correlation function establishment module, wherein the parameter correlation function establishment module is used to perform parameter control correlation analysis based on the simulation data set and multiple sensitive dimensions to establish a parameter correlation function, wherein the correlation is an influence characteristic on VOCs emission and energy consumption;

[0015] A target mathematical model building module, wherein the target mathematical model building module is used to interact with the hardware configuration and system configuration of the color aluminum spraying production line to establish a target mathematical model;

[0016] A control parameter optimization module, wherein the control parameter optimization module is used to optimize the control parameters based on the parameter correlation function and the production line environment and working conditions as constraints, combined with the target mathematical model, to determine a pre-processing parameter group, wherein the pre-processing parameter group is a parameter combination with the minimum VOCs emission and energy consumption;

[0017] A production management optimization module, wherein the production management optimization module is used to transmit the pre-processing parameter group to a production management system to optimize production management.

[0018] Through the energy consumption optimization method and system based on VOCs thermal conversion parameter sensitivity analysis proposed in this application, it is planned to determine the basic fuel and air volume ratio information and the key parameters of the conversion process; map the basic fuel and air volume ratio information and key parameters to simulate the thermal conversion process; perform parameter control correlation analysis and establish parameter association functions; interact with hardware configuration and system configuration to establish a target mathematical model; use the parameter association function and the production line environment and working conditions as constraints to optimize the control parameters and determine the pre-processing parameter group; transmit the pre-processing parameter group to the production management system for optimized production management. It solves the technical problems of poor accuracy in obtaining real-time parameters for VOCs thermal conversion, inaccurate sensitivity analysis results, and low parameter matching, which lead to high energy consumption and poor energy consumption optimization effects in the existing thermal conversion energy consumption optimization, and achieves the technical effect of accurately analyzing parameter sensitivity, reducing energy consumption, and improving energy consumption optimization effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0020] Figure 1 A schematic flow chart of an energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a process flow for simulating a thermal conversion process in an energy consumption optimization method based on sensitive analysis of VOCs thermal conversion parameters provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of a flow chart for optimizing control parameters in an energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis provided in an embodiment of the present application;

[0023] Figure 4 Schematic diagram of the structure of an energy consumption optimization system based on VOCs thermal conversion parameter sensitivity analysis provided in an embodiment of the present application.

[0024] Explanation of the reference numerals: thermal conversion process simulation module 10 , parameter correlation function establishment module 20 , target mathematical model construction module 30 , control parameter optimization module 40 , production management optimization module 50 . DETAILED DESCRIPTION

[0025] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0026] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0027] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0028] The present application embodiment provides an energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis, such as Figure 1 As shown, the method includes:

[0029] Step S100, determine the basic fuel and air volume ratio information of the pre-treated material and the key parameters of the conversion treatment, wherein the key parameters include the control parameters and auxiliary element parameters of the VOCs thermal conversion equipment. Determine the basic fuel and air volume ratio information of the pre-treated material. Fuel ratio refers to the calculation of the required basic fuel type and proportion based on the calorific value of the material and the combustion requirements; air volume calculation refers to the calculation of the required air volume based on the combustion reaction equation and the combustion characteristics of the material to ensure that the air volume can meet the combustion requirements and avoid heat loss caused by excessive air; determination of basic fuel and air volume ratio information refers to adjusting the fuel and air volume ratio through actual combustion tests, observing the combustion effect, emission indicators, etc., to find the best ratio scheme. The determination of basic fuel and air volume ratio information helps to ensure that the treated material can meet specific performance requirements; key parameters of the conversion treatment Key parameters, in VOCs thermal conversion, mainly include equipment control parameters and auxiliary factor parameters, these parameters are crucial to ensure the effective treatment of VOCs (volatile organic compounds), control parameters may include equipment operating temperature, pressure, air velocity (VOCs gas volume treated per unit volume of catalyst per unit time), etc., all of which will affect the conversion efficiency of VOCs, auxiliary factor parameters may include catalyst selection and proportion, state (liquid, gaseous, etc.), concentration, exhaust gas pretreatment requirements, fuel consumption rate and other parameters, these are all key factors to ensure stable operation of equipment and efficient treatment of VOCs. The basic fuel and air volume ratio information of pre-treated materials and the key parameters of conversion treatment are two important aspects that must be carefully controlled in the industrial processing process, they directly affect the quality of the final product and the efficiency of the processing process.

