Low-energy-consumption recovery method and system for organic waste gas

Abstract

The invention relates to the technical field of waste gas separation, and discloses a low-energy-consumption recovery method and system for organic waste gas, and the method comprises the steps: analyzing the volatile solvent component concentration, concentration fluctuation data, solvent recovery energy consumption correlation factors and temperature and humidity data of the organic waste gas through multi-source coupling, and obtaining a characteristic coupling feature set; analyzing key process conditions of waste gas separation based on the feature set feed-forward situation to obtain optimal energy consumption operation parameters; the parameters and conditions of the waste gas separation process are optimized in combination with the parameters and feature set dynamic constraint, and a Pareto optimal scheme is obtained; selecting a scheme as an optimal control strategy according to separation efficiency dynamic characteristics and energy consumption stability key factors in the feature set; the digital twinning collaborative deduction control strategy and feature set is coded into a separation control instruction; based on recovery energy efficiency verification and product quality detection indexes after actual operation, performing feedback optimization to obtain a low-energy-consumption instruction generation strategy; the low-energy-consumption recovery efficiency of the organic waste gas can be improved.

Description

Technical Field

[0001] This invention relates to the field of waste gas separation technology, and in particular to a low-energy-consumption method and system for recovering organic waste gas. Background Technology

[0002] Organic waste gases generally contain a variety of volatile solvents, and their effective separation and recovery are crucial to meeting environmental protection requirements and achieving resource recycling. Currently, separation and recovery technologies such as adsorption and condensation are widely used, with the core being balancing recovery efficiency and process energy consumption. To achieve this goal, existing technologies mainly set process parameters by monitoring waste gas concentrations and are gradually introducing parameter optimization models, process simulations, and even digital twin technologies to improve process simulation and optimization capabilities.

[0003] Existing technologies lack in-depth coupled analysis of multi-source data affecting the separation process. They typically view waste gas concentration in isolation, failing to integrate concentration fluctuations, key process factors, and environmental parameters for a comprehensive correlation analysis. This results in the inability to construct a comprehensive feature reflecting the relationship between waste gas characteristics and energy consumption. The data foundation is weak, and the parameter optimization methods for multiple objectives of low energy consumption and high efficiency are relatively static. They neither fully consider the dynamic feedforward influence of waste gas characteristics nor have a mechanism for systematically balancing and screening conflicting objectives under process constraints. Consequently, the obtained process parameters are difficult to maintain an optimal energy efficiency balance under fluctuating operating conditions, and the robustness of the solution is insufficient. Therefore, how to improve the low-energy recovery efficiency of organic waste gas has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a low-energy-consumption method and system for recovering organic waste gas, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a low-energy-consumption method for recovering organic waste gas, comprising: S1. Perform multi-source coupling analysis on the concentration of volatile solvent components, concentration fluctuation data, solvent recovery energy consumption correlation factor, and environmental temperature and humidity data of organic waste gas to obtain the characteristic coupling feature set of the organic waste gas. S2. Based on the aforementioned characteristic coupling feature set, a feedforward situation analysis is performed on the process conditions of the organic waste gas during the separation process to obtain the optimal energy consumption separation operation parameters of the organic waste gas during the separation process. S3. Based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set, the process conditions of the organic waste gas in the separation process are dynamically constrained and optimized to obtain the Pareto optimal separation scheme of the organic waste gas. S4. Based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, the Pareto optimal separation scheme is robustly selected, and the selection results are compiled into the optimal separation control strategy for the organic waste gas. S5. Perform digital twin co-induction on the optimal separation control strategy and the characteristic coupling feature set, and encode the induction result into the waste gas separation control command of the organic waste gas; S6. Based on the energy efficiency verification index and the quality index of the recovered product obtained after the operation of the waste gas separation control command, the generation strategy of the waste gas separation control command is optimized by feedback to obtain the low-energy-consumption separation command generation strategy of the organic waste gas.

[0006] In a preferred embodiment, the multi-source coupling analysis of the concentrations of volatile solvent components, concentration fluctuation data, solvent recovery energy consumption correlation factors, and environmental temperature and humidity data of the organic waste gas yields a characteristic coupling feature set of the organic waste gas, including: Concentration analysis was performed on the volatile solvent components of the organic waste gas to obtain accurate concentration data of the organic waste gas. Pattern recognition is performed on the concentration fluctuation data of the organic waste gas to obtain the fluctuation pattern characteristics of the organic waste gas. Energy consumption correlation mining was performed on the solvent recovery energy consumption correlation factors of the organic waste gas to obtain the energy consumption correlation characteristics of the organic waste gas. Environmental parameters are coupled to the ambient temperature and humidity data of the organic waste gas to obtain the environmental coupling factor of the organic waste gas. The precise concentration data, the fluctuation pattern characteristics, the energy consumption correlation characteristics, and the environmental coupling factor are fused into a characteristic coupling feature set of the organic waste gas.

[0007] In a preferred embodiment, the step of performing feedforward situational analysis on the process conditions of the organic waste gas during the separation process based on the characteristic coupling feature set to obtain the optimal energy consumption separation operation parameters of the organic waste gas during the separation process includes: The distribution pattern of volatile solvent concentration and fluctuation data in the characteristic coupling feature set is analyzed to obtain the dynamic distribution characteristics of the organic waste gas concentration. Based on the dynamic distribution characteristics of the concentration, the temperature, pressure and flow rate conditions of the organic waste gas during the separation process are analyzed to obtain the energy consumption sensitive parameters of the organic waste gas during the separation process. The energy-sensitive parameters are optimized and arranged by feedforward to obtain the optimal operating parameter sequence for the organic waste gas during the separation process; Based on the optimized operating parameter sequence, the separation energy consumption and recovery efficiency of the organic waste gas are synergistically balanced to obtain the optimal energy consumption separation operating parameters for the organic waste gas.

[0008] In a preferred embodiment, the energy consumption sensitive parameter is calculated using the following formula: ; In the formula, The optimal energy consumption separation operation parameters are... Let be the energy consumption sensitive parameter vector of the organic waste gas during the separation process. This serves as a unified quantization carrier for the sub-features within the aforementioned feature coupling feature set. Let be the energy consumption function of the organic waste gas during the separation process. Let be a function of the separation efficiency of volatile solvents in the organic waste gas. Let be the partial derivative of the organic waste gas separation efficiency with respect to the energy consumption sensitive parameter. The preset separation efficiency sensitive threshold, The optimization operator for minimizing the independent variable of the energy consumption sensitive parameter. This refers to the constraint operator in the process of synergistically balancing separation energy consumption and recovery efficiency of the organic waste gas.

[0009] In a preferred embodiment, the step of dynamically constraining and optimizing the process conditions of the organic waste gas during the separation process based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set to obtain the Pareto optimal separation scheme for the organic waste gas includes: Based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set, the process conditions of the organic waste gas in the separation process are dynamically constrained to obtain the process constraint conditions of the organic waste gas. Within the range defined by the process constraints, the separation efficiency and energy consumption of the volatile solvent components in the organic waste gas are balanced by multiple objectives to obtain the feasible domain of the process parameters of the organic waste gas. Pareto analysis was performed on the separation efficiency and process energy consumption target that are mutually constrained in the feasible region of the process parameters to obtain the Pareto optimal solution of the organic waste gas. Based on the real-time operating conditions and separation efficiency dynamic factors of the waste gas recovery in the characteristic coupling feature set, the optimal solution for the operating conditions of the organic waste gas is selected from the Pareto optimal solution set. The optimal solution for adapting the operating conditions is decompiled into a Pareto optimal separation scheme for the organic waste gas.

[0010] In a preferred embodiment, the robust selection of the Pareto optimal separation scheme based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, and the arrangement of the selection results into the optimal separation control strategy for the organic waste gas, includes: The separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set are extracted to obtain the polymerization factor of the organic waste gas. Based on the volatility information in the aggregation factor, the robustness of the Pareto optimal separation scheme is evaluated in multiple scenarios to obtain a robust evaluation set of the Pareto optimal separation scheme. Based on the robust evaluation set, the energy consumption adaptability and recovery efficiency threshold of the Pareto optimal separation scheme are selected in two dimensions to obtain the optimal separation scheme for the organic waste gas. The optimal separation scheme is programmed into the optimal separation and control strategy for the organic waste gas.

