Design support method, design support system, and design support program

Abstract

To support designing of manufacturing processes by optimizing raw material usage.SOLUTION: A manufacturing process design support method is provided, the method involving causing one or more computers to perform steps of: creating a process route for a substance corresponding to a raw material, including multiple uses and a distribution ratio for distributing the raw material to each of the multiple uses; optimizing the distribution ratio based on an objective function and constraints for computing a given evaluation index; and outputting the optimized distribution ratio.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present technology relates to a design support method, a design support system, and a design support program. [Background technology]

[0002] There are countless combinations of recovering substances such as carbon and converting them into chemical products, and it has been difficult to evaluate their advantages compared to processes for producing chemical products from fossil resources, etc. Previously, it was proposed to model alternative pathways for recovering and utilizing carbon and identify the optimal one (e.g., Non-Patent Document 1).

[0003] Furthermore, in the case of a process synthesis problem, it is difficult to set the target scope, prerequisites, objective function, etc., and it is often difficult to formulate it as a mathematical programming problem. Conventionally, a method has been proposed in which a structure that includes all structures to be considered as its substructures is considered, and the process synthesis problem is formulated as a linear programming problem based on that structure, and a solution is found (for example, Non-Patent Document 2). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Wonsuk Chung, Hyungmuk Lim, Jeehwan S. Lee, Ali S. Al-Hunaidy, Hasan Imran, Aqil Jamal b, Kosan Roh, Jay H. Lee, “Computer-aided identification and evaluation of technologies for sustainable carbon capture and utilization using a superstructure approach”, Journal of CO2 Utilization, 25 April 2022 [Non-patent document 2] Shinji Hasebe, "Process Synthesis Using Superstructures," Yokokan, April 2016, Vol. 10, No. 1, pp. 38-46 Summary of the Invention [Problem to be solved by the invention]

[0005] In chemical manufacturing, complex constraints such as cost and environmental impacts can make it difficult to determine, for example, what products and how much of each should be produced using a given raw material. The purpose of this disclosure is to optimize raw material usage and support the design of manufacturing processes. [Means for solving the problem]

[0006] The present disclosure includes the following aspects. [1] creating a process route for a raw material, the process route including a plurality of uses and a distribution ratio for distributing the raw material to each of the plurality of uses; Optimizing the distribution rate based on an objective function and constraints for calculating a predetermined evaluation index; outputting information representing the optimized distribution ratio; A manufacturing process design aid method, wherein the method is executed by one or more computers. [2] The constraints include internal constraints necessary to implement the usage and external constraints based on preconditions. The design support method described in [1]. [3] The internal constraints include material balances in carrying out the utilization method and / or product specifications that the product of the utilization method must meet. [2] The design support method described in [2]. [4] The external constraints include at least one of the demand for the product of the use, the supply capacity of the raw material, the greenhouse gas reduction target, and the budget. [2] The design support method described in [2]. [5] The evaluation indexes include economic indexes and environmental indexes. A design support method according to any one of [1] to [4]. [6] The evaluation index includes a plurality of indexes, The information representing the optimized distribution ratios includes information representing a plurality of distribution ratios corresponding to discrete data points in a Pareto solution. A design support method according to any one of [1] to [5]. [7] The optimization is performed by nonlinear programming. A design support method according to any one of [1] to [6]. [8] The raw material is a material recovered from the effluent of another process; the process route includes multiple recovery methods for recovering the material from one or more sources; The optimizing includes optimizing a distribution ratio for distributing the effluent to the plurality of recovery methods. A design support method according to any one of [1] to [7].

[0007] The content of the means for solving the problem can be provided as a device such as a computer, a system including multiple devices, a method executed by a computer, or a program executed by a computer. The program can also be executed over a network. A recording medium storing the program may also be provided. [Effects of the Invention]

[0008] The present invention can optimize the use of raw materials and assist in the design of manufacturing processes. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram for explaining an outline of the embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of the support device. [Figure 3]FIG. 3 is a processing flow diagram showing an example of the optimization processing. [Figure 4] FIG. 4 is a diagram illustrating an example of information related to costs and LCA. [Figure 5] FIG. 5 is a diagram for explaining the output results. [Figure 6] FIG. 6 is a diagram for explaining the output results. [Figure 7] FIG. 7 is a diagram for explaining the output results. [Figure 8] FIG. 8 is a diagram for explaining an overview of the first modification. [Figure 9] FIG. 9 is a diagram for explaining an outline of the second modification. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, the embodiments will be described with reference to the drawings.

