Line scheduling and switching management system based on energy efficiency data analysis
By quantifying the remaining energy level of equipment and optimizing the production sequence, the problems of energy waste and equipment wear in the existing scheduling system are solved, and the efficient use of energy and safe and stable operation of equipment are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BAIHE YONGHONG CHEM CO LTD
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-23
Smart Images
Figure CN121860362B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of industrial intelligent manufacturing and production energy management technology, specifically to a production line scheduling and switching management system based on energy efficiency data analysis. Background Technology
[0002] In the production environments of biopharmaceuticals and fine chemicals, core equipment such as fermenters and reactors need to be frequently switched between multiple product and batch orders. This process is accompanied by drastic temperature and pressure changes, which often results in the equipment retaining significant thermodynamic inertia and physical residual energy after shutdown.
[0003] For task allocation on such production lines, existing scheduling schemes generally adopt a time-efficiency-based management model, which focuses on calculating the overall efficiency of equipment and order delivery deadlines. This typically assumes that equipment is in a zero-energy state during task intervals and simply follows the first-in, first-out (FIFO) principle to determine the production sequence. While this model can maintain a basic production pace, it lacks awareness and utilization of residual heat or pressure potential energy within the equipment and fails to consider the matching of process energy levels between tasks. This easily leads to thermal conflicts between high-temperature tasks and immediately following low-temperature processes, forcing the production line to consume large amounts of steam or refrigerant for a forced state reset. This not only results in severe energy waste and increased operating costs but also accelerates equipment aging and increases the risk of mechanical failure due to frequent thermal stress shocks caused by drastic temperature differences.
[0004] Therefore, how to optimize the scheduling sequence by quantifying the thermodynamic state of equipment while ensuring timely order delivery, so as to reduce energy consumption during production line conversion and reduce equipment wear and tear, has become an urgent technical problem to be solved. Summary of the Invention
[0005] To address the aforementioned technical problems, this invention provides a production line scheduling and switching management system based on energy efficiency data analysis. Specifically, the technical solution of this invention includes:
[0006] The data acquisition module is used to acquire the operational characteristic parameters of the target production line. The operational characteristic parameters include: the set of orders to be scheduled, the baseline data of process energy consumption, the real-time thermodynamic status of the equipment, the end time of the previous batch, and the workshop environmental parameters.
[0007] The first processing module is used to determine the remaining energy level state of the production equipment over time after shutdown, based on the real-time thermodynamic state of the equipment, the end time of the previous batch, and the workshop environmental parameters.
[0008] The second processing module is used to calculate the conversion energy consumption value required for state switching between different production tasks based on the remaining energy level state and the process energy consumption baseline data, and to generate a switching energy consumption matrix.
[0009] The third processing module is used to determine the optimal production sequence with the lowest total conversion energy consumption under the delivery deadline constraint based on the set of orders to be scheduled and the switching energy consumption matrix.
[0010] The scheduling management module is used to determine the optimal material feeding time window and the corresponding equipment status reset instruction for each production task based on the optimal production sequence and the remaining energy level state.
[0011] Preferably, the first processing module determines the remaining energy level state of the production equipment over time after shutdown based on the real-time thermodynamic state of the equipment, the end time of the previous batch, and the workshop environmental parameters, including:
[0012] The real-time thermodynamic state of the equipment and the workshop environmental parameters are retrieved.
[0013] Using thermal decay calculation logic, based on the temperature difference between the current equipment temperature and the workshop ambient temperature, and combined with the preset equipment thermal inertia coefficient, the natural temperature decay curve of the production equipment during the shutdown waiting period is calculated.
[0014] Based on the natural temperature decay curve, the temperature value corresponding to the device when starting the next task at any future time point is calculated, and the temperature value is mapped to a thermal energy value, which is then defined as the remaining energy level state.
[0015] Preferably, the second processing module calculates the conversion energy consumption value required for state switching between different production tasks based on the remaining energy level state and the process energy consumption baseline data, including:
[0016] The process energy consumption baseline data is used to determine the target process energy level requirements and cleaning cleanliness requirements for the next task.
[0017] Calculate the thermal temperature control energy consumption required to adjust the production equipment from the remaining energy level state to the target process energy level requirement;
[0018] Calculate the chemical cleaning energy consumption required to adjust the production equipment from its current cleaning state to the required cleaning cleanliness level.
[0019] The sum of the energy consumption for thermal temperature regulation and the energy consumption for chemical cleaning is defined as the conversion energy consumption value.
[0020] Preferably, the third processing module determines the optimal production sequence with the lowest total conversion energy consumption under the delivery deadline constraint based on the set of orders to be scheduled and the switching energy consumption matrix, including:
[0021] Construct a multi-objective optimization function that includes total conversion energy consumption and delivery delay penalty variables, and calculate the value of the multi-objective optimization function.
