Modularized and multi-objective optimized carbon emission-energy consumption-cost collaborative optimization method for preparing oil through direct coal liquefaction

Abstract

The invention discloses a modularized and multi-objective optimized carbon emission-energy consumption-cost collaborative optimization method for direct coal liquefaction oil preparation, and relates to the technical field of coal chemical industry technical optimization. Comprises: determining an accounting boundary; constructing a system model of the direct coal liquefaction oil production process; determining emission sources of the chemical process module and the self-contained power plant module; a module based on a direct coal liquefaction oil production system model is linked with a plurality of emission reduction technology interfaces to construct an emission reduction technology module; respectively establishing objective functions; defining constraint conditions of the target function; constructing a multi-objective optimization function of direct coal liquefaction oil production; relevant parameters of the multi-objective optimization model are collected; solving a Pareto optimal solution according to a Python code constructed by a multi-objective optimization problem of direct coal liquefaction for oil production; and outputting an optimization result, namely a Pareto optimal solution set. The modular design is adopted, emission sources of all links of direct coal liquefaction oil preparation are accurately recognized, and a plug-and-play interface is constructed for various emission reduction technologies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal chemical technology optimization, more particularly to a modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of coal direct liquefaction oil. BACKGROUND

[0002] Coal direct liquefaction oil is an important way to convert coal into liquid fuel, and the current technology faces three major challenges: high carbon emission, high energy consumption and high cost.

[0003] Existing emission reduction measures (green electricity replacement, green hydrogen replacement, coupling CCS) mostly adopt a single technical path, and the emission reduction effect of green hydrogen replacement is significant, but the additional energy consumption of water electrolysis to produce hydrogen is extremely high, and the emission reduction effect of CCS technology is significant, but the additional energy consumption and cost are high. Green electricity replacement has a cost advantage because the current green electricity price is slightly lower than the coal power price, but the green electricity emission reduction range is small. There is a trade-off relationship between carbon emission, energy consumption and cost of coal direct liquefaction oil, and there is a lack of a systematic and synergistic optimization model, which will make it difficult to achieve the synergistic optimization of the three goals of carbon emission, energy consumption and cost.

[0004] Therefore, a modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of coal direct liquefaction oil is proposed to solve the problems existing in the prior art, which is a problem that needs to be solved by those skilled in the art. SUMMARY

[0005] Therefore, the present application provides a modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of coal direct liquefaction oil, which establishes a multi-objective optimization model by constructing a modular interface between the chemical process module of coal direct liquefaction oil and the emission reduction technology, systematically integrates green electricity replacement, green hydrogen replacement and CCS technology, and realizes the triple objective Pareto optimization of coal direct liquefaction oil.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme:

[0007] A modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of coal direct liquefaction oil, comprising the following steps:

[0008] S1, determining the accounting boundary;

[0009] S2, based on the accounting boundary, constructing a system model of coal direct liquefaction oil process, including a chemical process module, a self-provided power plant module and a purchased power module;

[0010] S3, determining the emission source of the chemical process module and the self-provided power plant module;

[0011] S4, based on the modules of the coal direct liquefaction oil system model, linking several kinds of emission reduction technology interfaces to construct an emission reduction technology module;

[0012] S5, respectively, establish green electricity replacement ratio x e , green hydrogen replacement ratio x h , CCS carbon capture rate x c as the decision variable of the objective function;

[0013] S6, the constraint conditions of the objective function are determined;

[0014] S7, a multi-objective optimization function of coal direct liquefaction oil is constructed;

[0015] S8, collect the related parameters of the multi-objective optimization model;

[0016] S9, according to the Python code constructed according to the multi-objective optimization problem of coal direct liquefaction oil, the NSGA-III algorithm is used to solve the Pareto optimal solution under the coordination of green electricity, green hydrogen, and coupled carbon capture and storage technology CCS;

[0017] S10, output the optimization result, that is, the Pareto optimal solution set.

