Emergency scheduling optimization method and system for coal chemical production

Abstract

The application discloses an emergency scheduling optimization method and system for coal chemical production, and the emergency scheduling optimization method comprises the following steps: setting input data and output parameters; based on the input data and the output parameters, a general emergency scheduling optimization model is established with the target of maximizing benefits; on the basis of the general emergency scheduling optimization model, the required constraint conditions are selected according to the actual requirements of users, and a personalized visual emergency scheduling optimization model is generated based on a coal chemical process production flow; for the personalized visual emergency scheduling optimization model, a scheduling case library and an emergency plan library are constructed; when an abnormal working condition is monitored, an alarm is given and the abnormal working condition is matched with the abnormal working condition in the scheduling case library; according to the matching condition of the monitored abnormal working condition and the abnormal working condition in the scheduling case library, an emergency scheduling optimization scheme in the emergency plan library is called or emergency optimization calculation is carried out. The application can improve the processing capacity of abnormal events, and ensure smooth and safe production of enterprises when an emergency working condition occurs.

Description

Technical Field

[0001] This invention relates to the field of industrial production scheduling technology, specifically to an emergency scheduling optimization method and system for coal chemical production. Background Technology

[0002] Chemical enterprises employ a continuous production process in which raw coal is processed into chemical products. This continuous production maximizes the enterprise's value chain. Therefore, rapid response and elimination of unsafe factors in the event of abnormal operating conditions are paramount for ensuring personnel safety and maximizing enterprise profitability. In emergency situations, the ability of dispatchers to formulate and issue effective and accurate dispatch plans based on the production status at the time of the emergency is crucial for emergency handling. However, in actual production, factors such as the individual qualities and experience of dispatchers, and enterprise cost control, often lead to delayed responses and unclear dispatch tasks when abnormal operating conditions occur during continuous production. This can result in equipment damage, significantly reduced enterprise profits, and in severe cases, even compromised safety and casualties. Furthermore, dispatch tasks based on individual experience are inherently subjective, inevitably leading to system instability during continuous production and hindering the ability to resolve problems quickly, thus harming enterprise profitability. Moreover, subjective emergency dispatch plans are prone to disconnect from actual production, often due to variations in the production status of equipment under different operating conditions. Typically, based on the establishment of a scheduling optimization model, it is necessary to comprehensively consider factors such as the production status of the equipment, abnormal locations, maximizing corporate benefits, and the inventory of products and raw materials to quickly and effectively formulate and issue emergency scheduling tasks.

[0003] Therefore, an emergency dispatch optimization calculation system that combines expert experience and knowledge that has been repeatedly refined with emergency dispatch calculation logic verified by historical data, and comprehensively considers factors such as safe production of equipment, enterprise product requirements, and maximization of benefits, is the key to ensuring the feasibility and effectiveness of emergency dispatch plans for coal chemical enterprises and increasing enterprise profits.

[0004] In terms of emergency dispatch optimization for chemical enterprises, the main patented technologies are: (1) emergency dispatch optimization methods, systems, equipment and media applicable to coal chemical industry (CN 113205234 B), (2) a safety management auxiliary decision-making system based on industrial intelligence (CN 111784191 A), and (3) a chemical enterprise management system and corresponding management method (CN 111121863A). Among them, patent (1) mainly relates to an emergency dispatch optimization method, system, equipment and medium applicable to coal chemical industry. The method includes: first, characterizing the process production flow of coal chemical industry to obtain a graphical process production flow, and then constructing an adjacency matrix; second, constructing the association relationship between utilities and materials; then, constructing a utilities-materials dispatch optimization model based on the association relationship, and generating constraints based on the adjacency matrix; then, obtaining abnormal working condition information, and matching the abnormal working condition with the working condition name in the list based on the preset abnormal working condition judgment criteria list; then, based on the matching situation, selecting countermeasures from the preset emergency plan library or performing dispatch optimization calculation based on the utilities-materials dispatch optimization model and constraints; finally, outputting the emergency dispatch optimization scheme. However, the technical solution of patent (1) does not provide a scheduling solution based on the experience of on-site experts in the emergency plan database. It only provides the load and flow data that the device can achieve for the dispatcher's reference, which cannot help the dispatcher to quickly issue emergency tasks. In addition, when performing emergency optimization calculations, patent (1) does not consider the priority production level of products in the factory and the controllable key flow streams. The proposed solution does not meet the actual production needs and may even result in a situation where the flow cannot be changed in a short period of time. Patent (2) discloses a safety management auxiliary decision-making system based on industrial intelligence, including an information update function module, GIS map display, scheduling calculation, scheduling plan display and intelligent emergency plan decision-making. Among them, the information update function module, i.e., information access, mainly includes the access of real-time information and the access of emergency resources; the GIS map display function module includes electronic map operation, satellite map operation, data mining and editing and network analysis and monitoring. The scheduling calculation function module includes statistical analysis of emergency scheduling tasks, archiving of historical data, intelligent emergency resource scheduling and algorithms. The scheduling plan display function module includes emergency personnel scheduling, resource distribution, types of resource needs, quantity of resource needs and optimal driving routes for emergency vehicles. Intelligent emergency response decision-making includes dynamic design of emergency response plans, archiving of historical plans, and a digital plan database. It also includes other auxiliary functions such as push notifications and flexible reporting. However, in continuous chemical production operations, patent (2) cannot quickly help companies make decisions to maintain stable production.Patent (3) provides a chemical enterprise management system and a corresponding management method. The management system includes a data acquisition module and a data analysis module. The data acquisition module includes at least a sensor unit and an electrical data acquisition unit. At least one sensor unit is installed on each chemical equipment in the chemical enterprise. The sensor unit collects the process parameters of the corresponding chemical equipment. The electrical data acquisition unit collects the electrical operating parameters of the corresponding chemical equipment. The data analysis module matches the corresponding mathematical analysis model according to the type of chemical equipment, and determines whether there is any abnormality in the chemical equipment based on the mathematical analysis model according to the process parameters and electrical operating parameters of the chemical equipment. However, Patent (3) only provides monitoring, early warning and scheduling schemes at the device level, lacking systematic thinking. It is impossible to achieve the balance of the production system by simply changing the device parameters. Summary of the Invention

[0005] In order to overcome the shortcomings of the above technologies, this invention provides an emergency dispatch optimization method and system for coal chemical production.