[0011] In a preferred embodiment, the step of performing digital twin co-inference on the optimal separation control strategy and the characteristic coupling feature set, and encoding the inference result into the waste gas separation control command for the organic waste gas, includes: Heterogeneous data aggregation is performed on the characteristic coupling feature set and the optimal separation control strategy to obtain the fused data base of the organic waste gas; Based on the fused data base, the low-energy separation process of the organic waste gas is dynamically and virtually reconstructed to obtain a virtual twin scene of the organic waste gas. In the virtual twin scenario, the recovery process of the organic waste gas is subjected to co-simulation analysis to obtain the dynamic evolution data of the organic waste gas during the separation process; Based on the dynamic evolution data, the low-energy recovery intention in the optimal separation control strategy is prioritized and analyzed to obtain the optimal operating parameter sequence for the organic waste gas. The optimal operating parameter sequence is compiled and packaged to obtain the waste gas separation control command for the organic waste gas.

[0012] In a preferred embodiment, the co-simulation analysis of the organic waste gas recovery process in the virtual twin scenario to obtain dynamic evolution data of the organic waste gas during the separation process includes: In the virtual twin scenario, the concentration distribution characteristics of volatile solvents in the organic waste gas and the energy transfer characteristics of the separation process are analyzed to obtain the simulation feature set of the organic waste gas; Based on the simulation feature set, the separation behavior of the organic waste gas under low energy consumption constraints is simulated to obtain the separation dynamic trajectory of the organic waste gas. Based on the separation dynamic trajectory, the energy consumption change and solvent recovery efficiency of the organic waste gas during the recovery process are synergistically characterized and analyzed to obtain the energy efficiency evolution data of the organic waste gas recovery. The dynamic evolution data of the organic waste gas is obtained by dynamically fusing and analyzing the dynamic trajectory of the separation behavior with the energy recovery efficiency evolution data.

[0013] In a preferred embodiment, the step of optimizing the generation strategy of the waste gas separation control command based on the energy efficiency verification index and the quality index of the recovered products obtained after the waste gas separation control command is executed, to obtain a low-energy-consumption separation command generation strategy for the organic waste gas, includes: The waste gas separation control command is executed, and the energy efficiency of the organic waste gas separation process is tracked in real time to obtain the separation energy consumption and recovery efficiency data of the organic waste gas during the separation process. The separation energy consumption and recovery efficiency data are analyzed by efficiency fusion to obtain the energy efficiency verification index for the recovery of organic waste gas; The recovered products of the organic waste gas are tested and analyzed to obtain the quality indicators of the recovered products of the organic waste gas. Based on the energy efficiency verification index and the quality index of the recovered products, the generation strategy of the waste gas separation control command is comprehensively analyzed to obtain the efficiency evaluation conclusion of the generation strategy. Based on the performance evaluation results, the generation strategy for the waste gas separation control command is dynamically optimized through feedback to obtain a low-energy-consumption separation command generation strategy for the organic waste gas.

[0014] To address the above problems, the present invention also provides a low-energy-consumption recovery system for organic waste gas, the system comprising: The multi-source coupling analysis module is used to perform multi-source coupling analysis on the concentration of volatile solvent components, concentration fluctuation data, solvent recovery energy consumption correlation factor and environmental temperature and humidity data of organic waste gas, so as to obtain the characteristic coupling feature set of the organic waste gas. The feedforward situation analysis module is used to perform feedforward situation analysis on the process conditions of the organic waste gas in the separation process based on the characteristic coupling feature set, so as to obtain the optimal energy consumption separation operation parameters of the organic waste gas in the separation process. The dynamic constraint optimization module is used to dynamically constrain and optimize the process conditions of the organic waste gas during the separation process based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set, so as to obtain the Pareto optimal separation scheme of the organic waste gas. The robustness selection module is used to robustly select the Pareto optimal separation scheme based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, and to compile the selection results into the optimal separation control strategy for the organic waste gas. The collaborative simulation module is used to perform digital twin collaborative simulation of the optimal separation control strategy and the characteristic coupling feature set, and encode the simulation results into the waste gas separation control command of the organic waste gas; The feedback optimization module is used to optimize the generation strategy of the waste gas separation control command based on the energy efficiency verification index and the quality index of the recovered product obtained after the waste gas separation control command is run, so as to obtain a low-energy-consumption separation command generation strategy for the organic waste gas.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention overcomes the problems of isolated data and one-sided analysis in existing technologies by performing integrated correlation analysis on the concentration of volatile solvent components, concentration fluctuations, energy consumption correlation factors, and environmental temperature and humidity, forming a characteristic coupling feature set. This feature set integrates accurate concentration data, fluctuation pattern characteristics, energy consumption correlation characteristics, and environmental coupling factors, providing a comprehensive and interconnected data foundation for subsequent process optimization, thereby more accurately reflecting the intrinsic relationship between waste gas characteristics and energy consumption.

[0016] 2. This invention utilizes feedforward situational analysis based on characteristic coupling feature sets, combined with Pareto optimality analysis and digital twin collaborative inference, to achieve multi-objective dynamic optimization of separation efficiency and energy consumption under process constraints. This method, through sensitive factor extraction, parameter sequence arrangement, and robustness selection, ensures that process parameters maintain optimal energy efficiency balance under fluctuating operating conditions, improving the system's adaptability and stability, thereby continuously achieving low-energy, high-efficiency separation in dynamic environments. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a low-energy-consumption method for recovering organic waste gas according to an embodiment of the present invention. Figure 2 A functional block diagram of a low-energy-consumption organic waste gas recovery system provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a low-energy-consumption method for recovering organic waste gas. The executing entity of this low-energy-consumption method for recovering organic waste gas includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the low-energy-consumption method for recovering organic waste gas can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a schematic flow chart of a low-energy-consumption method for recovering organic waste gas according to an embodiment of the present invention. In this embodiment, the low-energy-consumption method for recovering organic waste gas includes: S1. Perform multi-source coupling analysis on the concentration of volatile solvent components, concentration fluctuation data, solvent recovery energy consumption correlation factor, and environmental temperature and humidity data of organic waste gas to obtain the characteristic coupling feature set of the organic waste gas. In this embodiment of the invention, the multi-source coupling analysis of the concentration of volatile solvent components, concentration fluctuation data, solvent recovery energy consumption correlation factor, and environmental temperature and humidity data of the organic waste gas to obtain the characteristic coupling feature set of the organic waste gas includes: Concentration analysis was performed on the volatile solvent components of the organic waste gas to obtain accurate concentration data of the organic waste gas. Pattern recognition is performed on the concentration fluctuation data of the organic waste gas to obtain the fluctuation pattern characteristics of the organic waste gas. Energy consumption correlation mining was performed on the solvent recovery energy consumption correlation factors of the organic waste gas to obtain the energy consumption correlation characteristics of the organic waste gas. Environmental parameters are coupled to the ambient temperature and humidity data of the organic waste gas to obtain the environmental coupling factor of the organic waste gas. The precise concentration data, the fluctuation pattern characteristics, the energy consumption correlation characteristics, and the environmental coupling factor are fused into a characteristic coupling feature set of the organic waste gas.

[0021] Raw data on the concentration of volatile solvent components in organic waste gas was collected. The collected raw concentration data was purified using professional testing equipment to remove irrelevant information such as equipment errors and environmental interference. The data was then calibrated through repeated testing to obtain accurate concentration data that accurately reflects the actual content of each volatile solvent in the organic waste gas. This data provides the core concentration basis information for subsequent analyses.

[0022] Based on the obtained accurate concentration data, the concentration changes of volatile solvents are continuously monitored over a period of time to form complete concentration fluctuation data. These fluctuation data are systematically sorted out to observe the changes in concentration, such as rising, falling, and remaining stable in different time periods. Clear change types such as continuous rise, periodic fluctuation, and random fluctuation are identified. Finally, fluctuation pattern characteristics that can clearly show the trend of volatile solvent concentration changes are obtained, which provides support for understanding the dynamic change law of concentration.

[0023] This study collects various energy consumption data related to solvent recovery during the treatment of organic waste gas, including energy consumption for heating and energy consumption for power equipment operation. Combined with existing precise concentration data and fluctuation patterns, it analyzes the intrinsic relationship between these energy consumption data and the concentration and fluctuation of volatile solvents. The study identifies which factors directly lead to increased or decreased energy consumption, and the strength of the impact of different factors on energy consumption. Ultimately, it obtains energy consumption correlation characteristics that reflect the relationship between solvent recovery energy consumption and related factors, providing an energy consumption reference for the formulation of low-energy recovery strategies.