[0030] Step S200, mapping the basic fuel and air volume ratio information and the key parameters, assisting Aspen-Plus in simulating the thermal conversion process, and reading the simulation data set. The above basic fuel and air volume ratio information and key parameters are mapped to corresponding operating conditions and equipment setting parameters. Aspen-Plus is a widely used chemical process simulation software, which can simulate and optimize various chemical processes. According to the mapped basic fuel and air volume ratio information and key parameters, a corresponding process model is established in Aspen-Plus, for example, selecting appropriate physical property methods, defining components, setting feed streams, selecting or defining reaction kinetic models, setting operating conditions (such as temperature, pressure), configuring unit operations (such as heat exchangers, reactors, separators, etc.) and connecting various streams and unit operations. After the Aspen-Plus process simulation is set up and runs, the software will generate a large amount of simulation data, including temperature, pressure, composition, operating conditions, efficiency, energy consumption, etc. of each unit operation. Reading the simulation data set is to export and organize these data into a data set to evaluate the performance, efficiency and feasibility of the thermal conversion process.

[0031] In one possible implementation, Figure 2As shown, step S200 further includes step S210, determining a random variable based on the key parameter, wherein the random variable is at least one of the key parameters. From the important parameters (i.e., key parameters) that affect the performance and use effect of the equipment, select at least one parameter as a random variable. It also includes step S220, based on the random variable, performing parallel detection of the thermal conversion process simulation to determine the first simulation data. In the simulation process of the thermal conversion process, the random variable determined above is introduced, and multiple simulations are performed based on different values ​​of this variable to obtain a series of simulation results, which are referred to as the first simulation data, wherein parallel detection refers to performing multiple independent simulation experiments under the same conditions by changing the value of the random variable, and each experiment will obtain a simulation result, which can be compared and verified with each other to ensure the accuracy and reliability of the simulation, and the first simulation data refers to a series of simulation data obtained by the above parallel detection, reflecting the performance and output of the thermal conversion process under different random variable values. It also includes step S230, adjusting the random variable, performing multiple parallel detections of the thermal conversion process simulation, and obtaining the Nth simulation data. Adjust the random variables, and for each adjusted random variable value, perform an independent simulation experiment under the same initial conditions and boundary conditions, N represents the number of simulation experiments, and each time the random variable is adjusted and the simulation is performed, a new set of simulation data will be obtained. It also includes step S240, adding the first simulation data to the Nth simulation data into the simulation data set. All data obtained from each simulation based on different random variable values ​​(from the first simulation to the Nth simulation) are integrated into a unified data set to construct a comprehensive and complete data set containing multiple simulation results, so as to conduct subsequent comprehensive analysis and compare the impact of random variables on simulation results.

[0032] Step S300, based on the simulation data set, parameter control correlation analysis is performed based on multiple sensitive dimensions to establish a parameter correlation function, wherein the correlation is the impact characteristic on VOCs emissions and energy consumption. In-depth exploration of the relationship between the parameters in the simulation data set, especially how they jointly affect VOCs emissions and energy consumption, multiple sensitive dimensions refer to the analysis of the comprehensive impact of these parameters on VOCs emissions and energy consumption when multiple parameters (such as temperature, pressure, flow rate, etc.) change simultaneously. Parameter control correlation analysis is to determine which parameters have a significant correlation with VOCs emissions and energy consumption through statistical methods, such as correlation coefficient calculation, regression analysis, etc., to identify key influencing factors, that is, parameters that have a significant impact on VOCs emissions and energy consumption. The parameter correlation function is a mathematical function established based on the results of the above correlation analysis, which describes the quantitative relationship between each influencing parameter and VOCs emissions and energy consumption.

