Energy utilization optimization method in coal-to-methanol production process

Abstract

The invention relates to the technical field of energy management and optimization in a coal chemical process, and discloses an energy utilization optimization method in a coal-to-methanol production process. A continuous operation cycle is equally divided into a plurality of sub time periods, a heat absorption / heat release state sequence is established according to a time sequence, thermal power is recorded in each section, energy is converted, and net energy is uniformly counted according to states; accumulating full-period net energy and counting the number of state switching times, and introducing a unit switching loss upper limit to construct a comprehensive evaluation target; screening a feasible solution set by combining the lowest heat absorption amount and the lowest heat release amount of the process, and selecting a scheduling sequence with the maximum evaluation target in the set. And performing temperature prediction, boundary amplitude limiting and quota complementation by using the system heat capacity and the initial temperature to form an amplitude limiting temperature sequence, identifying a maximum continuous heat release section, implementing equalization processing and updating a power sequence under the condition of meeting the temperature and power boundary conditions, and finally outputting a scheduling, power and temperature sequence which can be executed on site.

Description

Technical Field

[0001] This invention relates to the field of energy management and optimization technology in coal chemical processes, specifically to an energy utilization optimization method for the coal-to-methanol production process. Background Technology

[0002] Coal-to-methanol processes typically include multiple units such as coal gasification, shift conversion, purification, methanol synthesis, and post-treatment. These units are interconnected by a complex heat exchange network formed by high-temperature coal gas, steam, and hot water. In long-term engineering practice, energy utilization optimization of the plant mainly relies on the static heat exchange network layout during the process design phase and experience-based adjustments during operation. For example, a common practice is to achieve heat balance through fixed heat exchanger combinations, steam class configurations, and simple load adjustments. During operation, operators manually adjust some valve positions or start / stop some heat exchange equipment based on monitored values ​​such as temperature and pressure to meet the requirements of changing production loads.

[0003] However, when the equipment is operating at low load, existing technologies generally lack a fine-grained scheduling mechanism for "periodic heat absorption and release behavior." Existing solutions often only focus on whether instantaneous operating conditions meet operational constraints, without dividing an operating cycle into discrete time periods and uniformly planning the heat absorption and release of each period and their temporal sequence. This leads to problems such as ineffective utilization of waste heat during low-load periods, passive delays in some heat absorption demands, or concentrated completion within a short period. Meanwhile, commonly used control methods are mostly conventional single-cycle or multi-cycle controllers, or process control strategies based on general optimization software. Their parameter configurations and objective functions are mostly geared towards steady-state indicators, insufficiently considering the coupling relationship between heat absorption and release in the time dimension, and lacking the ability to comprehensively characterize the relationship between "heat absorption workload," "heat release workload," and temperature constraints. Regarding the switching between heat absorption and release states, existing technologies typically do not quantitatively constrain the number of switching times and the resulting energy losses, leading to frequent start-ups and shutdowns of some equipment during low-load periods. This increases the risk of mechanical and thermal fatigue in valves, heat exchangers, and other equipment, and also causes intermittent waste of steam and waste heat. Furthermore, existing solutions largely rely on temperature upper and lower limit alarms and post-event manual intervention. They lack a clear mathematical description for calculating the "maximum energy allowed to absorb or release heat" within each time period, given the system's heat capacity and temperature boundaries. This easily leads to a reactive situation where temperature exceeds limits and corrections are made later, rather than preventing this through energy limiting and sequential allocation during the scheduling phase. For continuous heat release sections, existing technologies typically only set higher or lower heat release power based on instantaneous load demand, lacking a mechanism for evenly distributing heat release power across the entire time period. This easily results in large peak-to-valley differences in heat load and drastic fluctuations within the section, which is detrimental to the long-term stable operation of the equipment.

