Methanol hydrogen production system optimization method, system and equipment and storage medium

Abstract

The invention discloses a methanol-to-hydrogen system optimization method, system and equipment and a storage medium, and relates to the technical field of hydrogen preparation, and the method comprises the steps that the input temperature of each cold stream heat exchanger in the methanol-to-hydrogen process is obtained based on a preset cold stream segmentation temperature point; each cold stream in the methanol hydrogen production process is segmented according to each input temperature, and heat exchanged by each cold stream heat exchanger is obtained; matching each cold stream heat exchanger with each hot stream heat exchanger in the methanol-to-hydrogen process according to the exchanged heat to obtain matching position information; constructing a global optimization model based on the input temperatures and the matching position information; and solving the global optimization model based on a plurality of preset decision variables and a preset algorithm in the methanol-to-hydrogen process to obtain an optimization decision. According to the method, a plurality of decision variables of methanol hydrogen production are considered, so that the final optimization decision obtained according to the plurality of decision variables can consider a plurality of indexes at the same time, and the global optimization of the system is finally realized.

Description

Technical Field

[0001] The present application relates to the field of hydrogen production technology, and in particular to a method, system, equipment and storage medium for optimizing a methanol hydrogen production system. Background Art

[0002] Against the backdrop of energy transition and green chemical development, methanol-to-hydrogen technology, as a crucial bridge between traditional fossil fuels and clean energy, has attracted considerable attention for its high efficiency and low-carbon operation. The heat exchange network, a key component of a methanol-to-hydrogen system, is crucial for its overall energy efficiency and economic viability.

[0003] In conventional heat exchange networks, the installation location and capacity of heat exchangers often rely on engineers' experience or energy and economic evaluation by comparing multiple empirical solutions. Although this method can meet the basic operating requirements of the system to a certain extent, it is limited by personal experience and the single evaluation dimension. It is difficult to fully consider the dynamic changes of the system under different operating conditions, as well as the complex relationship between multiple objectives such as energy utilization, equipment investment, and operating costs.

[0004] Therefore, how to achieve the global optimal heat exchanger setting scheme while ensuring the efficient operation of the system has become a key issue that needs to be urgently solved in the current field of methanol hydrogen production technology.

[0005] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a methanol hydrogen production system optimization method, system, equipment and storage medium, aiming to solve the technical problem of how to achieve a globally optimal heat exchanger setting scheme while ensuring efficient system operation.

[0007] To achieve the above objectives, the present application proposes a method for optimizing a methanol-to-hydrogen system, the method comprising:

[0008] Obtaining the input temperature of each cold stream heat exchanger in the methanol-to-hydrogen process based on a preset cold stream segmentation temperature point;

[0009] Dividing each cold stream in the methanol-to-hydrogen process according to each input temperature to obtain the heat exchanged by each cold stream heat exchanger;

[0010] Matching each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol-to-hydrogen process according to the exchanged heat to obtain matching position information;

[0011] Building a global optimization model based on each of the input temperatures and the matching position information;

[0012] The global optimization model is solved based on multiple decision variables and a preset algorithm in the methanol-to-hydrogen process to obtain an optimized decision.

[0013] In one embodiment, the step of obtaining the input temperature of each cold stream heat exchanger in the methanol-to-hydrogen process based on the preset cold stream segmentation temperature point includes:

[0014] Obtaining the temperature difference of each cold stream;

[0015] For any one of the cold streams, obtaining the cold stream heat exchangers corresponding to the cold stream, wherein the cold stream heat exchangers are connected in sequence;

[0016] The input temperature of each of the cold stream heat exchangers is calculated in sequence based on the preset cold stream segmentation temperature point and the temperature rise difference.

[0017] In one embodiment, the step of dividing the cold streams in the methanol-to-hydrogen process according to the input temperatures includes:

[0018] Dividing the cold stream into a plurality of sub-streams according to the input temperatures;

[0019] The heat exchanged by each of the cold streams is calculated according to the input temperature corresponding to each of the sub-streams.

[0020] In one embodiment, the step of matching each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol-to-hydrogen process according to the exchanged heat comprises:

[0021] Sort the heat exchangers according to their temperature ranges;

[0022] According to the heat exchanged, the heat exchangers are matched in order from large to small.

[0023] In one embodiment, in the global optimization model, each of the input temperatures satisfies a preset temperature constraint condition, and the matching position information is represented by integer coding, satisfying a preset coding constraint range.

[0024] In one embodiment, the step of solving the global optimization model based on multiple decision variables and a preset algorithm in the methanol-to-hydrogen process to obtain an optimization decision includes:

[0025] Generate a curve graph of the global optimization model based on the decision variables through a preset algorithm;

[0026] A corresponding decision point is selected in the curve diagram according to a preset decision target, and each target input temperature and target matching position information corresponding to the decision point is an optimized decision for the decision target.

