Carbon energy flow modeling and energy saving and carbon reduction optimization method for molten salt manufacturing process based on petri net

Abstract

The application relates to a molten salt manufacturing process carbon energy flow modeling and energy-saving and carbon-reducing optimization method based on a Petri net, which comprises the following steps: S1, analyzing the substance and energy metabolism mechanism of a molten salt manufacturing process; S2, modeling the carbon energy flow of the molten salt manufacturing process based on a Petri net and defining a P / T system; S3, constructing a multi-objective function and constraint condition, and optimizing the model; S4, solving the model by adopting NSGA-II, obtaining the optimal production rate of equipment under different macro periods, and selecting the optimal production rate according to the demand of a molten salt production enterprise. Compared with the prior art, the molten salt manufacturing process carbon energy flow modeling and energy-saving and carbon-reducing optimization based on the Petri net can reduce the energy consumption of the molten salt production enterprise, can save the cost of the molten salt production enterprise, and can achieve the energy-saving and carbon-reducing effect.

Description

Technical Field

[0001] This invention relates to an energy-saving and carbon-reducing method, and more particularly to a method for carbon energy flow modeling and energy-saving and carbon-reducing optimization in molten salt manufacturing processes based on Petri nets. Background Technology

[0002] Under the pressures of global climate change and multiple environmental challenges, it is crucial to deepen energy conservation and low carbon emissions across all industries and improve energy efficiency. The low energy efficiency of traditional industries and the instability of solar and wind power necessitate the use of energy storage to address the mismatch between energy supply and demand in terms of time, space, and intensity. According to the latest "Global Energy Storage Market Tracking Report" released by the Zhongguancun Energy Storage Industry Technology Alliance, as of the third quarter of 2017, the top three types of energy storage projects in terms of cumulative installed capacity globally are pumped hydro storage, molten salt thermal energy storage, and electrochemical energy storage. Globally, molten salt thermal energy storage consistently holds the second position in terms of installed energy storage capacity.

[0003] Molten salt thermal energy storage is an emerging energy supply technology and an important component in guiding clean energy supply, improving the environment, and enhancing grid stability. In recent years, the application of molten salt thermal energy storage in my country has gradually gained popularity. In 2017 alone, seven molten salt thermal energy storage projects commenced construction, including the Dunhuang 100,000 kW molten salt tower solar thermal power generation project, the Luneng Qinghai Xizhou 50 MW tower molten salt solar thermal power plant, and the Inner Mongolia Marco Polo Dream City green molten salt heating and cooling project.

[0004] The manufacturing process of molten salt also generates significant carbon dioxide emissions and energy consumption. As demand for molten salt increases, companies will expand their production capacity. Therefore, focusing on energy conservation and carbon reduction in the molten salt manufacturing process is essential.

[0005] Because some equipment in the molten salt production process operates more flexibly due to the addition of frequency converters or built-in frequency conversion technology, the power consumption of the equipment has become exceptionally complex, increasing the complexity of carbon energy flow modeling for the entire production process. Furthermore, since there is no intermediate storage area between some upstream and downstream equipment in the production process, the continuity of the production process necessitates real-time matching of the operating speeds of the equipment, limiting the freedom of equipment operation optimization and causing some equipment to fail to operate at its optimal energy consumption point. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for carbon energy flow modeling and energy saving and carbon reduction optimization in molten salt manufacturing process based on Petri nets.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets includes the following steps:

[0009] S1. Analyze the material and energy metabolism mechanisms in the molten salt manufacturing process;

[0010] S2. Model the carbon energy stream in the molten salt manufacturing process based on Petri nets and define the P / T system;

[0011] S3. Construct multi-objective functions and constraints, and optimize the model;

[0012] S4. The model is solved using NSGA-II (Fast Non-Dominated Sorting Genetic Algorithm) with an elite strategy to obtain the optimal production rate of the equipment under different macro-cycles, and the optimal production rate is selected according to the needs of the molten salt production enterprise.