[0022] Based on the switching energy consumption matrix, a path search algorithm is used to traverse the task permutations and combinations in the set of orders to be scheduled, and to calculate the sum of switching energy consumption between adjacent tasks in each permutation and combination.
[0023] In response to the searched permutations satisfying the delivery deadline constraint, the permutation with the smallest multi-objective optimization function value is selected as the optimal production sequence.
[0024] Preferably, the scheduling management module determines the optimal material feeding time window for each production task based on the optimal production sequence and the remaining energy level state, including:
[0025] Calculate the forced temperature control energy consumption under the immediate feeding mode, and the temperature control energy consumption after natural decay under the delayed feeding mode;
[0026] Determine whether the following two conditions are met simultaneously: Condition 1 is that the energy consumption for temperature regulation after natural decay is lower than the energy consumption for forced temperature regulation; Condition 2 is that the completion time after the delay is not later than the order delivery deadline.
[0027] If both condition one and condition two are met simultaneously, the delayed time will be determined as the starting point of the optimal feeding time window.
[0028] If either condition one or condition two is not met, the equipment readiness time will be determined as the starting point of the optimal feeding time window.
[0029] Preferably, the system also includes a peak load early warning module, used for:
[0030] Based on the optimal production sequence and the process energy consumption baseline data, an energy load prediction curve for the production line in the future time period is generated.
[0031] Determine whether the peak value in the energy load prediction curve exceeds a preset energy supply threshold;
[0032] If the energy supply threshold is exceeded, an adjustment command is sent to the third processing module to trigger a staggered rearrangement of the optimal production sequence.
[0033] Preferably, the process energy consumption baseline data includes: standard temperature curves, pressure requirements, and energy consumption baselines per unit time for different product types in the fermentation, extraction, and concentration stages;
[0034] The real-time thermodynamic status of the equipment includes: the current internal temperature of the reaction vessel, the jacket temperature, the pipeline pressure, and the cleanliness level of the inner wall.
[0035] Preferably, when determining the optimal production sequence, the third processing module performs the following clustering strategy:
[0036] Identify the process temperature zone values for each task in the set of orders to be scheduled;
[0037] Tasks whose process temperature values are within the same preset range are clustered into continuous production groups;
[0038] Within the continuous production group, the groups are sorted in order of increasing energy consumption, and among different continuous production groups, they are sorted in order of increasing temperature difference to generate the optimal production sequence.
[0039] Compared with the prior art, the present invention has the following beneficial effects:
[0040] 1. This invention constructs a residual energy level state calculation model based on thermal decay logic, which explicitly defines the implicit thermal energy or pressure potential energy retained by equipment over time after shutdown. This mechanism overcomes the shortcomings of traditional scheduling systems that typically assume equipment is in a zero state between tasks, enabling scheduling decisions to accurately utilize the residual heat or cold energy inside the equipment. This avoids repeated heating or unnecessary forced cooling of equipment that already possesses a basic energy level, thereby reducing energy waste at its source.
[0041] 2. This invention breaks away from the rigid first-in-first-out (FIFO) scheduling model by calculating the switching energy consumption matrix, which includes both physical temperature control and chemical cleaning, and combining a multi-objective optimization function with a clustering strategy. The system can identify the energy flow affinity between tasks, clustering tasks with similar process temperature ranges or compatible cleaning requirements into continuous production groups, and sorting them according to energy consumption gradients. This sequence optimization based on fully connected graph edge weight data minimizes the energy level difference between adjacent tasks, significantly reducing the consumption of water, electricity, steam, and cleaning agents caused by frequent and large-scale temperature changes and intense cleaning.
[0042] 3. This invention proposes a logic for determining the optimal material feeding time window, proactively introducing a controlled waiting mechanism while meeting order delivery deadlines. The system can intelligently determine and utilize natural environmental temperature differences for natural cooling, replacing the forced refrigeration work driven by high-grade energy. This not only significantly reduces refrigerant consumption but also avoids thermal stress caused by extreme temperature fluctuations in a short period, thereby reducing the risk of damage to equipment welds and critical components, and extending the service life of core production equipment.
[0043] 4. This invention achieves dynamic control over production energy consumption by generating energy load prediction curves for future time periods and monitoring their relationship with energy supply thresholds in real time. When a potential energy overload risk is predicted, the system can automatically trigger peak-shifting rearrangement of the optimal production sequence, postponing or shifting high-energy-consuming tasks. This mechanism effectively achieves peak shaving and valley filling of production load, avoiding safety hazards such as tripping and unstable steam pressure caused by excessive instantaneous energy consumption, and reducing the enterprise's dependence on transformer capacity and basic electricity expenses. Attached Figure Description
[0044] The present invention will be further explained below with reference to the accompanying drawings and embodiments:
[0045] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.