[0027] In addition, to achieve the above objectives, the present application also proposes a methanol hydrogen production system, which includes:

[0028] A segmentation module is used to obtain the input temperature of each cold stream heat exchanger in the methanol hydrogen production process based on a preset cold stream segmentation temperature point; segment each cold stream in the methanol hydrogen production process according to each input temperature to obtain the heat exchanged by each cold stream heat exchanger;

[0029] a matching module, configured to match each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol-to-hydrogen process according to the exchanged heat, and obtain matching position information;

[0030] A modeling module, configured to construct a global optimization model based on the input temperatures and the matching position information;

[0031] The optimization module is used to solve the global optimization model based on multiple decision variables and preset algorithms in the methanol-to-hydrogen process to obtain an optimized decision.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a methanol hydrogen production device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the methanol hydrogen production system optimization method as described above.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the methanol hydrogen production system optimization method as described above are implemented.

[0034] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the methanol hydrogen production system optimization method as described above.

[0035] The present application provides a method for optimizing a methanol hydrogen production system. The present application first determines the input temperature of each cold stream heat exchanger in the methanol hydrogen production process according to a preset cold stream segmentation temperature, then segments each cold stream in the methanol hydrogen production process according to the input temperature of each cold stream heat exchanger to obtain the heat required to be exchanged by each cold stream heat exchanger, and then matches each cold stream heat exchanger with each hot stream heat exchanger in the methanol hydrogen production process according to the heat required to be exchanged by each cold stream heat exchanger to obtain initial matching position information. The input temperatures and matching position information are used as independent variables to construct a global optimization model. The decision variables change according to the changes in the independent variables. The global optimization model is solved according to the preset multiple decision variables and the preset algorithm to obtain the final optimization decision.

[0036] In summary, this application uses the input temperature and matching position information of each cold stream heat exchanger in the methanol hydrogen production process as independent variables to construct a global optimization model, and uses various indicators of methanol hydrogen production as decision variables. Taking into account multiple decision variables of methanol hydrogen production, the final optimization decision obtained based on multiple decision variables can consider multiple indicators at the same time, and ultimately achieve global optimization of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 A schematic diagram of a process flow diagram provided for Example 1 of the methanol hydrogen production system optimization method of this application;

[0040] Figure 2 This is a process flow chart of a methanol hydrogen production system involved in an embodiment of the methanol hydrogen production system optimization method of this application;

[0041] Figure 3 This is a conversion diagram of a heat exchange network calculation example of a methanol online hydrogen production system involved in an embodiment of the methanol hydrogen production system optimization method of this application;

[0042] Figure 4 This is a dot diagram of the optimization scheme involved in the embodiment of the methanol hydrogen production system optimization method of this application;

[0043] Figure 5 Schematic diagram of the heat exchange network of the methanol hydrogen production system involved in the embodiment of the methanol hydrogen production system optimization method of this application;

[0044] Figure 6 This is a schematic diagram of the module structure of the methanol hydrogen production system according to an embodiment of the present application;

[0045] Figure 7 Schematic diagram of the equipment structure of the hardware operating environment involved in the methanol hydrogen production system optimization method in the embodiment of the present application.

[0046] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solution of the embodiment of the present application is: based on the preset cold stream segmentation temperature point, the input temperature of each cold stream heat exchanger in the methanol hydrogen production process is obtained; according to each input temperature, the cold streams in the methanol hydrogen production process are segmented to obtain the heat exchanged by each cold stream heat exchanger; according to the exchanged heat, each cold stream heat exchanger is matched with each hot stream heat exchanger in the methanol hydrogen production process to obtain matching position information; based on each input temperature and the matching position information, a global optimization model is constructed; based on multiple decision variables and a preset algorithm preset in the methanol hydrogen production process, the global optimization model is solved to obtain an optimization decision.

[0050] Against the backdrop of energy transition and green chemical development, methanol-to-hydrogen technology, as a crucial bridge between traditional fossil fuels and clean energy, has attracted considerable attention for its high efficiency and low-carbon operation. The heat exchange network, a key component of a methanol-to-hydrogen system, is crucial for its overall energy efficiency and economic viability.

[0051] In conventional heat exchange networks, the installation location and capacity of heat exchangers often rely on engineers' experience or energy and economic evaluation by comparing multiple empirical solutions. Although this method can meet the basic operating requirements of the system to a certain extent, it is limited by personal experience and the single evaluation dimension. It is difficult to fully consider the dynamic changes of the system under different operating conditions, as well as the complex relationship between multiple objectives such as energy utilization, equipment investment, and operating costs.