[0024] Real-time temperature and humidity data of the environment where organic waste gas is located are collected. Combined with the previously obtained accurate concentration data, fluctuation pattern characteristics, and energy consumption correlation characteristics, the temperature and humidity data are integrated and analyzed with these acquired characteristic data to clarify the comprehensive impact of temperature and humidity changes on the stability of volatile solvent concentration and energy consumption. Finally, an environmental coupling factor that can fully reflect the interaction between ambient temperature and humidity and relevant data in the waste gas recovery and separation process is obtained, providing an environmental reference for process optimization.

[0025] The previously acquired precise concentration data, fluctuation pattern characteristics, energy consumption correlation characteristics, and environmental coupling factors are systematically integrated to ensure that the information is complementary, logically consistent, and without conflicts or omissions. This forms a comprehensive set covering the concentration of volatile solvents in organic waste gas, concentration fluctuation patterns, energy consumption correlation information, and environmental impact factors. Ultimately, a characteristic coupling feature set of organic waste gas is obtained, providing complete and comprehensive basic data support for a series of optimization efforts for low-energy separation and recovery processes of organic waste gas.

[0026] The beneficial effects include ensuring accurate and reliable volatile solvent concentration data, laying the foundation for subsequent analysis and process optimization, guaranteeing the scientific nature of low-energy recovery and separation, clarifying the variation law of volatile solvent concentration, facilitating targeted adjustment of process parameters, avoiding energy waste, assisting in low-energy recovery, clarifying the relationship between energy consumption and related factors, providing a basis for low-energy strategy formulation, reducing energy consumption in waste gas recovery and separation, considering the comprehensive influence of temperature and humidity, improving the adaptability and stability of low-energy processes, optimizing process comprehensiveness, integrating various key information to form a complete dataset, and providing comprehensive and systematic data support for the optimization of low-energy separation processes.

[0027] S2. Based on the aforementioned characteristic coupling feature set, a feedforward situation analysis is performed on the process conditions of the organic waste gas during the separation process to obtain the optimal energy consumption separation operation parameters of the organic waste gas during the separation process. In this embodiment of the invention, the step of performing feedforward situational analysis on the process conditions of the organic waste gas during the separation process based on the characteristic coupling feature set to obtain the optimal energy consumption separation operation parameters of the organic waste gas during the separation process includes: The distribution pattern of volatile solvent concentration and fluctuation data in the characteristic coupling feature set is analyzed to obtain the dynamic distribution characteristics of the organic waste gas concentration. Based on the dynamic distribution characteristics of the concentration, the temperature, pressure and flow rate conditions of the organic waste gas during the separation process are analyzed to obtain the energy consumption sensitive parameters of the organic waste gas during the separation process. The energy-sensitive parameters are optimized and arranged by feedforward to obtain the optimal operating parameter sequence for the organic waste gas during the separation process; Based on the optimized operating parameter sequence, the separation energy consumption and recovery efficiency of the organic waste gas are synergistically balanced to obtain the optimal energy consumption separation operating parameters for the organic waste gas.

[0028] The calculation formula for the energy consumption sensitive parameter is as follows: ; In the formula, The optimal energy consumption separation operation parameters are... Let be the energy consumption sensitive parameter vector of the organic waste gas during the separation process. This serves as a unified quantization carrier for the sub-features within the aforementioned feature coupling feature set. Let be the energy consumption function of the organic waste gas during the separation process. Let be a function of the separation efficiency of volatile solvents in the organic waste gas. Let be the partial derivative of the organic waste gas separation efficiency with respect to the energy consumption sensitive parameter. The preset separation efficiency sensitive threshold, The optimization operator for minimizing the independent variable of the energy consumption sensitive parameter. This refers to the constraint operator in the process of synergistically balancing separation energy consumption and recovery efficiency of the organic waste gas.

[0029] When analyzing the distribution patterns of volatile solvent concentration and fluctuation data in the characteristic coupling feature set, it is necessary to use the accurate concentration data and fluctuation pattern characteristics obtained from multi-source coupling analysis as a basis, continuously track the real-time changes in volatile solvent concentration in organic waste gas, and record the time intervals and amplitudes of concentration fluctuations. The collected information should be systematically sorted out to find the variation patterns of concentration in different time periods and under different environmental conditions. For example, the specific time period of concentration peak, the frequency of fluctuation, and the magnitude of fluctuation should be identified. The final dynamic distribution feature of concentration is a comprehensive presentation of these patterns, clearly reflecting the specific state of volatile solvent concentration changes with time and environmental factors.

[0030] Based on the obtained dynamic distribution characteristics of concentration, we conducted analysis of sensitive factors related to temperature, pressure, and flow rate during the separation of organic waste gas. We adjusted the temperature, pressure, and flow rate during the separation process, changing only one condition at a time while keeping the other two constant. We observed and recorded changes in the separation effect of volatile solvents and energy consumption in real time. For example, when the temperature increased, we recorded whether the separation efficiency improved and whether energy consumption increased; the same procedure was followed when the temperature decreased. The same procedure was applied to pressure and flow rate. By comparing the fluctuations in energy consumption and separation effect under different conditions, we determined the significance of each condition's impact on energy consumption. The specific value ranges of temperature, pressure, and flow rate that had the most significant impact on energy consumption were identified as energy-sensitive parameters.

[0031] Based on the established energy-sensitive parameters and combined with the concentration change trend of volatile solvents in the dynamic distribution characteristics of concentration, feedforward optimization is performed. The adjustment sequence and magnitude of the energy-sensitive parameters are planned in advance. For example, when the concentration is about to rise, the flow rate is adjusted to a suitable value within the range of the energy-sensitive parameters first, then the pressure is adjusted, and finally the temperature is adjusted, to ensure that the temperature, pressure, and flow rate are within the optimal range of the energy-sensitive parameters at each stage of concentration change. These temperature, pressure, and flow rate adjustment schemes planned according to the time sequence and concentration change trend are arranged sequentially to form an optimized operating parameter sequence.

[0032] Based on the optimized operating parameter sequence, each adjustment scheme in the sequence is executed one by one. During the execution, the separation energy consumption data and volatile solvent recovery efficiency data corresponding to each scheme are recorded in real time. The specific values ​​of energy consumption and efficiency under different schemes are compared. According to the basic requirements of waste gas recovery, the adjustment scheme with the lowest separation energy consumption under the premise that the recovery efficiency meets the specified standards is selected. The specific values ​​of temperature, pressure and flow rate corresponding to this scheme are the optimal energy consumption separation operating parameters, which not only ensure that volatile solvents can be effectively separated and recovered, but also achieve the goal of low energy consumption in the separation process.

[0033] In the formula, These are the optimal energy consumption separation operation parameters to be determined, used for adjusting the process conditions of organic waste gas separation, to meet the dual requirements of low energy consumption and high recovery efficiency of volatile solvents. They represent a vector of energy-sensitive parameters. The final result after optimization and selection based on constraints This is a vector of energy-sensitive parameters for the separation of organic waste gas. It is the result of analyzing the temperature, pressure, and flow rate conditions during the separation process based on the dynamic distribution characteristics of the concentration. It includes key process parameters affecting the energy consumption of waste gas separation and their specific value ranges, and is the core variable in the formula calculation. The unified quantitative carrier of sub-features in the characteristic coupling feature set is a multi-source coupling analysis step. This step performs coupling analysis on volatile solvent component concentration, concentration fluctuation data, solvent recovery energy consumption correlation factors, and environmental temperature and humidity data, ultimately integrating accurate concentration data, fluctuation pattern characteristics, energy consumption correlation characteristics, and environmental coupling factors to form a unified quantitative carrier of sub-features. This provides the basic data support for the subsequent calculation of the two functions. It is the partial derivative of the organic waste gas separation efficiency with respect to energy consumption sensitive parameters, which is calculated by the separation efficiency function. Follow The rate of change of each parameter is derived, reflecting the degree of influence of changes in a single energy-sensitive parameter on the separation efficiency of volatile solvents. This is a preset separation efficiency sensitivity threshold, set in advance according to industry standards and actual application needs for organic waste gas recovery. It is used to define the minimum acceptable range for volatile solvent separation efficiency, ensuring that waste gas recovery and separation effects meet standards. It is an optimization operator for minimizing the independent variable of energy-sensitive parameters, and its function is to... Within all possible values, select the energy consumption function that enables it. The independent variable that achieves the minimum value The core objective is to achieve low energy consumption in the waste gas separation process. It is a constraint operator in the process of balancing separation energy consumption and recovery efficiency of organic waste gas. It is used to limit the optimization range and ensure that the screened low energy consumption parameters simultaneously meet the requirements of volatile solvent separation efficiency, so as to avoid pursuing low energy consumption while ignoring the recovery effect.