[0006] This method and system comprehensively consider various constraints such as flow rate, equipment processing capacity, output requirements, raw material prices, product prices, utility balance, and abnormal locations. By establishing an emergency scheduling optimization model with product priority and key flow streams, it achieves dynamic monitoring and control of production process risks, improves the ability to handle abnormal events, and ensures that enterprises can maintain stable and safe production while providing emergency scheduling solutions for high-value product production during emergency situations. When abnormal situations occur, it reduces the response time of enterprise dispatchers, quantitatively calculates equipment load, effectively improves the accuracy and feasibility of scheduling tasks, and promptly eliminates safety hazards. At the same time, this invention can provide guarantees for enterprise production and operation efficiency, ensure normal production, reduce production losses, and maintain equipment safety.

[0007] The technical solution adopted by this invention to overcome its technical problems is:

[0008] An emergency dispatch optimization method for coal chemical production includes the following steps:

[0009] S1. Set the input data and output parameters;

[0010] S2. Based on input data and output parameters, establish a general emergency dispatch optimization model that includes product priority and key flow streams with the goal of maximizing benefits. The general emergency dispatch optimization model includes an objective function and several constraints.

[0011] S3. Based on the general emergency dispatch optimization model, select the required constraints according to the user's actual needs and generate a personalized visual emergency dispatch optimization model based on the coal chemical process production flow.

[0012] S4. For the personalized visual emergency dispatch optimization model, construct a dispatch case library for storing abnormal operating condition cases and an emergency plan library for storing emergency dispatch optimization schemes to deal with abnormal operating conditions.

[0013] S5 connects to the factory's real-time flow database and monitors the flow in real time. When an abnormal condition is detected, it issues an alarm and matches the abnormal condition with the abnormal condition in the scheduling case library. Based on the matching of the detected abnormal condition with the abnormal condition in the scheduling case library, it calls the emergency scheduling optimization plan in the emergency plan library or performs emergency optimization calculations, and then performs emergency scheduling command.

[0014] Furthermore, in step S1, the input data includes at least the processing capacity of the device, the properties of raw and auxiliary materials, consumption, flow rate limits, product output requirements, constraints on the hydrogen-to-carbon ratio of synthesis, tolerance for abnormal operating conditions, product priority, and judgment logic and emergency plans for known abnormal operating conditions; the output parameters include at least the scheduling events after scheduling optimization calculations, flow characteristics, abnormal tag numbers, device load, product output, device production consumption, and flow rate.

[0015] Furthermore, in step S2,

[0016] The objective function is:

[0017] y=priceofconsumption*m–sumofutility*priceofutility

[0018] In the formula, priceofconsumption is the price coefficient matrix of the variables, m is the flow rate of the pipeline, sumofutility is the quantity of public works, and priceofutility is the price corresponding to the public works.

[0019] The constraints include at least the following:

[0020] (1) Balance of feed and discharge between the distributor and the mixer:

[0021]

[0022]

[0023]

[0024]

[0025] In the formula, splitOut(i,j) is the flow rate of the j-th stream of the output flow of splitter i, splitin(i,j) is the flow rate of the j-th stream of the feed flow of splitter i, mixOut(i,j) is the flow rate of the j-th stream of the output flow of mixer i, mixin(i,j) is the flow rate of the j-th stream of the feed flow of mixer i, errori is the allowable error of the feed-output balance of splitter, and errorj is the allowable error of the feed-output balance of mixer.

[0026] (2) Equipment processing yield constraints:

[0027] unitOut(i,j)=∑unitIn(i,j)*unitproduct

[0028] In the formula, unitOut(i,j) is the output flow rate of the j stream of processing device i, unitIn(i,j) is the feed flow rate of the j stream of atmospheric and vacuum distillation device i, and unitproduct is the product output coefficient of the device;

[0029] (3) Upper and lower limits of equipment processing capacity constraints:

[0030]

[0031]

[0032] In the formula, 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) Material storage tank inventory constraints:

[0034] htlow(i)–ht0(i)≤tankin(i)–tankout(i)≤htup(i)-hto(i)

[0035] In the formula, tankin(i) is the feed flow rate of tank i, tankout(i) 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.

[0036] (5) Production quantity constraints:

[0037] productlimitlow ≤ Product quantity ≤ product limitup

[0038] In the formula, Productquantity is the product output, productlimitlow is the lower limit of product output, and productlimitup is the upper limit of product output;

[0039] (6) Constraints on the nature of the flow:

[0040] propertyMatrix(i,:)≤propertyConstraintUp(i,:)

[0041] -propertyMatrix(i,:)≤propertyConstraintLow(i,:)

[0042] In the formula, propertyMatrix is ​​the property matrix of the flow, with each row corresponding to a property, propertyConstraintUp is the upper limit of the flow property, and propertyConstraintLow is the lower limit of the flow property;

[0043] (7) Utility constraints:

[0044] unitutility(i,j)≤unitutilityUp(i,j)

[0045] -unitutility(i,j)≤unitutilityLow(i,j)

[0046] In the formula, unitutility(i,j) is the output or consumption of the j-th type of utility in the i-th secondary device, unitutilytyUp(i,j) is the upper limit of the output or consumption of the j-th type of utility in the i-th secondary device, and unitutilytyLow(i,j) is the lower limit of the output or consumption of the j-th type of utility in the i-th secondary device.

[0047] (8) Flow constraints between pipes:

[0048] m(j) = m(i,j)*k(i,j) + b(i,j)

[0049] 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.

[0050] (9) Material composition constraints:

[0051] (H2(i)-CO2(i)) / (CO(i)+CO2(i)) <ratiolimitup(i)+deltar(i)

[0052] (H2(i)-CO2(i)) / (CO(i)+CO2(i))>ratiolimitlow(i)+deltar(i)

[0053] 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.

[0054] (10) Real-time traffic constraints:

[0055] lb(j) = mreal(j)

[0056] ub(j) = mreal(j)

[0057] In the formula, lb(j) represents the lower limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, ub(j) represents the upper limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, and mreal(j) represents the real-time value of the flow of the key abnormal flow of the abnormal device when an abnormal operating condition occurs.