[0011] FIG. 1 is a diagram for explaining an outline of an embodiment. In this embodiment, we propose how to use a substance after recovering it from a mixture containing the substance to be used as a raw material. For example, we propose how to use recovered carbon dioxide (CO2) in a variety of ways and in what ratio. In the example of FIG. 1, the utilization methods include CO2 storage, methane (CH4) synthesis, methanol (CH3OH) synthesis, carbonate (e.g., DEC (Diethyl carbonate)) synthesis, and carbonate production. The allocation ratio is determined based on indicators such as Net Present Value (NPV), which comprehensively represents costs and benefits, and Potential CO2 Reduction (PCR). Indicators can also be optimized based on internal constraints required for implementing the utilization method, such as material balance and product specifications, and external constraints resulting from external prerequisites for the utilization method, such as demand, raw material supply capacity, CO2 reduction targets, and budgets.

[0012] <Device configuration> FIG. 2 is a diagram showing an example of the configuration of the support device 1. The support device 1 is a computer, and includes a processor 11, a storage device 12, an input / output device 13, and a communication interface (IF) 14. In this embodiment, the support device 1 performs processing for optimizing a chemical manufacturing process. The processor 11 is a calculation processor such as a CPU (Central Processing Unit). The processor 11 is a processing device that executes a program to perform processing according to the embodiment. For example, the processor 11 also functions as a process simulator or an optimization solver by executing a predetermined program. The storage device 12 includes a main storage device such as a random access memory (RAM) or a read-only memory (ROM), and an auxiliary storage device (secondary storage device) such as a hard-disk drive (HDD), a solid-state drive (SSD), or a flash memory. The main storage device temporarily stores programs read by the processor 11 and secures a working area for the processor 11. The auxiliary storage device stores programs executed by the processor 11 and information read and written by the programs. The storage device 12 stores data such as the cost required for chemical production and indicators related to the environmental friendliness of chemical production. The input / output device 13 is a user interface, such as an input device such as a keyboard or a mouse, an output device such as a monitor, or an input / output device such as a touch panel. The communication IF 14 is, for example, a network adapter, and communicates with other computers based on a predetermined protocol. The support device 1 transmits at least a part of the information stored in the storage device 12, such as the above-mentioned costs and predetermined index values, to other computers via the communication IF 14 and, for example, an IP (Internet Protocol) network. The information may be obtained from a computer.

[0013] <Optimization process> FIG. 3 is a processing flow diagram showing an example of optimization processing. The processing in FIG. 3 is started based on a user's operation. The processor 11 of the support device 1 acquires information about the raw material (FIG. 3: S1). In this step, the processor 11 presents a predetermined dialog box to the user via the input / output device 13, for example, and acquires information via the input / output device 13 based on the user's operation. The acquired information includes the type, concentration, supply amount (supply amount per unit time) of the raw material discharged from the emission source. For example, in the case of an LNG (Liquefied Natural Gas) gasifier, Information such as a concentration of 5 wt% and a flow rate of 100,000 tons / hour is acquired for CO2 emitted from a bin power plant. In this embodiment, the recovery method, such as absorption, membrane separation, or adsorption, is assumed to be predetermined. The information about the raw material may further include information indicating the purity of the raw material according to the recovery method.

[0014] After S1, the processor 11 performs cost, LCA (Life Cycle Assessment), and The information on the process is acquired (FIG. 3: S2). This information is also input via the input / output device 13 based on, for example, a user's operation. Alternatively, the information may be acquired from a database held by another device via a network based on, for example, a user's operation.

[0015] The cost information is information for calculating the cost required for each use. It may also include the sales price of the chemicals produced in each use. The LCA information is as follows: For example, this information can be obtained from an existing emissions intensity database, and represents the amount of CO2 emissions per unit of activity. Figure 4 shows an example of information related to costs and LCA. The table in Figure 4 includes attributes of target, price, and emissions intensity. In the target field, items representing the equipment, chemicals, and energy required to implement each use method and the products produced by each use method are registered.

