An energy consumption regulation system and method for an scr catalyst regeneration plant

Abstract

The application provides an SCR catalyst regeneration plant energy consumption regulation system and method, the system comprises an equipment energy consumption collection module, a production plan optimization module and a control module; the equipment energy consumption collection module comprises various sensors arranged at each key equipment and process node of the plant, is used for collecting equipment energy consumption, process parameters and production data in real time, and is uniformly uploaded to a flexible load analysis module through an industrial communication network; the production plan optimization module is used for constructing and solving a multi-objective optimization model according to the equipment energy consumption, process parameters and production data collected by the equipment energy consumption collection module in real time, and generating an optimal production scheduling strategy; the control module is used for converting the optimal production scheduling strategy into control instructions of each process equipment and controlling automatic operation of each process equipment. One technical effect of the application is to maximize the reduction of energy cost, obtain demand side response income, and finally realize the minimization of comprehensive operation cost.

Description

Technical Field

[0001] This invention belongs to the field of SCR catalyst regeneration technology, specifically relating to an energy consumption control system and method for an SCR catalyst regeneration plant. Background Technology

[0002] SCR catalyst regeneration involves multiple steps, including cleaning, washing, loading, drying, and packaging. It is usually an assembly line operation. During the entire process, about ten catalysts are simultaneously performing different processes. Factories typically produce in sequence and rarely pay attention to power consumption, which is a major cost of factory operation, especially since equipment such as drying ovens, evaporators, water pumps, and fans consume a lot of electricity and operate for long periods of time.

[0003] Currently, factories primarily consume rigid loads of electricity, focusing only on the energy efficiency of the equipment itself. They fail to optimize their electricity consumption behavior from a holistic and time-scale perspective, making it impossible to effectively save costs by utilizing the grid's time-of-use pricing policy, let alone participate in demand-side response and enjoy subsidies.

[0004] Therefore, there is an urgent need for an energy consumption control system and method for SCR catalyst regeneration plants to solve the above-mentioned technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art, and to provide a new technical solution for an energy consumption control system and method for an SCR catalyst regeneration plant.

[0006] According to a first aspect of the present invention, an energy consumption control system for an SCR catalyst regeneration plant is provided, comprising: The equipment energy consumption collection module includes various sensors deployed at key equipment and process nodes in the factory. These sensors are used to collect equipment energy consumption, process parameters, and production data in real time, and then upload them to the flexible load analysis module via an industrial communication network. Each of the sensors includes a smart meter, a temperature sensor, and a flow meter. The smart meter is used to monitor equipment power and energy consumption in real time, the temperature sensor is used to monitor the temperature of the furnace, module surface, and the environment, and the flow meter is used to monitor the flow rate of gas, cooling water, and cleaning fluid. The production planning optimization module is used to construct and solve a multi-objective optimization model that comprehensively considers economic efficiency, quality assurance and grid interaction based on the equipment energy consumption, process parameters and production data collected in real time by the equipment energy consumption collection module, and generate the optimal production scheduling strategy to minimize energy costs and achieve coordinated operation with the grid while ensuring the quality of catalyst regeneration. The control module is used to convert the optimal production scheduling strategy into control instructions for each process equipment after the production planning optimization module generates the optimal production scheduling strategy, and to control the automatic operation of each process equipment.

[0007] Optionally, during production execution, the sensors in the equipment energy consumption collection module continuously collect the actual operating data of the equipment in each process and upload it to the production planning optimization module in real time, forming a closed-loop control structure of optimization-execution-feedback-re-optimization.