[0052] Therefore, how to achieve the global optimal heat exchanger setting scheme while ensuring the efficient operation of the system has become a key issue that needs to be urgently solved in the current field of methanol hydrogen production technology.

[0053] In response to the above problems, the present application provides a method for optimizing a methanol hydrogen production system. The present application first determines the input temperature of each cold stream heat exchanger in the methanol hydrogen production process according to a preset cold stream segmentation temperature, and then segments each cold stream in the methanol hydrogen production process according to the input temperature of each cold stream heat exchanger to obtain the heat required to be exchanged by each cold stream heat exchanger. Then, according to the heat required to be exchanged by each cold stream heat exchanger, each cold stream heat exchanger is matched with each hot stream heat exchanger in the methanol hydrogen production process to obtain an initial matching position information. The input temperatures and matching position information are used as independent variables to construct a global optimization model. The decision variables change according to the changes in the independent variables. The global optimization model is solved according to the preset multiple decision variables and the preset algorithm to obtain the final optimization decision.

[0054] In summary, this application uses the input temperature and matching position information of each cold stream heat exchanger in the methanol hydrogen production process as independent variables to construct a global optimization model, and uses various indicators of methanol hydrogen production as decision variables. Taking into account multiple decision variables of methanol hydrogen production, the final optimization decision obtained based on multiple decision variables can consider multiple indicators at the same time, and ultimately achieve global optimization of the system.

[0055] In this embodiment, for ease of description, the methanol hydrogen production equipment is used as the execution subject for explanation.

[0056] Based on this, the embodiment of the present application provides a method for optimizing a methanol hydrogen production system, referring to Figure 1 , Figure 2 This is a flow chart of the first embodiment of the methanol-to-hydrogen system optimization method of the present application.

[0057] In this embodiment, the methanol-to-hydrogen system optimization method includes steps S10 to S50:

[0058] Step S10, obtaining the input temperature of each cold stream heat exchanger in the methanol-to-hydrogen process based on the preset cold stream segmentation temperature point;

[0059] It should be noted that in the process of methanol to hydrogen production, please refer to Figure 2The methanol-water solution is preheated to 240°C before entering the reforming reactor. The reformed gas is cooled to 40°C and then undergoes gas-liquid separation. The waste liquid is discharged to the outside, and the gas enters the pressure swing adsorption unit. The hydrogen-rich gas obtained by pressure swing adsorption separation is heat exchanged to 240°C before entering the methanation reactor. The desorbed gas can be fed as burnback gas to the catalytic oxidation reactor. The hydrogen product at the methanation reactor outlet is cooled to 70°C and fed to the downstream low-temperature proton exchange membrane fuel cell. Methanol and air enter the catalytic oxidation reactor. The reactions occurring during this process are shown in Table 1. The energy flow of the system is extracted, with the methanol-water feedstock preheating process considered cold stream 1 and the hydrogen-rich gas heating process considered cold stream 2. As the heat demander, the cold stream temperature increases from low to high. The reformed gas cooling process is considered hot stream 1, the product hydrogen cooling process is considered hot stream 2, the methanol catalytic oxidation flue gas cooling process is considered hot stream 3, and the burnback gas catalytic oxidation flue gas cooling process is considered hot stream 4.

[0060] Table 1 Chemical reactions in the methanol hydrogen production system

[0061]

[0062] The hot stream serves as the heat supply, with the stream temperature decreasing from high to low. Existing methanol-to-hydrogen systems integrate a methanol steam reforming reactor, a methanation reactor, a methanol catalytic oxidation reactor, and a burnback gas catalytic oxidation reactor. The methanol catalytic oxidation and burnback gas catalytic oxidation processes consist of two components: reaction heat and flue gas heat. The reaction heat is used to heat the methanol steam reforming hydrogen production process, while the flue gas heat is used for energy integration in the hot stream.

[0063] In this embodiment, in the methanol-to-hydrogen system, first, the input temperature of each cold stream heat exchanger in the methanol-to-hydrogen process is determined according to the preset cold stream segmentation temperature point.

[0064] Specifically, in a feasible implementation manner, the above step S10 may include steps S11 to S13:

[0065] Step S11, obtaining the temperature difference of each cold stream;

[0066] In this embodiment, the temperature change of each cold stream from the initial state to the final state in the methanol hydrogen production process is first monitored and recorded, and the temperature rise difference of each cold stream is obtained by calculating the difference between the initial temperature and the final temperature. The temperature rise difference reflects the heat absorption of each cold stream in the hydrogen production process.

[0067] Step S12: for any one of the cold streams, obtain the cold stream heat exchangers corresponding to the cold stream, wherein the cold stream heat exchangers are connected in sequence;

[0068] In this embodiment, for each cold stream, all heat exchangers that each cold stream passes through in the hydrogen production system are identified and determined, and the connection order of these heat exchangers is clarified.