[0047] Example 1:
[0048] Please see Figure 1 The production line scheduling and switching management system based on energy efficiency data analysis includes: a data acquisition module, used to acquire the operational characteristic parameters of the target production line, including: the set of orders to be scheduled, process energy consumption baseline data, real-time thermodynamic status of equipment, end time of the previous batch, and workshop environmental parameters;
[0049] The first processing module is used to determine the remaining energy level state of the production equipment over time after shutdown, based on the real-time thermodynamic state of the equipment, the end time of the previous batch, and workshop environmental parameters.
[0050] The second processing module is used to calculate the conversion energy consumption value required for state switching between different production tasks based on the remaining energy level state and process energy consumption baseline data, and to generate a switching energy consumption matrix.
[0051] The third processing module is used to determine the optimal production sequence with the lowest total conversion energy consumption under the constraint of meeting the delivery deadline, based on the set of orders to be scheduled and the switching energy consumption matrix; the scheduling management module is used to determine the best material feeding time window and the corresponding equipment status reset instruction for each production task based on the optimal production sequence and the remaining energy level status.
[0052] This embodiment provides a production line scheduling and switching management system based on energy efficiency data analysis; the system aims to solve the energy waste problem caused by focusing only on time efficiency (OEE) and ignoring the thermodynamic inertia of equipment in the existing extraction workshop scheduling.
[0053] The data acquisition module, as the system's perception layer, is used to acquire operational characteristic parameters of the target production line. Among these, the set of orders to be scheduled refers to production instructions issued by the ERP system, including product SKUs, batch sizes, and delivery deadlines; process energy consumption baseline data refers to the energy consumption fingerprints of different products under standard operating conditions; real-time thermodynamic status of the equipment, in this embodiment, specifically refers to the inner wall temperature, jacket temperature, and pressure values of the reactor or extraction tank read in real time by sensors; the end time of the previous batch refers to the precise timestamp when the preceding task completed its discharge and entered an idle state; workshop environmental parameters include workshop room temperature and cooling water inlet temperature.
[0054] The first processing module establishes a thermal decay model for the device, the purpose of which is to determine the remaining energy level states. In the prior art, scheduling systems typically assume that equipment is in a zero state between tasks; however, in this embodiment, the first processing module calculates the thermal energy or pressure potential energy retained by the production equipment over time after shutdown based on the real-time thermodynamic state of the equipment, the end time of the previous batch, and workshop environmental parameters; for example, a tank that has just finished high-temperature extraction at 120°C may still retain residual heat of 80°C 30 minutes after shutdown, and this 80°C is the residual energy level state.
[0055] The second processing module is used to construct the switching energy consumption matrix; based on the remaining energy level state and process energy consumption baseline data, the second processing module calculates the conversion energy consumption value required for state switching between different production tasks. The conversion energy consumption value refers to the sum of physical energy, such as steam and refrigerant, and chemical energy, such as cleaning agents and water for injection, required to adjust the equipment from its current remaining state to the initial state required for the next task.
[0056] The specific process for generating the switching energy consumption matrix is as follows: Assume the set of orders to be scheduled contains... One task; the second processing module initializes a task in memory. Two-dimensional array The system uses a double loop to traverse the task index. and ,in ;
[0057] when At that time, set ;when At that time, the system calls the energy consumption calculation subroutine to calculate the task. The initial state is the end process condition, and the task is... The initial process requirement is the target state, and the switching energy consumption is calculated. And store the value in The generated matrix It fully describes the energy flow resistance between all potential task pairs, providing a weighted edge data structure for a fully connected graph for subsequent optimization.
[0058] Based on this, the third processing module, acting as the brain of scheduling decisions, determines the optimal production sequence with the lowest total conversion energy consumption under the constraints of delivery deadlines, according to the set of orders to be scheduled and the switching energy consumption matrix. This module no longer simply follows the first-in, first-out (FIFO) principle, but sorts based on energy flow affinity to minimize the energy level difference between adjacent tasks.
[0059] The scheduling management module is used to execute specific instructions; based on the optimal production sequence and the remaining energy level status, the scheduling management module determines the best material feeding time window and the corresponding equipment status reset instruction for each production task.
[0060] This embodiment achieves digital takeover of hidden thermal energy assets in industrial production by constructing a closed-loop control architecture that includes thermodynamic state perception. The system can explicitly quantify the thermodynamic inertia of equipment, avoiding the cold and heat conflicts commonly seen in traditional scheduling, such as immediately scheduling a low-temperature task after a high-temperature task. This significantly reduces the unit energy consumption of the production line and reduces the thermal stress loss of equipment caused by drastic temperature changes while ensuring delivery.
[0061] To verify the actual technical effectiveness of this system, the technical team conducted a 30-day comparative test in the fermentation and extraction workshop of a biopharmaceutical company; the test subjects were six identical 5000L stainless steel fermenters.