[0008] According to a first aspect of the present invention, an energy consumption control method for an SCR catalyst regeneration plant is provided, applied to the energy consumption control system for an SCR catalyst regeneration plant as described in the first aspect, comprising the following steps: Step S1: Collect equipment energy consumption, process parameters and production data in real time through the equipment energy consumption collection module, and upload them to the flexible load analysis module through the industrial communication network. Step S2: The production planning optimization module starts an optimization calculation at a fixed period and solves a multi-objective optimization model based on the time-of-use electricity price signal for the next 24 hours, the demand-side response forecast or real-time instructions issued by the power grid, the current order production schedule and delivery time limit, and the operating status and availability of each piece of equipment. The optimal production scheduling strategy for the next period of time is obtained based on the multi-objective optimization model. Step S3: The optimal production scheduling strategy is transformed into control commands for each process equipment through the control module, and the equipment of each process is controlled to run automatically, so as to prioritize the operation of high energy-consuming processes during off-peak electricity price periods and strive for DSR economic benefits while ensuring the quality of regeneration.

[0009] Optionally, in step S2, when solving a multi-objective optimization model, the production planning optimization module also bases its solution on the latest measured data from the equipment energy consumption collection module.

[0010] Optionally, while ensuring the quality of recycling, efforts can be made to maximize the economic benefits of DSR, including: When a demand-side response instruction is received from the power grid, the set temperature of the drying furnace is temporarily and moderately reduced, and the operation of non-critical auxiliary equipment is suspended. By smoothly adjusting the set temperature, a flexible reduction in instantaneous power is achieved, so as to strive for DSR economic benefits while ensuring regeneration quality.

[0011] Optionally, high-energy-consuming processes may be prioritized for operation during off-peak electricity pricing periods, including: By taking advantage of the physical characteristics of the drying furnace, such as its large thermal inertia and good heat preservation performance, catalyst modules with long drying cycles and high overall energy consumption are preferentially scheduled to enter the drying furnace for heating during off-peak electricity price periods. During peak electricity price periods, no new modules are put into the drying furnace, and only necessary low-power heat preservation operation is maintained to achieve the optimal distribution of energy consumption costs.

[0012] Optionally, in order to prioritize the operation of high-energy-consuming processes during off-peak electricity hours and to strive for DSR economic benefits while ensuring regeneration quality, a thermal inertia dynamic model of the drying furnace is established. The thermal inertia dynamic model of the drying furnace is based on the design thermal parameters of the drying furnace and combined with real-time on-site operating data. The nonlinear functional relationship between the furnace temperature change rate and various influencing factors is constructed by combining data fitting and mechanism modeling. The real-time on-site operating data includes the core temperature at the furnace entrance, ambient temperature, circulating fan speed, moisture content of the modules to be dried, and module feeding rate.

[0013] Optionally, the thermal inertia dynamic model of the drying furnace is used to predict in real time the rate of increase or decrease of furnace temperature under different set temperature conditions; The thermal inertia dynamic model of the drying furnace is also used to dynamically calculate the real-time power required to maintain a specific process temperature. The thermal inertia dynamic model of the drying furnace is also used to quantitatively assess the changes in energy consumption during the temperature regulation process.

[0014] Optionally, a multi-objective optimization model is generated with the goal of minimizing the total overall operating cost, and the following objective function is established; Total cost = Electricity cost + Quality risk cost - DSR incentive revenue; in, ; ; ; In the above formula: The electricity price at time t is expressed in yuan / kWh. The power of the j-th module at time t in the i-th process is expressed in kW. This is a weighting factor, representing the impact of the actual temperature of the drying oven deviating from the design temperature on the quality, expressed in yuan / ℃. 2 ; The design target temperature of the drying oven, in °C; This refers to the actual operating temperature of the drying oven, in °C. The electricity price is subsidized, and the unit is yuan / kWh; This represents the load reduction amount, expressed in kW.

[0015] Optionally, the production planning optimization module incorporates a process logic model as a hard constraint for optimization, the hard constraint including: Process timing constraints: The start time of a downstream process must not be earlier than the end time of an upstream process; Delivery time constraints: The final completion time of all modules must not be later than the delivery deadline promised by the customer; Equipment operating boundary constraints: The heating / cooling rate of the drying oven must not exceed the safety limit; the maximum operating temperature must not exceed the design limit; and the number of modules in the oven at the same time must not exceed the drying oven capacity.