[0069] Step S13, calculating the input temperature of each of the cold stream heat exchangers in sequence based on the preset cold stream segmentation temperature point and the temperature rise difference.

[0070] In this embodiment, the temperature rise process of the cold stream is first divided into multiple intervals according to the preset cold stream segmentation temperature points. Then, the input temperature of the current heat exchanger is calculated based on the temperature rise difference of each interval and the output temperature (or initial temperature) of the previous heat exchanger. By calculating in sequence, the input temperature of each cold stream heat exchanger can be obtained.

[0071] Step S20, dividing each cold stream in the methanol-to-hydrogen process according to each input temperature to obtain heat exchanged by each cold stream heat exchanger;

[0072] In this embodiment, the input temperature of each cold stream heat exchanger is obtained to segment the cold streams in the methanol-to-hydrogen process. The segmentation is based on the heat exchange characteristics of each cold stream in different temperature ranges. Through calculation and analysis, the heat exchanged by each cold stream heat exchanger in its respective temperature range is determined.

[0073] Specifically, in a feasible implementation, the above step S20 may include steps S21 to S22:

[0074] Step S21, dividing the cold stream into a plurality of sub-streams according to the input temperatures;

[0075] In this embodiment, the flow process of each cold stream in the heat exchanger is analyzed in detail. Based on the input temperature of each cold stream heat exchanger obtained, each cold stream is divided at the key node of temperature change (such as the preset cold stream segmentation temperature point or the actual monitored temperature significant change point) to form multiple sub-streams, each sub-stream representing the flow portion of the cold stream in a specific temperature range in the heat exchanger.

[0076] Step S22: Calculate the heat exchanged by each of the cold streams according to the input temperature corresponding to each of the sub-streams.

[0077] In this embodiment, the input temperature and output temperature of each substream in the heat exchanger (or the input temperature of the next substream, or the outlet temperature of the heat exchanger for the last substream) are first determined. Then, the heat exchanged by each substream in the heat exchanger is calculated using thermodynamic principles or heat exchanger performance parameters, combined with physical properties such as the mass flow rate and specific heat capacity of the substream.

[0078] In one example, see Figure 3 , the process flow of methanol-to-hydrogen system is extracted and divided into units according to the difference in heat demand. The results are as follows Figure 3 As shown. The process system can be divided into six parts: the methanol water preheating process is set as cold stream 1, the process absorbs heat, and the temperature rises from 25°C to 240°C to meet the raw material inlet temperature requirement of the next stage reforming reactor, and the heat exchangers H11, H12, and H13 are preset to match the hot stream; the reforming gas cooling process at the outlet of the reforming reactor is set as hot stream 1, the process releases heat, and the temperature drops from 240°C to 40°C to meet the raw material inlet temperature requirement of the next stage gas-liquid separation and pressure swing adsorption, and the heat exchangers H21, H22, and H23 are preset to match the cold stream; the hydrogen-rich gas heating process at the outlet of the pressure swing adsorption is set as cold stream 2, the process absorbs heat, and the temperature rises from 25°C to 240°C to meet the raw material inlet temperature requirement of the next stage methanation reactor, and the heat exchangers H31, H32, and H33 are preset to match the hot stream; The cooling process of the pure hydrogen gas at the outlet of the alkylation reactor is set as cold stream 2. The process releases heat and the temperature drops from 240°C to 70°C to meet the raw material inlet temperature requirement of the low-temperature proton exchange membrane fuel cell in the next stage, and the heat exchangers H41, H42, and H43 are preset to match the hot stream. The exhaust gas generated by the pressure swing adsorption flushing process is sent to the catalytic oxidation reactor 1 as burnback gas. The cooling process of the tail gas at the outlet of the catalytic oxidation reactor is set as hot stream 3. The process releases heat and the temperature can drop from 240°C to ambient temperature. The heat exchangers H51, H52, and H53 are preset to match the cold stream. The fuel methanol and air enter the catalytic oxidation reactor 2. The cooling process of the tail gas at the outlet of the reactor is set as hot stream 4. The process releases heat and the temperature can drop from 240°C to ambient temperature. The heat exchangers H61, H62, and H63 are preset to match the cold stream. The catalytic oxidation reactor, methanation reactor, and reforming reactor are structurally coupled. The catalytic oxidation reactor serves as the system heat source, providing heat for the reaction process. Therefore, the reaction process is not converted into a cold stream, and hot streams 3 and 4 are considered thermal utilities. The cold utility of the online hydrogen production system is an air-cooled heat exchanger.

