A global planning and scheduling optimization method and system applicable to coal chemical production

Abstract

This invention discloses a global planning and scheduling optimization method and system applicable to coal chemical production. The global planning and scheduling optimization method includes: setting input data and output parameters; establishing a general global production planning and scheduling optimization model based on the input data and output parameters with the goal of maximizing efficiency; generating a personalized visual planning and scheduling optimization model by selecting the required constraints according to the user's actual needs; modifying and updating the personalized visual planning and scheduling optimization model, and then performing planning and scheduling optimization calculations to obtain the optimized flow rate; updating relevant parameters in the plant's production process flow model, calculating the components of the flow, calculating the load of the equipment, calculating the efficiency and output of the products, and performing cost analysis according to two classification methods: different processes and different products. This invention can automatically optimize and calculate to provide monthly production plan results, significantly improving the overall efficiency of the plant.

Description

Technical Field

[0001] This invention relates to the field of industrial production scheduling technology, specifically to a global planning and scheduling optimization method and system applicable to coal chemical production. Background Technology

[0002] The process of processing raw coal into chemical products involves issues such as production capacity, equipment depreciation, environmental protection, and energy consumption. This necessitates the coordinated optimization of coal quality, utilities, and production capacity through planning and scheduling to maximize the enterprise's value chain. Therefore, the formulation of production plans and scheduling tasks is a crucial link in the production management of coal chemical enterprises, and the quality of these plans and scheduling schemes reflects the level of the enterprise's economic benefits. Planning and scheduling encompass four levels: enterprise-level production planning, single-plant-level production planning, production scheduling, and unit operation. Firstly, in actual industrial production, the formulation of production plans and scheduling tasks is primarily done by scheduling personnel relying on their personal experience and knowledge, using tabular forms. This inherent subjectivity inevitably leads to insufficient coordination in material and utility balance, and the scheduling results may not be the most efficient for the enterprise. Furthermore, the inconsistency and frequent switching of unit processing plans cannot guarantee stable refinery production, and in severe cases, may cause safety issues. Secondly, production plans formulated subjectively by enterprises are prone to deviating from actual production. This is often because the yield and discharge characteristics of the unit change with the unit's production status and operating time. Typically, based on the established model, it is necessary to comprehensively consider potential influencing factors such as abnormal operating conditions, raw material procurement optimization, raw material processing volume optimization, market price fluctuations, and product structure optimization. Meanwhile, the occurrence of force majeure events such as equipment maintenance and repair tasks presents another challenge in quickly and effectively scheduling production.

[0003] Therefore, combining the expert experience and knowledge of production schedulers to conduct theoretical optimization calculations and overall analysis of planning and scheduling optimization, and to quickly and dynamically update the production schedule and scheduling optimization based on market and other factors, is the key to ensuring the feasibility and effectiveness of production planning for coal chemical enterprises and increasing enterprise profits.

[0004] In terms of planning optimization for chemical enterprises, existing patented technologies include: (1) a planning optimization method for chemical enterprise supply chain (CN 108108994 A), (2) a data-driven planning optimization method for petrochemical enterprises (CN 110009142 A), and (3) a smart production scheduling algorithm (CN 110189040 A). Patent (1) mainly provides a planning optimization method for chemical enterprise supply chain, which belongs to the field of optimization methods. It includes: obtaining initial parameters of the supply chain and storing the initial parameters of the supply chain in a data warehouse; combining the sales data of chemical enterprises over the years, determining the expected market demand relative to the chemical enterprises through self-matching demand forecasting to construct a supply chain planning optimization model, setting a planning optimization objective function and constraints in the supply chain planning optimization model, and obtaining the optimal solution of the planning objective function under the constraints; setting parameter step size to automatically construct different planning optimization scheduling schemes, calculating the profit corresponding to each planning scheme, and selecting the planning scheme with the highest profit to optimize the relevant parameters of the enterprise supply chain. However, this patent, from a supply chain perspective, has limitations in the constraints it considers. Different production schedules may be obtained under the same product price, and the results are uncertain as they are predictions. It fails to provide a specific scheduling scheme for achieving the plan, and cannot be realistic or tracked in a timely manner. Patent (2) mainly constructs an automatic optimization of the cutting temperature point by limiting the MILP constraint of a special sequence set, which solves the problem that the existing technology cannot accurately optimize the cutting temperature point of the atmospheric and vacuum distillation unit. In addition, it combines chemical and statistical knowledge to construct an auxiliary modeling technology based on data-driven modeling. The technology of this invention can use historical data and laboratory data to automatically obtain the best data combination and calculate the model parameters of the planned optimization processing unit, which solves the dependence of data modeling on manual labor in the existing technology. The invention automatically updates the interaction coefficients of the blending components required by the planned optimization blending module based on the oil blending component library and the oil blending model, which solves the shortcomings of the linear blending mode used in the existing technology. This patent is applicable to petrochemical enterprises, but because the processes of petrochemical enterprises and coal chemical enterprises are quite different, coal gasification production enterprises do not need to consider the mixing properties of products in petrochemical production, and focus more on gas components and public utility energy. Therefore, the planning optimization method proposed in this patent is not applicable to coal gasification processes. Patent (3) discloses a smart production scheduling algorithm, which includes the following steps: Step 1: Generate dynamic sales forecasts of delivery data through a manual planning dynamic forecasting system; Step 2: Intervene and adjust the demand through automatic machine forecasting and manual planning dynamic forecasting system to form a unified market demand; Step 3: Compare market demand with available product inventory, and calculate the planning urgency and gap of each commodity in combination with the daily delivery volume in the recent period, and sort them according to urgency, with higher urgency priority scheduling.The patent's approach to planning from the perspective of inventory and market has systemic risks. While inventory and market are important aspects in actual production, profitability is the key factor that ultimately determines the planning process for enterprises. This patent is unlikely to meet the profitability needs of enterprises. Furthermore, it does not integrate the model settings with actual production processes, making it impossible to update the model in a timely manner according to changes in the actual situation, and thus failing to align with actual production. Summary of the Invention