[0016] One technical advantage of this invention is that: In the embodiments of this application, the SCR catalyst regeneration plant energy consumption control system and method can transform the SCR catalyst regeneration process from a rigid load to a flexible and adjustable load. By interacting with the power grid, it automatically adjusts high-energy-consuming processes to operate during off-peak electricity price periods. Under the premise of ensuring production tasks, it dynamically adjusts equipment operating parameters and production sequence to maximize the reduction of energy costs and obtain demand-side response benefits, ultimately minimizing the overall operating costs (electricity costs, DSR reward benefits, and quality risk costs). Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the energy consumption control system for an SCR catalyst regeneration plant according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of an energy consumption control method for an SCR catalyst regeneration plant according to an embodiment of the present invention. Figure 3 This is a schematic flowchart of an energy consumption control method for an SCR catalyst regeneration plant according to another embodiment of the present invention.

[0018] In the diagram: 10. Equipment energy consumption collection module; 20. Production planning optimization module; 30. Control module. Detailed Implementation

[0019] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0020] The embodiments of this application will now be described in detail. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] According to a first aspect of the invention, see Figure 1 An energy consumption control system for an SCR catalyst regeneration plant is provided, comprising: The equipment energy consumption collection module 10 includes various sensors deployed at key equipment and process nodes in the factory. These sensors are used to collect equipment energy consumption, process parameters, and production data in real time, and then upload them to the flexible load analysis module via an industrial communication network. Each of the sensors includes a smart meter, a temperature sensor, and a flow meter. The smart meter is used to monitor equipment power and energy consumption in real time. The temperature sensor is used to monitor the temperature of the furnace, module surface, and the environment. The flow meter is used to monitor the flow rate of gas, cooling water, and cleaning fluid. The production planning optimization module 20 is used to construct and solve a multi-objective optimization model that comprehensively considers economic efficiency, quality assurance and grid interaction based on the equipment energy consumption, process parameters and production data collected in real time by the equipment energy consumption collection module, and generate the optimal production scheduling strategy to minimize energy costs and achieve coordinated operation with the grid while ensuring the quality of catalyst regeneration. The control module 30 is used to convert the optimal production scheduling strategy into control instructions for each process equipment after the production planning optimization module generates the optimal production scheduling strategy, and to control the automatic operation of each process equipment.

[0022] In this embodiment, the SCR catalyst regeneration plant energy consumption control system can transform the SCR catalyst regeneration process from a rigid load to a flexible and adjustable load. By interacting with the power grid, it automatically adjusts high-energy-consuming processes to operate during off-peak electricity price periods. Under the premise of ensuring production tasks, it dynamically adjusts equipment operating parameters and production sequence to maximize the reduction of energy costs and obtain demand-side response benefits, ultimately minimizing the overall operating costs (electricity costs, DSR reward benefits, and quality risk costs).

[0023] Optionally, during production execution, the sensors in the equipment energy consumption collection module continuously collect the actual operating data (i.e., production data) of the equipment in each process, and upload it in real time to the production planning optimization module (i.e., the feedback of the closed-loop effect after execution), forming a closed-loop control structure of optimization-execution-feedback-re-optimization.

[0024] In the above embodiments, not only is the automated execution of production plans achieved, but also the closed-loop data feedback enables the system to continuously evolve. Upgrading the traditional "open-loop scheduling" to "closed-loop intelligent control" significantly improves the overall operational efficiency of the recycling plant under complex electricity price environments and quality constraints, serving as a core support for achieving green, low-carbon, and intelligent production. Furthermore, this application endows the system with self-learning and adaptive capabilities, enabling it to continuously adapt to changes in production conditions and gradually improve energy consumption optimization and scheduling accuracy.