[0079] Step S30, matching each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol-to-hydrogen process according to the exchanged heat, to obtain matching position information;

[0080] In this embodiment, based on the calculated heat exchanged by each cold stream heat exchanger, each cold stream heat exchanger is matched with each hot stream heat exchanger in the methanol hydrogen production process. The matching principle is to ensure that the heat exchange between the cold stream and the hot stream is optimal, that is, to achieve efficient energy utilization. Through matching, the optimal connection position between each cold stream heat exchanger and the hot stream heat exchanger is determined to form matching position information.

[0081] Specifically, in a feasible implementation manner, the above step S30 may include steps S31 to S32:

[0082] Step S31, sorting the heat exchangers of the hot streams according to their temperature ranges;

[0083] In this embodiment, the outlet temperature or operating temperature range of all hot stream heat exchangers in the methanol-to-hydrogen process is monitored and recorded, and then, based on these temperature data, the hot stream heat exchangers are sorted from high to low (or from low to high, depending on the specific matching strategy) according to temperature.

[0084] Step S32: matching the heat exchangers with the heat streams in descending order according to the exchanged heat.

[0085] In this embodiment, the heat exchangers are first sorted from highest to lowest heat exchanged, based on the calculated heat exchanged by each cold heat exchanger. Each cold heat exchanger is then matched with the sorted hot heat exchangers in this order. The matching principle is to connect the cold heat exchanger with the highest heat exchange capacity to the hot heat exchanger with the appropriate temperature range to achieve efficient heat utilization. During the matching process, it is necessary to record which hot heat exchanger each cold heat exchanger is connected to, as well as the connection location information (i.e., matching location information).

[0086] Specifically, as an example, a system mass balance, energy balance, and technical and economic analysis model is constructed. Cold streams H1 and H2 are segmented according to the input cold stream segmentation temperature. That is, the temperature of heat exchangers H11, H12, H31, and H31 is input. The heat exchanged by heat exchangers H11, H12, H13, H31, H32, and H33 is calculated to complete the cold block segmentation. According to the cold stream information obtained in the first calculation, the segmented cold stream is matched with the hot stream. The cold and hot streams are matched according to the cold block matching position in the independent variable. That is, the heat exchanged by H11, H12, H13, H31, H32, and H33 is distributed according to the matching rules and used as the heat input for H21, H22, H41, H42, H51, H52, H61, and H62 to complete the hot stream matching.

[0087] Step S40, constructing a global optimization model based on the input temperatures and the matching position information;

[0088] In this embodiment, a global optimization model is constructed by combining the obtained input temperatures of each cold stream heat exchanger and the obtained matching position information. This model aims to comprehensively consider indicators such as the heat exchange conditions of each heat exchanger in the methanol hydrogen production system, energy utilization efficiency, economic benefits of the system, and overall system performance. The system state is described through mathematical expressions to provide objective functions and constraints for subsequent optimization solutions.

[0089] Furthermore, in a feasible implementation, in the global optimization model, each of the input temperatures satisfies a preset temperature constraint, and the matching position information is represented by integer coding, satisfying a preset coding constraint range.

[0090] In this example, after determining the input temperature and matching position information, a secondary calculation is required to refine the stream matching matrix. The streams are then fused using a stream fusion strategy to obtain an optimized heat exchange network solution, specifically the matching positions and heat transfer capacity information for the stream processes. The currently obtained heat exchange network solution only considers the segmentation and matching of cold stream blocks, as well as load energy conservation constraints. It does not consider constraints such as temperature feasibility, heat transfer temperature difference, and non-negative temperature during the heat exchange process.

[0091] The optimization objectives of the hydrogen production system are: minimize the heat exchanger area and make changes to the heat exchanger as small as possible to meet the equipment reserved space requirements and reduce the complexity of equipment modification; maximize the system efficiency and minimize the average annual hydrogen cost to meet the system thermodynamic and economic requirements. The objective function is as follows:

[0092] min.F(Area all , LCOH, -η) = f(T H11 , T H12 , T H31 , T H32 , MATCH)

[0093] Where, T H11 , T H12 , T H13 ——Input temperature (°C) of heat exchangers H11, H12, H31, and H32; MATCH——Matching position information of the heat stream corresponding to the heat exchanged by heat exchangers H11, H12, H13, H31, H32, and H33 after one calculation.

[0094] In the objective function, each variable has certain constraints, and the MATCH is represented in a coded form to facilitate simulation.

[0095] Specifically, the matching position information generation process is shown in Table 2.

[0096] Table 2 Pseudo code of matching position information generation process

[0097]

[0098] The program contains five independent variables and three decision variables. Each set of independent variables can constitute a heat exchange network solution. The system constraints are listed in Table 3. The first four independent variables are continuous variables with constraints. The fifth independent variable is an integer constraint representing the matching position, ranging from 1 to 20160. To ensure that this variable is an integer during the optimization process, it is rounded off.