[0034] The energy consumption function of the organic waste gas separation process The calculation formula is: ; In the formula, It is a unified quantization carrier of sub-features in the feature set of characteristic coupling. The baseline energy consumption value corresponding to the energy consumption correlation characteristics is the result of energy consumption correlation mining of solvent recovery energy consumption correlation factors in multi-source coupling analysis. It is the basic energy consumption reference value of the separation process determined based on historical waste gas recovery data, reflecting the basic energy consumption level when there is no environmental interference and the process parameters are stable. It is a unified quantitative carrier The environmental energy consumption correction coefficient corresponding to the environmental coupling factor is the result of coupling environmental parameters of environmental temperature and humidity data. It is used to quantify the impact of temperature and humidity on the separated energy consumption. For example, a correction coefficient greater than 1 under high temperature conditions indicates an increase in energy consumption, while a correction coefficient less than 1 under low temperature conditions indicates a decrease in energy consumption. , , It is an energy-sensitive parameter vector The temperature, pressure, and flow rate parameters in the formula are consistent with those in the core formula. Consistency of source is a core process parameter affecting the energy consumption of waste gas separation, directly determining the degree of energy loss per unit time. , , It is the unit energy consumption loss coefficient corresponding to temperature, pressure, and flow rate, based on industry-standard data for organic waste gas separation processes, combined with... The defined value ranges for energy-sensitive parameters represent the increase in energy consumption corresponding to changes in unit temperature, unit pressure, and unit flow rate, ensuring that the coefficients are consistent with... Precise matching of parameter attributes ensures the accuracy of energy consumption calculation.

[0035] Separation efficiency function of volatile solvents in organic waste gas The calculation formula is: ; It is a unified quantization carrier of sub-features in the feature set of characteristic coupling. The theoretical upper limit of separation corresponding to the precise data of medium concentration is the concentration analysis result of the volatile solvent component. It is the maximum separable proportion determined based on the current concentration of volatile solvent in the waste gas, reflecting the theoretical limit of volatile solvent recovery at this concentration. It is a unified quantitative carrier The efficiency attenuation coefficient corresponding to the fluctuation pattern characteristics is the result of pattern identification of concentration fluctuation data. The more violent the concentration fluctuation, the larger the coefficient is. It is used to quantify the degree of weakening of the separation efficiency of volatile solvents by fluctuations. For example, when the concentration fluctuates frequently and significantly, the attenuation coefficient increases and the actual value of separation efficiency decreases. , , Consistent with the energy consumption function, the energy-sensitive parameter vector derived from the disjunction The value of is a key process parameter that affects the separation efficiency of volatile solvents. It directly determines the separation rate and thoroughness of solvent molecules and waste gas. , , It is the separation efficiency improvement coefficient corresponding to temperature, pressure, and flow rate, combined with The corresponding energy-sensitive parameters were calibrated using experimental data, representing the improvement in separation efficiency corresponding to changes in unit temperature, unit pressure, and unit flow rate, respectively, ensuring that the coefficients are consistent with... The sensitive interval is adapted to ensure the rationality of efficiency calculation.

[0036] The overall computation first obtains a unified quantization carrier of sub-features in the characteristic coupling feature set through multi-source coupling analysis. and extract from it , , , Based on the basic data, energy consumption sensitive parameter vectors are then extracted based on the dynamic distribution characteristics of concentration. ,clear , , and corresponding , , , , , Coefficients. These data are then substituted into the energy consumption function. and separation efficiency function Calculate different The separation energy consumption and volatile solvent separation efficiency were calculated for each given value, followed by the partial derivative of the separation efficiency with respect to the energy consumption-sensitive parameter. Filter out those that meet the requirements That is, the separation efficiency meets the standard. The range of values. Finally, within this constraint range, through... The operator performs optimization to find the one that minimizes separation energy consumption. The selected value is the optimal energy consumption separation operation parameter. Ultimately, this achieves a synergistic unity of "efficient recovery of volatile solvents" and "low energy consumption" in the organic waste gas separation process, effectively improving the overall efficiency of waste gas recovery and separation.

[0037] The beneficial effects include: accurately grasping the dynamic distribution characteristics of volatile solvent concentrations, providing accurate basis for low-energy separation, assisting in the precise control of waste gas recovery and separation, identifying key process conditions affecting energy consumption, avoiding energy waste caused by blind adjustments, improving the pertinence of waste gas separation parameter adjustments, planning process parameter adjustment schemes in advance, adapting to dynamic concentration changes, ensuring continuous high efficiency and low consumption in waste gas recovery and separation, improving stability, balancing volatile solvent recovery efficiency and low energy consumption, achieving the organic unity of the two, and improving the overall efficiency of low-energy recovery of organic waste gas.

[0038] S3. Based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set, the process conditions of the organic waste gas in the separation process are dynamically constrained and optimized to obtain the Pareto optimal separation scheme of the organic waste gas. In this embodiment of the invention, the dynamic constraint optimization of the process conditions of the organic waste gas during the separation process based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set to obtain the Pareto optimal separation scheme for the organic waste gas includes: Based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set, the process conditions of the organic waste gas in the separation process are dynamically constrained to obtain the process constraint conditions of the organic waste gas. Within the range defined by the process constraints, the separation efficiency and energy consumption of the volatile solvent components in the organic waste gas are balanced by multiple objectives to obtain the feasible domain of the process parameters of the organic waste gas. Pareto analysis was performed on the separation efficiency and process energy consumption target that are mutually constrained in the feasible region of the process parameters to obtain the Pareto optimal solution of the organic waste gas. Based on the real-time operating conditions and separation efficiency dynamic factors of the waste gas recovery in the characteristic coupling feature set, the optimal solution for the operating conditions of the organic waste gas is selected from the Pareto optimal solution set. The optimal solution for adapting the operating conditions is decompiled into a Pareto optimal separation scheme for the organic waste gas.

[0039] The efficiency is dynamically constrained based on the optimal energy-consuming separation operating parameters and the characteristic coupling feature set. The optimal energy-consuming separation operating parameters are core operating parameters obtained through feedforward situational analysis, balancing separation efficiency and energy consumption. The characteristic coupling feature set includes accurate concentration data of organic waste gas, fluctuation pattern characteristics, energy consumption correlation characteristics, and environmental coupling factors. First, information directly related to the waste gas separation process is extracted from the characteristic coupling feature set, such as the dynamic change law of volatile solvent concentration and the energy consumption correlation under different environmental conditions. Then, combined with the benchmark standards such as temperature, pressure, and flow rate clearly defined in the optimal energy-consuming separation operating parameters, each process condition in the organic waste gas separation process is defined one by one, clarifying the allowable variation range of each process condition. Process parameter options that would cause energy consumption to exceed limits or separation efficiency to drop significantly are eliminated. The final process constraints are a set of clear process parameter control standards, clearly specifying the upper and lower limits and allowable fluctuation ranges of key process conditions such as temperature, pressure, and flow rate, ensuring that subsequent process adjustments always revolve around the goal of low-energy recovery of volatile solvents.

[0040] Multi-objective equilibrium work is conducted within the defined scope of process constraints. These constraints provide clear boundaries for subsequent parameter screening, avoiding ineffective analyses that exceed reasonable limits. For the two core objectives of separation efficiency and energy consumption of volatile solvents in organic waste gas, each possible combination of process parameters is systematically reviewed. For each set of process parameters, the corresponding volatile solvent separation efficiency and energy consumption values ​​are recorded. It is then determined whether the set of parameters simultaneously satisfies the conditions that the separation efficiency is not lower than the basic requirement and the energy consumption does not exceed the upper limit of the constraints. All process parameter combinations that meet these conditions are collected. This set of screened and feasible process parameter combinations constitutes the feasible region of process parameters, providing a comprehensive and accurate parameter range for further screening of the optimal solution.

[0041] Pareto analysis was performed on the separation efficiency and energy consumption targets within the feasible region of the process parameters. All combinations of process parameters within the feasible region met the basic separation and energy consumption requirements, but differences existed in separation efficiency and energy consumption between different combinations, and these two factors were mutually restrictive. Improving separation efficiency often required increasing energy consumption, while reducing energy consumption might affect separation efficiency. Each combination of process parameters within the feasible region was analyzed individually to determine if another combination could reduce energy consumption without decreasing the current separation efficiency, or improve separation efficiency without increasing the current energy consumption. If no such combination existed, then that combination was a Pareto optimal solution. All such combinations were collected and compiled to form the Pareto optimal solution, where each solution achieved a relatively optimal balance between separation efficiency and energy consumption.