[0004] Therefore, this case aims to propose an energy utilization optimization method for the coal-to-methanol production process. The continuous operating cycle is divided into several sub-time periods. The heat absorption or release state of each time period is identified and its energy is converted. A comprehensive evaluation index is constructed by dynamically calculating net energy and the number of state transitions. Under the premise of meeting the minimum total heat absorption and release constraint, the optimal scheduling sequence is selected from all feasible schemes. Based on the system heat capacity model, multiple temperature limits and compensation processes are applied. A balanced allocation is further performed on the continuous heat release section. Finally, a complete heat power execution sequence and temperature execution trajectory are output. Summary of the Invention

[0005] This invention provides an energy utilization optimization method for the coal-to-methanol production process, which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: an energy utilization optimization method for a coal-to-methanol production process, comprising:

[0007] Set the control cycle and divide it into multiple sub-time periods, and establish an operation status sequence in chronological order;

[0008] Record the heat absorption power or heat release power in each sub-time period and convert it into energy, and establish net energy inclusion rules according to the state;

[0009] Accumulate the net energy of the control cycle and count the number of state transitions in adjacent sub-time periods, set an upper limit for unit switching loss, and form an evaluation target;

[0010] Obtain the minimum total heat absorption and the minimum total heat release of the process, and screen the feasible solution set according to the total amount constraint;

[0011] Calculate the evaluation objective within the feasible solution set and select the scheduling sequence with the largest evaluation objective value;

[0012] Obtain the system heat capacity and initial temperature, and perform temperature prediction, boundary limiting and quota supplementation according to the selected schedule to form a limiting temperature sequence;

[0013] Identify the maximum continuous heat release section, and perform continuous heat release section equalization processing and update the power sequence under the condition of satisfying the temperature and power boundaries;

[0014] Output the optimal scheduling, execution power sequence, and final temperature sequence.

[0015] Optionally, the setting of the control period and its division into multiple sub-time periods, and the establishment of an operation state sequence in chronological order, specifically includes:

[0016] Set the total duration of the control cycle;

[0017] The control cycle is divided into several sub-time periods, and each sub-time period is assigned a unique number.

[0018] Set an operation status flag for each sub-time period, with values ​​of either heat absorption or heat release, which are mutually exclusive.

[0019] The operation status of each sub-time period is arranged in numerical order to form an operation status sequence.

[0020] Optionally, the step of recording the heat absorption or release power and converting it into energy in each sub-time period, and establishing net energy inclusion rules according to the state, specifically includes:

[0021] Record the heat absorption power in the sub-time periods marked as heat absorption, and convert it into heat absorption energy according to the duration of the corresponding sub-time period.

[0022] Record the heat release power in the sub-time periods marked as heat release, and convert it into heat release energy according to the duration of the corresponding sub-time period;

[0023] Establish rules for net energy inclusion: include exothermic energy in exothermic sub-periods and include endothermic energy as a deduction item in net energy in endothermic sub-periods.

[0024] Optionally, the cumulative net energy of the control cycle and the number of state transitions in adjacent sub-time periods are counted, and an upper limit for unit switching loss is set to form an evaluation target, specifically including:

[0025] The net energy of each sub-time period is accumulated within the control period to obtain the net energy of the control period;

[0026] Compare the operation status of two adjacent sub-time periods and count the number of status transitions;

[0027] Within the control cycle, the maximum values ​​of the heat absorption energy and the heat release energy of a single sub-time period are retrieved, and the larger one is taken as the upper limit of unit switching loss.

[0028] The evaluation objective is constructed based on the net energy of the control cycle, minus the product of the upper limit of unit switching loss and the number of state switching.

[0029] Optionally, obtaining the minimum total heat absorption and minimum total heat release in the process, and screening feasible solution sets according to total amount constraints, specifically includes:

[0030] To obtain the minimum total heat absorption specified by the process;

[0031] Under a given scheduling scheme, the heat absorption energy of each heat absorption sub-time period is accumulated to form the actual total heat absorption, and the actual total heat absorption is set to be no less than the minimum total heat absorption.

[0032] To obtain the minimum total heat release specified by the process;

[0033] Under a given scheduling scheme, the heat release energy of each heat release sub-time period is accumulated to form the actual total heat release, and the actual total heat release is set to be no less than the minimum total heat release.

[0034] All scheduling schemes that simultaneously meet the minimum total heat absorption and minimum total heat release limits are aggregated into a feasible solution set.

[0035] Optionally, the step of calculating the evaluation objective within the feasible solution set and selecting the scheduling sequence with the largest evaluation objective value specifically includes:

[0036] Enumerate the sequence of operation states with the same length as the control cycle in the feasible solution set;

[0037] For each operational state sequence, the net energy of the control cycle and the evaluation target are calculated based on the net energy inclusion rule and the switching penalty model.

[0038] The optimal schedule is selected from the sequence of operation states that maximizes the evaluation objective.