[0099] Table 3 Constraints of methanol hydrogen production system

[0100]

[0101] Step S50 , solving the global optimization model based on a plurality of preset decision variables and a preset algorithm in the process of producing hydrogen from methanol to obtain an optimization decision.

[0102] In this embodiment, the constructed global optimization model is solved based on multiple decision variables preset in the methanol hydrogen production process (such as system efficiency, annual average cost, heat exchange area, etc.) and preset algorithms (such as genetic algorithm, particle swarm algorithm, etc.). The goal of the solution is to find an optimal set of dependent variable values ​​so that the objective function of the global optimization model reaches the optimal value, thereby realizing efficient energy utilization and performance optimization of the methanol hydrogen production system.

[0103] Furthermore, in a feasible implementation manner, the above step S50 may include steps S51 to S52:

[0104] Step S51, generating a curve graph of the global optimization model based on the decision variables through a preset algorithm;

[0105] In this embodiment, a preset optimization algorithm is used to solve the global optimization model. During the solution process, the algorithm explores different value combinations of decision variables and calculates the objective function value of the global optimization model under each combination. Then, these decision variable value combinations are matched with the objective function value to generate a series of points. These points form a curve graph (or surface graph, depending on the number of decision variables) in two-dimensional or multi-dimensional space. This curve graph reflects the performance of the global optimization model under different decision variable values.

[0106] Step S52 : selecting a corresponding decision point in the curve graph according to a preset decision target. The target input temperature and target matching position information corresponding to the decision point are the optimization decisions for the decision target.

[0107] In this embodiment, the decision objective is first identified, such as maximizing energy efficiency, minimizing costs, or maximizing output. Then, within the generated graph, the decision point corresponding to the objective is found. This point is a point on the graph that represents the optimal set of decision variable values ​​that satisfies the objective. For multidimensional graphs, finding the decision point may require projection, slicing, or other methods. Finally, the target input temperatures and target matching position information corresponding to the decision point represent the optimized decision for the objective.

[0108] Specifically, as an example, a global optimization algorithm is used to solve the process, taking the NSGA-II algorithm as an example, and the Pareto curve method is used for target decision-making. The obtained optimization solution is shown in Table 4:

[0109] Table 4 Heat exchange network optimization scheme

[0110]

[0111]

[0112]

[0113]

[0114] To convert it into a dot plot, please refer to Figure 4 , Figure 4 The Pareto frontier curves for Area_all, η, and LCOH for different scenarios are shown, along with the corresponding annual average cost and payback period. Each point on the Pareto frontier corresponds to a non-dominated solution. Point A* corresponds to the minimum heat exchange area of ​​0.2113 m², the minimum system efficiency of 67.03%, and the maximum levelized cost of hydrogen of 2.8853 $ / kg. If minimizing heat exchange area alone is the optimization objective, this point represents the optimal solution, with an annual average system cost of 23,690.2 $ / y and a payback period of 1.60 years. The corresponding outlet temperatures for heat exchangers H11, H12, H31, and H32 are 84.1°C, 86.1°C, 84.0°C, and 127.6°C, respectively. The matching location information label is 4112. Point C* corresponds to the maximum heat exchange area of ​​0.3305 m², the maximum system efficiency of 69.24%, and the minimum levelized cost of hydrogen of 2.7192 $ / y.

[0115] If the optimization objectives are minimizing average annual cost and maximizing system efficiency, this point represents the optimal solution. At this point, the average annual system cost is $22,544.2 per year, the payback period is 1.58 years, and the corresponding outlet temperatures of heat exchangers H11, H12, H31, and H32 are 28.6°C, 121.7°C, 57.8°C, and 141.0°C, respectively. The matching location information tag is 3692. Point B* has the shortest Euclidean distance from the ideal solution and is the game point corresponding to the optimal solution for multi-objective optimization. At this point, the total heat exchange area is 0.2502 m2, the system efficiency is 68.22%, and the levelized cost of hydrogen is $2.7192 per kg. At this point, the average annual system cost is $23,054.5 per year, with a payback period of 1.58 years. The corresponding outlet temperatures of heat exchangers H11, H12, H31, and H32 are 64.2°C, 107.2°C, 69.6°C, and 128.0°C, respectively, and the matching location information tag is 4115. Table 5 lists the resulting parameters for different schemes. The results show that compared to the existing equipment, the heat exchange area increases from 0.0287 m² to 0.2113 m² to 0.3305 m². The optimized scheme is expected to improve system energy efficiency by 21.61% to 23.82%, and reduce the levelized cost of hydrogen by 1.36 to 1.54 $ / kg.