[0005] To overcome the shortcomings of the above-mentioned technologies, this invention provides a global planning and scheduling optimization method applicable to coal chemical production. This method comprehensively considers various constraints such as flow rate, equipment processing capacity, minimum output requirements, and utility balance. By establishing a personalized visual planning and scheduling optimization model, it achieves optimized arrangement of the entire plant's production process and utilities over a future period, ensuring stable and safe production while providing high-value product production solutions to improve enterprise production efficiency. This invention also provides a global planning and scheduling optimization system applicable to coal chemical production.

[0006] The technical solution adopted by this invention to overcome its technical problems is:

[0007] A global planning and scheduling optimization method applicable to coal chemical production includes the following steps:

[0008] S1. Set the input data and output parameters;

[0009] S2. Based on input data and output parameters, establish a general global production planning and scheduling optimization model that integrates public works and production processes with the goal of maximizing benefits. The general global production planning and scheduling optimization model includes an objective function and several constraints.

[0010] S3. Based on the general global production planning and scheduling optimization model, select the required constraints according to the user's actual needs to generate a personalized visual planning and scheduling optimization model.

[0011] S4. Modify and update the personalized visual planning and scheduling optimization model, and then perform planning and scheduling optimization calculations to obtain the flow rate of the optimized plan and scheduling.

[0012] S5. Update the relevant parameters in the whole plant production process model at least according to the preset equipment and process information; and calculate the components of the stream, the load of the equipment, the benefits and output of the product, and perform cost analysis according to the two classification methods of different processes and different products based on the stream flow rate obtained in step S4.

[0013] Furthermore, in step S1, the input data includes at least the processing capacity of the unit, raw material and auxiliary material procurement and consumption data, composition of syngas produced by gasification, flow rate limits, load limits imposed by product output requirements, hydrogen-to-carbon ratio constraints, maintenance and repair plans, market price data, and utility production and consumption constraints; the output parameters include at least the unit scheduling events after optimization calculations, flow characteristics, utility production and consumption, end-of-scheduling tank inventory, flow rate, and unit load.

[0014] Furthermore, in step S2,

[0015] The objective function is:

[0016] y=PriceCoefficient*m-sumOfCrude*crudePrice

[0017] In the formula, PriceCoefficient is the price coefficient matrix of the variables, m is the flow rate variable, sumOfCrude is the quantity of utilities, and crudePrice is the price corresponding to the utilities;

[0018] The constraints include at least the following:

[0019] (1) Equipment feed and discharge balance:

[0020]

[0021]

[0022]

[0023]

[0024] In the formula, spliterOut(i,j) is the flow rate of the j-th stream of the discharge stream of splitter i, spliterIn(i,j) is the flow rate of the j-th stream of the feed stream of splitter i, mixerOut(i,j) is the flow rate of the j-th stream of the discharge stream of mixer i, mixerIn(i,j) is the flow rate of the j-th stream of the feed stream of mixer i, deltai is the allowable error of the feed-discharge balance of the splitter, and deltaj is the allowable error of the feed-discharge balance of the mixer.

[0025] (2) Yield constraint:

[0026] unitOut(i,j)≤unitIn(i,j)*unitYield(i,j)+deltal

[0027] -unitOut(i,j)≤-unitIn(i,j)*unitYield(i,j)+deltal

[0028] In the formula, unitOut(i,j) is the output flow rate of the j-th stream of processing device i, unitIn(i,j) is the feed flow rate of the j-th stream of processing device i, deltal is the allowable error of the side-line yield of the device, and unityield(i,j) is the yield of the j-th stream of processing device i.

[0029] (3) Equipment processing capacity constraints:

[0030]

[0031]

[0032] In the formula, unitIn(i,j) is the flow rate of the j feed streams of processing device i, unitFeedUp(i) is the upper limit of the processing capacity of secondary device i, and unitFeedLow(i) is the lower limit of the processing capacity of secondary device i.

[0033] (4) Intermediate tank storage constraints:

[0034] tankIn(i)-tankOut(i,j)≤htUp(i)-ht0(i)

[0035] -tankIn(i)+tankOut(i,j)≤-htLow(i)+ht0(i)

[0036] In the formula, tankIn(i) is the feed flow rate of tank i, tankOut(i,j) is the discharge flow rate of tank i, htUp(i) is the upper limit of tank i, htLow(i) is the lower limit of tank i, and ht0(i) is the initial tank volume of tank i.

[0037] (5) Product output constraints:

[0038] productMatrix*m≤productLimtUp

[0039] -productMatrix*m≤-productLimtLow

[0040] In the formula, productMatrix is ​​the product matrix, productLimtUp is the upper limit matrix of product output, and productLimtLow is the lower limit matrix of product output.