[0062] Test data shows that before using this system, the average energy consumption per batch in the workshop was 450 kg of standard steam. After deploying this system, by optimizing the production sequence and utilizing the natural cooling window, the average energy consumption per batch was reduced to 365 kg of standard steam, a reduction of approximately 18.9% in unit energy consumption. At the same time, data from the stress sensor installed at the weld seam of the tank jacket showed that the number of stress alarms caused by temperature difference shocks >40℃ / min decreased from an average of 2.5 times per week to 0.25 times, a reduction of 90%, effectively verifying the system's practical effectiveness in reducing energy consumption and thermal stress loss.
[0063] Example 2:
[0064] The first processing module determines the remaining energy level state of the production equipment over time after shutdown, based on the equipment's real-time thermodynamic state, the end time of the previous batch, and workshop environmental parameters. This includes: calling up the equipment's real-time thermodynamic state and workshop environmental parameters; using thermal decay calculation logic, taking the temperature difference between the current equipment temperature and the workshop ambient temperature as a benchmark, and combining it with the preset equipment thermal inertia coefficient, calculating the natural temperature decay curve of the production equipment during the shutdown waiting period; based on the natural temperature decay curve, calculating the temperature value corresponding to the equipment when starting the next task at any future time point, mapping this temperature value to a thermal energy value, and defining the thermal energy value as the remaining energy level state.
[0065] This embodiment provides a detailed description of the specific implementation of determining the remaining energy level state in the first processing module; in order to accurately predict the energy residue of the equipment in the future, this embodiment introduces a thermal decay calculation logic based on Newton's law of cooling; the first processing module calls the real-time thermodynamic state of the equipment and workshop environmental parameters to calculate the natural temperature decay curve of the production equipment during the shutdown waiting period.
[0066] Specifically, the temperature of the equipment after shutdown time t The calculation is as follows:
[0067]
[0068] in, The data is sourced from real-time data collected by environmental sensors, and its physical meaning is the workshop ambient temperature, expressed in °C. The source is the actual measurement at the end of the previous batch, and the physical meaning is the initial temperature of the equipment, in °C. The source is a fitted regression; its physical meaning is the thermal inertia coefficient of the equipment, and its unit is... Its determining logic is as follows:
[0069] Collection of historical shutdown and cooling data points of the equipment ,in This represents the historical sampling time after the system was shut down. For the corresponding historical temperature of the equipment, based on a linearized model The slope is calculated using the least squares method, i.e.:
[0070]
[0071] in, This is the dimensionless logarithmic temperature difference calculated based on the measured temperature.
[0072] Based on the natural temperature decay curve, the first processing module calculates the temperature value of the device when the next task is started at any future time point, and maps this temperature value to a thermal energy value, i.e., enthalpy, defining the thermal energy value as the remaining energy level state. .
[0073] This embodiment introduces a specific thermal inertia coefficient. The nonlinear decay model solves the technical problem of excessive error in traditional linear estimation models under extreme temperature differences; the system can accurately predict how much heat energy is retained inside the equipment for free at any future feeding time, which provides a quantitative physical benchmark for subsequent judgment on whether to use waste heat or need cooling, avoiding blind operation caused by empiricism.
[0074] Example 3:
[0075] The second processing module calculates the conversion energy consumption value required for state switching between different production tasks based on the remaining energy level state and process energy consumption baseline data. This includes: calling the process energy consumption baseline data to determine the target process energy level requirements and cleaning cleanliness requirements for the next task; calculating the thermal temperature control energy consumption required to adjust the production equipment from the remaining energy level state to the target process energy level requirements; calculating the chemical cleaning energy consumption required to adjust the production equipment from the current cleaning state to the cleaning cleanliness requirements; and defining the sum of the thermal temperature control energy consumption and the chemical cleaning energy consumption as the conversion energy consumption value.
[0076] This embodiment details how the second processing module calculates the conversion energy consumption value; the second processing module calls the process energy consumption benchmark data to determine the target process energy level requirements for the next task, such as feeding materials at 60°C, and the cleaning cleanliness requirements, such as requiring sterile SIP or water-washed CIP.
[0077] This embodiment decouples the switching process into two dimensions: physical temperature regulation and chemical cleaning; converting energy consumption values. The calculation formula is as follows:
[0078]
[0079] in, The source is calculated, and the physical meaning is the energy consumption for thermodynamic temperature regulation; The source is calculated, and the physical meaning is the energy consumption of chemical cleaning;
[0080] Thermal temperature control energy consumption The calculation will move the production equipment from the residual energy level state. Corresponding temperature Adjust to the target process energy level requirements, corresponding temperature Energy required:
[0081]
[0082] in, The source is a property table, and the physical meaning is specific heat capacity; The source is equipment parameters, and the physical meaning is the equipment's equivalent mass;
[0083] In response to Greater than , This represents the energy consumption of refrigerant to remove heat; in response to Less than This represents the energy consumption for heating with steam;
[0084] Energy consumption of chemical cleaning The calculation formula is as follows:
[0085]
[0086] Index for the cleaning phase; This represents the total number of cleaning stages; Specific heat capacity of the cleaning fluid, unit: ; For the first Water consumption per stage, unit: ; For the first Target temperature for staged cleaning; Real-time inlet water temperature for the workshop water supply system; Rated power of the cleaning pump, unit: ; For the first Phase duration, in units: ; This is a unit conversion factor used to correct the units of the power-time product, ensuring consistency with the thermal energy term.