[0033] In a possible implementation, step S300 further includes step S310, systematically analyzing key parameters, setting parameter variables and parameter quantification, wherein the parameter variables include at least one. All important operation and control parameters in the thermal conversion process are comprehensively checked and set, especially the key parameters are systematically analyzed. Parameter variables refer to those parameters that may be adjusted or changed in simulation or actual operation, and are adjusted as needed to achieve the purpose of optimizing the process or meeting specific needs. For example, in the thermal conversion process, reaction temperature, pressure, flow rate, etc. may be set as parameter variables. In the process of traversing key parameters, at least one (or more) adjustable or variable parameters need to be determined; parameter quantification refers to those parameter values ​​that remain unchanged during the experiment or simulation process. These parameters are set to fixed values ​​under specific experimental or simulation conditions in order to more accurately study the impact of other variables on the process. It also includes step S320, identifying the basic fuel and air volume ratio information, and determining relevant indicators based on VOCs emissions and energy consumption. Identifying the basic fuel and air volume ratio information is to understand and analyze the proportional relationship of each component in the material. These proportional relationships constitute the basic fuel and air volume ratio information required for thermal conversion. According to VOCs emissions and energy consumption, some specific quantitative indicators are set or selected. There may be a variety of organic compounds in the pre-treated material, that is, VOCs to be thermally converted, all of which need to be reduced to reduce hazards. For example, emissions, density, organic compound content, VOCs emission concentration, emission rate, energy consumption rate, etc. are used to evaluate the environmental performance and energy efficiency of the production or processing process, and establish the relationship between the basic fuel and air volume ratio information and VOCs emissions and energy consumption, so as to more accurately evaluate how different basic fuel and air volume ratios affect VOCs emissions and energy consumption. It also includes step S330, determining the independent variable parameters based on the parameter variables, combining the simulation data set, performing indicator change analysis based on the relevant indicators, and determining multiple variable sequences. One or more of the previously set parameter variables are selected as independent variable parameters. The independent variable is a factor that can change independently and affect other variables (dependent variables). After the independent variable parameters are determined, the relationship between the independent variable parameters and the relevant indicators is analyzed using the collected simulation data set. It is possible to understand how the relevant indicators respond when the independent variables change, and identify a series of variable sequences that reflect the changes and trends of the relevant indicators (such as VOCs emissions and energy consumption) under different independent variable parameter settings. It also includes step S340, based on the multiple variable sequences, a multi-sensitive dimension parameter control correlation analysis is performed to determine the parameter association function.Perform parameter-control correlation analysis on multiple variable sequences in multiple sensitive dimensions, and consider the impact of multiple parameter changes on system output at the same time, revealing the intrinsic connection between these parameters and how they work together on system output. After completing the correlation analysis in multiple sensitive dimensions, one or more mathematical functions (i.e., parameter correlation functions) can be constructed to describe the quantitative relationship between parameters and system output, which can be used to predict VOCs emissions under given parameter settings.

[0034] In a possible implementation, step S340 further includes step S341, traversing the multiple variable sequences, combining regression analysis to perform fluctuation analysis based on the relevant indicators, and determining the sensitivity order of indicators. Traversing multiple variable sequences, through regression analysis, it can be determined which independent variables have the greatest impact on the dependent variables, wherein regression analysis is a statistical method for exploring the mathematical relationship between independent variables (here is the parameter variable) and dependent variables (here is the relevant indicators, such as VOCs emissions or energy consumption), and fluctuation analysis is mainly to observe the changes of relevant indicators under different parameter settings. By analyzing these fluctuations, it can be understood which parameters have the most significant impact on the indicators. The more the frequency of indicator changes under different variable scenarios, the larger the variable amount and the higher the sensitivity. The sensitivity order of indicators refers to the order of the degree of influence of different parameters on relevant indicators. It also includes step S342, based on the multiple variable sequences, analyzing the correlation and influence relationship between the unit parameter variation and the indicator variation as the first correlation function. In-depth exploration of the specific relationship between the parameter variation range (unit parameter variation) and the related index variation range (index variation). The unit parameter variation refers to the effect caused by each unit change of the parameter within its possible variation range. For example, if a parameter increases from 1 to 2, then its variation is 1; the index variation refers to the change in the related index (such as VOCs emissions, energy consumption, etc.) corresponding to the parameter variation. When the parameter changes, the related index will also change accordingly. The amplitude of this change is the index variation. By comparing and analyzing the unit parameter variation and the corresponding index variation, we can understand the correlation and influence relationship between the two and establish a mathematical function (i.e., the first correlation function) to describe the quantitative relationship between the unit parameter variation and the index variation. It also includes step S343, adding the parameter correlation function based on the index sensitivity order and the first correlation function. Integrating the index sensitivity order and the first correlation function into the original parameter correlation function means that when constructing or updating the parameter correlation function, we need to consider the relationship revealed by the parameter sensitivity order and the first correlation function. For example, if a parameter is determined to be highly sensitive, then in the parameter correlation function, the change of this parameter should be given a greater weight. At the same time, the specific relationship between the parameter and the index described by the first correlation function can ensure a more accurate reflection of the actual situation. It also includes step S344, wherein the first correlation function includes a positive correlation function of the influence of the unit parameter change amplitude on the correlation index, and a negative correlation function of the influence of the unit index change amplitude on the parameter.The first correlation function includes two parts, the positive correlation function of the influence of unit parameter change on the associated index, and the inverse correlation function of the influence of unit index change on the parameter, which describe the mutual influence relationship between the parameter and the index from different angles. Specifically, the positive correlation function of the influence of unit parameter change on the associated index describes how the associated index will change when the parameter changes by unit, and quantifies the direct impact of the parameter change on the index; the inverse correlation function of the influence of unit index change on the parameter describes the reverse impact on the parameter when the associated index changes by unit.