[0018] Optionally, the accounting boundary in S1 is the scope of the coal direct liquefaction oil enterprise plant.

[0019] Optionally, the chemical process module and the self-provided power plant module in S2 each include an independent coal pretreatment sub-module, a coal slurry preparation sub-module, a liquefaction sub-module, a gas purification sub-module, and a hydrogenation cracking and separation sub-module.

[0020] Optionally, the module based on the coal direct liquefaction oil system model in S4 links the specific content of the interfaces of several emission reduction technologies: the green electricity replacement interface to the power consumption end of each module, the green hydrogen replacement interface to the liquefaction sub-module, and the CCS technology interface to the carbon emission source of each module.

[0021] Optionally, the several emission reduction technologies in S4 include green electricity replacement, green hydrogen replacement, and coupled carbon capture and storage technology CCS.

[0022] Optionally, the specific content of the objective function in S5, which respectively establishes green electricity penetration ratio x e , green hydrogen penetration ratio x h , and CCS carbon capture rate x c as the decision variable, includes the objectives of minimizing carbon emissions, minimizing total energy consumption, and minimizing production cost:

[0023] Minimize carbon emissions:

[0024] f CO2 = f base -η e x e Elec base -ηh x h H total -η c x c F base ;

[0025] Minimize total energy consumption:

[0026]

[0027] Minimize production cost:

[0028] f cost =C base +ΔC e x e Elec base +ΔC h x h H total +ΔC c x c E base输入 ;

[0029] where f CO2 , f energy , f cost are the carbon emissions, energy consumption, and cost per ton of oil, respectively, in units of tCO2 / t, kgce / toe, and yuan / t; F base , Elec base , E base输入 , E base输出 , C base are the reference carbon emissions, reference electricity consumption, reference total input energy consumption, reference total output energy consumption, and reference cost per unit of product from a coal direct liquefaction oil plant, in units of tCO2 / t, kWh / t, kgce, kgce, and yuan / t, respectively; H total is the hydrogen consumption per unit of product, in units of kgH2 / t; x e , x h , x c are the green electricity replacement ratio, green hydrogen replacement ratio, and CCS carbon capture rate, respectively; η e is the carbon emission reduction per unit of green electricity replacement, in units of kgCO2 eq / kWh; η h is the carbon emission reduction per unit of green hydrogen replacement, in units of kgCO2 eq / kg H2; η c is the carbon emission reduction per unit of captured CO2, η c = 1; ΔE h输入 and ΔE c输入 are the additional input energy consumption from green hydrogen replacement and coupling with CCS, in units of kgce; M is the product output of coal direct liquefaction oil, in units of tons of standard oil (toe); ΔCe , AC h , AC c respectively are green electricity replacement, green hydrogen replacement and coupled CCS added cost.

[0030] Optionally, the specific content of the constraint condition of the objective function in S6 is:

[0031] 0 <= x e , x h <= 1;

[0032] x c <= 0.97;

[0033] f CO2 > > 0;

[0034] f energy <= 1500;

[0035] Wherein, f CO2 ton of oil carbon emission, unit tCO2 / t, f energy ton of oil energy consumption, unit kgce / toe, x e , x h , x c respectively are green electricity replacement ratio, green hydrogen replacement ratio and CCS carbon capture rate.

[0036] Optionally, the specific content of the multi-objective optimization function of coal direct liquefaction oil in S7 is:

[0037]

[0038] Wherein, minF(x) is a multi-objective optimization function, T is a matrix transpose, is a constraint condition range, f CO2 ton of oil carbon emission, unit tCO2 / t, f energy ton of oil energy consumption, unit kgce / toe, f cost (x) is ton of oil cost, unit yuan / t, x e , x h , x c respectively are green electricity replacement ratio, green hydrogen replacement ratio and CCS carbon capture rate.