[0058] Furthermore, in step S4, the construction of the scheduling case library specifically includes:

[0059] 1) The process scheduling model built on the production real-time monitoring system is connected to the enterprise's real-time database and monitored in real time;

[0060] 2) When an abnormal situation is detected, the recording and comparison function of the scheduling case library is triggered;

[0061] 3) Determine if the bit number of the abnormal data is a critical bit number and if it already exists in the scheduling case library: if it is a critical bit number and does not exist in the scheduling case library, proceed to the next step; otherwise, discard the abnormal data.

[0062] 4) Once the abnormal data is confirmed, it is saved to the scheduling case library.

[0063] Furthermore, in step S4, the construction of the emergency response plan database specifically includes:

[0064] 1) Select abnormal operating condition cases stored in the scheduling case library and construct contingency plans for abnormal operating condition cases;

[0065] 2) Confirm and modify the operating status when abnormal conditions occur;

[0066] 3) Confirm the stable operating condition results after the occurrence of abnormal operating conditions;

[0067] 4) Provide the necessary adjustment amounts and operating procedures for each department based on stable operating conditions;

[0068] 5) Once the required adjustment amount and operation process are confirmed, save them to the emergency plan database.

[0069] Furthermore, both the dispatch case library and the emergency plan library are configured to allow for modification of plans based on actual dispatching needs.

[0070] Furthermore, in step S5, based on the matching of the monitored abnormal operating conditions with the abnormal operating conditions in the scheduling case library, the emergency scheduling optimization scheme in the emergency plan library is invoked or emergency optimization calculations are performed, specifically including:

[0071] If the detected abnormal operating condition matches the abnormal operating condition in the scheduling case library, the emergency scheduling optimization scheme in the emergency plan library will be directly invoked.

[0072] If the detected abnormal operating conditions do not match the abnormal operating conditions in the scheduling case library, emergency optimization calculations will be performed.

[0073] Furthermore, emergency optimization calculations specifically include:

[0074] 1) Locate all abnormal flow streams when abnormal operating conditions occur, and determine the key abnormal flow streams and their corresponding devices;

[0075] 2) Input the key abnormal change streams into the emergency dispatch optimization calculation algorithm as calculation constraints, and read the currently set product priorities for optimization calculation;

[0076] 3) Under the existing production conditions, reduce or stop the load of several devices on the production line in order of priority from low to high until the system stability requirements are met.

[0077] Furthermore, the emergency dispatch optimization calculation algorithm specifically includes:

[0078] 1) The calculation of loads in the same series is as follows:

[0079] averloadMatrix=devicesload / sumofdevices

[0080] When the processing capabilities of devices in the same series differ, and the upper limit of one of the devices is reached:

[0081] averloadMatrix(i)=unitFeedup(i);

[0082] Where averloadMatrix is ​​the average load of a single device in the series, devicesload is the total load of the devices in the series, sumofdevices is the number of devices in the series, averloadMatrix(i) is the load of the i-th device, and unitFeedup(i) is the upper limit of the processing capacity of the i-th device;

[0083] 2) Algorithm for calculating unit output and consumption:

[0084] d. Calculated by unit consumption: devicein(i,j) = deviceout(i,j) * consumption(i,j)

[0085] e. Calculate by yield: deviceout(i,j) = devicein(i,j) * yield(i,j)

[0086] f. Calculated using linear yield: deviceout(i,j)=∑(devicein(i,j)*yield(i,j))

[0087] Where devicein(i,j) is the feed rate of the j-th stream of the i-th device, deviceout(i,j) is the output rate of the j-th stream of the i-th device, consumption(i,j) is the unit consumption coefficient of the j-th stream of the i-th device, and yield(i,j) is the yield coefficient of the j-th stream of the i-th device.

[0088] 3) Calculation function for the inlet and outlet balance of the mixer and splitter:

[0089] ∑mixandsplitout=∑mixandsplitin

[0090] Where Mixandsplitout is the flow rate of the outlet stream of the mixer and splitter, and mixandsplittin is the flow rate of the inlet stream of the mixer and splitter.

[0091] 4) Component calculation function, calculated proportionally as follows:

[0092] ∑(consumption(i,j)*loadin(i,j))=∑(consumption(i,k)*loadout(i,k))

[0093] Wherein, consumption(i,j) is a certain component of the j-th feed stream of the i-th device, loadin(i,j) is the flow rate of the j-th feed stream of the i-th device, consumption(i,k) is a certain component of the k-th discharge stream of the i-th device, and loadout(i,k) is the flow rate of the j-th feed stream of the i-th device.

[0094] This invention also discloses an emergency dispatch optimization system for abnormal operating conditions in coal chemical production, comprising:

[0095] The data structure setting module is used to set input data and output parameters;

[0096] The general emergency dispatch optimization model construction module is used to build a general emergency dispatch optimization model based on input data and output parameters, with the goal of maximizing benefits. The general emergency dispatch optimization model includes an objective function and several constraints.

[0097] The personalized visual emergency dispatch optimization model building module is used to generate a personalized visual emergency dispatch optimization model based on the user's actual needs and the coal chemical process production flow, on the basis of the general emergency dispatch optimization model.

[0098] The module for building the scheduling case library and the emergency plan library is used to build a scheduling case library for storing abnormal operating condition cases and an emergency plan library for storing emergency scheduling optimization solutions for dealing with abnormal operating conditions, based on the personalized visual emergency scheduling optimization model.

[0099] The emergency dispatch optimization module is used to connect to the factory's real-time flow database and monitor the flow in real time. When an abnormal condition is detected, an alarm is triggered and the abnormal condition is matched with the abnormal condition in the dispatch case library. Based on the matching result of the detected abnormal condition with the abnormal condition in the dispatch case library, the emergency dispatch optimization scheme in the emergency plan library is invoked or emergency optimization calculation is performed, and then emergency dispatch command is carried out.

[0100] Furthermore, it also includes a result presentation module, which is at least used to visualize and interact with the optimization results obtained by the emergency dispatch optimization module.

[0101] The beneficial effects of this invention are:

[0102] 1. This invention simplifies the plant-wide emergency scheduling problem into a product priority production scheduling problem within a scheduling optimization problem based on existing operating conditions. An emergency scheduling optimization model is established with the goal of maximizing efficiency, incorporating product priority and key flow streams. Furthermore, the emergency scheduling optimization model in this invention is visualized. The emergency scheduling optimization algorithm is built using a data-driven model, which, compared to ant colony algorithms and genetic algorithms, offers higher model solving efficiency and more feasible calculation results.