[0035] Step S400, hardware configuration and system configuration of the interactive color aluminum spraying production line, to establish the target mathematical model. The hardware configuration and system configuration of the interactive color aluminum spraying production line are used to establish the target mathematical model, wherein the hardware configuration may include pre-treatment equipment (surface treatment of aluminum), powder spraying system / paint spraying equipment (evenly spraying the paint on the surface of aluminum), oven (curing powder or drying paint), conveying system (moving aluminum in the production line), other auxiliary equipment (electric control system, heat source system, etc.); the system configuration may include control system (responsible for the automation control of the entire production line), quality monitoring system (real-time monitoring of various parameters in the spraying process), safety protection system (to ensure the safety of personnel and the normal operation of equipment), etc.

[0036] Step S500, taking the parameter association function and the production line environment and working conditions as constraints, combined with the target mathematical model, the control parameter optimization is performed to determine the pre-processing parameter group, which is a parameter combination with the minimum VOCs emission and energy consumption. Taking the parameter association function and the production line environment and working conditions as constraints, combined with the target mathematical model, the control parameter optimization is performed to find a set of parameter settings that can minimize VOCs emissions and energy consumption while satisfying the production environment and equipment constraints, wherein the production line environment includes equipment capacity, process requirements, safety specifications and other actual production constraints. By integrating the parameter association function and the production line environment constraints into the target mathematical model, a parameter combination that can optimize the production target is obtained, that is, a pre-processing parameter group is obtained, which is a parameter combination with the minimum VOCs emission and energy consumption. In summary, the control parameter optimization process is to find a set of optimal pre-processing parameter settings by integrating the parameter association function, the production line environment constraints and the target mathematical model, while ensuring production quality and efficiency, while minimizing VOCs emissions and energy consumption.

[0037] In one possible implementation, Figure 3As shown, step S500 further includes step S510, determining the VOCs type of the pretreated material. The materials involved in the pretreatment stage are analyzed and identified for the components of volatile organic compounds (VOCs), and thermal conversion treatment is performed to reduce emissions for the various harmful organic compounds that exist. Based on different parameter control priorities, a balanced analysis is performed to ensure overall optimization. It also includes step S520, if there are multiple types of VOCs, priority is sorted based on the hazard degree and content ratio, and the VOCs avoidance principle is determined, among which specific hazards have a high priority. When the pre-treated material contains multiple types of VOCs, in order to effectively manage and control these hazardous substances, it is necessary to prioritize them, including the hazard degree and content ratio, where the hazard degree is an important factor in evaluating the potential impact of VOCs on the environment and human health. Some VOCs may have special toxicity, carcinogenicity, teratogenicity or mutagenicity, so their hazard degree is higher. Specific hazard refers to the special toxicity or health effects of certain VOCs, such as the carcinogenicity of benzene. VOCs with specific hazards will be given a higher priority when sorting, and non-specific hazards are smaller, for example, causing headaches, inattention, boredom, fatigue, etc.; the content of VOCs in the material is also an important reference for sorting, and VOCs with high content may pose a greater threat to the environment and human health. The VOCs avoidance principle means that when dealing with multiple VOCs, those substances with high hazard degree and / or high content should be treated first, especially when some VOCs have specific hazards, they should be regarded as high priority, even if their content may not be the highest. It also includes step S530, based on the VOCs avoidance principle, the overall equilibrium constraint of the control parameter optimization is performed. When optimizing control parameters, a variety of factors must be considered comprehensively. Not only must the principle of reducing the emission of highly hazardous VOCs be followed, but the overall efficiency and stability of the production process must also be ensured to achieve optimal overall performance, so that various performance indicators (such as production efficiency, product quality, environmental emissions, etc.) can reach a relatively optimal state.