[0039] Through the above technical solution, compared with the prior art, the present application provides a modular and multi-objective optimization coal direct liquefaction oil carbon emission-energy consumption-cost collaborative optimization method, which has the following advantages:

[0040] 1) Adopting modular design, accurately identifying the emission source of each link of coal direct liquefaction oil, constructing plug-and-play interface for various emission reduction technologies; the design supports efficient replacement of emission reduction modules, avoids global recalculation, and significantly improves system optimization efficiency;

[0041] 2) Applying NSGA-III algorithm, efficiently searching for optimal solution in three-dimensional target space composed of carbon emission, energy consumption and cost by introducing reference point guiding mechanism and adaptive genetic operators (SBX crossover, PM mutation), the algorithm effectively solves the problem of mutual conflict between carbon emission, energy consumption and cost in single target optimization, and realizes the collaborative optimization of the three in the process of coal direct liquefaction oil. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0043] Figure 1 A flow chart of a modular and multi-objective optimization coal direct liquefaction oil carbon emission-energy consumption-cost collaborative optimization method provided by the present application;

[0044] Figure 2 A coal direct oil liquefaction modularization and emission reduction technology interface diagram under enterprise plant accounting boundary provided by the present application;

[0045] Figure 3 A flow chart of NSGA-III algorithm provided by the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] Referring to Figure 1 The present application discloses a modular and multi-objective optimization coal direct liquefaction oil carbon emission-energy consumption-cost collaborative optimization method, comprising the following steps:

[0048] S1, determining the accounting boundary;

[0049] S2, based on the accounting boundary, a system model of coal direct liquefaction oil process is constructed, including a chemical process module, a self-provided power plant module and a purchased power module;

[0050] S3, determining the emission sources of the chemical process module and the self-provided power plant module;

[0051] Specifically, the emission sources are determined according to the actual production of the factory; generally, the emission sources include direct emission sources and indirect emission sources; the direct emission sources include process treatment process emissions in the chemical process module, and combustion emissions of coal, diesel and steam in the self-provided power plant module; the indirect emissions mainly include purchased power consumption.

[0052] S4, based on the module of the coal direct liquefaction oil system model, a plurality of emission reduction technology interfaces are linked to construct an emission reduction technology module;

[0053] S5, the objective functions with green electricity replacement ratio x e , green hydrogen replacement ratio x h , and CCS carbon capture rate x c as decision variables are respectively established;

[0054] S6, the constraint conditions of the objective functions are determined;

[0055] S7, a multi-objective optimization function of coal direct liquefaction oil is constructed;

[0056] S8, the related parameters of the multi-objective optimization model are collected;

[0057] Specifically, the data of monthly material input and output, production operation, and various component analysis reports of a certain coal direct liquefaction oil plant, as well as the carbon content of raw materials, energy and products are collected;

[0058] The carbon inventory and energy consumption report of a certain coal direct liquefaction oil plant is collected to obtain the benchmark carbon emission (tCO2 / t), benchmark power consumption (kWh / t), benchmark total input energy consumption (kgce), benchmark total output energy consumption (kgce), benchmark cost (yuan / t), and unit product hydrogen consumption (kgH2 / t);

[0059] The carbon emission factors of regional coal power, green power, coal hydrogen and green hydrogen are collected;

[0060] The unit cost of regional coal power, green power, coal hydrogen, green hydrogen and CCS technology is collected.

[0061] S9, according to the Python code constructed for the multi-objective optimization problem of coal direct liquefaction oil, the NSGA-III algorithm is used to solve the Pareto optimal solution under the cooperation of green electricity, green hydrogen and coupled carbon capture and storage technology CCS;

[0062] Specifically, the NSGA-III algorithm flowchart is shown inFigure 3 As shown, it includes six steps: initialization of population, non-dominated sorting, reference point association, environment selection, genetic operation, and termination condition judgment.

[0063] In the initialization of the population, an initial population is randomly generated, with a population size of N, and each individual contains three decision variables (green electricity replacement ratio x e , green hydrogen replacement ratio x h , CCS carbon capture rate x c ) and meets the constraint conditions.