[0103] 2. This invention can automatically optimize and calculate to provide a steady-state production schedule that meets product priorities during emergency scheduling. Compared with manual emergency scheduling, this emergency scheduling scheme can significantly reduce emergency scheduling response time, improve overall plant efficiency, and quickly reach a new equilibrium.

[0104] 3. Compared with the patent with publication number CN113205234B, this invention is based on maximizing enterprise benefits and is more in line with actual production requirements. For the emergency dispatch calculation module, based on the steady-state result calculated by the abnormal position number, the dispatcher can make its own judgment on whether it meets the production requirements and issue dispatch tasks within a certain period of time. Compared with the method described in the patent with publication number CN113205234B, it is safer and more referential.

[0105] 4. This invention can configure constraints through a simple tolerance adjustment method, thereby enabling the customized development of emergency dispatch optimization methods.

[0106] 5. This invention combines expert experience with an emergency dispatch optimization scheme that conforms to the actual factory operation. The dispatch case library and emergency plan library can be modified by enterprises according to the actual dispatch plan, which increases the effectiveness of the plan, the results are more realistic, and have a high degree of security.

[0107] 6. The emergency dispatch optimization system of the present invention can provide a comparative display of the actual operation of the entire plant and the steady-state operation of emergency dispatch optimization, so that operators can understand it at a glance.

[0108] 7. This invention does not require the installation of additional testing equipment. Attached Figure Description

[0109] Figure 1 This is a flowchart illustrating the emergency dispatch optimization method for coal chemical production as described in Embodiment 1 of the present invention.

[0110] 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.

[0111] Figure 3 This is a schematic diagram of a personalized visual emergency dispatch optimization model built by a coal chemical production enterprise according to actual needs, as described in Embodiment 1 of the present invention.

[0112] Figure 4 This is a schematic diagram of the emergency dispatch triggering process in Embodiment 1 of the present invention. Detailed Implementation

[0113] 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.

[0114] Example 1

[0115] This embodiment discloses an emergency dispatch optimization method for coal chemical production, such as... Figure 1 As shown, the steps include:

[0116] S1. Set the input data and output parameters.

[0117] S2. Based on the input data and output parameters, establish a general emergency dispatch optimization model that includes product priority and key flow streams with the goal of maximizing benefits. The general emergency dispatch optimization model includes an objective function and several constraints.

[0118] S3. Based on the general emergency dispatch optimization model, select the required constraints according to the user's actual needs and generate a personalized visual emergency dispatch optimization model based on the coal chemical process production flow.

[0119] S4. For the personalized visual emergency dispatch optimization model, construct a dispatch case library for storing abnormal operating condition cases and an emergency plan library for storing emergency dispatch optimization schemes to deal with abnormal operating conditions.

[0120] S5 connects to the factory's real-time flow database and monitors the flow in real time. When an abnormal condition is detected, it issues an alarm and matches the abnormal condition with the abnormal condition in the scheduling case library. Based on the matching of the detected abnormal condition with the abnormal condition in the scheduling case library, it calls the emergency scheduling optimization plan in the emergency plan library or performs emergency optimization calculations, and then performs emergency scheduling command.

[0121] 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.

[0122] Specifically, the emergency dispatch optimization method for coal chemical production described in this embodiment includes the following:

[0123] S1. Set the input data and output parameters.

[0124] The emergency dispatch optimization method for coal chemical production is based on the emergency dispatch optimization system for coal chemical production. First, it is necessary to sort out the input data and output parameters of the emergency dispatch optimization system.

[0125] Specifically, the input data includes at least the processing capacity of the unit, the properties of raw and auxiliary materials, consumption, flow rate limits, product output requirements, constraints on the hydrogen-to-carbon ratio of synthesis, tolerance for abnormal operating conditions, product priority, and judgment logic and emergency plans for known abnormal operating conditions; the output parameters include at least the scheduling events after scheduling optimization calculations, flow characteristics, abnormal tag numbers, unit load, product output, unit production consumption, and flow rate.

[0126] S2. Based on the input data and output parameters, establish a general emergency dispatch optimization model that includes product priority and key flow streams with the goal of maximizing benefits. The general emergency dispatch optimization model includes an objective function and several constraints.

[0127] Specifically, the emergency response problem is simplified into a benefit maximization problem in the scheduling optimization problem after an abnormal operating condition occurs. A general emergency scheduling optimization model is established with benefit maximization as the objective, which includes product priority and critical streams. The general emergency scheduling optimization model includes an objective function and several constraints, as follows:

[0128] The objective function is:

[0129] y=priceofconsumption*m–sumofutility*priceofutility

[0130] In the formula, priceofconsumption is the price coefficient matrix of the variables, m is the flow rate of the pipeline, sumofutility is the quantity of public works, and priceofutility is the price corresponding to the public works.

[0131] The constraints include at least the following:

[0132] (1) Balance of feed and discharge between the distributor and the mixer:

[0133]

[0134]

[0135]

[0136]

[0137] In the formula, splitOut(i,j) is the flow rate of the j-th stream of the output flow of splitter i, splitin(i,j) is the flow rate of the j-th stream of the feed flow of splitter i, mixOut(i,j) is the flow rate of the j-th stream of the output flow of mixer i, mixin(i,j) is the flow rate of the j-th stream of the feed flow of mixer i, errori is the allowable error of the feed-output balance of splitter, and errorj is the allowable error of the feed-output balance of mixer.

[0138] (2) Equipment processing yield constraints:

[0139] unitOut(i,j)=∑unitIn(i,j)*unitproduct

[0140] In the formula, unitOut(i,j) is the output flow rate of the j stream of processing device i, unitIn(i,j) is the feed flow rate of the j stream of atmospheric and vacuum distillation device i, and unitproduct is the product output coefficient of the device;

[0141] (3) Upper and lower limits of equipment processing capacity constraints:

[0142]

[0143]

[0144] In the formula, 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;

[0145] (4) Material storage tank inventory constraints:

[0146] htlow(i)–ht0(i)≤tankin(i)–tankout(i)≤htup(i)-hto(i)

[0147] In the formula, tankin(i) is the feed flow rate of tank i, tankout(i) 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.