[0042] Based on the real-time operating conditions of waste gas recovery and the dynamic factors of separation efficiency in the characteristic coupling feature set, a screening process is performed. Real-time operating conditions refer to the actual operating status of waste gas treatment, including actual conditions such as waste gas emission volume, real-time inlet gas concentration, and equipment operating load. Dynamic factors of separation efficiency are key information reflecting the trend of efficiency changes during the separation process, demonstrating the stability and sustainability of separation efficiency under different process parameters. The process parameters corresponding to each solution in the Pareto optimal solution set are matched one-to-one with the real-time operating conditions of waste gas recovery to check whether the set of parameters can adapt to the current actual operating conditions such as waste gas emission volume and inlet gas concentration. Simultaneously, combined with the dynamic factors of separation efficiency, it is determined whether the separation efficiency can remain stable and meet the expected standards after the application of the set of parameters. Solutions that cannot adapt to the real-time operating conditions or cannot guarantee stable separation efficiency are eliminated. The final combination of process parameters that is highly adapted to the real-time operating conditions and can ensure separation efficiency is the optimal solution for operating condition adaptation, making the optimal solution more closely match the actual waste gas recovery operation scenario.

[0043] The optimal solution for adapting to the operating conditions is compiled. This optimal solution represents the combination of process parameters that best fits the actual operating conditions, obtained after multiple rounds of screening. The process parameters, such as temperature, pressure, and flow rate, contained in this optimal solution are clearly quantified and defined. The specific values, adjustment timings, control precision requirements, and corresponding operating procedures for each parameter are meticulously analyzed. These scattered parameter information and operational requirements are integrated and transformed into standardized, clear, and directly guiding operating instructions and process specifications for waste gas separation equipment. The resulting Pareto optimal separation scheme is a complete and highly practical waste gas separation process execution plan that can be directly applied to the low-energy recovery and separation process of organic waste gas, ensuring efficient recovery of volatile solvents and precise energy consumption control.

[0044] The beneficial effects include ensuring the scientific and targeted nature of process constraints, laying the foundation for low-energy and efficient recovery of volatile solvents from organic waste gas, ensuring that process adjustments revolve around core objectives, forming a precise and comprehensive feasible domain of process parameters, eliminating invalid parameter combinations, providing a high-quality range for subsequent screening, focusing on the goals of volatile solvent recovery and low energy consumption, achieving a relatively optimal balance between separation efficiency and process energy consumption, providing a high-quality set of alternatives for subsequent screening, improving the rationality and feasibility of low-energy waste gas recovery, allowing the optimal solution to accurately adapt to actual operating conditions, avoiding the disconnect between theory and practice, ensuring stable volatile solvent recovery effects, improving the operability of waste gas recovery and separation, forming a standardized and operable separation scheme, promoting the implementation of low-energy recovery strategies, improving the standardization and efficiency of organic waste gas separation, and achieving efficient recovery of volatile solvents and effective control of energy consumption.

[0045] S4. Based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, the Pareto optimal separation scheme is robustly selected, and the selection results are compiled into the optimal separation control strategy for the organic waste gas. In this embodiment of the invention, the robust selection of the Pareto optimal separation scheme based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, and the arrangement of the selection results into the optimal separation control strategy for the organic waste gas, includes: The separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set are extracted to obtain the polymerization factor of the organic waste gas. Based on the volatility information in the aggregation factor, the robustness of the Pareto optimal separation scheme is evaluated in multiple scenarios to obtain a robust evaluation set of the Pareto optimal separation scheme. Based on the robust evaluation set, the energy consumption adaptability and recovery efficiency threshold of the Pareto optimal separation scheme are selected in two dimensions to obtain the optimal separation scheme for the organic waste gas. The optimal separation scheme is programmed into the optimal separation and control strategy for the organic waste gas.

[0046] When extracting features from the dynamic factor of separation efficiency and the stable factor of energy consumption in the characteristic coupling feature set, it is first clarified that the dynamic factor of separation efficiency reflects the real-time changes in the separation effect during the waste gas recovery and separation process, and the stable factor of energy consumption reflects whether the energy consumption during the waste gas recovery and separation process is stable. The core content of these two factors is extracted from the characteristic coupling feature set, including the changes in separation efficiency and the specific manifestations of efficiency improvement or decline in the dynamic factor of separation efficiency, and key information such as the range of energy consumption fluctuations and the duration of stability in the stable factor of energy consumption. Then, these extracted core information are integrated, and redundant content is removed to form a comprehensive information set that can fully cover the dynamic changes in separation efficiency and the stable state of energy consumption. This comprehensive information set is the aggregation factor, which provides a unified and comprehensive reference for subsequent multi-scenario robustness assessment.

[0047] When evaluating the robustness of Pareto optimal separation schemes across multiple scenarios based on the volatility information in the aggregation factor, the volatility information of the aggregation factor includes specific details such as the frequency and amplitude of separation efficiency fluctuations, as well as the range and duration of energy consumption fluctuations. First, various real-world operating scenarios are set up, covering common actual operating conditions such as different volatile solvent concentrations, different ambient temperatures and humidity levels, and different waste gas treatment volumes. Under each scenario, the waste gas recovery and separation process is simulated according to the requirements of the Pareto optimal separation scheme. The execution status of the scheme in each scenario is recorded in real time, including whether the separation efficiency remains stable, whether energy consumption is within a reasonable range, and whether the scheme can quickly adapt to changes in the scenario. Then, the execution status under each scenario is objectively evaluated, and the evaluation results are categorized and organized by scenario to form a robust evaluation set containing the robustness performance of the scheme under each scenario. The robust evaluation set clearly presents the adaptability and stable performance of the Pareto optimal separation scheme under different real-world operating conditions.

[0048] When selecting Pareto optimal separation schemes based on a robust evaluation set using two dimensions, energy consumption adaptability refers to the degree to which the energy consumption of the scheme matches the energy consumption budget and energy supply capacity in actual production. The recovery efficiency threshold refers to the minimum recovery efficiency standard that the scheme must achieve. First, energy consumption data and recovery efficiency data of each Pareto optimal separation scheme under different scenarios are extracted from the robust evaluation set. Each scheme's energy consumption is checked one by one to see if it does not exceed the preset energy consumption budget and whether it matches the actual energy supply capacity. Schemes that meet the energy consumption adaptability requirements are selected. Then, from these schemes that meet the energy consumption adaptability requirements, schemes whose recovery efficiency reaches or exceeds the preset recovery efficiency threshold are further selected. If there are multiple schemes that meet both dimensions, the scheme that can stably meet the requirements in more scenarios is selected. The scheme determined after double screening is the optimal separation scheme. The optimal separation scheme can stably achieve efficient recovery of volatile solvents while ensuring low energy consumption.

[0049] When formulating the optimal separation scheme into the optimal separation control strategy, first comprehensively review all the core elements of the optimal separation scheme, including specific operational requirements such as temperature, pressure, and flow rate that need to be controlled during the waste gas separation process, the execution sequence of each operation, the execution time nodes, and the basis for adjustments based on changes in operating conditions. Then, according to the operating logic and control specifications of the waste gas recovery and separation equipment, organize these reviewed contents into a clear, logically coherent sequence of operation instructions that can be directly recognized and executed by the equipment. Clarify the specific execution content, triggering conditions, and expected effects of each operation instruction, and finally form the optimal separation control strategy. The optimal separation control strategy can directly guide the actual operation of the organic waste gas recovery and separation equipment, ensuring that the equipment efficiently completes the separation and recovery of volatile solvents with low energy consumption.

[0050] The beneficial effects include integrating key performance and energy consumption information of waste gas recovery and separation, avoiding one-sided analysis, providing a comprehensive reference for evaluation, ensuring the accuracy of low-energy recovery-related assessments, assisting in process optimization, verifying the adaptability and stability of Pareto optimal separation schemes under multiple operating conditions, avoiding limitations of single scenarios, reducing actual operational risks, supporting low-energy and high-efficiency recovery, accurately balancing low-energy consumption and volatile solvent recovery performance, avoiding problems of excessive energy consumption or insufficient recovery efficiency, improving the practicality and economy of the process, transforming the optimal scheme into executable instructions for equipment, reducing human operation errors, and improving the operability and operational reliability of low-energy recovery of organic waste gas.