[0038] In one possible implementation, the target mathematical model in step S500 includes an inversion and calculation layer, a global balance layer and a verification and analysis layer. The control parameter optimization is performed in combination with the target mathematical model, and further includes step S540, reading the thermal conversion quality standard, combining the inversion and calculation layer, and determining a single control parameter that meets the thermal conversion quality standard, and the thermal conversion quality standard corresponds one-to-one to the VOCs type. Among them, the inversion and calculation layer mainly uses actual observation data or experimental results to infer the internal state, parameters or initial conditions of the system through mathematical methods. In the VOCs treatment process, the key parameters such as the efficiency and reaction rate constant of the treatment system can be inverted and inferred through the monitored emission data; the global balance layer comprehensively considers multiple aspects of the system (such as economic benefits, environmental impact, resource utilization, etc.) to find the optimal balance point of the overall performance of the system. In VOCs treatment, the global balance layer may consider multiple goals such as treatment efficiency, energy consumption, and treatment cost to find a treatment solution that is both economical and environmentally friendly; the verification and analysis layer is responsible for verifying and analyzing the model, testing the accuracy and reliability of the model, and evaluating the deviation of the model's prediction results. After establishing the mathematical model of VOCs treatment, the verification and analysis layer will use experimental data and field monitoring data to verify the model's prediction results to ensure the effectiveness and applicability of the model. Read and understand the thermal conversion quality standards set for different types of VOCs, use the inversion layer method to deduce the individual control parameters that meet these standards based on the actual thermal conversion effect and emission standards, and ensure that each type of VOCs has its corresponding optimized control parameter settings to achieve efficient and safe thermal conversion treatment. Among them, the thermal conversion quality standard refers to the treatment effect or emission standard set for different types of VOCs during the thermal conversion process, which may vary depending on the type, toxicity, and difficulty of treatment of VOCs. For example, some toxic or difficult-to-degrade VOCs may need to reach Higher processing efficiency or lower emission concentration; single control parameters may include temperature, pressure, reactant concentration, etc., which directly affect the efficiency and effect of thermal conversion. By adjusting these parameters, the removal efficiency and final emission concentration of VOCs in the thermal conversion process can be controlled. Different types of VOCs have their specific processing requirements and emission standards. Therefore, when determining the single control parameters, it is necessary to consider and optimize each type of VOCs separately. It also includes step S550, transferring the single control parameters to the global balance layer, combining the VOCs avoidance principle to balance the parameters and determine the pre-processing parameter group. The single control parameters are transferred to the global balance layer, and after the parameter balance control of the global balance layer in combination with the VOCs avoidance principle, a set of optimized pre-processing parameter groups will be obtained, which comprehensively considers the processing efficiency, energy consumption, and environmental emissions to achieve the optimization of the overall performance. It also includes step S560, based on the verification analysis layer, the pre-processing parameter group is subjected to production line simulation verification.Input the pre-processing parameter group into the verification and analysis layer of the target mathematical model to perform production line simulation verification to verify whether the pre-processing parameter group can achieve the expected effect in the real production environment and whether there are areas that need adjustment or optimization. If it is found that the simulation results are significantly different from expectations or the performance is poor, then the pre-processing parameter group needs to be verified and adjusted.

[0039] In a possible implementation, step S560 further includes step S561, identifying the simulation data set, determining the change trend of VOCs, wherein the change characteristics include the ratio of each VOCs type and the thermal conversion rate. The simulation data set refers to the data on VOCs content, type and conversion status obtained through the production line simulation verification, and the data is analyzed to determine the change trend of VOCs under different conditions. The change characteristics include the ratio of each VOCs type and the thermal conversion rate. The ratio of each VOCs type refers to the ratio of different VOCs types in the data set. By analyzing the ratio of each VOCs in the simulation data set, it can be understood which VOCs dominate under different conditions (such as temperature, pressure, reaction time, etc.), and which VOCs content is decreasing or increasing; the thermal conversion rate refers to the speed at which VOCs undergo chemical reactions or decomposition under specific conditions (such as high temperature, the presence of catalysts, etc.). The level of the thermal conversion rate directly affects the treatment efficiency and final emission concentration of VOCs. The method also includes step S562, identifying the VOCs change trend, locating the segmentation node based on the trend decline threshold, partitioning the entire parameter control cycle, and determining the processing partitions, wherein the processing partitions include the front zone, the middle zone, and the back zone. Analyze the continuous monitoring data of VOCs concentration or emission to identify its changing trend over time or other factors (such as temperature, humidity, treatment conditions, etc.). After identifying the changing trend of VOCs, it is necessary to find one or more points (i.e., split nodes) that divide the overall trend into different stages. These split nodes are usually determined based on a preset decline threshold. For example, when the concentration or emission of VOCs decreases below a specific threshold, it can be considered that the trend has changed significantly, and this point can be used as a split node. After determining the split node, the entire control cycle (i.e., the entire process from the beginning of treatment to the end of treatment) can be divided into different areas or stages, and the entire treatment cycle can be divided into the front zone, the middle zone, and the back zone. Among them, the front zone includes the initial reduction stage of VOCs. At this time, the treatment effect may not be very obvious, but the concentration or emission of VOCs has begun to decline; the middle zone is the stage where the treatment effect is most significant, and the concentration or emission of VOCs will drop significantly at this stage; the back zone is the final stage of the treatment process, at which time the concentration or emission of VOCs has been reduced to a lower level and the treatment effect tends to be stable. The process also includes step S563, adjusting the parameters of the pre-processing parameter group based on the processing partition. As the processing progresses, the pre-set processing parameters are optimized and adjusted in a targeted manner according to the processing partitions (front zone, middle zone, and back zone). At different processing stages, the density and proportion of VOCs are different, and the processing parameters are adjusted accordingly, combined with the dynamic situation, to effectively improve the final processing effect.