[0087] parameter The values are retrieved from the preset process parameter database based on cleaning cleanliness requirements such as CIP / SIP levels; parameters Derived from standard fluid property tables; parameters The data originates from real-time temperature monitoring of the workshop's water supply system; parameters The rated power is derived from the nameplate of the cleaning pump.
[0088] This embodiment employs an energy decoupling and superposition calculation strategy, which can identify hidden energy costs during production switchovers; for example, although some tasks have similar temperatures, they may lead to... Smaller, but prone to cross-contamination requiring vigorous cleaning If the temperature reading is too high, the system will recognize that the switch is not optimal, thus avoiding scheduling misjudgments caused by focusing only on temperature as a single dimension.
[0089] Example 4:
[0090] The third processing module determines the optimal production sequence with the lowest total conversion energy consumption under the delivery deadline constraint based on the set of orders to be scheduled and the switching energy consumption matrix. This includes: constructing a multi-objective optimization function containing variables of total conversion energy consumption and delivery delay penalty, and calculating the value of the multi-objective optimization function; based on the switching energy consumption matrix, using a path search algorithm to traverse the task permutations and combinations in the set of orders to be scheduled, and calculating the sum of switching energy consumption between adjacent tasks in each permutation and combination; and in response to the searched permutations and combinations satisfying the delivery deadline constraint, selecting the permutation and combination with the smallest multi-objective optimization function value as the optimal production sequence.
[0091] This embodiment describes in detail how the third processing module determines the optimal production sequence; in order to achieve a balance between delivery timeliness and energy conservation, the third processing module constructs a multi-objective optimization function that includes a total conversion energy consumption variable and a delivery delay penalty variable;
[0092]
[0093] in, Optimize function values for multiple objectives (unit: ); This represents the total number of tasks to be scheduled. For the first in the sequence The and the first Energy consumption for switching between tasks (unit: ); Time-energy conversion weight (unit: ), used to quantify time-dimensional delays into energy consumption costs; The task delay penalty variable is used to ensure the computability of the optimization function. The task time state transition logic is defined as follows: Assume the optimal production sequence is an ordered set. For the first in the sequence Let there be a task, and its original index in the order set be... Its preceding task index is ,Task Actual completion time Determined by the recursive formula:
[0094]
[0095] in, For the preceding task The actual completion time, if For the first task, then The current system time; For the task Material readiness time; For the task The baseline processing time; For the task End of mission The time required for the transition and cleaning process between steps;
[0096] Task delay penalty variable (unit: The calculation formula is as follows:
[0097]
[0098] The actual completion time of the task; The deadline for task delivery;
[0099] Based on the switching energy consumption matrix, i.e., the set of switching energy consumption between all pairs of tasks, the third processing module uses an improved ant colony algorithm to traverse the task permutations and combinations in the set of orders to be scheduled. The improved ant colony algorithm, specifically designed for energy consumption optimization scenarios, constructs a pheromone update mechanism based on a heuristic function of the reciprocal of energy consumption and an elite retention strategy. Specifically, ants start from the current task... Move to the next task probability The calculation is as follows:
[0100]
[0101] in, For the transition probability; The concentration of pheromones; As a heuristic factor; This is a set of unvisited tasks. For pheromone importance factors; This is a heuristic factor and an importance factor.
[0102] The heuristic factor is defined as the reciprocal of the switching energy consumption:
[0103]
[0104] As a heuristic factor; To switch energy consumption; To prevent corrections where the denominator is zero;
[0105] This gives the algorithm a tendency to prioritize low-energy paths from the early stages of the search. In this embodiment, the value is 0.01, and the pheromone update rule adopts the following elite strategy formula:
[0106]
[0107] in, For the first The pheromone concentration at the next iteration For the first The pheromone concentration at the next iteration Volatility coefficient; The pheromone increment contributed to elite ants; only if the path The total objective function value in this iteration When the minimum optimal sequence is reached, apply an increment. ;otherwise ;
[0108] This is the pheromone enhancement constant, and its value is usually set to the order of magnitude of the predicted value of the objective function. of to This is multiplied by a factor of 1 to ensure that the pheromone increment has a significant guiding effect in terms of numerical value; this mechanism ensures that the algorithm converges quickly to a low-energy path.
[0109] The system responds when the searched permutations and combinations satisfy the hard delivery deadline constraint, i.e. Within an acceptable range, select the multi-objective optimization function value. The smallest permutation or combination is the optimal production sequence.
[0110] This embodiment transforms the complex scheduling problem into a constrained Traveling Salesman Problem (TSP) variant for solution; by introducing weights... The system can flexibly switch between a high-weight mode for "rushing to meet deadlines" and a low-weight mode for "energy saving" and allow for moderate waiting in exchange for energy efficiency, thus achieving dynamic optimization of production efficiency and operating costs.