[0041] (6) Material property constraints:

[0042] productMatrix(i,:)′≤propertyConstraintUp(i,:)′

[0043] -productMatrix(i,:)′≤-propertyConstraintLow(i,:)′

[0044] In the formula, productMatrix(i,:) is the flow property matrix of the i-th row, with each row corresponding to a property; propertyConstraintUp(i,:) is the upper limit matrix of the flow property of the i-th row, with each row corresponding to a property; and propertyConstraintLow(i,:) is the lower limit matrix of the flow property of the i-th row, with each row corresponding to a property.

[0045] (7) Constraints on the output and consumption of public works:

[0046] unitutility(i,j)≤unitutilityUp(i,j)

[0047] -unitutility(i,j)≤-unitutilityLow(i,j)

[0048] In the formula, unitutility(i,j) is the production / consumption of the j-th type of utility in the i-th secondary device; unitutilytyUp(i,j) is the upper limit of the production / consumption of the j-th type of utility in the i-th secondary device; and unitutilytyLow(i,j) is the lower limit of the production / consumption of the j-th type of utility in the i-th secondary device.

[0049] (8) Constraints on the relationship between flow rates:

[0050] m(j) <m(i,j)*k(i,j)+b(i,j)+delta8

[0051] -m(j)<-(m(i,j)*k(i,j)+b(i,j)+delta8

[0052] In the formula, m(j) represents the constrained flow rate of the j-th flow, m(i,j) represents the flow rate value of the i-th flow associated with the j-th flow, and k(i,j) and b(i,j) represent the coefficient and constant relationship between the i-th flow and the j-th flow, respectively.

[0053] (9) Hydrogen-to-carbon ratio constraint:

[0054] (H2(i)-CO2(i)) / (CO(i)+CO2(i)) <ratiolimitup(i)+deltar(i)

[0055] (H2(i)-CO2(i)) / (CO(i)+CO2(i))>ratiolimitlow(i)+deltar(i)

[0056] In the formula, H2(i) represents the proportion of hydrogen in the i-th stream, CO2(i) represents the proportion of carbon dioxide in the i-th stream, CO(i) represents the proportion of carbon monoxide in the i-th stream, ratiolimitup(i) represents the upper limit of the hydrogen-carbon ratio of the i-th stream, ratiolimitlow(i) represents the lower limit of the hydrogen-carbon ratio of the i-th stream, and deltar(i) represents the allowable error of the hydrogen-carbon ratio of the i-th stream.

[0057] Furthermore, in step S4, the personalized visual planning and scheduling optimization model is modified and updated, including at least the flow stream model, device model, flowchart model, product pricing system, cycle length, and maintenance plan. Specifically, this includes the following:

[0058] (1) Based on the personalized visual planning and scheduling optimization model built in step S3, at least modify the model constraints such as basic equipment information, processing capacity, load calculation, material feeding and discharging relationship, and correlation relationship;

[0059] (2) Read and modify the prices of raw materials, utilities, intermediate materials and products in the personalized visual planning and scheduling optimization model from the enterprise's real-time database;

[0060] (3) Confirm and revise the maintenance and repair plan and minimum production plan for this production cycle;

[0061] (4) Call the calculation module in the personalized visual planning and scheduling optimization model to perform global planning and scheduling optimization calculations, so as to complete the monthly business plan and optimized scheduling and production arrangement, and view the detailed daily plan and shift plan after decomposition.

[0062] Furthermore, in step (4), the global planning and scheduling optimization calculation specifically includes:

[0063] (41) Taking into account scheduling events that may occur in actual production, including at least equipment maintenance events, raw material and product price fluctuations, and storage tank inventory restrictions;

[0064] (42) By combining planning and scheduling, the planned quantities of raw materials and products for production within a cycle are calculated, and scheduling information related to the flow rate, composition, material properties and equipment load of the pipeline streams is given.

[0065] (43) Taking into account the overall plant material balance and utility balance, the impact of utility on cost.

[0066] Furthermore, in step S4, the monthly operating plan and optimized scheduling production plan are configured to be modifiable according to the actual plan.

[0067] Furthermore, when the prices of raw materials or products change, a scheduling plan with a shorter cycle than the current cycle is generated through planning and scheduling optimization calculations.

[0068] This invention also discloses a global planning and scheduling optimization system applicable to coal chemical production, comprising:

[0069] The data structure setting module is used to set input data and output parameters;

[0070] A general global production planning and scheduling optimization model is used to establish a general global production planning and scheduling optimization model that integrates utilities and production processes based on input data and output parameters with the goal of maximizing benefits. The general global production planning and scheduling optimization model includes an objective function and several constraints.

[0071] The personalized visual planning and scheduling optimization model building module is used to generate personalized visual planning and scheduling optimization models based on the general global production planning and scheduling optimization model and according to the user's actual needs by selecting the required constraints.

[0072] The planning and scheduling optimization calculation module is used to modify and update the personalized visual planning and scheduling optimization model, and then perform planning and scheduling optimization calculations to obtain the optimized flow rate.

[0073] The model parameter update and analysis calculation module is used to update the relevant parameters in the whole plant production process model based on at least the preset equipment and process information; and to calculate the components of the stream, the load of the equipment, the benefits and output of the product, and perform cost analysis according to two classification methods: different processes and different products, based on the stream flow rate obtained in step S4.

[0074] Furthermore, it also includes a result presentation module, which is at least used to visualize and facilitate human-computer interaction with the planning and scheduling results obtained by the planning and scheduling optimization calculation module.