[0148] (5) Production quantity constraints:

[0149] productlimitlow ≤ Product quantity ≤ product limitup

[0150] In the formula, Productquantity is the product output, productlimitlow is the lower limit of product output, and productlimitup is the upper limit of product output;

[0151] (6) Constraints on the nature of the flow:

[0152] propertyMatrix(i,:)≤propertyConstraintUp(i,:)

[0153] -propertyMatrix(i,:)≤propertyConstraintLow(i,:)

[0154] In the formula, propertyMatrix is ​​the property matrix of the flow, with each row corresponding to a property, propertyConstraintUp is the upper limit of the flow property, and propertyConstraintLow is the lower limit of the flow property;

[0155] (7) Utility constraints:

[0156] unitutility(i,j)≤unitutilityUp(i,j)

[0157] -unitutility(i,j)≤unitutilityLow(i,j)

[0158] In the formula, unitutility(i,j) is the output or consumption of the j-th type of utility in the i-th secondary device, unitutilytyUp(i,j) is the upper limit of the output or consumption of the j-th type of utility in the i-th secondary device, and unitutilytyLow(i,j) is the lower limit of the output or consumption of the j-th type of utility in the i-th secondary device.

[0159] (8) Flow constraints between pipes:

[0160] m(j) = m(i,j)*k(i,j) + b(i,j)

[0161] 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.

[0162] (9) Material composition constraints:

[0163] (H2(i)-CO2(i)) / (CO(i)+CO2(i)) <ratiolimitup(i)+deltar(i)

[0164] (H2(i)-CO2(i)) / (CO(i)+CO2(i))>ratiolimitlow(i)+deltar(i)

[0165] 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.

[0166] (10) Real-time traffic constraints:

[0167] lb(j) = mreal(j)

[0168] ub(j) = mreal(j)

[0169] In the formula, lb(j) represents the lower limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, ub(j) represents the upper limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, and mreal(j) represents the real-time value of the flow of the key abnormal flow of the abnormal device when an abnormal operating condition occurs.

[0170] The 10 constraints listed above are common to coal chemical production enterprises and may not be applicable to every coal chemical production enterprise. Different enterprises may require different constraints. These 10 constraints are provided for coal chemical enterprises with different needs to choose from.

[0171] like Figure 2 The image shown is a schematic diagram of the full-process technology model interface of a coal chemical production enterprise.

[0172] S3. Based on the general emergency dispatch optimization model, select the required constraints according to the user's actual needs and generate a personalized visual emergency dispatch optimization model based on the coal chemical process production flow.

[0173] Different enterprise users often have different focuses regarding emergency dispatch operations. Based on a general emergency dispatch optimization model, users select the necessary constraints according to their actual business needs and build a personalized, visual emergency dispatch optimization model according to the coal chemical process flow of the plant. This customized visual emergency dispatch optimization model facilitates real-time monitoring. For example... Figure 3 The diagram shows a personalized visual emergency dispatch optimization model built by a coal chemical production enterprise based on actual needs. Through this diagram, real-time production data can be compared with the data optimized by the emergency dispatch optimization model in this embodiment.

[0174] S4. For the personalized visual emergency dispatch optimization model, construct a dispatch case library for storing abnormal operating condition cases and an emergency plan library for storing emergency dispatch optimization schemes to deal with abnormal operating conditions.

[0175] Specifically, the scheduling case library is a database used to store various abnormal operating condition cases. Past case data is input into this library for dispatchers to review operating conditions. When key indicators similar to these anomalies appear, and the system meets the conditions in the scheduling case, it is determined that such an abnormal operating condition case has occurred. Each abnormal operating condition requires a solution, that is, a plan for adjustment. These solutions are stored in the emergency case library, which contains methods for handling abnormal operating conditions. Existing methods generally provide flow rate information; this embodiment presents specific handling methods as contingency plans. That is, how to adjust, operate, and the operational process of each device or equipment are all included in the specific plan. When a case in the scheduling case library occurs, the corresponding solution in the emergency plan library is retrieved, and after confirmation by the operator, it is directly issued to one or more devices in a specific workshop for emergency handling.

[0176] In this embodiment, the construction of the scheduling case library specifically includes:

[0177] 1) The process scheduling model built on the production real-time monitoring system is connected to the enterprise's real-time database and monitored in real time;

[0178] 2) When an abnormal situation is detected, the recording and comparison function of the scheduling case library is triggered;

[0179] 3) Determine if the bit number of the abnormal data is a critical bit number and if it already exists in the scheduling case library: if it is a critical bit number and does not exist in the scheduling case library, proceed to the next step; otherwise, discard the abnormal data.

[0180] 4) Once the abnormal data is confirmed, it is saved to the scheduling case library.

[0181] In this embodiment, the construction of the emergency response plan database specifically includes:

[0182] 1) Select abnormal operating condition cases stored in the scheduling case library and construct contingency plans for abnormal operating condition cases;

[0183] 2) Confirm and modify the operating status when abnormal conditions occur;

[0184] 3) Confirm the stable operating condition results after the occurrence of abnormal operating conditions;

[0185] 4) Provide the necessary adjustment amounts and operating procedures for each department based on stable operating conditions;

[0186] 5) Once the required adjustment amount and operation process are confirmed, save them to the emergency plan database.

[0187] In this preferred embodiment, both the scheduling case library and the emergency plan library are configured to allow for modification of plans based on actual scheduling. This is because during production, enterprises constantly encounter various unstable operating conditions, such as problems with one or more devices. These conditions are unforeseen, and future occurrences are unknown. When such conditions occur, the indicators for these conditions can be added to the scheduling case library, and the corresponding solutions in the emergency plan library can be modified to better reflect reality, increasing the effectiveness of the plans and enhancing their safety.

[0188] S5 connects to the factory's real-time flow database and monitors the flow in real time. When an abnormal condition is detected, an alarm is triggered and the abnormal condition is matched with the abnormal conditions in the scheduling case library. Based on the matching result of the detected abnormal condition with the abnormal conditions in the scheduling case library, an emergency scheduling optimization plan from the emergency plan library is invoked or an emergency optimization calculation is performed, and then emergency scheduling command is carried out. Figure 4 The diagram shown is a flowchart illustrating the process of triggering emergency dispatch.