[0025] According to a second aspect of the invention, see Figure 2 and Figure 3 A method for controlling energy consumption in an SCR catalyst regeneration plant is provided, which is applied to the energy consumption control system of the SCR catalyst regeneration plant as described in the first aspect, and includes the following steps: Step S1: Collect equipment energy consumption, process parameters and production data in real time through the equipment energy consumption collection module, and upload them to the flexible load analysis module through the industrial communication network. Step S2: The production planning optimization module starts an optimization calculation at a fixed period and solves a multi-objective optimization model based on the time-of-use electricity price signal for the next 24 hours, the demand-side response forecast or real-time instructions issued by the power grid, the current order production schedule and delivery time limit, and the operating status and availability of each piece of equipment. The optimal production scheduling strategy for the next period of time is obtained based on the multi-objective optimization model. Step S3: The optimal production scheduling strategy is transformed into control commands for each process equipment through the control module, and the automatic operation of each process equipment is controlled (i.e., production plan) to prioritize the operation of high-energy-consuming processes during off-peak electricity price periods and strive for DSR economic benefits while ensuring regeneration quality.

[0026] In this embodiment of the application, the energy consumption control method of the SCR catalyst regeneration plant can transform the SCR catalyst regeneration process from a rigid load to a flexible and adjustable load. By interacting with the power grid, it automatically adjusts high-energy-consuming processes to operate during off-peak electricity price periods. Under the premise of ensuring production tasks, it dynamically adjusts equipment operating parameters and production sequence to maximize the reduction of energy costs and obtain demand-side response benefits, ultimately minimizing the overall operating costs (electricity costs, DSR reward benefits, and quality risk costs).

[0027] Optionally, in step S2, when solving a multi-objective optimization model, the production planning optimization module also uses the latest measured data from the equipment energy consumption collection module to achieve closed-loop effect feedback after execution. This not only improves the model's prediction accuracy and enhances scheduling reliability, but also avoids quality risks or energy waste caused by external disturbances, improves system robustness, and facilitates closed-loop optimization, enabling continuous learning and evolution.

[0028] Optionally, while ensuring the quality of recycling, efforts can be made to maximize the economic benefits of DSR, including: When a demand-side response instruction is received from the power grid, the set temperature of the drying furnace is temporarily and moderately reduced, and the operation of non-critical auxiliary equipment is suspended. By smoothly adjusting the set temperature, a flexible reduction in instantaneous power is achieved, so as to strive for DSR economic benefits while ensuring regeneration quality.

[0029] In the above embodiments, Optionally, high-energy-consuming processes may be prioritized for operation during off-peak electricity pricing periods, including: By taking advantage of the physical characteristics of the drying furnace, such as its large thermal inertia and good heat preservation performance, catalyst modules with long drying cycles and high overall energy consumption are preferentially scheduled to enter the drying furnace for heating during off-peak electricity price periods. During peak electricity price periods, no new modules are put into the drying furnace, and only necessary low-power heat preservation operation is maintained to achieve the optimal distribution of energy consumption costs.

[0030] In the above implementation, by "peak shifting and valley filling", the main energy consumption load is shifted from high-price periods to low-price periods. Under the premise of ensuring the continuity of the regeneration process and the stability of quality, the overall electricity procurement cost is significantly reduced, and the optimal distribution of energy consumption expenditure in the time dimension is achieved.

[0031] Optionally, in order to prioritize the operation of high-energy-consuming processes during off-peak electricity hours and to strive for DSR economic benefits while ensuring regeneration quality, a thermal inertia dynamic model of the drying furnace is established. The thermal inertia dynamic model of the drying furnace is based on the design thermal parameters of the drying furnace and combined with real-time on-site operating data. The nonlinear functional relationship between the furnace temperature change rate and various influencing factors is constructed by combining data fitting and mechanism modeling, that is, to achieve flexible load regulation for demand-side response (DSR). The real-time on-site operating data includes the core temperature in front of the furnace, the ambient temperature, the speed of the circulating fan, the moisture content of the module to be dried, and the module feeding rate into the furnace.