[0039] Optionally, the process of obtaining the system heat capacity and initial temperature, and performing temperature prediction, boundary limiting, and quota supplementation according to the selected schedule to form a limiting temperature sequence specifically includes:

[0040] Obtain the system heat capacity and initial temperature, and set the minimum and maximum temperature boundaries;

[0041] The heat absorption and release energy of each sub-time period is initialized to the original value, a limited temperature sequence is established, and the first term is set as the initial temperature;

[0042] By recursively extrapolating the energy balance over time, a predicted temperature sequence is obtained.

[0043] At the beginning of each sub-time period, the temperature at the beginning of the sub-time period is used as the current limiting temperature. The maximum energy increase and the maximum energy decrease allowed up to the highest temperature boundary and the lowest temperature boundary are calculated.

[0044] When the current limiting temperature is higher than the highest temperature boundary and the current sub-time period is in the state of heat absorption, the heat absorption energy of the current sub-time period is set to zero; when the current limiting temperature is lower than the lowest temperature boundary and the current sub-time period is in the state of heat release, the heat release energy of the current sub-time period is set to zero.

[0045] When the predicted temperature is higher than the maximum temperature boundary and the current sub-time period is in the state of heat absorption, the heat absorption energy of the current sub-time period is limited according to the allowable value of the maximum temperature boundary; when the predicted temperature is lower than the minimum temperature boundary and the current sub-time period is in the state of heat release, the heat release energy of the current sub-time period is limited according to the allowable value of the minimum temperature boundary; when the predicted temperature is between the two temperature boundaries, the energy of the current sub-time period is kept below the corresponding upper limit of the original heat absorption capacity or the original heat release capacity of the equipment.

[0046] Update the temperature value at the end of the current sub-time period in the limited temperature sequence using the adjusted energy;

[0047] The total heat absorbed and released after the statistical limit are compared with the minimum total heat absorbed and the minimum total heat released to obtain the heat absorption gap and the heat release gap, respectively.

[0048] The starting temperature for the quota replenishment process is set to the current limit temperature. Replenishment is carried out in each sub-time period in chronological order: In the sub-time period marked as heat absorption, heat absorption exceeding the heat absorption gap is replenished, provided that the original heat absorption capacity of the equipment is not exceeded and the maximum temperature boundary is not exceeded; In the sub-time period marked as heat release, heat release exceeding the heat release gap is replenished, provided that the temperature is not lower than the minimum temperature boundary and the original heat release capacity of the equipment is not exceeded; After each sub-time period is completed, the temperature at the end of the corresponding sub-time period is updated and the process is rolled over to the next sub-time period.

[0049] When both types of gaps are zero, it is determined that the minimum total heat absorption and minimum total heat release constraints are met; if gaps still exist, it is determined that the current parameter set is not feasible, and the parameters are reset by one or more of the following methods: increasing the maximum temperature boundary, decreasing the minimum temperature boundary, reducing the minimum total heat absorption, reducing the original heat release capacity of the equipment, or increasing the number of segments.

[0050] Optionally, the identification of the maximum continuous heat release segment, and the execution of continuous heat release segment equalization processing and power sequence update under the condition of satisfying temperature and power boundaries, specifically includes:

[0051] Identify each maximum continuous exothermic zone, record the start and end indices, and build an index set;

[0052] Establish coverage and mutual exclusion relationships: all exothermic sub-time periods are completely covered by the index set, and no two index sets overlap;

[0053] The total heat release of each continuous heat release segment is obtained by accumulating the heat release energy after limiting and quota supplementation, and the number of sub-time periods contained in the continuous heat release segment is calculated.

[0054] Within the continuous heat release section, the total heat release candidate is evenly distributed to each sub-time period, and the hypothetical temperature trajectory of the continuous heat release section is constructed based on the current limiting temperature at the start of the quota replenishment phase.

[0055] Feasibility assessment: If the assumed temperature trajectory is not lower than the minimum temperature boundary and the candidate heat release does not exceed the original heat release capacity of the equipment, uniform distribution is deemed feasible.

[0056] Where feasible, the final heat release of each sub-time period within the continuous heat release section is set to an average value, and the new heat release power is calculated accordingly; where not feasible, the heat release energy and power of the continuous heat release section are kept constant.

[0057] Optionally, the output optimal scheduling, execution power sequence, and final temperature sequence specifically include:

[0058] Output the optimal scheduling operation state sequence;

[0059] Segment-by-segment generation of execution power: within sub-time periods marked as heat absorption, the power is calculated by converting the final heat absorption energy with the duration of the corresponding sub-time period; within sub-time periods marked as heat release, the new heat release power is obtained after equalization processing.