[0189] Specifically, based on the matching of monitored abnormal operating conditions with those in the scheduling case library, emergency scheduling optimization schemes from the emergency plan library are invoked or emergency optimization calculations are performed, including:

[0190] If the detected abnormal operating condition matches an abnormal operating condition in the scheduling case library, then the emergency scheduling optimization scheme in the emergency plan library is directly invoked, such as... Figure 4 As shown, the relevant solutions in the emergency response plan library are provided for dispatchers to refer to. This method is intuitive and can quickly respond to abnormal operating conditions.

[0191] If the detected abnormal operating conditions do not match the abnormal operating conditions in the scheduling case library, emergency optimization calculations are performed. The emergency optimization calculations specifically include:

[0192] 1) Locate all abnormal flow streams when abnormal operating conditions occur, and determine the key abnormal flow streams and their corresponding devices;

[0193] 2) Input the key abnormal change streams into the emergency dispatch optimization calculation algorithm as calculation constraints, and read the currently set product priorities for optimization calculation;

[0194] 3) Under the existing production conditions, reduce or stop the load of several devices on the production line in order of priority from low to high until the system stability requirements are met.

[0195] Furthermore, the emergency dispatch optimization calculation algorithm specifically includes:

[0196] 1) The calculation of loads in the same series is as follows:

[0197] averloadMatrix=devicesload / sumofdevices

[0198] When the processing capabilities of devices in the same series differ, and the upper limit of one of the devices is reached:

[0199] averloadMatrix(i)=unitFeedup(i);

[0200] Where averloadMatrix is ​​the average load of a single device in the series, devicesload is the total load of the devices in the series, sumofdevices is the number of devices in the series, averloadMatrix(i) is the load of the i-th device, and unitFeedup(i) is the upper limit of the processing capacity of the i-th device;

[0201] 2) Algorithm for calculating unit output and consumption:

[0202] g. Calculated by unit consumption: devicein(i,j) = deviceout(i,j) * consumption(i,j)

[0203] h. Calculate by yield: deviceout(i,j) = devicein(i,j) * yield(i,j)

[0204] i. Calculated using linear yield: deviceout(i,j)=∑(devicein(i,j)*yield(i,j))

[0205] Where devicein(i,j) is the feed rate of the j-th stream of the i-th device, deviceout(i,j) is the output rate of the j-th stream of the i-th device, consumption(i,j) is the unit consumption coefficient of the j-th stream of the i-th device, and yield(i,j) is the yield coefficient of the j-th stream of the i-th device.

[0206] 3) Calculation function for the inlet and outlet balance of the mixer and splitter:

[0207] ∑mixandsplitout=∑mixandsplitin

[0208] Where Mixandsplitout is the flow rate of the outlet stream of the mixer and splitter, and mixandsplittin is the flow rate of the inlet stream of the mixer and splitter.

[0209] 4) Component calculation function, calculated proportionally as follows:

[0210] ∑(consumption(i,j)*loadin(i,j))=∑(consumption(i,k)*loadout(i,k))

[0211] Wherein, consumption(i,j) is a certain component of the j-th feed stream of the i-th device, loadin(i,j) is the flow rate of the j-th feed stream of the i-th device, consumption(i,k) is a certain component of the k-th discharge stream of the i-th device, and loadout(i,k) is the flow rate of the j-th feed stream of the i-th device.

[0212] Example 2

[0213] This embodiment discloses an emergency dispatch and optimization system for abnormal operating conditions in coal chemical production, including:

[0214] The data structure setting module is used to set input data and output parameters;

[0215] The general emergency dispatch optimization model construction module is used to build a general emergency dispatch optimization model based on input data and output parameters, with the goal of maximizing benefits. The general emergency dispatch optimization model includes an objective function and several constraints.

[0216] The personalized visual emergency dispatch optimization model building module is used to generate a personalized visual emergency dispatch optimization model based on the user's actual needs and the coal chemical process production flow, on the basis of the general emergency dispatch optimization model.

[0217] The module for building the scheduling case library and the emergency plan library is used to build a scheduling case library for storing abnormal operating condition cases and an emergency plan library for storing emergency scheduling optimization solutions for dealing with abnormal operating conditions, based on the personalized visual emergency scheduling optimization model.

[0218] The emergency dispatch optimization module is used to connect to the factory's real-time flow database and monitor the flow in real time. When an abnormal condition is detected, an alarm is triggered and the abnormal condition is matched with the abnormal condition in the dispatch case library. Based on the matching result of the detected abnormal condition with the abnormal condition in the dispatch case library, the emergency dispatch optimization scheme in the emergency plan library is invoked or emergency optimization calculation is performed, and then emergency dispatch command is carried out.

[0219] Furthermore, the system also includes a results presentation module, which is used at least to visualize and facilitate human-computer interaction with the optimization results obtained by the emergency dispatch optimization module. Specifically, the data that can be presented includes at least the production schedule plan for a future period, the current operating status of the equipment, the formulated emergency dispatch adjustment plan, and the enterprise benefit data under the stable state of the emergency plan. Human-computer interaction allows users to modify the data in the dispatch case library and the emergency plan library through manual input, enhancing the accuracy of dispatch case matching and the feasibility of emergency plans. When an emergency occurs, the system automatically performs calculations and analyses, and dispatchers can push the emergency plan to relevant operators through publication.