[0116] Table 5 Comparison of different solutions

[0117]

[0118]

[0119] Please refer to Figure 5 , Figure 5 Figure (a) is the existing equipment heat exchange network, Figure (b) is the A* scheme heat exchange network; Figure (c) is the B* scheme heat exchange network; Figure (d) is the C* scheme heat exchange network. Figure 5 Changes to the network structure allow for more efficient matching between streams and between streams and utilities, enabling improved system energy utilization and efficiency. However, an analysis of the network structure reveals that the optimization solution derived from the existing equipment and temperature segmentation method fails to utilize the heat from the backflow gas generated by the pressure swing adsorption (PSA) flushing process. Methanol is fed directly into the reforming reactor without complete preheating, and pure hydrogen generated from the PSA is fed directly into the methanation reactor without complete preheating. This increased heat demand in the reactors leads to increased methanol consumption.

[0120] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the methanol hydrogen production system optimization method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0121] This application also provides a methanol hydrogen production system, please refer to Figure 6, the methanol hydrogen production system comprises:

[0122] The segmentation module 10 is used to obtain the input temperature of each cold stream heat exchanger in the methanol hydrogen production process based on the preset cold stream segmentation temperature point; segment each cold stream in the methanol hydrogen production process according to the input temperature to obtain the heat exchanged by each cold stream heat exchanger;

[0123] A matching module 20 is configured to match each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol-to-hydrogen process according to the exchanged heat, and obtain matching position information;

[0124] A modeling module 30, configured to construct a global optimization model based on the input temperatures and the matching position information;

[0125] The optimization module 40 is used to solve the global optimization model based on a plurality of preset decision variables and a preset algorithm in the methanol-to-hydrogen process to obtain an optimization decision.

[0126] Optionally, the segmentation module 10 is further configured to:

[0127] Obtaining the temperature difference of each cold stream;

[0128] For any one of the cold streams, obtaining the cold stream heat exchangers corresponding to the cold stream, wherein the cold stream heat exchangers are connected in sequence;

[0129] The input temperature of each of the cold stream heat exchangers is calculated in sequence based on the preset cold stream segmentation temperature point and the temperature rise difference.

[0130] Optionally, the segmentation module 10 is further configured to:

[0131] Dividing the cold stream into a plurality of sub-streams according to the input temperatures;

[0132] The heat exchanged by each of the cold streams is calculated according to the input temperature corresponding to each of the sub-streams.

[0133] Optionally, the matching module 20 is further configured to:

[0134] Sort the heat exchangers according to their temperature ranges;

[0135] According to the heat exchanged, the heat exchangers are matched in order from large to small.

[0136] Optionally, the optimization module 40 is further configured to:

[0137] Generate a curve graph of the global optimization model based on the decision variables through a preset algorithm;

[0138] A corresponding decision point is selected in the curve diagram according to a preset decision target, and each target input temperature and target matching position information corresponding to the decision point is an optimized decision for the decision target.

[0139] The methanol-to-hydrogen system provided in this application, employing the methanol-to-hydrogen system optimization method described in the aforementioned embodiments, can address the technical problem of achieving a globally optimal heat exchanger configuration while ensuring efficient system operation. Compared to the prior art, the methanol-to-hydrogen system provided in this application achieves the same beneficial effects as the methanol-to-hydrogen system optimization method described in the aforementioned embodiments. Other technical features of the methanol-to-hydrogen system are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0140] The present application provides a methanol hydrogen production device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the methanol hydrogen production system optimization method in the above-mentioned embodiment one.

[0141] Reference below Figure 7 , which shows a structural schematic diagram of a methanol hydrogen production device suitable for implementing an embodiment of the present application. Figure 7 The methanol hydrogen production equipment shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0142] like Figure 7As shown, the methanol-to-hydrogen plant may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the methanol-to-hydrogen plant. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. Communication device 1009 can allow the methanol-to-hydrogen plant to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a methanol-to-hydrogen plant with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or provided instead.

[0143] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0144] The methanol hydrogen production equipment provided in this application, utilizing the methanol hydrogen production system optimization method described in the aforementioned embodiment, can address the technical problem of achieving a globally optimal heat exchanger configuration while ensuring efficient system operation. Compared to the prior art, the methanol hydrogen production equipment provided in this application achieves the same beneficial effects as the methanol hydrogen production system optimization method described in the aforementioned embodiment. Other technical features of this methanol hydrogen production equipment are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0145] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0146] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0147] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the methanol-to-hydrogen system optimization method in the above-mentioned embodiment.

[0148] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0149] The computer-readable storage medium may be included in the methanol-to-hydrogen device; or may exist independently without being assembled into the methanol-to-hydrogen device.