[0051] S5. Perform digital twin co-induction on the optimal separation control strategy and the characteristic coupling feature set, and encode the induction result into the waste gas separation control command of the organic waste gas; In this embodiment of the invention, the step of performing digital twin co-inference on the optimal separation control strategy and the characteristic coupling feature set, and encoding the inference result into the waste gas separation control command for the organic waste gas, includes: Heterogeneous data aggregation is performed on the characteristic coupling feature set and the optimal separation control strategy to obtain the fused data base of the organic waste gas; Based on the fused data base, the low-energy separation process of the organic waste gas is dynamically and virtually reconstructed to obtain a virtual twin scene of the organic waste gas. In the virtual twin scenario, the recovery process of the organic waste gas is subjected to co-simulation analysis to obtain the dynamic evolution data of the organic waste gas during the separation process; Based on the dynamic evolution data, the low-energy recovery intention in the optimal separation control strategy is prioritized and analyzed to obtain the optimal operating parameter sequence for the organic waste gas. The optimal operating parameter sequence is compiled and packaged to obtain the waste gas separation control command for the organic waste gas.

[0052] In the virtual twin scenario, the recovery process of the organic waste gas is subjected to co-simulation analysis to obtain dynamic evolution data of the organic waste gas during the separation process, including: In the virtual twin scenario, the concentration distribution characteristics of volatile solvents in the organic waste gas and the energy transfer characteristics of the separation process are analyzed to obtain the simulation feature set of the organic waste gas; Based on the simulation feature set, the separation behavior of the organic waste gas under low energy consumption constraints is simulated to obtain the separation dynamic trajectory of the organic waste gas. Based on the separation dynamic trajectory, the energy consumption change and solvent recovery efficiency of the organic waste gas during the recovery process are synergistically characterized and analyzed to obtain the energy efficiency evolution data of the organic waste gas recovery. The dynamic evolution data of the organic waste gas is obtained by dynamically fusing and analyzing the dynamic trajectory of the separation behavior with the energy recovery efficiency evolution data.

[0053] The characteristic coupling feature set includes precise concentration data, fluctuation pattern characteristics, energy consumption correlation characteristics, and environmental coupling factors of volatile solvents in organic waste gas. The optimal separation control strategy includes process parameter combinations and operational procedures adapted for low-energy separation of waste gas. Heterogeneous data refers to these two types of data from different sources. Integrating these data, eliminating duplicate and conflicting information, and supplementing the logical relationships between data ensures that all data related to low-energy separation of waste gas and recovery of volatile solvents is complete and logically consistent, ultimately forming a fused data base. This fused data base is a comprehensive data set covering waste gas characteristics and separation control requirements, providing comprehensive data support for subsequent virtual reconstruction.

[0054] Based on fused data, and following the actual industrial process of low-energy separation of organic waste gas, the structure and operating principle of the separation equipment are reproduced, the flow path and separation steps of volatile solvents in the equipment are replicated, and the influence of ambient temperature and humidity on the separation process is simulated. At the same time, the energy consumption law reflected by the energy consumption correlation characteristics is integrated into the virtual environment to construct a virtual twin scenario that is highly consistent with the actual waste gas separation process. This scenario can map the various states in the actual separation process in real time, providing a virtual platform for simulation analysis of low-energy separation of waste gas and recovery of volatile solvents.

[0055] In a virtual twin scenario, the changes in the content of volatile solvents in different areas of the separation equipment are tracked in real time to determine the distribution of areas with high and low volatile solvent concentrations and to clarify the trend of concentration changes over time. At the same time, the energy transfer path between the equipment, volatile solvents and the environment during the separation process is recorded, and the efficiency of energy transfer is statistically analyzed. These concentration distribution-related information and energy transfer-related information are organized and integrated to form a simulation feature set, providing an accurate basis for subsequent separation behavior simulation.

[0056] Based on the simulation feature set and strictly adhering to the low energy consumption constraint, the volatile solvent concentration distribution and energy transfer information in the simulation feature set are combined to simulate the complete movement path of the volatile solvent in the separation equipment from entry to separation and recovery. The residence time, movement speed and state change of the volatile solvent in each separation stage are recorded to form a separation dynamic trajectory. This trajectory fully presents the separation movement process of the volatile solvent under the low energy consumption constraint.

[0057] Based on the separation dynamic trajectory, the total energy consumed in the waste gas separation process is recorded stage by stage. At the same time, the mass and volume of volatile solvents successfully recovered in each stage are counted. The recovery efficiency is obtained by calculating the proportion of recovered volatile solvents to the total amount processed. The energy consumption data and recovery efficiency data of each stage are organized in chronological order to form recovery energy efficiency evolution data. This data clearly reflects the relationship between energy consumption and volatile solvent recovery efficiency in the low-energy separation process of waste gas.

[0058] By integrating the volatile solvent movement information recorded by the separation dynamic trajectory with the energy consumption and efficiency changes reflected by the recovery energy efficiency evolution data, the intrinsic relationship between the two is explored. For example, the impact of the trajectory change of the volatile solvent at a certain movement stage on energy consumption and recovery efficiency is investigated. This results in dynamic evolution data, which comprehensively covers the movement state of volatile solvents, energy consumption, and dynamic changes in volatile solvent recovery efficiency during the waste gas separation process, providing a complete basis for subsequent parameter analysis.

[0059] Supported by dynamic evolution data, the core objective of low-energy recovery in the optimal separation control strategy is clarified. It is determined which operation links and process parameters are more critical to achieving low-energy recovery of volatile solvents. These key operations and parameters are ranked according to their importance. Process parameters such as temperature, pressure, and flow rate are organized into an ordered combination according to the sequence and importance of the separation process to form the optimal operating parameter sequence. This sequence directly points to the core requirement of low-energy separation of waste gas and efficient recovery of volatile solvents.

[0060] For each process parameter and sorting logic in the optimal operating parameter sequence, it is transformed into an instruction language that the waste gas separation equipment can recognize and execute. The specific execution time, execution duration and control precision of each parameter are clarified. At the same time, these instructions are integrated and packaged according to the sequence of the separation process to ensure the integrity and executability of the instructions. Finally, the waste gas separation control instructions are formed. These instructions can directly drive the equipment to carry out low-energy separation work and ensure the efficient recovery of volatile solvents.

[0061] The beneficial effects include ensuring the integrity and consistency of the fused data base, providing comprehensive and reliable data support for low-energy separation of waste gas and recovery of volatile solvents, replicating the actual separation process with a virtual twin scenario, reducing energy consumption and equipment wear from real-world trial and error, ensuring the accuracy of simulation analysis, accurately grasping the concentration distribution and energy transfer patterns of volatile solvents, improving the relevance of low-energy separation simulation, clearly presenting the separation path of volatile solvents, providing a clear tracking object for energy consumption and recovery efficiency analysis, enhancing the relevance of analysis, monitoring the dynamics of energy consumption and volatile solvent recovery efficiency in real time, providing data support for parameter optimization, helping to reduce costs and increase efficiency, integrating key separation information, providing a complete basis for formulating the optimal operating parameter sequence, ensuring the scientific nature of the strategy, clarifying the key operations and parameter sequence of low-energy separation, improving the systematic nature of waste gas separation operations, laying the foundation for control command generation, realizing the transformation of separation strategies into executable commands for equipment, promoting the implementation of low-energy recovery, and improving the stability and low-energy effect of volatile solvent recovery.

[0062] S6. Based on the energy efficiency verification index and the quality index of the recovered product obtained after the operation of the waste gas separation control command, the generation strategy of the waste gas separation control command is optimized by feedback to obtain the low-energy separation command generation strategy of the organic waste gas. In this embodiment of the invention, the step of optimizing the generation strategy of the waste gas separation control command based on the energy efficiency verification index and the quality index of the recovered product obtained after the waste gas separation control command is executed, to obtain a low-energy-consumption separation command generation strategy for the organic waste gas, includes: The waste gas separation control command is executed, and the energy efficiency of the organic waste gas separation process is tracked in real time to obtain the separation energy consumption and recovery efficiency data of the organic waste gas during the separation process. The separation energy consumption and recovery efficiency data are analyzed by efficiency fusion to obtain the energy efficiency verification index for the recovery of organic waste gas; The recovered products of the organic waste gas are tested and analyzed to obtain the quality indicators of the recovered products of the organic waste gas. Based on the energy efficiency verification index and the quality index of the recovered products, the generation strategy of the waste gas separation control command is comprehensively analyzed to obtain the efficiency evaluation conclusion of the generation strategy. Based on the performance evaluation results, the generation strategy for the waste gas separation control command is dynamically optimized through feedback to obtain a low-energy-consumption separation command generation strategy for the organic waste gas.