[0220] 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. An emergency dispatch optimization method for coal chemical production, characterized in that, Including the following steps: S1. Set input data and output parameters; the input data should include at least the processing capacity of the device, the properties of raw and auxiliary materials, consumption, flow rate limits, product output requirements, constraints on the hydrogen-to-carbon ratio of synthesis, tolerance for abnormal operating conditions, product priority, and judgment logic and emergency plans for known abnormal operating conditions. The output parameters should include at least the scheduling event after scheduling optimization calculation, stream nature, abnormal tag number, unit load, product output, unit production consumption, and stream flow rate; S2. Based on input data and output parameters, establish a general emergency dispatch optimization model that includes product priority and key flow streams with the goal of maximizing benefits. The general emergency dispatch optimization model includes an objective function and several constraints. The objective function is: y = priceofconsumption * m – sumofutility * priceofutility In the formula, priceofconsumption is the price coefficient matrix of the variables, m is the flow rate of the pipeline, sumofutility is the quantity of public works, and priceofutility is the price corresponding to the public works. The constraints include at least the following: (1) Balance of feed and discharge between the distributor and the mixer: In the formula, splitOut(i,j) is the flow rate of the j-th stream of the output flow of splitter i, splitin(i,j) is the flow rate of the j-th stream of the feed flow of splitter i, mixOut(i,j) is the flow rate of the j-th stream of the output flow of mixer i, mixin(i,j) is the flow rate of the j-th stream of the feed flow of mixer i, errori is the allowable error of the feed-output balance of splitter, and errorj is the allowable error of the feed-output balance of mixer. (2) Equipment processing yield constraints: unitOut (i, j) = ∑unitIn(i, j) * unitproduct In the formula, unitOut(i, j) is the output flow rate of the j stream of processing device i, unitIn(i, j) is the feed flow rate of the j stream of atmospheric and vacuum distillation device i, and unitproduct is the product output coefficient of the device; (3) Upper and lower limits of equipment processing capacity constraints: In the formula, 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; (4) Material storage tank inventory constraints: htlow(i) – ht0(i) ≤ tankin(i) – tankout(i) ≤ htup(i) -hto(i) In the formula, tankin (i) is the feed flow rate of tank i, tankout (i) 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) Production quantity constraints: productlimitlow ≤ Productquantity ≤ productlimitup In the formula, Productquantity is the product output, productlimitlow is the lower limit of product output, and productlimitup is the upper limit of product output; (6) Constraints on the nature of the strands: propertyMatrix(i,:) ≤ propertyConstraintUp(i,:) -propertyMatrix(i,:) ≤ propertyConstraintLow(i,:) In the formula, propertyMatrix is ​​the property matrix of the flow, with each row corresponding to a property, propertyConstraintUp is the upper limit of the flow property, and propertyConstraintLow is the lower limit of the flow property; (7) Public works constraints: unitutility(i,j) ≤ unitutilityUp(i,j) -unitutility(i,j) ≤ unitutilityLow(i,j) In the formula, unitutility(i,j) is the output or consumption of the j-th type of utility in the i-th secondary device, unitutilytyUp(i,j) is the upper limit of the output or consumption of the j-th type of utility in the i-th secondary device, and unitutilytyLow(i,j) is the lower limit of the output or consumption of the j-th type of utility in the i-th secondary device. (8) Flow constraints between pipes: m(j) = m(i , j) * k(i , j) + b(i , j) 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. (9) Material composition constraints: (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. (10) Real-time flow constraints: lb(j) = mreal(j) ub(j) = mreal(j) In the formula, lb(j) represents the lower limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, ub(j) represents the upper limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, and mreal(j) represents the real-time value of the flow of the key abnormal flow of the abnormal device when an abnormal operating condition occurs. S3. Based on the general emergency dispatch optimization model, select the required constraints according to the user's actual needs and generate a personalized visual emergency dispatch optimization model based on the coal chemical process production flow. S4. For the personalized visual emergency dispatch optimization model, construct a dispatch case library for storing abnormal operating condition cases and an emergency plan library for storing emergency dispatch optimization schemes to deal with abnormal operating conditions. S5 connects to the factory's real-time flow database and monitors the flow in real time. When an abnormal condition is detected, it issues an alarm and matches the abnormal condition with the abnormal condition in the scheduling case library. Based on the matching of the detected abnormal condition with the abnormal condition in the scheduling case library, it calls the emergency scheduling optimization plan in the emergency plan library or performs emergency optimization calculations, and then performs emergency scheduling command.

2. The emergency dispatch optimization method for coal chemical production according to claim 1, characterized in that, In step S4, the construction of the scheduling case library specifically includes: 1) The process scheduling model built on the production real-time monitoring system is connected to the enterprise's real-time database and monitored in real time; 2) When an abnormal situation is detected, the recording and comparison function of the scheduling case library is triggered; 3) Determine if the bit number of the abnormal data is a critical bit number and if it already exists in the scheduling case library: if it is a critical bit number and does not exist in the scheduling case library, proceed to the next step; otherwise, discard the abnormal data. 4) Once the abnormal data is confirmed, it is saved to the scheduling case library.

3. The emergency dispatch optimization method for coal chemical production according to claim 1, characterized in that, Step S4, the construction of the emergency response plan database specifically includes: 1) Select abnormal operating condition cases stored in the scheduling case library and construct contingency plans for abnormal operating condition cases; 2) Confirm and modify the operating status when abnormal conditions occur; 3) Confirm the stable operating condition results after the occurrence of abnormal operating conditions; 4) Provide the necessary adjustment amounts and operating procedures for each department based on stable operating conditions; 5) Once the required adjustment amount and operation process are confirmed, they are saved to the emergency plan database.

4. The emergency dispatch optimization method for coal chemical production according to claim 1, characterized in that, Both the dispatch case library and the emergency plan library are configured to allow for modification of plans based on actual dispatching needs.

5. The emergency dispatch optimization method for coal chemical production according to claim 1, characterized in that, In step S5, based on the matching of the monitored abnormal operating conditions with the abnormal operating conditions in the scheduling case library, the emergency scheduling optimization scheme in the emergency plan library is invoked or emergency optimization calculations are performed, specifically including: If the detected abnormal operating condition matches the abnormal operating condition in the scheduling case library, the emergency scheduling optimization scheme in the emergency plan library will be directly invoked. If the detected abnormal operating conditions do not match the abnormal operating conditions in the scheduling case library, emergency optimization calculations will be performed.

6. The emergency dispatch optimization method for coal chemical production according to claim 5, characterized in that, Emergency optimization calculations specifically include: 1) Locate all abnormal flow streams when abnormal operating conditions occur, and determine the key abnormal flow streams and their corresponding devices; 2) Input the key abnormal change streams into the emergency dispatch optimization calculation algorithm as calculation constraints, and read the currently set product priorities for optimization calculation; 3) Under the existing production conditions, reduce or stop the load of several devices on the production line in order of priority from low to high until the system stability requirements are met.