[0016] The price field is registered as the market price per unit, which corresponds to the cost information mentioned above. The market price can be obtained from publicly available information such as the Ministry of Economy, Trade and Industry's Annual Report on Production Statistics (https: / / www.meti.go.jp / statistics / tyo / seidou / index.html) or from local data. The cost of storage includes transportation costs and the cost of using high-pressure wells for storage. Transportation costs are set based on existing information. For example, the cost of fuel consumption (tons d) is set based on information such as IEAHG, “The Status and Challenges of CO2 Shipping Infrastructures,” 28 July 2020. The cost of the equipment according to the scale of the equipment can be calculated according to the type of equipment, for example, the volume, area, load, etc. For example, the cost of the equipment according to the scale of the equipment can be calculated according to the type of equipment, for example, the volume, area, load, etc. The cost of the equipment may be calculated based on the calculation method described in the literature.

[0017] The amount of CO2 emitted per unit is registered in the emission intensity field, which corresponds to the LCA information mentioned above. The emission intensity is calculated from the inventory database "IDEA" (https: / / riss.aist.go.jp / lca-consortium / activity / lca-idea / ). It is possible to use publicly available information such as the above, or information contained in the database of LCA software such as SimaPro. Note that the information is not limited to the example in Figure 4, and may further include information on expenses equivalent to the running costs of the equipment, useful life, depreciation costs, etc.

[0018] The process information is information representing reactions for producing chemical products and the like in each application. The process information is, for example, information representing the process design of a chemical plant, and is a model that can be input into a process simulator. The model can be defined by connecting unit operations such as distillation, extraction, crystallization, membrane separation, dust collection, sedimentation, filtration, and gas absorption with streams that represent the connections between the unit operations. Each unit operation is expressed by a physical model including balance equations representing material balance and energy balance, as well as rate equations such as reaction rate equations and mass and heat transfer rate equations. Furthermore, for each stream, operating parameters such as temperature, pressure, and flow rate, as well as material balances such as composition, can be defined so that the product meets specified product specifications. The process design information is input via the input / output device 13, for example, based on user operation. In this embodiment, information representing the process flow is input for each of methanation, methanol synthesis, carbonate synthesis, carbonate production, and the like.

[0019] After S2, the processor 11 creates a process route (S3 in FIG. 3). In this step, for example, the raw material input in S1 is associated with the expected usage, and information indicating allocation to the usage methods shown in FIG. 1 is created. For such a process route, the optimal chemical manufacturing process is proposed by determining the ratios χ1, α1, β1, γ1, and δ1 that allocate the amount m1 of captured CO2 to each of the usage methods, such as storage, methanation, methanol synthesis, carbonate synthesis, and carbonate production.

[0020] After S3, the processor 11 sets an objective function (FIG. 3: S4). In this step, evaluation indicators such as NPV and PCR are set based on, for example, a user's operation. NPV is an economic indicator and can be calculated based on, for example, the following formula (1). NPV=-(C i +C w )+(RX)×(1-t)×{1-(1+i) -n} / i+D×t×{1-(1+i)} -m / i+(C s +C w ) / (1+i) n (1) In addition, C i is the fixed equipment cost, which can be calculated by reading it from the database shown in Figure 4, or by calculating the cost according to the equipment size using a predetermined formula. Cw is the working capital cost, which can be calculated by reading the costs of the chemicals and electricity required for the process from the database shown in Figure 4 and adding them up. C s is the residual value, and the value after the useful life can be calculated using a predetermined formula. R is the revenue from product sales, which can be calculated based on the product price and demand. X is the annual expense. D is the annual depreciation cost, which can be calculated using a predetermined formula based on the useful life of each piece of equipment. t is the carbon tax rate. i is the after-tax rate of return. n is the useful life of the plant. m is the depreciation period. As described above, costs and profits can be calculated using the prices shown in Figure 4. The tax rate may be determined in advance, or input may be accepted in S4. The useful life and depreciation period may be determined in advance for each type of equipment, or input may be accepted in S4. The above formula (1) is an objective function that takes a larger value as costs decrease and profits increase, and a high NPV is desirable. Costs and profits are calculated by integrating values ​​for each of multiple uses. Therefore, the scale of the process (and therefore the equipment scale, costs, profits, and NPV value) will change depending on the amount of CO2 captured m1 shown in Figure 1 and the ratio at which it is allocated to multiple uses.