[0111] In a simulation example involving 10 orders, the energy consumption performance of the traditional FIFO (First-In, First-Out) strategy and the optimized strategy of this example were compared. The calculation results show that the total conversion energy consumption of the sequence generated by the FIFO strategy is significantly higher than that of the optimized sequence generated by this module. Under the premise that all tasks meet their delivery deadlines, the optimized sequence achieves substantial energy savings. This data specifically supports the technical effectiveness of multi-objective optimization functions in reducing total conversion energy consumption in actual scheduling scenarios.
[0112] Example 5:
[0113] Based on the optimal production sequence and the remaining energy level state, the scheduling management module determines the optimal material feeding time window for each production task, including:
[0114] Calculate the forced temperature control energy consumption under the immediate feeding mode, and the temperature control energy consumption after natural decay under the delayed feeding mode;
[0115] Determine whether the following two conditions are met simultaneously: Condition 1 is that the energy consumption for temperature regulation after natural decay is lower than the energy consumption for forced temperature regulation; Condition 2 is that the completion time after the delay is not later than the order delivery deadline.
[0116] If both condition one and condition two are met simultaneously, the delayed time will be determined as the starting point of the optimal feeding time window.
[0117] If either condition one or condition two is not met, the equipment readiness time will be determined as the starting point of the optimal feeding time window.
[0118] The system in the time interval Internal startup univariate optimization algorithm; constructing a method for calculating delay duration Local energy consumption function ,in, The delay duration is Temperature regulation energy consumption after natural decay. The energy consumption penalty coefficient per unit waiting time is set as follows:
[0119]
[0120] in, This refers to the device's standby power. The unit price of electricity; This is a conversion factor for the unit steam price, used to convert the cost of electricity into an equivalent steam energy consumption unit, thereby unifying the time cost into the joule or equivalent economic cost for easier calculation. Perform weighted summation in the following manner;
[0121] Characterizes the equivalent energy loss converted from idle capacity of equipment; uses the golden section method to... Perform an iterative search, with the iteration terminating when the interval length is less than a preset precision, such as 5 minutes; calculate to obtain smallest ,Will Determine the starting point of the optimal feeding time window;
[0122] If either condition one or condition two is not met, the equipment readiness time will be determined as the starting point of the optimal feeding time window.
[0123] This embodiment details how the scheduling management module determines the optimal feeding time window, which is a key step in the present invention to achieve the goal of trading time for space, i.e., energy. The scheduling management module calculates the energy consumption under two scenarios for the determined next task.
[0124] Forced temperature control energy consumption in immediate feeding mode Assuming the cleaning and temperature adjustment process begins immediately after the previous task is completed, the equipment temperature difference is at its maximum at this time. The calculation formula is as follows:
[0125]
[0126] Specific heat capacity; Equivalent mass of the equipment; The target process temperature; The instantaneous temperature of the equipment at the end of the previous task;
[0127] The formula for calculating free heat dissipation based on ambient temperature difference is as follows:
[0128]
[0129] Energy consumption for temperature regulation after natural decay. Equipment temperature after delay;
[0130] in, Calculations based on Newton's law of cooling yielded the following:
[0131]
[0132] Workshop ambient temperature, Initial temperature Thermal inertia coefficient of the equipment Delay duration;
[0133] The module determines whether the following two conditions are met simultaneously:
[0134] Condition one: That is, waiting can bring significant energy savings;
[0135] Condition two: ,in, Equipment readiness time Delay duration Task processing time, Delivery period;
[0136] That is, the delayed completion time shall not be later than the order delivery deadline;
[0137] In response to the simultaneous fulfillment of conditions one and two, the system determines the delayed time as the starting point of the optimal feeding time window; at this time, the system will issue a standby command and use natural cooling instead of the refrigeration unit to perform work; in response to the failure to meet either condition, such as an urgent order or slow natural cooling, the equipment readiness time will be immediately determined as the starting point of the optimal feeding time window.
[0138] This embodiment breaks away from the rigid thinking of "equipment cannot be stopped" in traditional industrial production. In non-bottleneck processes or non-urgent orders, the system proactively introduces a controlled waiting mechanism, using the entropy increase in nature, i.e., natural cooling, to replace the consumption of high-grade energy such as electricity or steam, achieving a highly cost-effective energy-saving effect without affecting the final delivery.
[0139] The following describes the implementation effect of this module with specific parameters: Assume the equivalent mass of the equipment. Specific heat capacity Initial equipment temperature at the end of the previous task The next target temperature Workshop ambient temperature ;
[0140] If the immediate feeding mode is adopted, forced cooling is required. The calculated energy consumption for forced temperature regulation ;
[0141] If a delayed feeding mode is used, set the delay duration. Equipment thermal inertia coefficient The calculated temperature after natural cooling ;
[0142] At this point, only forced cooling is needed. The calculated temperature regulation energy after natural decay If the delivery deadline is met, the system will determine... By implementing a delayed feeding strategy, the cold energy consumption was reduced by approximately 77.2% compared to immediate feeding.