[0150] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the methanol hydrogen production equipment, the methanol hydrogen production equipment is enabled to: obtain the input temperature of each cold stream heat exchanger in the methanol hydrogen production process based on the preset cold stream segmentation temperature point; segment each cold stream in the methanol hydrogen production process according to each input temperature to obtain the heat exchanged by each cold stream heat exchanger; match each cold stream heat exchanger with each hot stream heat exchanger in the methanol hydrogen production process according to the exchanged heat to obtain matching position information; construct a global optimization model based on each input temperature and the matching position information; solve the global optimization model based on multiple decision variables and a preset algorithm preset in the methanol hydrogen production process to obtain an optimization decision.

[0151] The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0152] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0153] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0154] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned methanol-to-hydrogen system optimization method. This computer-readable storage medium addresses the technical problem of achieving a globally optimal heat exchanger configuration while ensuring efficient system operation. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the methanol-to-hydrogen system optimization method provided in the aforementioned embodiments and are not further elaborated here.

[0155] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned methanol-to-hydrogen system optimization method when executed by a processor.

[0156] The computer program product provided in this application solves the technical problem of achieving a globally optimal heat exchanger configuration while ensuring efficient system operation. Compared to the prior art, the beneficial effects of the computer program product provided in this application are similar to those of the methanol-to-hydrogen system optimization method provided in the aforementioned embodiment, and are not further elaborated here.

[0157] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for optimizing a methanol-to-hydrogen system, characterized in that: The methanol hydrogen production system optimization method comprises: Based on the preset cold stream split temperature point, the input temperature of each cold stream heat exchanger in the methanol hydrogen production process is obtained; Dividing each cold stream in the methanol-to-hydrogen process according to each input temperature to obtain the heat exchanged by each cold stream heat exchanger; Matching each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol-to-hydrogen process according to the exchanged heat to obtain matching position information; Building a global optimization model based on each of the input temperatures and the matching position information; The global optimization model is solved based on multiple decision variables and a preset algorithm in the methanol-to-hydrogen process to obtain an optimized decision.

2. The method for optimizing a methanol-to-hydrogen system according to claim 1, wherein: The step of obtaining the input temperature of each cold stream heat exchanger in the methanol-to-hydrogen process based on the preset cold stream segmentation temperature point includes: Obtaining the temperature difference of each cold stream; For any one of the cold streams, obtaining the cold stream heat exchangers corresponding to the cold stream, wherein the cold stream heat exchangers are connected in sequence; The input temperature of each of the cold stream heat exchangers is calculated in sequence based on the preset cold stream segmentation temperature point and the temperature rise difference.

3. The method for optimizing a methanol-to-hydrogen system according to claim 1, wherein: The step of dividing each cold stream in the methanol-to-hydrogen process according to each input temperature comprises: Dividing the cold stream into a plurality of sub-streams according to the input temperatures; The heat exchanged by each of the cold streams is calculated according to the input temperature corresponding to each of the sub-streams.

4. The method for optimizing a methanol-to-hydrogen system according to claim 1, wherein: The step of matching each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol to hydrogen process according to the exchanged heat comprises: Sort the heat exchangers according to their temperature ranges; According to the heat exchanged, the heat exchangers are matched in order from large to small.

5. The method for optimizing a methanol-to-hydrogen system according to claim 1, wherein: In the global optimization model, each input temperature satisfies a preset temperature constraint condition, and the matching position information is represented by integer coding, satisfying a preset coding constraint range.

6. The method for optimizing a methanol-to-hydrogen system according to any one of claims 1 to 5, wherein: The step of solving the global optimization model based on multiple decision variables and a preset algorithm in the methanol-to-hydrogen process to obtain an optimization decision includes: Generate a curve graph of the global optimization model based on the decision variables through a preset algorithm; A corresponding decision point is selected in the curve diagram according to a preset decision target, and each target input temperature and target matching position information corresponding to the decision point is an optimized decision for the decision target.

7. A methanol hydrogen production system, characterized in that: The methanol hydrogen production system comprises: A segmentation module is used to obtain the input temperature of each cold stream heat exchanger in the methanol hydrogen production process based on a preset cold stream segmentation temperature point; and to segment each cold stream in the methanol hydrogen production process according to each input temperature to obtain the heat exchanged by each cold stream heat exchanger; a matching module, configured to match each of the cold stream heat exchangers with each of the hot stream heat exchangers in the methanol-to-hydrogen process according to the exchanged heat, and obtain matching position information; A modeling module, configured to construct a global optimization model based on the input temperatures and the matching position information; The optimization module is used to solve the global optimization model based on multiple decision variables and preset algorithms in the methanol-to-hydrogen process to obtain an optimized decision.

8. A methanol hydrogen production device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the methanol-to-hydrogen system optimization method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the methanol hydrogen production system optimization method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the methanol-to-hydrogen system optimization method according to any one of claims 1 to 6 are implemented.

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