[0064] The objective function values f CO2 , f energy , f cost of each individual in the population are calculated, non-dominated sorting is performed, and the population is divided into multiple non-dominated levels (Fronts). The first level (Front 1) is the solution in the current population that is not dominated by any other individual, the second level (Front 2) is the solution that is dominated by the first level individual but not by other level individuals, and so on.

[0065] In the reference point association, pre-defined reference points (which are uniformly distributed in the objective space) are used to maintain the diversity of the population. By standardizing the objective values and calculating the distance of each individual to the reference line, each individual is associated to the nearest reference point:

[0066]

[0067] where f = (f CO2 , f energy , f cost ), z j is the jth reference point, and z is the whole of z 1 , z 2 , …, z K , K takes 12, and d(f, Z) is the distance from f to Z.

[0068] In the environment selection, individuals are selected from the non-dominated levels in turn until the population size reaches N. The specific steps are as follows:

[0069] Starting from the first level, individuals in the non-dominated layer are added to the next generation population layer by layer until a certain layer (assuming the Lth layer) cannot be completely added.

[0070] For the Lth layer, use the reference point association mechanism to select some individuals to maintain diversity, and preferentially select individuals associated with reference points associated with fewer individuals.

[0071] In genetic operations, genetic operations (crossover and mutation) are performed on the current population to generate offspring. Specifically, simulated binary crossover (SBX) is used to perform a crossover operation on selected parent individuals p1 and p2 to generate offspring individuals c1 and c2. The crossover probability P... c =0.9, distribution index η c =15.

[0072]

[0073] The offspring are mutated using polynomial mutation (PM), with a mutation probability P. m =0.1, distribution index η m =20.

[0074]

[0075] Where, δ i By the distribution index η m Control, x′ i The value of the decision variable after the mutation, x i δ is the current value of the decision variable that is about to mutate. i For variable asynchronous length, The maximum allowed value for the decision variable. x i This represents the minimum allowed value for the decision variable.

[0076] In the termination condition judgment, it is determined whether the number of iterations has reached the preset maximum number of iterations (e.g., 200 generations) or whether the solution set has converged (e.g., the change in the optimal solution is less than a threshold for several consecutive generations). If the termination condition is met, the non-dominated solution set (Pareto optimal solution set) in the current population is output; otherwise, the process returns to the non-dominated sorting step to continue iteration.

[0077] S10. Output the optimization results, i.e., the Pareto optimal solution set.

[0078] Furthermore, the accounting boundary in S1 is the plant area of ​​the coal direct liquefaction to oil production enterprise.

[0079] Specifically, since the modular optimization of coal direct liquefaction to oil emission reduction technology is aimed at the entire production process of coal direct liquefaction to oil, excluding raw material mining, raw material transportation, product transportation, etc., the carbon emission, energy consumption, and cost accounting boundaries are determined based on the scope of the enterprise's factory production activities.

[0080] Furthermore, both the chemical process module and the self-owned power plant module in S2 include independent coal pretreatment submodules, coal slurry preparation submodules, liquefaction submodules, gas purification submodules, and hydrocracking and separation submodules.

[0081] Specifically, the liquefaction submodule includes a coal gasification hydrogen production submodule and a water-gas conversion submodule.

[0082] Furthermore, in S4, the modules based on the coal direct liquefaction to oil system model link several emission reduction technology interfaces as follows: the green electricity substitution interface is connected to the power consumption end of each module, the green hydrogen substitution interface is connected to the liquefaction sub-module, and the CCS technology interface is connected to the carbon emission source of each module.

[0083] For details, see Figure 2 As shown, the green electricity substitution technology interface connects to the power consumption terminals of each sub-module within the chemical process module, replacing the original coal-fired power generation and purchased coal-fired power with purchased green electricity. Its emission reduction technology module considers two scenarios: one is replacing the original process's power demand with green electricity derived from photovoltaic power generation, and the other is replacing the original process's power demand with green electricity derived from wind power.