7. The emergency dispatch optimization method for coal chemical production according to claim 6, characterized in that, The emergency dispatch optimization calculation algorithm specifically includes: 1) The calculation of loads in the same series is as follows: averloadMatrix = devicesload / sumofdevices When the processing capabilities of devices in the same series differ, and the upper limit of one of the devices is reached: averloadMatrix(i) = unitFeedup(i); Where averloadMatrix is ​​the average load of a single device in the series, devicesload is the total load of the devices in the series, sumofdevices is the number of devices in the series, averloadMatrix(i) is the load of the i-th device, and unitFeedup(i) is the upper limit of the processing capacity of the i-th device; 2) Algorithm for calculating unit output and consumption: a. Calculated by unit consumption: devicein(i,j) = deviceout(i,j) * consumption(i,j) b. Calculate by yield: deviceout(i,j) = devicein(i,j) * yield(i,j) c. Calculated using linear yield: deviceout(i,j) = ∑( devicein(i,j) * yield(i,j) Where devicein(i,j) is the feed rate of the j-th stream of the i-th device, deviceout(i,j) is the output rate of the j-th stream of the i-th device, consumption(i,j) is the unit consumption coefficient of the j-th stream of the i-th device, and yield(i,j) is the yield coefficient of the j-th stream of the i-th device. 3) Calculation function for the inlet and outlet balance of the mixer and splitter: ∑mixandsplitout = ∑mixandsplitin Where Mixandsplitout is the flow rate of the outlet stream of the mixer and splitter, and mixandsplittin is the flow rate of the inlet stream of the mixer and splitter. 4) Component calculation function, calculated proportionally as follows: ∑(consumption(i,j) * loadin (i,j)) =∑( consumpution(i,k) * loadout(i,k)) Wherein, consumption(i,j) is a certain component of the j-th feed stream of the i-th device, loadin(i,j) is the flow rate of the j-th feed stream of the i-th device, consumption(i,k) is a certain component of the k-th discharge stream of the i-th device, and loadout(i,k) is the flow rate of the j-th feed stream of the i-th device.

8. An emergency dispatching and optimization system for abnormal operating conditions in coal chemical production, characterized in that, include: The data structure setting module is used to set input data and output parameters. The input data includes at least the processing capacity of the device, the properties of raw and auxiliary materials, consumption, flow rate limits, product output requirements, constraints on the hydrogen-to-carbon ratio of synthesis, tolerance for abnormal operating conditions, product priority, and judgment logic and emergency plans for known abnormal operating conditions. The output parameters should include at least the scheduling event after scheduling optimization calculation, stream nature, abnormal tag number, unit load, product output, unit production consumption, and stream flow rate; The general emergency dispatch optimization model construction module is used to build a general emergency dispatch optimization model based on input data and output parameters, with the goal of maximizing benefits. The general emergency dispatch optimization model includes an objective function and several constraints. The objective function is: y = priceofconsumption * m – sumofutility * priceofutility In the formula, priceofconsumption is the price coefficient matrix of the variables, m is the flow rate of the pipeline, sumofutility is the quantity of public works, and priceofutility is the price corresponding to the public works. The constraints include at least the following: (1) Balance of feed and discharge between the distributor and the mixer: In the formula, splitOut(i,j) is the flow rate of the j-th stream of the output flow of splitter i, splitin(i,j) is the flow rate of the j-th stream of the feed flow of splitter i, mixOut(i,j) is the flow rate of the j-th stream of the output flow of mixer i, mixin(i,j) is the flow rate of the j-th stream of the feed flow of mixer i, errori is the allowable error of the feed-output balance of splitter, and errorj is the allowable error of the feed-output balance of mixer. (2) Equipment processing yield constraints: unitOut (i, j) = ∑unitIn(i, j) * unitproduct In the formula, unitOut(i, j) is the output flow rate of the j stream of processing device i, unitIn(i, j) is the feed flow rate of the j stream of atmospheric and vacuum distillation device i, and unitproduct is the product output coefficient of the device; (3) Upper and lower limits of equipment processing capacity constraints: In the formula, 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; (4) Material storage tank inventory constraints: htlow(i) – ht0(i) ≤ tankin(i) – tankout(i) ≤ htup(i) -hto(i) In the formula, tankin (i) is the feed flow rate of tank i, tankout (i) 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) Production quantity constraints: productlimitlow ≤ Productquantity ≤ productlimitup In the formula, Productquantity is the product output, productlimitlow is the lower limit of product output, and productlimitup is the upper limit of product output; (6) Constraints on the nature of the strands: propertyMatrix(i,:) ≤ propertyConstraintUp(i,:) -propertyMatrix(i,:) ≤ propertyConstraintLow(i,:) In the formula, propertyMatrix is ​​the property matrix of the flow, with each row corresponding to a property, propertyConstraintUp is the upper limit of the flow property, and propertyConstraintLow is the lower limit of the flow property; (7) Public works constraints: unitutility(i,j) ≤ unitutilityUp(i,j) -unitutility(i,j) ≤ unitutilityLow(i,j) In the formula, unitutility(i,j) is the output or consumption of the j-th type of utility in the i-th secondary device, unitutilytyUp(i,j) is the upper limit of the output or consumption of the j-th type of utility in the i-th secondary device, and unitutilytyLow(i,j) is the lower limit of the output or consumption of the j-th type of utility in the i-th secondary device. (8) Flow constraints between pipes: m(j) = m(i , j) * k(i , j) + b(i , j) 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. (9) Material composition constraints: (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. (10) Real-time flow constraints: lb(j) = mreal(j) ub(j) = mreal(j) In the formula, lb(j) represents the lower limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, ub(j) represents the upper limit of the flow of the j-th flow in the emergency dispatch optimization algorithm, and mreal(j) represents the real-time value of the flow of the key abnormal flow of the abnormal device when an abnormal operating condition occurs. The personalized visual emergency dispatch optimization model building module is used to generate a personalized visual emergency dispatch optimization model based on the user's actual needs and the coal chemical process production flow, on the basis of the general emergency dispatch optimization model. The module for building the scheduling case library and the emergency plan library is used to build a scheduling case library for storing abnormal operating condition cases and an emergency plan library for storing emergency scheduling optimization solutions for dealing with abnormal operating conditions, based on the personalized visual emergency scheduling optimization model. The emergency dispatch optimization module is used to connect to the factory's real-time flow database and monitor the flow in real time. When an abnormal condition is detected, an alarm is triggered and the abnormal condition is matched with the abnormal condition in the dispatch case library. Based on the matching result of the detected abnormal condition with the abnormal condition in the dispatch case library, the emergency dispatch optimization scheme in the emergency plan library is invoked or emergency optimization calculation is performed, and then emergency dispatch command is carried out.

9. The emergency dispatching and optimization system for abnormal operating conditions in 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 optimization results obtained by the emergency dispatch optimization module.

Images