[0032] In the above implementation, the load reduction can be provided as an ancillary service to the power grid, and the factory can obtain DSR subsidy revenue from the power grid company, thus achieving a win-win situation of economic benefits and social responsibility.

[0033] It should be noted that the drying oven is a major energy-consuming device, and starting it requires a large amount of energy, with high energy consumption during continuous operation. This invention, based on the high thermal inertia of the drying oven, achieves the following objectives: (a) Prioritize modules with long drying cycles and high energy consumption to enter the furnace during off-peak electricity price periods, and keep the drying furnace in a module-free state during peak electricity price periods, maintaining the heat preservation power with low energy consumption, so as to achieve the optimal distribution of energy consumption costs. (b) Upon receiving a demand-side response (DSR) signal, the instantaneous power is reduced smoothly by temporarily adjusting parameters, thereby transforming the rough start-up and shutdown into an appropriate increase or decrease in temperature to balance economy and regeneration quality.

[0034] Optionally, the thermal inertia dynamic model of the drying furnace is used to predict in real time the rate of increase or decrease of furnace temperature under different set temperature conditions; The thermal inertia dynamic model of the drying furnace is also used to dynamically calculate the real-time power required to maintain a specific process temperature. The thermal inertia dynamic model of the drying furnace is also used to quantitatively assess the changes in energy consumption during the temperature regulation process.

[0035] In other words, the thermal inertia dynamic model of the drying furnace can clearly determine how long it takes for the temperature to drop or rise to the target value. For example, how long does it take to drop the set temperature from 250℃ to 230℃? How much power can be saved during this period? When electricity prices are low, if the temperature needs to be increased, how much power and how long will it take to restore it to 280℃? This provides a basis for subsequent power calculations.

[0036] The above implementation not only enables a leap from "experience-based control" to "predictive control," improving temperature control accuracy, reducing temperature overshoot or undershoot, and ensuring process stability, but also makes energy-saving effects quantifiable and comparable, providing a solid basis for the production planning optimization module to make the optimal trade-off between "energy saving" and "quality." Furthermore, it enhances the credibility and revenue stability of participation in the electricity market, achieving a win-win situation of "energy saving" and "revenue generation."

[0037] Optionally, a multi-objective optimization model is generated with the goal of minimizing the total overall operating cost, and the following objective function is established; Total cost = Electricity cost + Quality risk cost - DSR incentive revenue; in, ; ; ; In the above formula: The electricity price at time t is expressed in yuan / kWh. The power of the j-th module at time t in the i-th process is expressed in kW. This is a weighting factor, representing the impact of the actual temperature of the drying oven deviating from the design temperature on the quality, expressed in yuan / ℃. 2 ; The design target temperature of the drying oven, in °C; This refers to the actual operating temperature of the drying oven, in °C. The electricity price is subsidized, and the unit is yuan / kWh; This represents the load reduction amount, expressed in kW.

[0038] The above implementation achieves a balance between economy and quality, ensuring that the optimization results save energy without compromising core process indicators.

[0039] It should be noted that the cost of electricity is essentially the cost of several modules in a project. The power of each module at each process is multiplied by the corresponding electricity price at that moment to obtain the sum of the electricity consumption of all processes in that module. After summing again, the total electricity consumption of all processes in all modules is obtained.

[0040] The quality risk cost assessment considers the impact on quality caused by deviations in the actual drying oven temperature from the design temperature in response to load reductions. This avoids the potential harm to quality caused by indiscriminately lowering the drying temperature to save electricity. By analyzing historical production data, a statistical relationship is established between temperature deviation and non-conforming product rate and rework costs, thus deriving an empirical α value. Specifically, when the temperature deviation is small, catalyst activity recovery may fluctuate slightly, but is usually within acceptable limits, and the loss is negligible, resulting in a small α value. When the temperature deviation is moderate, the catalyst activity recovery rate decreases significantly, the risk of non-conforming products increases, and some modules may need to be reworked, increasing internal loss costs; the α value should be significant to allow the algorithm to proactively avoid such deviations. When the temperature deviation is large, improper drying may even damage the mechanical strength of the catalyst module or cause complete failure, resulting in batch accidents; the α value should be very high to ensure that such major risks do not occur.