[0013] Furthermore, in step S1, the carbon emission factors for electricity and steam are obtained according to the "Guidelines for Greenhouse Gas Emission Accounting Methods and Reporting of Chemical Production Enterprises in China". The total carbon emissions of the molten salt manufacturing process are divided into direct carbon emissions from chemical reactions and indirect carbon emissions corresponding to equipment energy consumption, as shown in the following formula:

[0014] E g =E r +E te

[0015] In the formula, E g E represents the total carbon emissions during the molten salt manufacturing process. r E represents direct carbon emissions from chemical reactions. te Carbon emissions indirectly generated from the energy consumption of equipment.

[0016] Furthermore, the direct carbon emissions E produced by the aforementioned chemical reaction r for:

[0017]

[0018] In the formula, M r,a M represents the relative molecular mass of the acidic raw material. r,b 44 represents the relative molecular mass of the basic prototype, and 44 represents the relative molecular mass of carbon dioxide. g This represents the total amount of reactants and raw materials.

[0019] Furthermore, the carbon emissions E indirectly generated by the energy used in the aforementioned equipment te The result needs to be obtained by adding up the values ​​from each production device, as shown in the following formula:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] E te =E p +E re +E cl +E th +E cf +E dry

[0027] In the formula, E te Let T represent the total energy consumption and indirect carbon emissions of all equipment, j be the number of equipment, i be the number of times the equipment changes production rate, and T be the total energy consumption and indirect carbon emissions of all equipment. ji Let E be the time from the last change in production rate of the j-th device to the next change in production rate, referred to as the macro cycle. pump For the carbon emissions of solution pumps, E re To neutralize the carbon emissions generated by the reactor, E cl E represents carbon emissions from evaporator crystallizers. th E is the carbon emission generated by the thickener. cf For the carbon emissions generated by centrifuges, E dry For the carbon emissions generated by the dryer, P ji Let S be the power of the j-th device in the i-th macroscopic cycle. ji Let EF be the hourly steam consumption of the j-th device in the i-th macro cycle. e The carbon emission factor of electricity consumption, EF h The carbon emission factor is the carbon emission factor for consuming steam.

[0028] Furthermore, the P / T system in step S2 is a seven-tuple Θ = {PTW} in W out MVK}:

[0029] P represents the set of places, P = P n ∪P m ∪P c Different styles and colors of libraries are used to represent the state and flow of energy, matter, and carbon. n =P ne ∪P ns P represents the location of energy. ne The flow of electrical energy is represented by a serrated circle, P.ns The flow of steam is represented by a double dashed circle. The evolution of the number of energy reservoirs reflects the energy support for production and quantitatively reflects energy consumption, specifically presented as energy flow; P m The storage locations of materials are represented by solid double circles. Materials can be categorized into raw materials, auxiliary materials, intermediate products, and final products. The changes in the quantity within these storage locations reflect the consumption and transformation of materials, specifically representing material flow; P c The locations where carbon emissions are located are represented by hollow circles. The evolution of the quantity in these locations reflects the amount of carbon emissions during the production process, specifically presented as carbon flows.

[0030] T represents the transition set, which is the set of transition elements, T = {T1 T2 … T}. n},in T stands for continuous transition, representing equipment that produces continuously;

[0031] W in Define R as the input function. + Let f(V) be the set of positive real numbers. i If is a function of rate, then:

[0032]

[0033] In the formula, W in (T i ,P j ) represents the transition T i Pointing to the place P j W in (T i ,P j This indicates the quantity of materials, energy, and carbon dioxide entering and exiting the equipment; the quantity is related to the operating speed of the equipment.

[0034] W out Define R as the output function. + Let f(V) be the set of positive real numbers. i If is a function of rate, then:

[0035]

[0036] In the formula, W out (P j ,T i ) represents the place P j Pointer transition T i W out (P j ,T i This indicates the amount of materials, energy, and carbon dioxide entering the equipment, and the amount is related to the operating speed of the equipment.

[0037] M = {M0, M} τ} where M0 represents the initial inventory of raw materials, auxiliary materials, intermediate products, and final products, as well as the energy storage, and M is usually represented as an m-dimensional non-negative integer vector, where m is the number of stock locations; M τ This represents the inventory levels of each raw material, auxiliary material, intermediate product, and final product, as well as the energy consumption, after a production time of τ.