[0021] Furthermore, when dealing with multiple indicators, it is desirable to normalize the indicator values. The normalized NPV can be calculated, for example, using the following formula (2). Normalized NPV=exp(NPV / abs(max NPV)) ···(2) In this example, if the NPV is greater than zero, the normalized NPV will be greater than 1. If the NPV is less than zero, the normalized NPV will be less than 1. Note that the above formulas (1) and (2) are merely examples, and the method for calculating NPV is not limited to formulas (1) and (2). For example, some elements, such as carbon tax, may not be used, or other elements may be added to the calculation.

[0022] PCR is an environmental indicator and can be calculated, for example, based on the following formula (3): PCR=m CO2_stored +m CO2_utilized +m CO2_avoided -m CO2_emitted ···(3) m CO2_emitted is the CO2 emissions associated with energy use. In other words, it is the amount of CO2 emitted to process raw materials using each method of use, and can be calculated using the emission intensity shown in Figure 4. CO2_stored is the amount of CO2 stored. In other words, it is the amount of CO2 allocated to "storage." In the example in Figure 1, it can be calculated as χ1 × m1. m CO2_utilized is the amount of CO2 used. In other words, it is the amount of CO2 processed as a raw material by a method other than storage. In the example shown in Figure 1, it can be calculated as (α1 + β1 + γ1 + δ1) × m1. m CO2_avoidedis the amount of CO2 emissions avoided by replacing the existing method. For the existing method, the user sets one utilization method, such as methane synthesis (methanation), and calculates the CO2 emissions reduction compared to using the total amount of raw material m1 for methanation using the emission intensity shown in Figure 4. The CO2 emissions of methane synthesized using the existing method can be stored in a database that stores costs and LCA data shown in Figure 4. In other words, the above equation (3) is an objective function that takes a larger value the smaller the CO2 emissions, and a high PCR is desirable. Note that the CO2 emissions, etc., are also integrated for each of the multiple utilization methods. Therefore, the PCR value changes depending on the amount of CO2 captured m1 shown in Figure 1 and the ratio at which it is allocated to the multiple utilization methods.

[0023] Furthermore, the normalized PCR can be calculated, for example, by the following formula (4). Normalized PCR=exp(PCR / abs(max PCR)) ···(4) In this example, when PCR is greater than zero, it indicates that CO2 emissions have been reduced, and the normalized PCR is greater than 1. When PCR is less than zero, it indicates that CO2 emissions have increased, and the normalized PCR is less than 1. Note that the above-mentioned formulas (3) and (4) are also examples, and the method of calculating PCR is not limited to formulas (3) and (4). For example, m CO2_avoided Some elements may not be used, or other elements may be added for calculation.

[0024] After S4, the processor 11 sets internal and external constraints (S5 in FIG. 3). The internal constraints include at least one of the material balance based on the process flow described above and the product specifications. The product specifications determine the operating parameters and material balance required to obtain a product that satisfies the specifications. The external constraints include at least one of the demand for the product, the supply capacity of raw materials, the target CO2 emission reduction amount, and the budget.

[0025] After S5, the processor 11 performs optimization calculations (S6 in Figure 3). In this step, an existing optimization solver is used, for example, to determine how to allocate raw materials to usages that will yield desirable index values, while satisfying the internal and external constraints described above. The energy costs, equipment scale, and costs required for each usage calculated by the process simulator change depending on the ratio of raw materials allocated to multiple usages (e.g., χ1, α1, β1, γ1, and δ1 in Figure 1), and the magnitude of the calculated index value also changes accordingly. Therefore, depending on the constraints, a higher index value may be achieved by using all the raw materials in one usage, or by allocating the raw materials to multiple usages. When performing multi-objective optimization using multiple indexes, such as NPV and PCR, a Pareto solution is obtained.

[0026] Note that CO2 membrane separation can be expressed by a nonlinear mathematical formula. When at least one of capture, utilization, and storage includes a process expressed by a nonlinear mathematical formula, a solution that maximizes the above-mentioned objective function can be obtained by a general nonlinear programming method. In other words, when a linear model cannot be sufficiently approximated, an optimization problem that includes a nonlinear model in the objective function or constraints can be solved by a nonlinear solver.

[0027] After S6, the processor 11 outputs the results (FIG. 3: S7). In this step, the index values ​​calculated in S6 and the distribution rates of ingredients to each use are displayed, for example, via the input / output device 13. Note that information indicating the results may be sent to another computer via the communication IF 14.