[0040] Step S600, the pre-processing parameter group is transmitted to the production management system for optimized production management. The determined pre-processing parameter group is input into the production management system to achieve accurate management of the VOCs thermal conversion process, including improving the overall VOCs treatment efficiency, reducing energy consumption, reducing VOCs emissions, improving product quality, etc., to ensure the stability and reliability of the production process.

[0041] In the above, refer to Figure 1 The energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to an embodiment of the present invention is described in detail. Figure 4 An energy consumption optimization system based on VOCs thermal conversion parameter sensitivity analysis according to an embodiment of the present invention is described.

[0042] The energy consumption optimization system based on VOCs thermal conversion parameter sensitivity analysis according to the embodiment of the present invention is used to solve the technical problems existing in the existing thermal conversion energy consumption optimization, such as poor accuracy in obtaining VOCs thermal conversion real-time parameters, inaccurate sensitivity analysis results, and low parameter matching, which in turn leads to high energy consumption and poor energy consumption optimization effect, and achieves the technical effect of accurately analyzing parameter sensitivity, reducing energy consumption, and improving energy consumption optimization effect. The energy consumption optimization system based on VOCs thermal conversion parameter sensitivity analysis includes: a thermal conversion process simulation module 10, a parameter correlation function establishment module 20, a target mathematical model construction module 30, a control parameter optimization module 40, and a production management optimization module 50.

[0043] A thermal conversion process simulation module 10, which is used to determine the basic fuel and air volume ratio information of the pre-treated material and the key parameters of the conversion process, wherein the key parameters include the control parameters and auxiliary element parameters of the VOCs thermal conversion equipment, map the basic fuel and air volume ratio information and the key parameters, assist Aspen-Plus in simulating the thermal conversion process, and read the simulation data set;

[0044] A parameter correlation function establishment module 20, wherein the parameter correlation function establishment module 20 is used to perform parameter control correlation analysis based on the simulation data set and multiple sensitive dimensions to establish a parameter correlation function, wherein the correlation is an influence characteristic on VOCs emission and energy consumption;

[0045] A target mathematical model building module 30, wherein the target mathematical model building module 30 is used to interact with the hardware configuration and system configuration of the aluminum color spraying production line to establish a target mathematical model;

[0046] A control parameter optimization module 40, wherein the control parameter optimization module 40 is used to optimize the control parameters by taking the parameter correlation function and the production line environment and working conditions as constraints and combining the target mathematical model to determine a pre-processing parameter group, wherein the pre-processing parameter group is a parameter combination with the minimum VOCs emission and energy consumption;

[0047] The production management optimization module 50 is used to transmit the pre-processing parameter group to the production management system to optimize the production management.

[0048] The specific configuration of the thermal conversion process simulation module 10 will be described in detail below. As described above, the thermal conversion process simulation module 10 may further include: determining a random variable based on the key parameter, the random variable being at least one of the key parameters; performing parallel detection of the thermal conversion process simulation based on the random variable to determine first simulation data; adjusting the random variable, performing multiple parallel detections of the thermal conversion process simulation to obtain Nth simulation data; and adding the first simulation data to the Nth simulation data into the simulation data set.