[0084] The green hydrogen substitution technology interface connects to the coal gasification hydrogen production submodule within the chemical process module and the self-owned power plant module, replacing the original coal gasification hydrogen production process with water electrolysis. Its emission reduction technology module considers two scenarios: one, the electricity for green hydrogen production comes from a mixture of coal-fired and green electricity; two, all the electricity for green hydrogen production comes from green electricity.

[0085] The CCS technology interface connects to the main carbon emission sources of each module in the chemical process module and the self-contained power plant module, capturing emitted CO2. Its emission reduction technology module considers two scenarios: one where only the chemical process module is coupled with CCS technology, and the other where both the chemical process module and the self-contained power plant process module are coupled with CCS technology.

[0086] Furthermore, several emission reduction technologies in S4 include: green electricity substitution, green hydrogen substitution, and coupled carbon capture and storage (CCS) technology.

[0087] Specifically, the first step is to determine the decision variables, including the green electricity penetration rate x. e Green hydrogen permeation ratio x h CCS carbon capture rate x c The boundary range is:

[0088] x e ∈[0,1];

[0089] x h ∈[0,1];

[0090] x c ∈[0,0.97];

[0091] Where, x c The value of ≤0.97 is because the current upper limit of CCS capture technology is close to 0.97. If the technology breaks through in the future, this value can be further close to 1.

[0092] Furthermore, in S5, a system is established with green electricity penetration ratio x e, green hydrogen penetration ratio x h , CCS carbon capture rate x c The specific content of the objective function, which is a decision variable, is as follows: the objectives of minimizing carbon emissions, minimizing total energy consumption, and minimizing production cost:

[0093] Minimize carbon emissions:

[0094] f CO2 = F base - η e x e Elec base - η h x h H total - η c x c F base ;

[0095] Minimize total energy consumption:

[0096]

[0097] Minimize production cost:

[0098] f cost = C base + ΔC e x e Elec base + ΔC h x h H total + ΔC c x c E base输入 ;

[0099] In the formula, f CO2 , f energy , f cost are the carbon emissions per ton of oil, the energy consumption per ton of oil, and the cost per ton of oil, respectively, with units of tCO2 / t, kgce / toe, and yuan / t; F base , Elec base , E base输入 , E base输出 , C base are the reference carbon emissions per unit product, the reference power consumption, the reference total input energy consumption, the reference total output energy consumption, and the reference cost of the coal direct liquefaction oil plant, respectively, with units of tCO2 / t, kWh / t, kgce, kgce, and yuan / t; H total is the hydrogen consumption per unit product, with a unit of kgH2 / t; x e , x h , x c are the green electricity replacement ratio, the green hydrogen replacement ratio, and the CCS carbon capture rate, respectively; ηe is the carbon emission reduction per unit of green electricity replacement, in kgCO2 eq / kWh; η h is the carbon emission reduction per unit of green hydrogen replacement, in kgCO2 eq / kg H2; η c is the carbon emission reduction per unit of CO2 capture; η c = 1; ΔE h输入 and ΔE c输入 are the input energy consumption of green hydrogen replacement and coupling CCS, respectively, in kgce; M is the product output of coal direct liquefaction to oil, in tons of standard oil (toe); ΔC e , ΔC h , and ΔC c are the costs of green electricity replacement, green hydrogen replacement, and coupling CCS, respectively.

[0100] Specifically,

[0101] η e = EF 煤电 - EF 绿电 ;

[0102] η c = EF 煤氢 - EF 绿氢 ;

[0103] wherein η e is the carbon emission reduction per unit of green electricity replacement, in kgCO2 eq / kWh, η c is the carbon emission reduction per unit of CO2 capture, in kgCO2 eq / t, and η c = 1; EF 煤电 , EF 绿电 , EF 煤氢 , and EF 绿氢 are the carbon emission factors of regional coal power, green electricity, coal hydrogen, and green hydrogen, respectively.