[0038] V is the transition excitation rate function, representing the production rate of the equipment. The set of V is V = {V1 V2 … V}. n}; Because the equipment has a maximum and minimum production capacity, V i ∈(V min V max ), where V i V represents the i-th device. min V represents the minimum production rate of the equipment. max Indicates the maximum production rate of the equipment;

[0039] K represents the upper and lower limits of the number of tokens that can be stored in the warehouse, and also represents the upper and lower limits of the raw materials, auxiliary materials, intermediate outputs, and final products that can be stored in the buffer zone between devices. i ∈(K min K max ), K min K represents the minimum size of the buffer. max This indicates the maximum capacity of the buffer.

[0040] Furthermore, in step S3, a multi-objective function is established to minimize carbon emissions, maximize production capacity, and minimize energy consumption:

[0041] E min,g =E r +E te

[0042]

[0043]

[0044]

[0045] In the formula, i represents the types of intermediate and final products in the molten salt production process, W is the proportionality coefficient, V is the production speed, Y is the production capacity of the equipment, P is the electrical energy consumed by the equipment, S is the amount of steam consumed by the equipment, and T is the time of each macroscopic cycle.

[0046] Furthermore, the constraints in step S3 include equipment production capacity constraints, buffer capacity constraints, collaborative cascade equipment production capacity constraints, production condition constraints on equipment production capacity, and chemical reaction equilibrium constraints.

[0047] Furthermore, the formula for constraining the equipment production capacity is as follows:

[0048] V imin ≤V i ≤V imax

[0049] The formula for buffer capacity constraint is:

[0050] K imin ≤K i ≤K imax

[0051] The formula for the production capacity constraint of collaborative cascade equipment is:

[0052] V d =α i ×V u

[0053] The formula for constraining equipment production capacity by production conditions is:

[0054] δ i-1 V i-1 ≤V i ≤δ i+1 V i+1

[0055] The chemical reaction equilibrium constraint formula is:

[0056] β1V r,a =β2V r,b

[0057] Where α, δ, and β are proportionality coefficients, and V d V represents the production rate of upstream equipment. u V represents the production rate of downstream equipment. r,a With V r,b These represent the rates at which the two raw materials are introduced into the neutralization reaction, and K is the size of the buffer zone.

[0058] Furthermore, in step S4, NSGA-II divides the multi-objective domain into the Pareto domain and uses a method for calculating congestion and an elite strategy to preserve the optimal solution of the energy consumption point, and finally obtains the optimal solution set.

[0059] Furthermore, the optimal solution set is selected based on the actual production needs of the molten salt production enterprise. If the enterprise has no requirements for the production rate, the lowest value of the division between the carbon emission objective function and the production capacity objective function is selected as the optimal energy consumption point.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. This invention establishes a carbon energy flow analysis model for the molten salt production process based on Petri nets, deeply explores the carbon energy flow coupling relationship between production equipment, optimizes the model by constructing multi-objective functions and constraints, and finally solves the model using a fast non-dominated sorting genetic algorithm with an elitist strategy to obtain the optimal production rate of the equipment under different macro-cycles, thereby reducing excessive carbon dioxide emissions caused by some equipment in the molten salt production process and saving energy consumption.

[0062] 2. This invention sets a K value in the P / T system, where K is the upper and lower limit of the number of tokens that can be stored in the warehouse, representing the upper and lower limits of the raw materials, auxiliary materials, intermediate outputs, and final products that can be stored in the buffer between equipment. The K value is used to constrain the capacity of materials or products in the buffer for use by subsequent equipment, thereby effectively optimizing the operating speed of the entire production process.

[0063] 3. This invention solves the model by using NSGA-II with an elitist strategy. NSGA-II divides the multi-objectives into the Pareto domain and uses a method of calculating congestion degree and an elitist strategy to preserve the optimal solution of the energy consumption point, thus obtaining the optimal solution set. The optimal solution can be selected according to the actual production needs of the enterprise, making it applicable to the vast majority of enterprises and practical. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the process of the present invention;

[0065] Figure 2 This is a schematic diagram of the structure of the present invention;

[0066] Figure 3 This is a schematic diagram of the molten salt manufacturing process.