[0075] The beneficial effects of this invention are:

[0076] 1. This invention simplifies the whole plant production scheduling problem into the form of production scheduling optimization problem in planning optimization and scheduling optimization problems. With the goal of maximizing benefits, it establishes a global production planning and scheduling optimization model that integrates public works and production processes. Moreover, the global production planning and scheduling optimization model in this invention is visualized and adopts global planning and scheduling optimization calculation. Compared with ant colony algorithm, genetic algorithm, etc., the model has high solution efficiency and high feasibility of calculation results.

[0077] 2. This invention can automatically optimize and calculate the monthly production plan, and this production plan can significantly improve the overall efficiency of the plant compared with the manually formulated production plan.

[0078] 3. This invention takes into account the uncertainty of maintenance and repair plans. The global production planning and scheduling optimization model can adjust the actual operation of the equipment affected by uncertain factors such as equipment failure or planned maintenance according to the preset special handling methods for equipment maintenance and repair, so as to achieve a production plan that fits the actual situation.

[0079] 4. This invention comprehensively considers the coupling relationship between the public works system and the material system, and aims to maximize benefits while ensuring safe production, and rationally arranges the load of each device, fuel gas replenishment, etc.

[0080] 5. The global planning and scheduling optimization system of the present invention can provide a comparative display of the actual operation of the entire plant and the planning and scheduling results obtained by the planning and scheduling optimization calculation module, so that operators can understand it at a glance.

[0081] 6. This invention does not require the installation of additional testing equipment. Attached Figure Description

[0082] Figure 1 This is a flowchart illustrating the global planning and scheduling optimization method applicable to coal chemical production as described in Embodiment 1 of the present invention.

[0083] Figure 2 This is a schematic diagram of the full-process process model interface of a coal chemical production enterprise as described in Embodiment 1 of the present invention.

[0084] Figure 3 This is a schematic diagram of a personalized visual planning and scheduling optimization model built by a coal chemical production enterprise according to actual needs, as described in Embodiment 1 of the present invention. Detailed Implementation

[0085] To facilitate a better understanding of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The following are merely exemplary and do not limit the scope of protection of the present invention.

[0086] Example 1

[0087] This embodiment discloses a global planning and scheduling optimization method applicable to coal chemical production, such as... Figure 1 As shown, the steps include:

[0088] S1. Set the input data and output parameters.

[0089] S2. Based on input data and output parameters, establish a general global production planning and scheduling optimization model that integrates public works and production processes with the goal of maximizing benefits. The general global production planning and scheduling optimization model includes an objective function and several constraints.

[0090] S3. Based on the general global production planning and scheduling optimization model, select the required constraints according to the user's actual needs to generate a personalized visual planning and scheduling optimization model.

[0091] S4. Modify and update the personalized visual planning and scheduling optimization model, and then perform planning and scheduling optimization calculations to obtain the flow rate of the optimized flow.

[0092] S5. Update the relevant parameters in the whole plant production process model at least according to the preset equipment and process information; and calculate the components of the stream, the load of the equipment, the benefits and output of the product, and perform cost analysis according to the two classification methods of different processes and different products based on the stream flow rate obtained in step S4.

[0093] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and is not limited to the embodiments described herein. These embodiments are provided to enable those skilled in the art to understand the present invention more clearly and thoroughly.

[0094] Specifically, the global planning and scheduling optimization method applicable to coal chemical production described in this embodiment includes the following:

[0095] S1. Set the input data and output parameters.

[0096] The global planning and scheduling optimization method for coal chemical production is based on the global planning and scheduling optimization method system for coal chemical production. First, it is necessary to sort out the input data and output parameters of the global planning and scheduling optimization system applicable to coal chemical production.

[0097] Specifically, the input data includes at least the unit's processing capacity, raw material and auxiliary material procurement and consumption data, composition of syngas produced by gasification, flow rate limits, load limits imposed by product output requirements, hydrogen-to-carbon ratio constraints, maintenance and repair plans, market price data, and utility production and consumption constraints; the output parameters include at least the optimized unit scheduling events, flow characteristics, utility production and consumption, end-of-scheduling tank inventory, flow rate, and unit load.

[0098] S2. Based on input data and output parameters, establish a general global production planning and scheduling optimization model that integrates public works and production processes with the goal of maximizing benefits. The general global production planning and scheduling optimization model includes an objective function and several constraints.

[0099] Specifically, the objective function is:

[0100] y=PriceCoefficient*m-sumOfCrude*crudePrice

[0101] In the formula, PriceCoefficient is the price coefficient matrix of the variables, m is the flow rate variable, sumOfCrude is the quantity of utilities, and crudePrice is the price corresponding to the utilities;

[0102] The constraints include at least the following:

[0103] (1) Equipment feed and discharge balance:

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, spliterOut(i,j) is the flow rate of the j-th stream of the discharge stream of splitter i, spliterIn(i,j) is the flow rate of the j-th stream of the feed stream of splitter i, mixerOut(i,j) is the flow rate of the j-th stream of the discharge stream of mixer i, mixerIn(i,j) is the flow rate of the j-th stream of the feed stream of mixer i, deltai is the allowable error of the feed-discharge balance of the splitter, and deltaj is the allowable error of the feed-discharge balance of the mixer.