[0049] The specific configuration of the parameter association function establishment module 20 will be described in detail below. As described above, based on the simulation data set, parameter control correlation analysis is performed based on multiple sensitive dimensions to establish a parameter association function. The parameter association function establishment module 20 further includes: systematic analysis of key parameters, setting parameter variables and parameter quantification, wherein the parameter variables include at least one; identifying the basic fuel and air volume ratio information, and determining related indicators based on VOCs emissions and energy consumption; determining independent variable parameters based on the parameter variables, combining the simulation data set, performing indicator change analysis based on the related indicators, and determining multiple variable sequences; based on the multiple variable sequences, performing parameter control correlation analysis in multiple sensitive dimensions to determine the parameter association function.

[0050] The specific configuration of the parameter association function establishment module 20 will be described in detail below. As described above, based on the simulation data set, parameter control correlation analysis is performed based on multiple sensitive dimensions to establish a parameter association function. The parameter association function establishment module 20 further includes: traversing the multiple variable sequences, combining regression analysis to perform fluctuation analysis based on the relevant indicators, and determining the indicator sensitivity order; based on the multiple variable sequences, analyzing the correlation and influence relationship between the unit parameter variation and the indicator variation as the first association function; based on the indicator sensitivity order and the first association function, adding the parameter association function; wherein the first association function includes a positive correlation function of the influence of the unit parameter variation on the associated indicator, and a negative correlation function of the influence of the unit indicator variation on the parameter.

[0051] The specific configuration of the control parameter optimization module 40 will be described in detail below. As described above, the control parameter optimization module 40 is combined with the target mathematical model to perform control parameter optimization, and further includes: determining the VOCs type of the pre-treated material; if there are multiple VOCs types, prioritizing them based on the hazard degree and content ratio, and determining the VOCs avoidance principle, wherein specific hazards have a high priority; based on the VOCs avoidance principle, performing overall equilibrium constraints for control parameter optimization.

[0052] The specific configuration of the control parameter optimization module 40 will be described in detail below. As described above, the target mathematical model includes an inversion calculation layer, a global balance layer and a verification analysis layer. The control parameter optimization module 40 is combined with the target mathematical model to perform control parameter optimization. The control parameter optimization module 40 further includes: reading the thermal conversion quality standard, combining the inversion calculation layer, and determining the single control parameter that meets the thermal conversion quality standard, and the thermal conversion quality standard corresponds to the VOCs type one by one; transferring the single control parameter to the global balance layer, combining the VOCs avoidance principle to perform parameter balance control, and determine the pre-processing parameter group; based on the verification analysis layer, the pre-processing parameter group is subjected to production line simulation verification.

[0053] The specific configuration of the control parameter optimization module 40 will be described in detail below. As described above, if the simulation verification result does not meet the standard, the control parameter optimization module 40 may further include: identifying the simulation data set, determining the VOCs change trend, wherein the change characteristics include the proportion of each VOCs type and the thermal conversion rate; identifying the VOCs change trend, locating the segmentation node based on the trend drop threshold, partitioning the entire parameter control cycle, determining the processing partition, the processing partition includes the front zone, the middle zone and the back zone; based on the processing partition, adjusting the parameters of the pre-processing parameter group.

[0054] The energy consumption optimization system based on VOCs thermal conversion parameter sensitivity analysis provided in the embodiment of the present invention can execute the energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0056] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. An energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis, characterized in that: The method comprises: Determine the basic fuel and air volume ratio information of the pre-treated material and the key parameters of the conversion process, wherein the key parameters include the control parameters and auxiliary element parameters of the VOCs thermal conversion equipment; Mapping the basic fuel and air volume ratio information and the key parameters, assisting Aspen-Plus in simulating the thermal conversion process flow, and reading the simulation data set; Based on the simulation data set, parameter control correlation analysis is performed based on multiple sensitive dimensions to establish a parameter correlation function, wherein the correlation is the impact characteristic on VOCs emissions and energy consumption; Hardware configuration and system configuration of interactive color aluminum spraying production line, and establishment of target mathematical model; Taking the parameter correlation function and the production line environment and working conditions as constraints, the control parameters are optimized in combination with the target mathematical model to determine a pre-processing parameter group, wherein the pre-processing parameter group is a parameter combination with the minimum VOCs emission and energy consumption; The pre-processing parameter group is transmitted to the production management system for optimized production management.