[0063] The system executes waste gas separation control commands, causing the waste gas recovery and separation equipment to start and operate according to the requirements of the commands. Throughout the entire organic waste gas separation process, energy efficiency is continuously tracked in real time. Separation energy consumption refers to the total energy consumed in the process of separating volatile solvents from organic waste gas. Recovery efficiency data refers to the ratio of the amount of volatile solvents actually recovered to the total amount of volatile solvents originally contained in the organic waste gas. By recording the energy consumption during equipment operation and simultaneously calculating the total amount of volatile solvents in the organic waste gas entering the equipment per unit time and the amount of volatile solvents actually collected after separation, the separation energy consumption and recovery efficiency data of organic waste gas during the separation process are finally obtained.

[0064] The energy efficiency verification index is an indicator that comprehensively reflects the matching degree between energy consumption and volatile solvent recovery effect in the process of waste gas recovery and separation. Based on the previously obtained separation energy consumption and recovery efficiency data, the relevant information of the two is integrated and analyzed to comprehensively sort out the correlation between the amount of separation energy consumption and the level of recovery efficiency, and to clarify whether the energy consumption corresponding to the current recovery efficiency is within a reasonable range. Through such integration and sorting, the energy efficiency verification index that can accurately reflect the current energy efficiency level of organic waste gas recovery and separation is obtained.

[0065] The recovered products refer to the volatile solvents separated and recovered from organic waste gas after the execution of the waste gas separation control command. The recovered products are subjected to comprehensive testing and analysis to check the purity of the recovered products, confirm the purity of the volatile solvents and whether there are other impurities, and test the stability of the components of the recovered products to see if the components of the recovered products collected at different time periods are consistent. Based on the results of these tests and analyses, the quality indicators of the recovered products of organic waste gas are compiled.

[0066] Based on the energy efficiency verification indicators and the quality indicators of the recovered products, a comprehensive analysis of the generation strategy for waste gas separation control instructions is conducted. The energy efficiency verification indicators reflect the matching between energy consumption and recovery effect, while the quality indicators of the recovered products reflect the quality status of the recovered products. By combining these two indicators, the analysis examines whether the generation strategy can simultaneously meet the requirements of low energy consumption and efficient and high-quality recovery of volatile solvents in practical applications. It also determines whether the current generation strategy has situations where energy consumption is too high but the recovery efficiency or product quality does not meet expectations, or where the product quality meets the standards but the energy consumption exceeds the reasonable range. After systematic analysis, the effectiveness evaluation conclusion of the generation strategy is obtained.

[0067] Based on the performance evaluation results, the generation strategy for waste gas separation control commands is dynamically optimized through feedback. If the evaluation results indicate that the energy consumption is too high, adjustments are made to the relevant links in the generation strategy that affect energy consumption. If there are defects in the product quality, the content related to separation purity in the generation strategy is optimized. Through such targeted and gradual adjustments and improvements, a low-energy-consumption separation command generation strategy for organic waste gas is finally formed. This strategy can minimize the energy consumption in the organic waste gas recovery and separation process while ensuring the efficiency of volatile solvent recovery and product quality.

[0068] The beneficial effects include providing accurate and complete data on separation energy consumption and recovery efficiency, laying a solid foundation for the evaluation of waste gas separation control command generation strategies, assisting in the scientific advancement of low-energy-consumption recovery and separation of organic waste gas, forming a recovery energy efficiency verification index that reflects the matching degree between energy consumption and recovery effect, providing key basis for judging the rationality of generation strategies, assisting in the low-energy-consumption optimization of waste gas recovery and separation, clarifying the quality of volatile solvent recovery, ensuring that the recovered products meet the standards, providing quality reference for generation strategy optimization, improving the practicality of organic waste gas recovery and separation, comprehensively grasping the actual application effect of generation strategies, accurately identifying shortcomings, avoiding blind adjustments, ensuring low-energy-consumption and high-efficiency organic waste gas recovery and separation, improving generation strategies, balancing volatile solvent recovery efficiency, product quality and energy consumption, promoting the development of organic waste gas recovery and separation towards low-energy consumption, high-quality and high-efficiency, and improving the overall recovery benefits.

[0069] like Figure 2 The diagram shown is a functional block diagram of a low-energy-consumption organic waste gas recovery system provided in an embodiment of the present invention.

[0070] The low-energy-consumption organic waste gas recovery system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the low-energy-consumption organic waste gas recovery system 100 may include a multi-source coupling analysis module 101, a feedforward situation analysis module 102, a dynamic constraint optimization module 103, a robustness selection module 104, a collaborative inference module 105, and a feedback optimization module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0071] In this embodiment, the functions of each module / unit are as follows: The multi-source coupling analysis module 101 is used to perform multi-source coupling analysis on the concentration of volatile solvent components, concentration fluctuation data, solvent recovery energy consumption correlation factor and environmental temperature and humidity data of organic waste gas, so as to obtain the characteristic coupling feature set of the organic waste gas. The feedforward situation analysis module 102 is used to perform feedforward situation analysis on the process conditions of the organic waste gas in the separation process based on the characteristic coupling feature set, so as to obtain the optimal energy consumption separation operation parameters of the organic waste gas in the separation process. The dynamic constraint optimization module 103 is used to dynamically constrain and optimize the process conditions of the organic waste gas in the separation process based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set, so as to obtain the Pareto optimal separation scheme of the organic waste gas. The robustness selection module 104 is used to robustly select the Pareto optimal separation scheme based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, and to compile the selection results into the optimal separation control strategy for the organic waste gas. The collaborative simulation module 105 is used to perform digital twin collaborative simulation of the optimal separation control strategy and the characteristic coupling feature set, and encode the simulation result into the waste gas separation control command of the organic waste gas. The feedback optimization module 106 is used to optimize the generation strategy of the waste gas separation control command based on the energy efficiency verification index and the quality index of the recovered product obtained after the waste gas separation control command is run, so as to obtain a low-energy-consumption separation command generation strategy for the organic waste gas.

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0073] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0075] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0076] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A low energy consumption recovery method of organic exhaust gas, characterized by, The method comprises: S1, multi-source coupling analysis is performed on the volatile solvent component concentration, concentration fluctuation data, solvent recovery energy consumption correlation factor and environmental temperature and humidity data of the organic waste gas, to obtain a characteristic coupling feature set of the organic waste gas; S2, based on the characteristic coupling feature set, feedforward situation analysis is performed on the process conditions of the organic waste gas in the separation process, to obtain optimal energy consumption separation operation parameters of the organic waste gas in the separation process; S3, based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set, dynamic constraint optimization is performed on the process conditions of the organic waste gas in the separation process, to obtain a Pareto optimal separation scheme of the organic waste gas; S4, based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, the Pareto optimal separation scheme is selected for robustness, and the selection result is arranged as an optimal separation control strategy of the organic waste gas; S5, digital twin is cooperated with the optimal separation control strategy and the characteristic coupling feature set for deduction, and the deduction result is encoded as a waste gas separation control instruction of the organic waste gas; S6, based on the recovery energy efficiency verification index and the recovery product quality index obtained after the waste gas separation control instruction is run, the generation strategy of the waste gas separation control instruction is optimized by feedback, to obtain a low-energy consumption separation instruction generation strategy of the organic waste gas.

2. The method of claim 1, wherein the organic exhaust gas is recovered at a low energy consumption. The multi-source coupling analysis on the volatile solvent component concentration, concentration fluctuation data, solvent recovery energy consumption correlation factor and environmental temperature and humidity data of the organic waste gas, to obtain the characteristic coupling feature set of the organic waste gas, comprises: Concentration analysis is performed on the volatile solvent component concentration of the organic waste gas, to obtain concentration accurate data of the organic waste gas; Pattern recognition is performed on the concentration fluctuation data of the organic waste gas, to obtain fluctuation mode features of the organic waste gas; Energy consumption correlation mining is performed on the solvent recovery energy consumption correlation factor of the organic waste gas, to obtain energy consumption correlation characteristics of the organic waste gas; Environmental parameter coupling is performed on the environmental temperature and humidity data of the organic waste gas, to obtain environmental coupling factors of the organic waste gas; The concentration accurate data, the fluctuation mode features, the energy consumption correlation characteristics and the environmental coupling factors are fused into the characteristic coupling feature set of the organic waste gas.