[0067] Figure 4 A schematic diagram of a Petri net model of the molten salt manufacturing process;

[0068] Figure 5 Here is the flowchart for the NSGA-II algorithm;

[0069] Figure 6 A schematic diagram showing carbon emissions and energy consumption at different iteration numbers;

[0070] Figure 7 A schematic diagram illustrating changes in carbon emissions and carbon-based energy use;

[0071] Figure 8 This is a diagram illustrating the changes in raw materials, finished products, and intermediate products. Detailed Implementation

[0072] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0073] like Figure 1 As shown, a method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets includes the following steps:

[0074] S1. Analyze the material and energy metabolism mechanisms in the molten salt manufacturing process;

[0075] S2. Model the carbon energy stream in the molten salt manufacturing process based on Petri nets and define the P / T system;

[0076] S3. Construct multi-objective functions and constraints, and optimize the model;

[0077] S4. The model is solved using NSGA-II with an elite strategy to obtain the optimal production rate of the equipment under different macroeconomic cycles, and the optimal production rate is selected according to the needs of the molten salt production enterprise.

[0078] like Figure 3 As shown, step S1 analyzes the process flow of molten salt manufacturing. Since the molten salt manufacturing process requires the consumption of various resources, including material resources such as acidic and alkaline liquids and reducing agents, and energy sources such as electricity, coal, natural gas, and steam, the consumption of these resources will generate carbon emissions. Based on the full life-cycle carbon footprint accounting method, the door-to-door carbon footprint accounting boundary is determined. Following the guidance of the "Guidelines for Greenhouse Gas Emission Accounting Methods and Reporting of Chemical Production Enterprises in China (Trial Implementation)," the carbon emission factors for electricity and steam are obtained. The total carbon emissions of the molten salt manufacturing process are divided into direct carbon emissions from chemical reactions and indirect carbon emissions corresponding to equipment energy consumption, as shown in the following formula:

[0079] E g =E r +E te

[0080] In the formula, E g E represents the total carbon emissions during the molten salt manufacturing process. r E represents direct carbon emissions from chemical reactions. te Carbon emissions indirectly generated from the energy consumption of equipment.

[0081] Direct carbon emissions E from chemical reactions r for:

[0082]

[0083] In the formula, M r,a M represents the relative molecular mass of the acidic raw material. r,b 44 represents the relative molecular mass of the basic prototype, and 44 represents the relative molecular mass of carbon dioxide. g This represents the total amount of reactants and raw materials.

[0084] Carbon emissions indirectly generated by equipment energy consumption E te The result needs to be obtained by adding up the values ​​from each production device, as shown in the following formula:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] E te =E p +E re +E cl +E th +E cf +E dry

[0092] In the formula, E te Let T represent the total energy consumption and indirect carbon emissions of all equipment, j be the number of equipment, i be the number of times the equipment changes production rate, and T be the total energy consumption and indirect carbon emissions of all equipment. ji Let E be the time from the last change in production rate of the j-th device to the next change in production rate, referred to as the macro cycle. pump For the carbon emissions of solution pumps, E re To neutralize the carbon emissions generated by the reactor, E cl E represents carbon emissions from evaporator crystallizers. th E is the carbon emission generated by the thickener. cf For the carbon emissions generated by centrifuges, E dry For the carbon emissions generated by the dryer, P ji Let S be the power of the j-th device in the i-th macroscopic cycle. ji Let EF be the hourly steam consumption of the j-th device in the i-th macro cycle. e The carbon emission factor of electricity consumption, EF h The carbon emission factor is the carbon emission factor for consuming steam.

[0093] Furthermore, the P / T system in step S2 is a seven-tuple Θ = {PTW} in W out MVK}:

[0094] P represents the set of places, P = P n ∪P m ∪P c Different styles and colors of libraries are used to represent the state and flow of energy, matter, and carbon. n =P ne ∪P ns P represents the location of energy. ne The flow of electrical energy is represented by a serrated circle, P. ns The flow of steam is represented by a double dashed circle. The evolution of the number of energy reservoirs reflects the energy support for production and quantitatively reflects energy consumption, specifically presented as energy flow; P m The storage locations of materials are represented by solid double circles. Materials can be categorized into raw materials, auxiliary materials, intermediate products, and final products. The changes in the quantity within these storage locations reflect the consumption and transformation of materials, specifically representing material flow; P c The locations where carbon emissions are located are represented by hollow circles. The evolution of the quantity in these locations reflects the amount of carbon emissions during the production process, specifically presented as carbon flows.