[0109] (2) Yield constraint:

[0110] unitOut(i,j)≤unitIn(i,j)*unitYield(i,j)+deltal

[0111] -unitOut(i,j)≤-unitIn(i,j)*unitYield(i,j)+deltal

[0112] In the formula, unitOut(i,j) is the output flow rate of the j-th stream of processing device i, unitIn(i,j) is the feed flow rate of the j-th stream of processing device i, deltal is the allowable error of the side-line yield of the device, and unityield(i,j) is the yield of the j-th stream of processing device i.

[0113] (3) Equipment processing capacity constraints:

[0114]

[0115]

[0116] In the formula, unitIn(i,j) is the flow rate of the j feed streams of processing device i, unitFeedUp(i) is the upper limit of the processing capacity of secondary device i, and unitFeedLow(i) is the lower limit of the processing capacity of secondary device i.

[0117] (4) Intermediate tank storage constraints:

[0118] tankIn(i)-tankOut(i,j)≤htUp(i)-ht0(i)

[0119] -tankIn(i)+tankOut(i,j)≤-htLow(i)+ht0(i)

[0120] In the formula, tankIn(i) is the feed flow rate of tank i, tankOut(i,j) is the discharge flow rate of tank i, htUp(i) is the upper limit of tank i, htLow(i) is the lower limit of tank i, and ht0(i) is the initial tank volume of tank i.

[0121] (5) Product output constraints:

[0122] productMatrix*m≤productLimtUp

[0123] -productMatrix*m≤-productLimtLow

[0124] In the formula, productMatrix is ​​the product matrix, productLimtUp is the upper limit matrix of product output, and productLimtLow is the lower limit matrix of product output.

[0125] (6) Material property constraints:

[0126] productMatrix(i,:)′≤propertyConstraintUp(i,:)′

[0127] -productMatrix(i,:)′≤-propertyConstraintLow(i,:)′

[0128] In the formula, productMatrix(i,:) is the flow property matrix of the i-th row, with each row corresponding to a property; propertyConstraintUp(i,:) is the upper limit matrix of the flow property of the i-th row, with each row corresponding to a property; and propertyConstraintLow(i,:) is the lower limit matrix of the flow property of the i-th row, with each row corresponding to a property.

[0129] (7) Constraints on the output and consumption of public works:

[0130] unitutility(i,j)≤unitutilityUp(i,j)

[0131] -unitutility(i,j)≤-unitutilityLow(i,j)

[0132] In the formula, unitutility(i,j) is the production / consumption of the j-th type of utility in the i-th secondary device; unitutilytyUp(i,j) is the upper limit of the production / consumption of the j-th type of utility in the i-th secondary device; and unitutilytyLow(i,j) is the lower limit of the production / consumption of the j-th type of utility in the i-th secondary device.

[0133] (8) Constraints on the relationship between flow rates:

[0134] m(j) <m(i,j)*k(i,j)+b(i,j)+delta8

[0135] -m(j)<-(m(i,j)*k(i,j)+b(i,j)+delta8

[0136] In the formula, m(j) represents the constrained flow rate of the j-th flow, m(i,j) represents the flow rate value of the i-th flow associated with the j-th flow, and k(i,j) and b(i,j) represent the coefficient and constant relationship between the i-th flow and the j-th flow, respectively.

[0137] (9) Hydrogen-to-carbon ratio constraint:

[0138] (H2(i)-CO2(i)) / (CO(i)+CO2(i)) <ratiolimitup(i)+deltar(i)

[0139] (H2(i)-CO2(i)) / (CO(i)+CO2(i))>ratiolimitlow(i)+deltar(i)

[0140] In the formula, H2(i) represents the proportion of hydrogen in the i-th stream, CO2(i) represents the proportion of carbon dioxide in the i-th stream, CO(i) represents the proportion of carbon monoxide in the i-th stream, ratiolimitup(i) represents the upper limit of the hydrogen-carbon ratio of the i-th stream, ratiolimitlow(i) represents the lower limit of the hydrogen-carbon ratio of the i-th stream, and deltar(i) represents the allowable error of the hydrogen-carbon ratio of the i-th stream.

[0141] The nine constraints listed above are common to coal chemical production enterprises, but they may not be applicable to every coal chemical production enterprise. Different enterprises may require different constraints. These nine constraints are provided for coal chemical enterprises with different needs to choose from.

[0142] S3. Based on the general global production planning and scheduling optimization model, select the required constraints according to the user's actual needs to generate a personalized visual planning and scheduling optimization model.

[0143] Different enterprise users will have different production plans and scheduling arrangements. Based on a general global production planning and scheduling optimization model, users select the necessary constraints according to their actual business needs and build a personalized visual planning and scheduling optimization model based on the coal chemical process flow of their plant—that is, a customized visual planning and scheduling optimization model. For example... Figure 3 The image shows a schematic diagram of a customized visual planning and scheduling optimization model built by a coal chemical production enterprise based on its actual needs.

[0144] S4. Modify and update the personalized visual planning and scheduling optimization model, and then perform planning and scheduling optimization calculations to obtain the flow rate of the optimized flow.