2. The energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to claim 1, characterized in that: The method of performing thermal conversion process simulation further comprises: Determining a random variable based on the key parameter, the random variable being at least one of the key parameters; Based on the random variables, performing parallel detection of thermal conversion process simulation to determine first simulation data; Adjusting the random variables, performing multiple parallel tests of the thermal conversion process simulation, and obtaining Nth simulation data; The first simulation data to the Nth simulation data are added into the simulation data set.

3. The energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to claim 1, characterized in that: The method further comprises: The system analyzes key parameters, sets parameter variables and parameter quantification, wherein the parameter variables include at least one; Identify the basic fuel and air volume ratio information, and determine relevant indicators based on VOCs emissions and energy consumption; Determine independent variable parameters based on the parameter variables, perform indicator change analysis based on the relevant indicators in combination with the simulation data set, and determine multiple variable sequences; Based on the multiple variable sequences, a parameter control correlation analysis of multiple sensitive dimensions is performed to determine the parameter association function.

4. The energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to claim 3 is characterized in that: The method further comprises: Traversing the plurality of variable sequences, performing fluctuation analysis based on the relevant indicators in combination with regression analysis, and determining the sensitivity order of the indicators; Based on the multiple variable sequences, analyzing the correlation and influence relationship between the unit parameter variation and the index variation as a first correlation function; Adding the parameter association function based on the indicator sensitivity order and the first association function; The first correlation function includes a positive correlation function of the influence of the unit parameter variation on the correlation index, and a negative correlation function of the influence of the unit index variation on the parameter.

5. The energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to claim 1, characterized in that: In combination with the target mathematical model, the control parameter optimization is performed, and the method further comprises: Determining the VOCs type of the pre-treated material; If there are multiple types of VOCs, the priority is sorted based on the hazard degree and content ratio to determine the VOCs avoidance principle, among which specific hazards have a high priority; Based on the VOCs avoidance principle, an overall equilibrium constraint is applied to optimize the control parameters.

6. The energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to claim 5, characterized in that: The target mathematical model includes an inversion and calculation layer, a global balancing layer, and a verification and analysis layer. The control parameter optimization is performed in combination with the target mathematical model. The method further includes: Reading the thermal conversion quality standard, combining the inversion calculation layer, and determining the single control parameter that meets the thermal conversion quality standard, wherein the thermal conversion quality standard corresponds to the VOCs type one by one; The single control parameter is transferred to the global balancing layer, and parameter balancing is controlled in combination with the VOCs avoidance principle to determine the pre-processing parameter group; Based on the verification analysis layer, the pre-processing parameter group is subjected to production line simulation verification.

7. The energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to claim 6, characterized in that: If the simulation verification result does not meet the standard, the method further includes: Identifying the simulated data set and determining a VOCs change trend, wherein the change characteristics include a ratio of each VOCs type and a thermal conversion rate; Identify the VOCs change trend, locate the segmentation node based on the trend decline threshold, partition the entire control cycle, and determine the processing partition, the processing partition includes the front zone, the middle zone and the back zone; Based on the processing partitions, the pre-processing parameter group is adjusted.

8. Energy consumption optimization system based on VOCs thermal conversion parameter sensitivity analysis, characterized in that: The system is used to implement the energy consumption optimization method based on VOCs thermal conversion parameter sensitivity analysis according to any one of claims 1 to 7, and the system comprises: A thermal conversion process simulation module, which is used to determine the basic fuel and air volume ratio information of the pre-treated material and the key parameters of the conversion process, wherein the key parameters include the control parameters and auxiliary element parameters of the VOCs thermal conversion equipment, map the basic fuel and air volume ratio information and the key parameters, assist Aspen-Plus in simulating the thermal conversion process, and read the simulation data set; A parameter correlation function establishment module, wherein the parameter correlation function establishment module is used to perform parameter control correlation analysis based on the simulation data set and multiple sensitive dimensions to establish a parameter correlation function, wherein the correlation is an influence characteristic on VOCs emission and energy consumption; A target mathematical model building module, wherein the target mathematical model building module is used to interact with the hardware configuration and system configuration of the color aluminum spraying production line to establish a target mathematical model; A control parameter optimization module, wherein the control parameter optimization module is used to optimize the control parameters based on the parameter correlation function and the production line environment and working conditions as constraints, combined with the target mathematical model, to determine a pre-processing parameter group, wherein the pre-processing parameter group is a parameter combination with the minimum VOCs emission and energy consumption; A production management optimization module, wherein the production management optimization module is used to transmit the pre-processing parameter group to a production management system to optimize production management.

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