3. The method of claim 1, wherein the organic exhaust gas is recovered at a low energy consumption. The feedforward situation analysis on the process conditions of the organic waste gas in the separation process based on the characteristic coupling feature set, to obtain the optimal energy consumption separation operation parameters of the organic waste gas in the separation process, comprises: Distribution law situation analysis is performed on the volatile solvent concentration and fluctuation data in the characteristic coupling feature set, to obtain concentration dynamic distribution features of the organic waste gas; Based on the concentration dynamic distribution features, sensitive factor extraction is performed on the temperature, pressure and flow rate conditions of the organic waste gas in the separation process, to obtain energy consumption sensitive parameters of the organic waste gas in the separation process; Feedforward optimization arrangement is performed on the energy consumption sensitive parameters, to obtain an optimal operation parameter sequence of the organic waste gas in the separation process; Based on the optimal operation parameter sequence, the energy consumption and recovery efficiency of the organic waste gas separation are balanced to obtain the optimal energy consumption separation operation parameter of the organic waste gas.

4. The method of claim 3, wherein the organic waste gas is recovered at a low energy consumption. The calculation formula of the energy consumption sensitive parameter is as follows: ； wherein, is the optimal energy consumption separation operation parameter, is the energy consumption sensitive parameter vector of the organic waste gas in the separation process, is the uniform quantization carrier of the sub-feature in the characteristic coupling feature set, is the energy consumption function of the organic waste gas in the separation process, is the separation efficiency function of the volatile solvent in the organic waste gas, is the partial derivative of the separation efficiency of the organic waste gas with respect to the energy consumption sensitive parameter, is the preset separation efficiency sensitive threshold, is the minimum value of the energy consumption sensitive parameter, is the constraint condition operator of the organic waste gas in the separation energy consumption and recovery efficiency collaborative trade-off process.

5. The method of claim 1, wherein the low energy consumption recovery of organic waste gas is characterized by, Based on the optimal energy consumption separation operation parameter and the characteristic coupling feature set, the process conditions of the organic waste gas in the separation process are dynamically constrained and optimized to obtain the Pareto optimal separation scheme of the organic waste gas, including: Based on the optimal energy consumption separation operation parameter and the characteristic coupling feature set, the process conditions of the organic waste gas in the separation process are dynamically constrained and optimized to obtain the Pareto optimal separation scheme of the organic waste gas, including: Under the range defined by the process constraint condition, the separation efficiency and energy consumption of the volatile solvent component in the organic waste gas are balanced to obtain the process parameter feasible region of the organic waste gas. The separation efficiency and process energy consumption in the process parameter feasible region are analyzed by Pareto to obtain the Pareto optimal solution of the organic waste gas. According to the waste gas recovery real-time working condition and separation efficiency dynamic factor in the characteristic coupling feature set, the working condition adaptive optimal solution of the organic waste gas is selected from the Pareto optimal solution set. The working condition adaptive optimal solution is compiled into the Pareto optimal separation scheme of the organic waste gas.

6. The method of claim 1, wherein the low energy consumption recovery of organic waste gas is characterized by, Based on the separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set, the Pareto optimal separation scheme is selected for robustness, and the selection result is arranged as the optimal separation control strategy of the organic waste gas, including: The separation efficiency dynamic factor and energy consumption stability factor in the characteristic coupling feature set are analyzed to obtain the aggregation factor of the organic waste gas. According to the fluctuation information in the aggregation factor, the Pareto optimal separation scheme is evaluated for multi-scenario robustness to obtain a robust evaluation set of the Pareto optimal separation scheme. Based on the robust evaluation set, the energy consumption adaptability and recovery efficiency threshold of the Pareto optimal separation scheme are selected in two dimensions to obtain the optimal separation scheme of the organic waste gas. The optimal separation scheme is arranged as the optimal separation control strategy of the organic waste gas.

7. The method of claim 1, wherein the low energy consumption recovery of organic waste gas is characterized by, The optimal separation control strategy and the characteristic coupling feature set are digitally twin coordinated and deduced, and the deduced result is encoded as the waste gas separation control instruction of the organic waste gas, including: The heterogeneous data of the characteristic coupling feature set and the optimal separation control strategy are aggregated to obtain the fusion data base of the organic waste gas. Based on the fusion data base, the low-energy separation process of the organic waste gas is dynamically virtually reconstructed to obtain the virtual twin scene of the organic waste gas. In the virtual twin scene, the recovery process of the organic waste gas is cooperatively simulated and analyzed to obtain the dynamic evolution data of the organic waste gas in the separation process. Based on the dynamic evolution data, the low-energy recovery intention in the optimal separation control strategy is prioritized and quantitatively analyzed to obtain the optimal operation parameter sequence of the organic waste gas. The optimal operation parameter sequence is compiled and packaged to obtain a waste gas separation control instruction of the organic waste gas.

8. The method of claim 7, wherein the organic waste gas is recovered at a low energy consumption. In the virtual twin scene, the recovery process of the organic waste gas is cooperatively simulated and analyzed to obtain dynamic evolution data of the organic waste gas in the separation process, including: In the virtual twin scene, the concentration distribution characteristics of the volatile solvent in the organic waste gas and the energy transfer characteristics of the separation process are analyzed to obtain a simulation feature set of the organic waste gas; Based on the simulation feature set, the separation behavior of the organic waste gas under the low-energy consumption constraint condition is trajectory simulated to obtain a separation dynamic trajectory of the organic waste gas; According to the separation dynamic trajectory, the energy consumption change and the solvent recovery efficiency of the organic waste gas in the recovery process are cooperatively characterized and analyzed to obtain recovery energy efficiency evolution data of the organic waste gas; The separation behavior dynamic trajectory and the recovery energy efficiency evolution data are dynamically fused and analyzed to obtain dynamic evolution data of the organic waste gas.

9. The method of claim 1, wherein the organic exhaust gas is recovered at a low energy consumption, and The recovery energy efficiency verification index and the recovery product quality index obtained after the waste gas separation control instruction is executed are used to feed back and optimize the generation strategy of the waste gas separation control instruction to obtain a low-energy consumption separation instruction generation strategy of the organic waste gas, including: The waste gas separation control instruction is executed, and the energy efficiency of the separation process of the organic waste gas is tracked in real time to obtain separation energy consumption and recovery efficiency data of the organic waste gas in the separation process; The separation energy consumption and the recovery efficiency data are fused and analyzed to obtain a recovery energy efficiency verification index of the organic waste gas; The recovery product of the organic waste gas is detected and analyzed to obtain a recovery product quality index of the organic waste gas; According to the recovery energy efficiency verification index and the recovery product quality index, the generation strategy of the waste gas separation control instruction is comprehensively analyzed to obtain an efficiency evaluation conclusion of the generation strategy; According to the efficiency evaluation conclusion, the generation strategy of the waste gas separation control instruction is dynamically fed back and optimized to obtain a low-energy consumption separation instruction generation strategy of the organic waste gas.

10. A low energy consumption recovery system of organic off-gas, characterized by, A low-energy consumption recovery method of an organic waste gas for realizing the method of claim 1, the system comprising: A multi-source coupling analysis module is configured to perform multi-source coupling analysis on the volatile solvent component concentration, concentration fluctuation data, solvent recovery energy consumption correlation factor, and environmental temperature and humidity data of the organic waste gas to obtain a characteristic coupling feature set of the organic waste gas. A feedforward situation analysis module is configured to perform feedforward situation analysis on the process conditions of the separation process of the organic waste gas based on the characteristic coupling feature set to obtain optimal energy consumption separation operation parameters of the organic waste gas in the separation process. A dynamic constraint optimization module is configured to perform dynamic constraint optimization on the process conditions of the separation process of the organic waste gas based on the optimal energy consumption separation operation parameters and the characteristic coupling feature set to obtain a Pareto optimal separation scheme of the organic waste gas. a robust selection module configured to perform robust selection on the Pareto optimal separation scheme based on a separation performance dynamic factor and an energy consumption stability factor in the characteristic coupling feature set, and compile a result of the robust selection as an optimal separation control strategy of the organic waste gas; a co-evolution module configured to perform digital twin co-evolution on the optimal separation control strategy and the characteristic coupling feature set, and encode a result of the co-evolution as a waste gas separation control instruction of the organic waste gas; a feedback optimization module configured to perform feedback optimization on a generation strategy of the waste gas separation control instruction based on a recovery energy efficiency verification index and a recovery product quality index obtained after running of the waste gas separation control instruction, and obtain a low-energy-consumption separation instruction generation strategy of the organic waste gas.

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