[0145] In this embodiment, the personalized visual planning and scheduling optimization model is modified and updated, including at least the flow stream model, device model, flowchart model, product pricing system, cycle length, and maintenance plan. Specifically, this includes the following:

[0146] (1) Based on the personalized visual planning and scheduling optimization model built in step S3, at least modify the model constraints such as basic equipment information, processing capacity, load calculation, material feeding and discharging relationship, and correlation relationship;

[0147] (2) Read and modify the prices of raw materials, utilities, intermediate materials and products in the personalized visual planning and scheduling optimization model from the enterprise's real-time database;

[0148] (3) Confirm and revise the maintenance and repair plan and minimum production plan for this production cycle;

[0149] (4) Invoke the calculation module in the personalized visual planning and scheduling optimization model to perform global planning and scheduling optimization calculations, in order to complete the monthly business plan and optimized scheduling production arrangement, and view the detailed daily and shift plans after decomposition. Specifically, the global planning and scheduling optimization calculations include:

[0150] (41) Taking into account scheduling events that may occur in actual production, including at least equipment maintenance events, raw material and product price fluctuations, and storage tank inventory restrictions;

[0151] (42) By combining planning and scheduling, the planned quantities of raw materials and products for production within a cycle are calculated, and scheduling information related to the flow rate, composition, material properties and equipment load of the pipeline streams is given.

[0152] (43) Taking into account the overall plant material balance and utility balance, the impact of utility on cost.

[0153] In a preferred embodiment, in step S4, the monthly operating plan and optimized scheduling plan are configured to be modifiable according to the actual plan. This is because in actual production, various operating conditions may arise from time to time, such as changes in the price of raw materials or products. Setting the monthly operating plan and optimized scheduling plan to be modifiable according to the actual plan allows for better planning and optimization, making the plan more realistic and increasing its effectiveness.

[0154] For example, when the price of raw materials or products changes, a scheduling plan with a shorter cycle than the current cycle can be generated through planning and scheduling optimization calculations, such as generating an hourly scheduling plan.

[0155] S5. Update the relevant parameters in the overall plant production process model based on the preset equipment and process information; and calculate the components of the flow stream, the load on the equipment, the benefits and output of the product, and perform cost analysis according to two classification methods: different processes and different products, based on the flow stream volume obtained in step S4.

[0156] In this embodiment, step S5, which involves updating the relevant parameters in the whole plant production process model based on the preset device and process information, specifically means that the personalized visual planning and scheduling optimization model established in step S3 does not change much within a certain period. For example, when a new production plan is arranged in August, the production plan in July can be referenced, and the parameters of the whole plant production process model can be adjusted according to the production requirements in August and the actual on-site working conditions.

[0157] In this embodiment, based on the flow rate obtained in step S4, the components of the flow are calculated, the load on the device is calculated, the benefits and output of the product are calculated, and cost analysis is performed according to two classification methods: different processes and different products. The two cost calculation methods yield two types of results: if the cost is calculated by process, the raw materials used in each process are the products of the previous process; this process produces multiple products and serves as the raw material for the next process. If the cost is calculated by product, the raw materials in the cost analysis refer to the total amount of raw materials input into the plant and other materials consumed in producing that product. These two calculation methods can guide production and control costs from different perspectives. When calculated by process, the results can guide cost control for each production process; when calculated by product, the results are of reference value to planners, schedulers, and managers when making production planning decisions: enabling clear overall planning of the output of different product lines.

[0158] Through the above steps S1-S5, a global planning and scheduling optimization method applicable to coal chemical production can be derived. This method can automatically optimize and calculate the monthly production plan results, and the production plan results can significantly improve the overall efficiency of the plant compared with the manually formulated production plan.

[0159] Typical application examples are as follows:

[0160] A schematic diagram of the full-process technology model interface of a coal chemical production enterprise is shown below. Figure 2 As shown in Table 1, the actual plant load during a certain period is as follows. Based on actual production data, plant processing capacity, and maintenance status, a configuration design is performed, taking into account flow constraints, hydrogen-to-carbon ratio constraints, flow correlation constraints, and utility balance. The global planning and scheduling optimization method provided in this embodiment is used to establish a plant-wide production process model, as shown in Table 1. Figure 2 As shown in Table 2, the unit load table obtained by the collaborative optimization calculation of material balance and utility balance with the goal of maximizing efficiency is presented. Compared with manual scheduling, this method shows a bias towards producing products with high marginal contribution while maintaining a stable balance. Operating based on the calculation results of the planning and scheduling optimization system can reduce energy consumption and significantly improve the overall marginal contribution of the plant's production.

[0161] Table 1. Actual Production Unit Load During a Certain Period

[0162]

[0163] Table 2. Unit load table obtained from optimization calculation.

[0164]

[0165]

[0166] Example 2

[0167] This embodiment discloses a global planning and scheduling optimization system applicable to coal chemical production, including:

[0168] The data structure setting module is used to set input data and output parameters;

[0169] A general global production planning and scheduling optimization model is used to establish a general global production planning and scheduling optimization model that integrates utilities and production processes based on input data and output parameters with the goal of maximizing benefits. The general global production planning and scheduling optimization model includes an objective function and several constraints.

[0170] The personalized visual planning and scheduling optimization model building module is used to generate personalized visual planning and scheduling optimization models based on the general global production planning and scheduling optimization model and according to the user's actual needs by selecting the required constraints.

[0171] The planning and scheduling optimization calculation module is used to modify and update the personalized visual planning and scheduling optimization model, and then perform planning and scheduling optimization calculations to obtain the optimized flow rate.

[0172] The model parameter update and analysis calculation module is used to update the relevant parameters in the whole plant production process model based on at least the preset equipment and process information; and to calculate the components of the stream, the load of the equipment, the benefits and output of the product, and perform cost analysis according to two classification methods: different processes and different products, based on the stream flow rate obtained in step S4.