[0104]

[0105]

[0106] wherein ΔE h输入 and ΔE c输入 are the input energy consumption of green hydrogen replacement and coupling CCS, respectively, in kgce; k i is the conversion standard coal coefficient of the input i-th energy and energy-consuming working medium, in kilogram of standard coal per ton (kgce / t) or kilogram of standard coal per kilowatt-hour [kgce / (kW·h)] or kilogram of standard coal per cubic meter (kgce / m 3 ); E iis the energy and energy-consuming working medium physical quantity of the ith non-raw material energy input in the production process, with units of tons (t) or kilowatt-hours (kW·h) or cubic meters (m 3 ) ;

[0107] ΔC e = C 煤电 -C 绿电 ;

[0108] ΔC h = C 煤氢 -C 绿氢 ;

[0109] ΔC c = C CCS ;

[0110] wherein, ΔC e , ΔC h , ΔC c are the costs of green electricity replacement, green hydrogen replacement and coupling CCS respectively; C 煤电 , C 绿电 , C 煤氢 , C 绿氢 , C CCS are the unit costs of coal-fired power, green electricity, coal hydrogen, green hydrogen and CCS technology, with units of yuan / kWh, yuan / kgH2 or yuan / tCO2.

[0111] Further, the specific content of the constraint condition of the objective function in S6 is:

[0112] 0≤x e ,x h ≤1;

[0113] x c ≤0.97;

[0114] f CO2 >>0;

[0115] f energy ≤1500;

[0116] wherein, f CO2 tons of oil carbon emissions, with units of tCO2 / t, f energy tons of oil energy consumption, with units of kgce / toe, x e , x h , x c are the green electricity replacement ratio, green hydrogen replacement ratio and CCS carbon capture rate respectively.

[0117] Specifically, f energy≤1500 is the upper limit of the energy consumption per unit of oil produced by direct coal liquefaction, which is 1500 kgce / toe according to the Coal-to-Oil Unit Product Energy Consumption Limit (GB 30180-2024).

[0118] Further, the specific content of constructing the multi-objective optimization function of direct coal liquefaction oil in S7 is as follows:

[0119]

[0120] Wherein, minF(x) is a multi-objective optimization function, T is a matrix transpose, is a constraint condition range, f CO2 is the carbon emission per ton of oil, in tCO2 / t, f energy is the energy consumption per ton of oil, in kgce / toe, f cost (x) is the cost per ton of oil, in yuan / t, x e , x h , x c are the green electricity replacement ratio, the green hydrogen replacement ratio and the CCS carbon capture rate respectively.

[0121] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0122] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A modular and multi-objective optimization method for the coordinated optimization of carbon emissions, energy consumption, and cost in direct coal liquefaction to oil production, characterized in that, Includes the following steps: S1. Determine the accounting boundaries; S2. Based on the accounting boundary, construct a system model for the direct coal liquefaction to oil production process, including a chemical process module, a self-owned power plant module, and a purchased power module; S3. Identify the emission sources for the chemical process module and the self-owned power plant module; S4. A module based on the coal direct liquefaction to oil system model, linking several emission reduction technology interfaces to construct an emission reduction technology module; S5. Establish the green electricity substitution ratio x respectively. e Green hydrogen replacement ratio x h CCS carbon capture rate x c The objective function for the decision variables; S6. Define the constraints of the objective function; S7. Construct a multi-objective optimization function for direct coal liquefaction to oil production; S8. Collect relevant parameters of the multi-objective optimization model; S9. Based on the multi-objective optimization problem of coal direct liquefaction to oil production, Python code was constructed, and the NSGA-III algorithm was used to solve the Pareto optimal solution under the synergy of green electricity, green hydrogen, and coupled carbon capture and storage (CCS) technology. S10. Output the optimization results, i.e., the Pareto optimal solution set.

2. The modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of direct coal liquefaction to oil production according to claim 1, characterized in that, The accounting boundary in S1 is the plant area of ​​the coal direct liquefaction to oil production enterprise.