[0041] The DSR (Distributed Revenue Sharing) incentive works as follows: When the reliability of the power grid system is threatened, users are alerted via electricity price signals to reduce unnecessary loads, ensuring grid stability. Users who respond to grid demands and reduce their electricity load receive a subsidy; the subsidy price multiplied by the load reduction amount equals the revenue.

[0042] Optionally, based on the objective function of minimizing the total operating cost, the energy consumption of the recycling plant is transformed into a problem of finding the minimum value of the function. However, this process must adhere to certain boundary conditions, namely, the built-in process model. Therefore, the production planning optimization module incorporates a process logic model, which is used to simulate the logical relationships and running times of each process (dust removal, water washing, pickling, drying, and loading). As hard constraints for the optimization solution, these hard constraints include: Process timing constraints: The start time of a downstream process must not be earlier than the end time of an upstream process; Delivery time constraints: The final completion time of all modules must not be later than the delivery deadline promised by the customer; Equipment operating boundary constraints: The heating / cooling rate of the drying oven must not exceed the safety limit; the maximum operating temperature must not exceed the design limit; and the number of modules in the oven at the same time must not exceed the drying oven capacity.

[0043] The above implementation ensures that the optimization results conform to the actual production process logic and avoids scheduling instructions that violate the production process.

[0044] Example 1 In this embodiment, assuming 100 standard SCR modules are regenerated, the device energy harvesting module will operate first: The ideal temperature target for the drying oven is T = 280℃, with an allowable operating range of 260~280℃. However, the greater the deviation, the higher the quality risk and cost. Within the range of 275~280℃, α = 1 yuan / ℃. 2 (A temperature deviation of 1°C from the target value for 1 hour will incur a quality risk cost of 1 yuan. At this point, a slight temperature deviation has a minimal impact.) During the heating period of the drying oven, the heating rate calculated by the drying oven's thermal inertia dynamic model is 2°C / min; when modules are being dried in the drying oven, the power is 500kW (drying); the cooling rate is 4°C / min; under heat preservation conditions (no modules in the drying oven), the power is 50kW (heat dissipation power).

[0045] The boundary conditions for electricity pricing are as follows: off-peak electricity price (0:00~8:00) is 0.3 yuan / kWh, normal electricity price (08:00~17:00, 22:00~24:00) is 0.7 yuan / kWh, and peak electricity price (17:00~22:00) is 1.2 yuan / kWh. During the DSR period from 18:00 to 19:00, the grid requests load reduction, with a subsidy of 0.8 yuan / kWh.

[0046] Production optimization module optimization objective: Calculate total cost = electricity cost + quality risk cost - DSR reward revenue.

[0047] Without this optimization, the production plan would be as follows: (1) The oven operates at a constant temperature of 280℃ throughout the day, and remains in drying mode until 100 pieces are produced, taking approximately 50 hours. This includes 16 hours during off-peak electricity pricing, 24 hours during normal electricity pricing, and 10 hours during peak electricity pricing. Without DSR peak shaving and temperature adjustment, the total operating cost is: Electricity cost = off-peak electricity cost + normal electricity cost + peak electricity cost = 0.3×16×500 + 0.7×24×500 + 1.2×10×500 = 16800 yuan, of which DSR bonus is 0 and quality risk cost is 0, so the total cost is 16800 yuan.

[0048] To find the optimal solution and schedule production, the drying time of more modules is strictly arranged during the most economical off-peak hours (00:00~08:00). Then, during normal periods, the load that needs to be operated during peak periods is reduced. Of course, under extreme conditions, all drying is carried out using off-peak electricity prices, and the rest of the time is spent on heat preservation. Although the cost of off-peak electricity prices is reduced, the overall construction period is too long and the overall cost is high. Therefore, it is necessary to balance the drying and heat preservation time periods, combine peak and off-peak electricity prices, calculate the most economical electricity price, and couple DSR rewards and quality risk costs.