[0173] Furthermore, it also includes a results presentation module, which is used at least to visualize and facilitate human-computer interaction with the planning and scheduling results obtained by the planning and scheduling optimization calculation module. Specifically, the data that can be presented includes at least the plant production plan for a future period, current flow rate, material flow, raw material usage, optimization of utility production and consumption, expected product cost and marginal contribution data, etc., and a report export function window can also be provided.

[0174] The above description only outlines the basic principles and preferred embodiments of the present invention. Those skilled in the art can make many changes and modifications based on the above description, and these changes and modifications should fall within the protection scope of the present invention.

Claims

1. A global planning and scheduling optimization method applicable to coal chemical production, characterized in that, Including the following steps: S1. Set the input data and output parameters; S2. Based on input data and output parameters, establish a general global production planning and scheduling optimization model that integrates public works and production processes with the goal of maximizing benefits. The general global production planning and scheduling optimization model includes an objective function and several constraints. The constraints include at least the following: Constraints on the relationship between flow rates: m(j)< m(i , j) * k(i , j) + b(i , j) + delta8; -m(j) <-(m(i , j) * k(i , j) + b(i , j) + delta8; In the formula, m(j) represents the constrained flow rate of the j-th stream, m(i, j) represents the flow rate value of the i-th stream associated with the j-th stream, and k(i, j) and b(i, j) represent the coefficient and constant relationship between the i-th stream and the j-th stream, respectively. Hydrogen-to-carbon ratio constraint: (H2(i) - CO2(i)) / (CO(i) + CO2(i)) < ratiolimitup (i) + deltar (i); (H2(i) - CO2(i)) / (CO(i) + CO2(i)) > ratiolimitlow (i) + deltar (i); In the formula, H2(i) represents the proportion of hydrogen component in the i-th stream, CO2(i) represents the proportion of carbon dioxide component in the i-th stream, CO(i) represents the proportion of carbon monoxide component in the i-th stream, ratiolimitup(i) represents the upper limit of the hydrogen-carbon ratio in the i-th stream, ratiolimitlow(i) represents the lower limit of the hydrogen-carbon ratio in the i-th stream, and deltar(i) represents the allowable error of the hydrogen-carbon ratio in the i-th stream. S3. Based on the general global production planning and scheduling optimization model, select the required constraints according to the user's actual needs to generate a personalized visual planning and scheduling optimization model. S4. Modify and update the personalized visual planning and scheduling optimization model, including at least modifying and updating the flow stream model, device model, flowchart model, product pricing system, cycle length, and maintenance plan. Then perform planning and scheduling optimization calculations to obtain the flow stream flow optimized by planning and scheduling. S5. Update the relevant parameters in the whole plant production process model at least according to the preset equipment and process information; and calculate the components of the stream, the load of the equipment, the benefits and output of the product, and perform cost analysis according to the two classification methods of different processes and different products based on the stream flow rate obtained in step S4.

2. The global planning and scheduling optimization method applicable to coal chemical production according to claim 1, characterized in that, In step S1, the input data includes at least the processing capacity of the unit, raw material and auxiliary material procurement and consumption data, composition of syngas produced by gasification, flow rate limits, load limits imposed by product output requirements, hydrogen-to-carbon ratio constraints, maintenance and repair plans, market price data, and utility production and consumption constraints; the output parameters include at least the unit scheduling events after optimization calculation, flow characteristics, utility production and consumption, end-of-scheduling tank inventory, flow rate, and unit load.

3. The global planning and scheduling optimization method applicable to coal chemical production according to claim 1, characterized in that, In step S2, The objective function is: ； In the formula, PriceCoefficient is the price coefficient matrix of the variables, m is the flow rate variable, sumOfCrude is the quantity of utilities, and crudePrice is the price corresponding to the utilities; The constraints also include: (1) Equipment feed and discharge balance: ； ； ； ； In the formula, spliterOut(i,j) is the flow rate of the j-th stream of the discharge stream of splitter i, spliterIn(i,j) is the flow rate of the j-th stream of the feed stream of splitter i, mixerOut(i,j) is the flow rate of the j-th stream of the discharge stream of mixer i, mixerIn(i,j) is the flow rate of the j-th stream of the feed stream of mixer i, deltai is the allowable error of the feed-discharge balance of the splitter, and deltaj is the allowable error of the feed-discharge balance of the mixer. (2) Yield constraint: unitOut(i,j)≤unitIn(i,j)* unitYield(i,j)+ deltal; -unitOut(i,j)≤-unitIn(i,j)* unitYield(i,j)+ deltal; In the formula, unitOut(i,j) is the output flow rate of the j-th stream of processing device i, unitIn(i,j) is the feed flow rate of the j-th stream of processing device i, deltal is the allowable error of the side-line yield of the device, and unityield(i,j) is the yield of the j-th stream of processing device i. (3) Equipment processing capacity constraints: ； ； In the formula, unitIn(i,j) represents the flow rate of feed stream j of processing device i. This represents the upper limit of the processing capacity of secondary device i. This represents the lower limit of the processing capacity of secondary device i; (4) Intermediate tank storage constraints: ； ； In the formula, tankIn(i) is the feed flow rate of tank i, tankOut(i,j) is the discharge flow rate of tank i, htUp(i) is the upper limit of tank i, htLow(i) is the lower limit of tank i, and ht0(i) is the initial tank volume of tank i. (5) Product output constraints: ； ； In the formula, productMatrix is ​​the product matrix, productLimtUp is the upper limit matrix of product output, and productLimtLow is the lower limit matrix of product output. (6) Material property constraints: ； ； In the formula, productMatrix(i,:) is the flow property matrix of the i-th row, with each row corresponding to a property; propertyConstraintUp(i,:) is the upper limit matrix of the flow property of the i-th row, with each row corresponding to a property; and propertyConstraintLow(i,:) is the lower limit matrix of the flow property of the i-th row, with each row corresponding to a property. (7) Constraints on the output and consumption of public works: ； ； In the formula, unitutility(i,j) is the production / consumption of the j-th type of utility in the i-th secondary device; unitutilytyUp(i,j) is the upper limit of the production / consumption of the j-th type of utility in the i-th secondary device; and unitutilytyLow(i,j) is the lower limit of the production / consumption of the j-th type of utility in the i-th secondary device.