[0095] T represents the transition set, which is the set of transition elements, T = {T1 T2 … T}. n},in T stands for continuous transition, representing equipment that produces continuously;

[0096] W in Define R as the input function. + Let f(V) be the set of positive real numbers. i If is a function of rate, then:

[0097]

[0098] In the formula, W in (T i ,P j ) represents the transition T i Pointing to the place P j W in (T i ,P j This indicates the quantity of materials, energy, and carbon dioxide entering and exiting the equipment; the quantity is related to the operating speed of the equipment.

[0099] W out Define R as the output function. + Let f(V) be the set of positive real numbers.i If is a function of rate, then:

[0100]

[0101] In the formula, W out (P j ,T i ) represents the place P j Pointer transition T i W out (P j ,T i This indicates the amount of materials, energy, and carbon dioxide entering the equipment, and the amount is related to the operating speed of the equipment.

[0102] M = {M0, M} τ} where M0 represents the initial inventory of raw materials, auxiliary materials, intermediate products, and final products, as well as the energy storage, and M is usually represented as an m-dimensional non-negative integer vector, where m is the number of stock locations; M τ This represents the inventory levels of each raw material, auxiliary material, intermediate product, and final product, as well as the energy consumption, after a production time of τ.

[0103] V is the transition excitation rate function, representing the production rate of the equipment. The set of V is V = {V1 V2 … V}. n}; Because the equipment has a maximum and minimum production capacity, V i ∈(V min V max ), where V i V represents the i-th device. min V represents the minimum production rate of the equipment. max Indicates the maximum production rate of the equipment;

[0104] K represents the upper and lower limits of the number of tokens that can be stored in the warehouse, and also represents the upper and lower limits of the raw materials, auxiliary materials, intermediate outputs, and final products that can be stored in the buffer zone between devices. i ∈(K min K max ), K min K represents the minimum size of the buffer. max This indicates the maximum capacity of the buffer.

[0105] According to Petri's definition, such as Figure 4 As shown, the parameters in Petri are shown in Tables 1 and 2:

[0106] Table 1. Locations and their meanings in the Petri net model.

[0107]

[0108]

[0109] Table 2. Transitions and their meanings in the Petri net model.

[0110]

[0111] And the correlation matrix is ​​obtained based on the Petri net:

[0112]

[0113] In step S3, a multi-objective function is established to minimize carbon emissions, maximize production capacity, and minimize energy consumption:

[0114] E min,g =E r +E te

[0115]

[0116]

[0117]

[0118] In the formula, i represents the types of intermediate and final products in the molten salt production process, W is the proportionality coefficient, V is the production speed, Y is the production capacity of the equipment, P is the electrical energy consumed by the equipment, S is the amount of steam consumed by the equipment, and T is the time of each macroscopic cycle.

[0119] The constraints in step S3 include equipment production capacity constraints, buffer capacity constraints, cooperative cascade equipment production capacity constraints, production condition constraints on equipment production capacity, and chemical reaction equilibrium constraints.

[0120] The formula for constraining equipment production capacity is:

[0121] V imin ≤V i ≤V imax

[0122] The formula for buffer capacity constraint is:

[0123] K imin ≤K i ≤K imax

[0124] The formula for the production capacity constraint of collaborative cascade equipment is:

[0125] V d =α i ×V u

[0126] The formula for constraining equipment production capacity by production conditions is:

[0127] δ i-1 V i-1 ≤V i ≤δ i+1 V i+1

[0128] The chemical reaction equilibrium constraint formula is:

[0129] β1V r,a =β2V r,b

[0130] Where α, δ, and β are proportionality coefficients, and V d V represents the production rate of upstream equipment. u V represents the production rate of downstream equipment. r,a With V r,b These represent the rates at which the two raw materials are introduced into the neutralization reaction, and K is the size of the buffer zone.