[0028] Figures 5 to 7 are diagrams for explaining the output results. In the line graphs shown in Figures 5(A) and (B), Figures 6(C) and (D), and Figures 7(E) and (F), the horizontal axis represents normalized PCR and the vertical axis represents normalized NPV. In addition, the 100% stacked vertical bar graphs corresponding to the data points represent the allocation rate of raw materials to each usage.

[0029] (A) and (B) in Figure 5, (C) and (D) in Figure 6, and (E) and (F) in Figure 7 are the results of optimization based on the constraints shown in the following table. For convenience, a combination of the setting values ​​of the constraints will be called a "scenario." [Table 1]

[0030] When optimization was performed under the constraints shown in Scenario A in the table, the results were as shown in Figure 5(A). Note that Scenario A and Scenario B, described below, differ in the external constraint of DEC demand. In other words, assuming there is no upper limit to DEC demand, as shown in Scenario A, and ignoring the CO2 reduction potential (PCR), the profit (NPV) would be higher if the raw material were used to synthesize carbonate (DEC). Furthermore, it can be seen that in order to increase PCR, the proportion of raw material allocated to methanol synthesis must be increased. In this way, S7 outputs results that include information representing multiple allocation rates corresponding to, for example, discrete data points in the Pareto solution. Based on the balance between NPV and PCR shown in these results, users can consider how to use raw materials.

[0031] When optimization was performed under the constraints shown in Scenario B in the table, the results were as shown in Figure 5 (B). As shown in Scenario B, if an upper limit is placed on DEC demand, even when PCR is ignored, the amount of raw material allocated to carbonate (DEC) synthesis remains constant, and the NPV reaches a plateau. Therefore, the results show that it is desirable to allocate a portion of the raw material to methanol synthesis and carbonate production as well. It can also be seen that in order to increase PCR, it is necessary to increase the proportion of raw material allocated to methanol synthesis.

[0032] When optimization was performed under the constraints shown in Scenario C in the table, the results were as shown in Figure 6 (C). Scenario C differs from Scenario A above in the external constraint of DEC demand. Note that carbon pricing is the price placed on CO2 emissions and affects carbon taxes and emissions trading. In other words, as shown in Scenario C, when carbon pricing is taken into account, the overall NPV decreases.

[0033] When optimization was performed under the constraints shown in Scenario D in the table, the results were as shown in Figure 6 (D). Scenario D differs from Scenario B described above in terms of the external constraint of carbon price. As shown in Scenario D, assuming a certain carbon price, the NPV is higher if some of the CO2 is stored rather than used to synthesize methane or methanol. Scenario D also differs from Scenario C described above in terms of the demand for DEC. In Scenario D, the amount of raw material allocated to DEC synthesis is fixed, and the NPV reaches a plateau. The results also show that it is preferable to store some of the raw materials.

[0034] When optimization is performed under the constraints shown in Scenario E in the table, the results are as shown in Figure 7 (E). Scenario E differs from Scenario A above in the external constraint of the DEC price. If the DEC price falls from Scenario A to Scenario C, the allocation of raw materials to DEC synthesis is avoided overall, and instead raw materials are allocated to storage. The overall NPV also falls.

[0035] When optimization was performed under the constraints shown in Scenario F in the table, the results were as shown in Figure 7(F). Scenario F and the above-mentioned Scenario E differ in the type of H2. Specifically, Scenario E uses grey hydrogen for methanation. On the other hand, Scenario F uses green hydrogen for methanation. Grey hydrogen is hydrogen produced using fossil fuels, and relatively large amounts of CO2 are emitted during the production process. Green hydrogen is hydrogen produced, for example, by electrolyzing water, and no CO2 is emitted during the production process. Therefore, in Scenario F, the price of H2 and the CO2 emission coefficient are higher than in Scenario E. Furthermore, as shown in the line graph in Figure 7(F), the higher the PCR, the higher the NPV.

[0036] <Effects> As described above, according to this embodiment, it is possible to optimize the use of raw materials and support the design of manufacturing processes. Furthermore, by performing optimization under constraints, it is possible to perform optimization taking into consideration the difficulty of production, market size (demand), profit margin, location conditions (in other words, the difficulty of storage), raw material procurement method, etc., and to support the identification of a chemical manufacturing process that is preferable from the viewpoint of indicators such as economic efficiency and environmental friendliness.