[0049] After production scheduling, drying takes place from 22:00 to 17:00. At 17:00, the temperature is lowered to 80℃ and maintained at that temperature. DSR electricity pricing begins at 18:00. Temperature is raised again at 22:00, reaching 280℃ at 23:00 for continuous drying. The total duration is from 22:00 on the first day to 10:00 on the fourth day. The off-peak electricity price period is 24 hours, the regular electricity price period is 26 hours, and the peak electricity price period is 12 hours with the temperature maintained. Therefore, the electricity cost = off-peak electricity cost + regular electricity cost + peak electricity cost = 0.3 × 24 × 500 + 0.7 × 26 × 500 + 1.2 × 12 × 30 = 13132 yuan; the DSR bonus = 0.8 × 1 × 3 × 470 = 1128 yuan; the quality risk cost is 0; the total cost is 10004 yuan, resulting in a saving of 6796 yuan for every 100 catalyst pieces produced.

[0050] This example uses 100 catalysts as a case study for ease of understanding. In reality, the system performs cost accounting every certain period (2 hours or less) based on the grid price and makes subsequent adjustments. If drying needs to be carried out during peak electricity price periods due to time constraints, a strategy of further reducing the temperature to reduce power and obtain revenue can be adopted. In this case, since the temperature reduction is small, the impact on quality risk costs is feasible as long as it is less than the sum of DSR revenue and electricity revenue.

[0051] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. An energy consumption control system for an SCR catalyst regeneration plant, characterized in that, include: The equipment energy consumption collection module includes various sensors deployed at key equipment and process nodes in the factory. These sensors are used to collect equipment energy consumption, process parameters, and production data in real time, and then upload them to the flexible load analysis module via an industrial communication network. Each of the sensors includes a smart meter, a temperature sensor, and a flow meter. The smart meter is used to monitor equipment power and energy consumption in real time, the temperature sensor is used to monitor the temperature of the furnace, module surface, and the environment, and the flow meter is used to monitor the flow rate of gas, cooling water, and cleaning fluid. The production planning optimization module is used to construct and solve a multi-objective optimization model that comprehensively considers economic efficiency, quality assurance and grid interaction based on the equipment energy consumption, process parameters and production data collected in real time by the equipment energy consumption collection module, and generate the optimal production scheduling strategy to minimize energy costs and achieve coordinated operation with the grid while ensuring the quality of catalyst regeneration. The control module is used to convert the optimal production scheduling strategy into control instructions for each process equipment after the production planning optimization module generates the optimal production scheduling strategy, and to control the automatic operation of each process equipment.

2. The SCR catalyst regeneration plant energy consumption control system according to claim 1, characterized in that, During production execution, the sensors in the equipment energy consumption collection module continuously collect the actual operating data of the equipment in each process, which is then uploaded to the production planning optimization module in real time, forming a closed-loop control structure of optimization-execution-feedback-re-optimization.

3. A method for controlling energy consumption in an SCR catalyst regeneration plant, characterized in that, The energy consumption control system for an SCR catalyst regeneration plant as described in any one of claims 1 to 2 includes the following steps: Step S1: Collect equipment energy consumption, process parameters and production data in real time through the equipment energy consumption collection module, and upload them to the flexible load analysis module through the industrial communication network. Step S2: The production planning optimization module starts an optimization calculation at a fixed period and solves a multi-objective optimization model based on the time-of-use electricity price signal for the next 24 hours, the demand-side response forecast or real-time instructions issued by the power grid, the current order production schedule and delivery time limit, and the operating status and availability of each piece of equipment. The optimal production scheduling strategy for the next period of time is obtained based on the multi-objective optimization model. Step S3: The optimal production scheduling strategy is transformed into control commands for each process equipment through the control module, and the equipment of each process is controlled to run automatically, so as to prioritize the operation of high energy-consuming processes during off-peak electricity price periods and strive for DSR economic benefits while ensuring the quality of regeneration.