3. The modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of direct coal liquefaction to oil production according to claim 1, characterized in that, In S2, both the chemical process module and the self-owned power plant module include independent sub-modules for coal pretreatment, coal slurry preparation, liquefaction, gas purification, and hydrocracking and separation.

4. The modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of direct coal liquefaction to oil production according to claim 3, characterized in that, The module in S4 based on the coal direct liquefaction to oil system model links several emission reduction technology interfaces as follows: the green electricity substitution interface is connected to the power consumption end of each module, the green hydrogen substitution interface is connected to the liquefaction sub-module, and the CCS technology interface is connected to the carbon emission source of each module.

5. The modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of direct coal liquefaction to oil production according to claim 1, characterized in that, Several emission reduction technologies in S4 include: green electricity substitution, green hydrogen substitution, and coupled carbon capture and storage (CCS) technology.

6. The modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of direct coal liquefaction to oil production according to claim 1, characterized in that, In S5, the green electricity penetration ratio x was established respectively. e Green hydrogen permeation ratio x h CCS carbon capture rate x c The specific content of the objective function for the decision variables includes the objectives of minimizing carbon emissions, minimizing total energy consumption, and minimizing production costs. Minimize carbon emissions: f CO2 =F base -or e x e Elec base -or h x h H total -or c x c F base ; Minimize total energy consumption: Minimize production costs: f cost =C base +ΔC e x e Elec base +ΔC h x h H total +ΔC c x c E base输入 ; In the formula, f CO2 f energy f cost These represent carbon emissions per ton of oil (tCO2 / t), energy consumption per ton of oil (kgce / toe), and cost per ton of oil (yuan / t); F base Elec base E base输入 E base输出 C base The figures are: benchmark carbon emissions per unit product of a coal direct liquefaction to oil plant, in tCO2 / t; benchmark electricity consumption, in kWh / t; benchmark total input energy consumption, in kgce; benchmark total output energy consumption, in kgce; and benchmark cost, in yuan / t. total This refers to the hydrogen consumption per unit of product, expressed in kgH2 / t; x e x h x c These are the green electricity substitution ratio, green hydrogen substitution ratio, and CCS carbon capture rate, respectively; η e ηh represents the carbon emission reduction per unit of green electricity substitution, expressed in kgCO2 eq / kWh; ηh represents the carbon emission reduction per unit of green hydrogen substitution, expressed in kgCO2 eq / kgH2; η c η represents the carbon emission reduction per unit of CO2 captured. c =1; ΔE h输入 and ΔE c输入 The input energy consumption for green hydrogen substitution and CCS coupling are respectively expressed in kgce; M is the product output of direct coal liquefaction to oil, expressed in tons of standard oil (toe); ΔC e ΔC h ΔC c These represent the additional costs associated with green electricity substitution, green hydrogen substitution, and coupling with CCS.

7. The modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of direct coal liquefaction to oil production according to claim 6, characterized in that, The specific constraints on the objective function specified in S6 are as follows: 0≤x e ,x h ≤1; x c ≤0.97； f CO2 ＞＞0； f energy ≤1500； Among them, f CO2 Carbon emissions per ton of oil, expressed in tCO2 / t, f energy Energy consumption per ton of oil, expressed in kgce / toe, x e x h x c These are the green electricity substitution ratio, the green hydrogen substitution ratio, and the CCS carbon capture rate, respectively.

8. The modular and multi-objective optimization method for carbon emission-energy consumption-cost synergistic optimization of direct coal liquefaction to oil production according to claim 6, characterized in that, The specific content of constructing the multi-objective optimization function for direct coal liquefaction in S7 is as follows: Where minF(x) is the multi-objective optimization function, and T is the matrix transpose. For the range of constraints, f CO2 Carbon emissions per ton of oil, expressed in tCO2 / t, f energy Energy consumption per ton of oil, expressed in kgce / toe, f cost (x) represents the cost per ton of oil, expressed in yuan / ton. e x h x c These are the green electricity substitution ratio, the green hydrogen substitution ratio, and the CCS carbon capture rate, respectively.

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