[0143] Example 6:
[0144] The system also includes a peak load early warning module, which is used to: generate an energy load prediction curve for the production line in the future time period based on the optimal production sequence and process energy consumption baseline data; determine whether the peak value in the energy load prediction curve exceeds the preset energy supply threshold; if it exceeds the energy supply threshold, send an adjustment command to the third processing module to trigger the staggered rearrangement of the optimal production sequence.
[0145] This embodiment adds a peak load early warning module. Based on the optimal production sequence and process energy consumption baseline data, including the energy consumption baseline per unit time, the module overlays the expected energy consumption curves of all equipment on the time axis to generate the energy load prediction curve of the production line in the future time period. The module determines whether the peak value in the energy load prediction curve exceeds the preset energy supply threshold, such as the maximum steam output of the steam boiler or the transformer capacity limit. In response to exceeding the threshold, the module sends an adjustment instruction to the third processing module to forcibly postpone or advance the start time of certain high-energy-consuming tasks, triggering the staggered rearrangement of the optimal production sequence.
[0146] This embodiment effectively avoids safety hazards affecting product quality, such as tripping or unstable steam pressure caused by excessive instantaneous load, through forward-looking load forecasting and peak shaving mechanisms. At the same time, by adopting peak shaving and valley filling strategies, it reduces the enterprise's demand for transformer capacity and basic electricity expenses, and improves the operational stability of the energy system.
[0147] Example 7:
[0148] The process energy consumption baseline data includes: standard temperature curves, pressure requirements, and energy consumption baselines per unit time for different product types in the fermentation, extraction, and concentration stages; the real-time thermodynamic status of the equipment includes: the current internal temperature of the reaction vessel, jacket temperature, pipeline pressure, and internal wall cleanliness level.
[0149] This embodiment specifically defines the physical meaning of the data to ensure the feasibility of the solution. The process energy consumption baseline data includes standard temperature curves for different product types in the fermentation, extraction, and concentration stages, such as heating rate, holding time, pressure requirements, and energy consumption baseline per unit time, such as the number of kg of steam consumed per hour. These data are derived from the average values of historical production batch records. The real-time thermodynamic status of the equipment includes: the current internal temperature of the reaction vessel, which directly determines the thermal state of the material contact surface; the jacket temperature, which reflects the residual state of the heat exchange medium; the pipeline pressure, which determines whether pressure relief and energy release are required; and the cleanliness level of the inner wall, which is determined by conductivity sensors or image recognition to determine the cleaning energy consumption.
[0150] This embodiment clarifies the boundaries and definitions of physical parameters, giving the modeling of thermodynamic states the support of physical entities rather than abstract concepts. This ensures that the calculation model can truly reflect the physical conditions of the production site, thereby guaranteeing the accuracy and feasibility of the energy consumption calculation results.
[0151] Example 8:
[0152] When determining the optimal production sequence, the third processing module executes the following clustering strategy: identifies the process temperature range values of each task in the set of orders to be scheduled; clusters tasks with process temperature range values in the same preset range into continuous production groups; sorts them within the continuous production groups in order of energy consumption from low to high, and sorts them between different continuous production groups in order of temperature difference from small to large, so as to generate the optimal production sequence.
[0153] This embodiment describes the specific clustering strategy of the third processing module during optimization, which is a heuristic optimization method. The third processing module performs the following steps: identifying the process temperature zone values of each task in the set of orders to be scheduled, such as high temperature zone 80-100℃, medium temperature zone 40-60℃, and low temperature zone 0-10℃; clustering tasks with process temperature zone values in the same preset range, such as tasks in the high temperature zone, into continuous production groups; performing a sorting operation; within the continuous production group, sorting according to energy consumption from low to high or cleanliness from low to high, to reduce fine-tuning temperature and cleaning; between different continuous production groups, sorting according to the gradient of temperature difference from small to large, such as high temperature group to medium temperature group and then to low temperature group, simulating the natural cooling process.
[0154] This embodiment uses a biomimetic sorting strategy to mimic the domino effect; it utilizes the residual heat or cold energy of the previous group of tasks to serve the next group of tasks, thus conforming to the natural laws of thermodynamics to the greatest extent. Compared with a purely random search algorithm, this strategy can significantly reduce invalid search paths and converge to the global optimum faster.