[0131] In step S4, NSGA-II divides the multi-objective into the Pareto domain and uses a method of calculating congestion and an elite strategy to preserve the optimal solution of the energy consumption point, and finally obtains the optimal solution set. The optimal solution set is selected according to the actual production needs of the molten salt production enterprise. If the enterprise has no requirements for the production rate, the lowest value of the division between the carbon emission objective function and the production capacity objective function is selected as the optimal energy consumption point.

[0132] This section uses simulation to verify the results.

[0133] The NSGA-II algorithm was programmed using Matlab, with a population size of 70, a crossover probability of 0.8, and a mutation probability of 0.2. The carbon emissions, electricity consumption, and steam consumption of the molten salt manufacturing system were calculated at different iteration numbers. Figure 6 As shown, Table 3 is the event table for the molten salt production optimization process.

[0134] Table 3 Event Table for Molten Salt Production Optimization Process

[0135]

[0136]

[0137] The entire system operated for 4.815 hours, generating 23.1106 tons of CO2, consuming 1244.577 kilowatts of electricity, and consuming 59.6836 tons of steam. Its carbon emissions, steam consumption, and electricity consumption are as follows: Figure 7 As shown. Its raw material consumption, product production status, and intermediate product changes are as follows: Figure 8As shown in the diagram, the production system will make full use of these intermediate storage areas throughout the entire production process, enabling the equipment to operate at the optimal energy consumption point under this macro-cycle, thereby achieving energy saving and carbon reduction.

[0138] As shown in Table 4, the total carbon emissions of each molten salt manufacturing system were reduced by 3.49% after optimization. However, since the carbon emissions generated by the chemical reactions during the molten salt production process are irreducible, the total carbon emissions of the operating equipment were reduced by 4.97%, electricity consumption by 3.14%, and steam consumption by 5.19%.

[0139] Table 4 Comparison of Molten Salt Production Before and After Optimization

[0140]

[0141] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets, characterized in that, Includes the following steps: S1. Analyze the material and energy metabolism mechanisms in the molten salt manufacturing process; S2. Based on Petri nets, model the carbon energy stream in the molten salt manufacturing process and define the P / T system. The P / T system is a seven-tuple. : Represented as a collection of places, Different styles and colors of libraries are used to represent the state and flow of energy, matter, and carbon. Indicates the location of energy. The flow of electrical energy is represented by a serrated circle. The flow of steam is represented by a double dashed circle. The evolution of the number of energy reservoirs reflects the energy support for production and quantitatively reflects the energy consumption, specifically presented as energy flow. The warehouse where the materials are located is represented by a solid double circle. The materials are divided into raw materials, auxiliary materials, intermediate products or final products. The change in the quantity in the warehouse reflects the consumption and transformation of the materials, which is specifically presented as the material flow. The locations where carbon emissions are located are represented by hollow circles. The evolution of the quantity in these locations reflects the amount of carbon emissions during the production process, specifically presented as carbon flows. Represented as a transition set, it is a set of transition elements. ,in , Equipment representing continuous production and transition; Define the input function. For the set of positive real numbers, If is a function of rate, then: In the formula, Indicates change Pointing to the storehouse , This indicates the amount of materials, energy, and carbon dioxide emitted from the equipment, and the amount is related to the operating speed of the equipment; Define the output function. For the set of positive real numbers, If is a function of rate, then: In the formula, Indicates the place of storage Pointing change , This indicates the amount of materials, energy, and carbon dioxide entering the equipment, and the amount is related to the operating speed of the equipment. ,in This indicates the initial inventory levels of raw materials, auxiliary materials, intermediate products, and final products, as well as the energy reserves at the start of production. M It is usually represented as an m-dimensional non-negative integer vector, where m is the number of loci; Indicated as production The inventory levels of each raw material, auxiliary material, intermediate product, and final product, as well as the energy consumption, after a certain period of time; Let be the transition excitation rate function, representing the production rate of the equipment. The set is Because the equipment has maximum and minimum production capacity, ,in Indicates the first i Taiwan equipment, Indicates the minimum production rate of the equipment. Indicates the maximum production rate of the equipment; This represents the upper and lower limits of the number of tokens that can be stored in the warehouse, and the upper and lower limits of the raw materials, auxiliary materials, intermediate outputs, and final products that can be stored in the buffer zone between devices. , This represents the minimum size of the buffer. Indicates the maximum capacity of the buffer; S3. Construct multi-objective functions and constraints, and optimize the model; S4. The model is solved using NSGA-II with an elite strategy to obtain the optimal production rate of the equipment under different macroeconomic cycles, and the optimal production rate is selected according to the needs of the molten salt production enterprise.