[0037] <Variation 1> Not only the utilization method, but also multiple raw material emission sources (supply sources) and / or raw material recovery methods may be included, and various combinations may be created in the process route creation (S3 in FIG. 3). FIG. 8 is a diagram for explaining an overview of a modified example. In the example in FIG. 8, multiple CO2 emission sources and multiple recovery methods (absorption method, membrane separation method, adsorption method) are included. In this way, it becomes possible to consider the optimal utilization method from the viewpoints of economic efficiency and environmental friendliness in a more realistic manner.

[0038] Depending on the combination of recovery method and utilization method, problems may arise such as the raw material being difficult to use in subsequent processes due to insufficient purity or the raw material containing water, etc. Therefore, the user may exclude some process routes in advance in S3 of Fig. 3, thereby reducing the number of combinations of recovery method and utilization method and reducing the load on the optimization process.

[0039] <Variation 2> FIG. 9 is a diagram showing an example of calculating index values ​​such as NPV and PCR for the production of chemical products using existing production methods. The example in FIG. 9 includes a case in which methane is refined using fossil resources and a case in which methanol is synthesized using fossil resources. In other words, index values ​​for a conventional general production method are calculated for a substance produced in the use method of the above-mentioned embodiment. In this way, users can use it as a comparison (standard) when considering the use method of the embodiment.

[0040] <Other> Although the embodiments of the present invention have been exemplified above, the scope of the present invention is not limited to these, and a person skilled in the art can make an appropriate selection while referring to the above conditions.

[0041] The raw material in the above-described embodiment is not limited to CO2, but may be other greenhouse gases (Green House Gases, The indicators are not limited to NPV and PCR, and either one may be used, or may be used in place of or in addition to these. Additionally, other indicators may be used.

[0042] Furthermore, the above-described process simulator and optimization solver may be existing software or proprietary software. Although the embodiment has been described assuming that all processing is performed by a single computer, the support device 1 may instead provide a system in which multiple computers connected to each other via a network share and execute the processing according to the embodiment or execute it in parallel. For example, the process simulator and optimization solver may be executed by different devices. The tables shown in FIG. 4 and other figures are examples of databases, and may be properly normalized to store information in multiple tables, or may be denormalized to store information collectively in a single table.

[0043] The present invention includes a computer program for executing the above-described processing, and a computer-readable recording medium having the program recorded thereon. The recording medium having the program recorded thereon enables the above-described processing by causing a computer to execute the program.

[0044] Here, a computer-readable recording medium refers to a recording medium that stores information such as data and programs electrically, magnetically, optically, mechanically, or chemically and can be read by a computer. Among such recording media, those that can be removed from a computer include flexible disks, magneto-optical disks, optical disks, magnetic tapes, memory cards, etc. Furthermore, recording media that are fixed to a computer include HDDs, SSDs (Solid State Drives), ROMs, etc. [Explanation of symbols]

[0045] 1: Support device 11: Processor 12: Storage device 13: Input / output device 14: Communication interface

Claims

1. creating a process route for a raw material, the process route including a plurality of uses and a distribution ratio for distributing the raw material to each of the plurality of uses; Optimizing the distribution rate based on an objective function and constraints for calculating a predetermined evaluation index; outputting information representing the optimized distribution ratio; A manufacturing process design support method in which one or more computers execute the above.

2. The constraints include internal constraints necessary to implement the usage and external constraints based on preconditions. The design support method according to claim 1 .

3. The internal constraints include material balances in carrying out the utilization method and / or product specifications that the product of the utilization method must meet. The design support method according to claim 2.

4. The external constraints include at least one of the demand for the product of the use, the supply capacity of the raw material, the greenhouse gas reduction target, and the budget. The design support method according to claim 2.

5. The evaluation indexes include economic indexes and environmental indexes. The design support method according to claim 1 .

6. The evaluation index includes a plurality of indexes, The information representing the optimized distribution ratios includes information representing a plurality of distribution ratios corresponding to discrete data points in a Pareto solution. The design support method according to claim 1 .

7. The optimization is performed by nonlinear programming. The design support method according to claim 1 .

8. The raw material is a material recovered from the effluent of another process; the process route includes multiple recovery methods for recovering the material from one or more sources; The optimizing includes optimizing a distribution ratio for distributing the effluent to the plurality of recovery methods. The design support method according to claim 1 .

9. A design support system comprising one or more computers that executes the design support method according to any one of claims 1 to 8.

10. A program for causing one or more computers to execute the design support method according to any one of claims 1 to 8.

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