4. The energy consumption control method for an SCR catalyst regeneration plant according to claim 3, characterized in that, In step S2, when solving a multi-objective optimization model, the production planning optimization module also uses the latest measured data from the equipment energy consumption collection module.

5. The energy consumption control method for an SCR catalyst regeneration plant according to claim 4, characterized in that, Strive for DSR economic benefits while ensuring recycling quality, including: When a demand-side response instruction is received from the power grid, the set temperature of the drying furnace is temporarily and moderately reduced, and the operation of non-critical auxiliary equipment is suspended. By smoothly adjusting the set temperature, a flexible reduction in instantaneous power is achieved, so as to strive for DSR economic benefits while ensuring regeneration quality.

6. The energy consumption control method for an SCR catalyst regeneration plant according to claim 5, characterized in that, High-energy-consuming processes will be prioritized for operation during off-peak electricity pricing periods, including: By taking advantage of the physical characteristics of the drying furnace, such as its large thermal inertia and good heat preservation performance, catalyst modules with long drying cycles and high overall energy consumption are preferentially scheduled to enter the drying furnace for heating during off-peak electricity price periods. During peak electricity price periods, no new modules are put into the drying furnace, and only necessary low-power heat preservation operation is maintained to achieve the optimal distribution of energy consumption costs.

7. The energy consumption control method for an SCR catalyst regeneration plant according to claim 6, characterized in that, To achieve the goals of prioritizing high-energy-consuming processes during off-peak electricity hours and maximizing DSR economic benefits while ensuring regeneration quality, a thermal inertia dynamic model of the drying furnace is established. This model is based on the design thermal parameters of the drying furnace and incorporates real-time operational data. It constructs a nonlinear functional relationship between the furnace temperature change rate and various influencing factors through a combination of data fitting and mechanism modeling. The real-time operational data includes the core temperature at the furnace entrance, ambient temperature, circulating fan speed, moisture content of the modules to be dried, and module feeding rate.

8. The energy consumption control method for an SCR catalyst regeneration plant according to claim 7, characterized in that, The thermal inertia dynamic model of the drying furnace is used to predict in real time the rate of increase or decrease of furnace temperature under different set temperature conditions. The thermal inertia dynamic model of the drying furnace is also used to dynamically calculate the real-time power required to maintain a specific process temperature. The thermal inertia dynamic model of the drying furnace is also used to quantitatively assess the changes in energy consumption during the temperature regulation process.

9. The energy consumption control method for an SCR catalyst regeneration plant according to claim 8, characterized in that, A multi-objective optimization model is established with the objective of minimizing the total overall operating cost, and the following objective function is defined: Total cost = Electricity cost + Quality risk cost - DSR incentive revenue; in, ; ； ； In the above formula: The electricity price at time t is expressed in yuan / kWh. The power of the j-th module at time t in the i-th process is expressed in kW. This is a weighting factor, representing the impact of the actual temperature of the drying oven deviating from the design temperature on the quality, expressed in yuan / ℃. 2 ; The design target temperature of the drying oven, in °C; This refers to the actual operating temperature of the drying oven, in °C. The electricity price is subsidized, and the unit is yuan / kWh; This represents the load reduction amount, expressed in kW.

10. The energy consumption control method for an SCR catalyst regeneration plant according to claim 9, characterized in that, The production planning optimization module has a built-in process logic model, which serves as a hard constraint for optimization. This hard constraint includes: Process timing constraints: The start time of a downstream process must not be earlier than the end time of an upstream process; Delivery time constraints: The final completion time of all modules must not be later than the delivery deadline promised by the customer; Equipment operating boundary constraints: The heating / cooling rate of the drying oven must not exceed the safety limit; the maximum operating temperature must not exceed the design limit; and the number of modules in the oven at the same time must not exceed the drying oven capacity.

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