4. The global planning and scheduling optimization method applicable to coal chemical production according to claim 1, characterized in that, In step S4, the personalized visual planning and scheduling optimization model is modified and updated, including at least the flow stream model, device model, flowchart model, product pricing system, cycle length, and maintenance plan. Specifically, this includes the following: (1) Based on the personalized visual planning and scheduling optimization model built in step S3, at least modify the model constraints such as the basic information of the device, processing capacity, load calculation, material feeding and discharging relationship, and correlation relationship; (2) Read and modify the prices of raw materials, utilities, intermediate materials and products in the personalized visual planning and scheduling optimization model from the enterprise's real-time database; (3) Confirm and revise the maintenance and repair plan and minimum production plan for this production cycle; (4) Call the calculation module in the personalized visual planning and scheduling optimization model to perform global planning and scheduling optimization calculations, so as to complete the monthly business plan and optimized scheduling and production arrangement, and view the detailed daily plan and shift plan after decomposition.

5. The global planning and scheduling optimization method applicable to coal chemical production according to claim 4, characterized in that, In step (4), the global planning and scheduling optimization calculation specifically includes: (41) Taking into account scheduling events that may occur in actual production, including at least equipment maintenance events, raw material and product price fluctuations, and storage tank inventory restrictions; (42) By combining planning and scheduling, the planned quantities of raw materials and products for production within a cycle are calculated, and scheduling information related to the flow rate, composition, material properties and equipment load of the pipeline streams is given. (43) Take into account the overall plant material balance and utility balance, and the impact of utility on cost.

6. The global planning and scheduling optimization method applicable to coal chemical production according to claim 4, characterized in that, In step S4, the monthly business plan and optimized scheduling production plan are configured to be modifiable according to the actual plan.

7. The global planning and scheduling optimization method applicable to coal chemical production according to claim 6, characterized in that, When the prices of raw materials or products change, a scheduling plan with a shorter cycle than the current cycle is generated through planning and scheduling optimization calculations.

8. A global planning and scheduling optimization system suitable for coal chemical production, characterized in that, include: The data structure setting module is used to set input data and output parameters; A general global production planning and scheduling optimization model is used to establish a general global production planning and scheduling optimization model that integrates utilities and production processes based on input data and output parameters with the goal of maximizing benefits. The general global production planning and scheduling optimization model includes an objective function and several constraints. The constraints include at least the following: Constraints on the relationship between flow rates: m(j)< m(i , j) * k(i , j) + b(i , j) + delta8; -m(j) <-(m(i , j) * k(i , j) + b(i , j) + delta8; In the formula, m(j) represents the constrained flow rate of the j-th stream, m(i, j) represents the flow rate value of the i-th stream associated with the j-th stream, and k(i, j) and b(i, j) represent the coefficient and constant relationship between the i-th stream and the j-th stream, respectively. Hydrogen-to-carbon ratio constraint: (H2(i) - CO2(i)) / (CO(i) + CO2(i)) < ratiolimitup (i) + deltar (i); (H2(i) - CO2(i)) / (CO(i) + CO2(i)) > ratiolimitlow (i) + deltar (i); In the formula, H2(i) represents the proportion of hydrogen component in the i-th stream, CO2(i) represents the proportion of carbon dioxide component in the i-th stream, CO(i) represents the proportion of carbon monoxide component in the i-th stream, ratiolimitup(i) represents the upper limit of the hydrogen-carbon ratio in the i-th stream, ratiolimitlow(i) represents the lower limit of the hydrogen-carbon ratio in the i-th stream, and deltar(i) represents the allowable error of the hydrogen-carbon ratio in the i-th stream. The personalized visual planning and scheduling optimization model building module is used to generate personalized visual planning and scheduling optimization models based on the general global production planning and scheduling optimization model and according to the user's actual needs by selecting the required constraints. The planning and scheduling optimization calculation module is used to modify and update the personalized visual planning and scheduling optimization model. This includes at least modifying and updating the flow stream model, device model, flowchart model, product pricing system, cycle length, and maintenance plan. Then, the planning and scheduling optimization calculation is performed to obtain the flow stream flow optimized by the planning and scheduling. The model parameter update and analysis calculation module is used to update the relevant parameters in the whole plant production process model based on at least the preset equipment and process information; and to calculate the components of the stream, the load of the equipment, the benefits and output of the product, and perform cost analysis according to two classification methods: different processes and different products, based on the stream flow rate obtained in step S4.

9. The global planning and scheduling optimization system for coal chemical production according to claim 8, characterized in that, It also includes a results presentation module, which is used at least to visualize and facilitate human-computer interaction with the planning and scheduling results obtained by the planning and scheduling optimization calculation module.

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