2. The method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets according to claim 1, characterized in that, In step S1, the carbon emission factors for electricity and steam are obtained according to the "Guidelines for Greenhouse Gas Emission Accounting and Reporting of Chemical Production Enterprises in China". The total carbon emissions of the molten salt manufacturing process are divided into direct carbon emissions from chemical reactions and indirect carbon emissions corresponding to equipment energy consumption, as shown in the following formula: In the formula, This refers to the total carbon emissions during the molten salt manufacturing process. Direct carbon emissions from chemical reactions Carbon emissions indirectly generated from the energy consumption of equipment.

3. The method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets according to claim 2, characterized in that, The direct carbon emissions generated by the aforementioned chemical reaction for: In the formula, The relative molecular mass of the acidic raw material. 44 represents the relative molecular mass of the basic prototype, and 44 represents the relative molecular mass of carbon dioxide. This represents the total amount of reactants and raw materials.

4. The method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets according to claim 2, characterized in that, The carbon emissions indirectly generated by the energy used in the aforementioned equipment The result needs to be obtained by adding up the values ​​from each production device, as shown in the following formula: In the formula, Indirect carbon emissions from the total energy consumption of all equipment. j For the number of devices, i The number of times the equipment changes production rate. For the first j The time from the last change in production rate of a piece of equipment to the next change in production rate is called the macro cycle. Carbon emissions from solution pumps To neutralize the carbon emissions generated by the reactor, Carbon emissions from the evaporator crystallizer Carbon emissions generated by the thickener For the carbon emissions generated by centrifuges, Carbon emissions from the dryer For the first j The equipment in the first i Power of a macroeconomic cycle For the first j The equipment in the first i The amount of steam consumed per hour in a macroeconomic cycle. Carbon emissions from electricity consumption The carbon emission factor is the carbon emission factor for consuming steam.

5. The method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets according to claim 1, characterized in that, In step S3, a multi-objective function is established to minimize carbon emissions, maximize production capacity, and minimize energy consumption. In the formula, i This refers to the types of intermediate and final products in the molten salt production process. This is the proportionality coefficient. For production speed, Y For the production capacity of the equipment, P The electrical energy consumed by the equipment. S This refers to the amount of steam consumed by the equipment. T The time for each macroeconomic cycle.

6. The method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets according to claim 1, characterized in that, The constraints in step S3 include equipment production capacity constraints, buffer capacity constraints, collaborative cascade equipment production capacity constraints, production condition constraints on equipment production capacity, and chemical reaction equilibrium constraints.

7. The method for carbon energy flow modeling and energy-saving and carbon reduction optimization in molten salt manufacturing process based on Petri nets according to claim 6, characterized in that, The formula for constraining equipment production capacity is: The formula for buffer capacity constraint is: The formula for the production capacity constraint of collaborative cascade equipment is: The formula for the constraint of production conditions on equipment production capacity is: The chemical reaction equilibrium constraint formula is: in, , , This is the proportionality coefficient. For the production rate of upstream equipment, For the production rate of downstream equipment, and These represent the rates at which the two raw materials are introduced into the neutralization reaction. K This represents the size of the buffer.

8. The method for carbon energy flow modeling and energy-saving and carbon reduction optimization in molten salt manufacturing process based on Petri nets according to claim 1, characterized in that, In step S4, NSGA-II divides the multi-objective domain into the Pareto domain and uses a method of calculating congestion and an elite strategy to preserve the optimal solution of the energy consumption point, and finally obtains the optimal solution set.

9. The method for carbon energy flow modeling and energy-saving and carbon-reduction optimization in molten salt manufacturing process based on Petri nets according to claim 8, characterized in that, The optimal solution set is selected based on the actual production needs of molten salt production enterprises. If the enterprise has no requirements for production rate, the lowest value of the division between the carbon emission objective function and the production capacity objective function is selected as the optimal energy consumption point.

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