[0155] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A production line scheduling and changeover management system based on energy efficiency data analysis, characterized in that, include: The data acquisition module is used to acquire the operational characteristic parameters of the target production line. The operational characteristic parameters include: the set of orders to be scheduled, the baseline data of process energy consumption, the real-time thermodynamic status of the equipment, the end time of the previous batch, and the workshop environmental parameters. The first processing module is used to determine the remaining energy level state of the production equipment over time after shutdown, based on the real-time thermodynamic state of the equipment, the end time of the previous batch, and the workshop environmental parameters. The second processing module is used to calculate the conversion energy consumption value required for state switching between different production tasks based on the remaining energy level state and the process energy consumption baseline data, and to generate a switching energy consumption matrix. The third processing module is used to determine the optimal production sequence with the lowest total conversion energy consumption under the delivery deadline constraint based on the set of orders to be scheduled and the switching energy consumption matrix. The scheduling management module is used to determine the optimal material feeding time window and the corresponding equipment status reset instruction for each production task based on the optimal production sequence and the remaining energy level status. The first processing module determines the remaining energy level state of the production equipment over time after shutdown, based on the real-time thermodynamic state of the equipment, the end time of the previous batch, and the workshop environmental parameters, including: The real-time thermodynamic state of the equipment and the workshop environmental parameters are retrieved. Using thermal decay calculation logic, based on the temperature difference between the current equipment temperature and the workshop ambient temperature, and combined with the preset equipment thermal inertia coefficient, the natural temperature decay curve of the production equipment during the shutdown waiting period is calculated. Based on the natural temperature decay curve, calculate the temperature value of the device when starting the next task at any future time point, and map the temperature value to a thermal energy value, defining the thermal energy value as the remaining energy level state. The second processing module calculates the conversion energy consumption value required for state switching between different production tasks based on the remaining energy level state and the process energy consumption baseline data, including: The process energy consumption baseline data is used to determine the target process energy level requirements and cleaning cleanliness requirements for the next task. Calculate the thermal temperature control energy consumption required to adjust the production equipment from the remaining energy level state to the target process energy level requirement; Calculate the chemical cleaning energy consumption required to adjust the production equipment from its current cleaning state to the required cleaning cleanliness level. The sum of the energy consumption for thermal temperature regulation and the energy consumption for chemical cleaning is defined as the conversion energy consumption value.
2. The production line scheduling and switching management system based on energy efficiency data analysis according to claim 1, characterized in that, The third processing module determines the optimal production sequence with the lowest total conversion energy consumption under the delivery deadline constraint based on the set of orders to be scheduled and the switching energy consumption matrix, including: Construct a multi-objective optimization function that includes total conversion energy consumption and delivery delay penalty variables, and calculate the value of the multi-objective optimization function. Based on the switching energy consumption matrix, a path search algorithm is used to traverse the task permutations and combinations in the set of orders to be scheduled, and to calculate the sum of switching energy consumption between adjacent tasks in each permutation and combination. In response to the searched permutations satisfying the delivery deadline constraint, the permutation with the smallest multi-objective optimization function value is selected as the optimal production sequence.
3. The production line scheduling and switching management system based on energy efficiency data analysis according to claim 1, characterized in that, The scheduling management module determines the optimal material feeding time window for each production task based on the optimal production sequence and the remaining energy level state, including: Calculate the forced temperature control energy consumption under the immediate feeding mode, and the temperature control energy consumption after natural decay under the delayed feeding mode; Determine whether the following two conditions are met simultaneously: Condition 1 is that the energy consumption for temperature regulation after natural decay is lower than the energy consumption for forced temperature regulation; Condition 2 is that the completion time after the delay is not later than the order delivery deadline. If both condition one and condition two are met simultaneously, the delayed time will be determined as the starting point of the optimal feeding time window. If either condition one or condition two is not met, the equipment readiness time will be determined as the starting point of the optimal feeding time window.
4. The production line scheduling and switching management system based on energy efficiency data analysis according to claim 1, characterized in that, It also includes a peak load early warning module, used for: Based on the optimal production sequence and the process energy consumption baseline data, an energy load prediction curve for the production line in the future time period is generated. Determine whether the peak value in the energy load prediction curve exceeds a preset energy supply threshold; If the energy supply threshold is exceeded, an adjustment command is sent to the third processing module to trigger a staggered rearrangement of the optimal production sequence.
5. The production line scheduling and switching management system based on energy efficiency data analysis according to claim 1, characterized in that, The process energy consumption baseline data includes: Standard temperature profiles, pressure requirements, and baseline energy consumption per unit time for different product types in the fermentation, extraction, and concentration stages; The real-time thermodynamic status of the equipment includes: the current internal temperature of the reaction vessel, the jacket temperature, the pipeline pressure, and the cleanliness level of the inner wall.
6. The production line scheduling and switching management system based on energy efficiency data analysis according to claim 1, characterized in that, When determining the optimal production sequence, the third processing module executes the following clustering strategy: Identify the process temperature zone values for each task in the set of orders to be scheduled; Tasks whose process temperature values are within the same preset range are clustered into continuous production groups; Within the continuous production group, the groups are sorted in order of increasing energy consumption, and among different continuous production groups, they are sorted in order of increasing temperature difference to generate the optimal production sequence.