[0060] The starting point of the final execution temperature sequence is set as the initial temperature, and the final temperature sequence is obtained by recursively calculating the energy balance segment by segment.

[0061] The present invention has the following beneficial effects:

[0062] 1. The continuous production cycle is divided into fixed-length sub-segments. For each sub-segment, two mutually exclusive states, "endothermic" and "exothermic," are defined, and a complete sequence of operating states is determined sequentially. This approach not only eliminates the possibility of state confusion within a large cycle but also refines the temporal granularity of energy scheduling, facilitating subsequent energy consumption and temperature analysis. Unlike the past approach of treating the entire cycle as a single mode, this subdivision makes the energy output direction of each time segment clearly identifiable, making it easier to identify efficient endothermic and exothermic windows. Since the heat capacity and response characteristics of coal-to-methanol units vary significantly under different loads, this method, through fine-grained modeling, can fully capture the impact of process fluctuations on energy efficiency, laying a solid foundation for subsequent net energy statistics and switching penalty modeling. Furthermore, the setting of mutually exclusive state flags ensures that the scheduling algorithm can systematically traverse all possible endothermic and exothermic combinations during enumeration, improving search quality.

[0063] 2. Within each sub-time period, this scheme records the actual heat absorption and release power of the device, converts them into energy values ​​based on the duration of the sub-time period, and then establishes a "net energy inclusion rule": energy efficiency benefits are directly included for the heat release segment, while the heat absorption segment is treated as an energy consumption deduction item. This innovative approach transforms traditionally dispersed thermal energy data into a unified net energy measure, removing the algorithm's reliance on complex energy balance formulas. The overall energy efficiency performance of each scheme can be quickly obtained through simple addition and subtraction operations. Compared to previous approaches that only focused on unidirectional energy saving or single heat recovery technologies, this rule considers both the energy consumption and benefits at the heat absorption and release ends, providing a more objective net energy efficiency indicator. Simultaneously, through unified data scaling, it provides directly callable basic quantities for subsequent comprehensive optimization and switching penalty calculations. This scheme combines readability and computational efficiency, avoiding a surge in algorithm complexity while perfectly meeting the real-time scheduling needs of production sites, achieving effective synergy between data processing and optimization calculations.

[0064] 3. The net energy of all sub-time periods is accumulated to form the cycle net energy efficiency. Simultaneously, each heat absorption / excitation state switch is statistically analyzed and multiplied by the upper limit of unit switching loss before deduction, ultimately constructing a comprehensive evaluation target. The concept of a "switching penalty" is introduced, considering both maximizing energy efficiency and suppressing the additional energy waste and equipment stress caused by high-frequency switching. Compared to traditional methods that only aim for maximum net energy, this model explicitly quantifies switching costs at the algorithm level, resulting in optimization results that are both efficient and stable. Furthermore, the upper limit of unit switching loss is taken as the maximum value of heat absorption and excitation energy throughout the cycle as the boundary, providing an adaptive parameter for evaluating switching costs. This ensures that the penalty is neither too high, leading to excessive adherence to a single mode, nor too low, causing frequent switching. This innovation can effectively extend equipment life and reduce the risk of transient loads during switching in real-world production, while also considering overall energy efficiency, achieving a balance between safety and efficiency.

[0065] 4. To address the mandatory requirements of the production process regarding minimum heat absorption and release, this scheme introduces dual lower bound constraints when screening feasible schedules, retaining only the set of schemes that simultaneously satisfy the minimum total heat absorption and release. This innovative approach integrates process requirements with scheduling optimization upfront, ensuring that the algorithm's search space contains only feasible scheduling vectors and reducing unnecessary computation. Compared to the traditional method of generating all schemes first and then performing subsequent constraint verification, this method introduces process constraints as generation conditions simultaneously. The feasible solution set has the characteristic of "generating it once and immediately conforming to the process," improving algorithm efficiency and feasibility. Simultaneously, the dual constraint mechanism effectively avoids the risk of product quality degradation or process interruption due to excessive energy saving, thus finding the optimal balance between energy efficiency and process safety.

[0066] 5. Within the feasible solution set, this scheme, through enumeration or other search strategies, calculates the comprehensive evaluation objective one by one based on the aforementioned net energy inclusion rules and switching penalty model, and selects the operational state vector corresponding to the maximum value. By directly using the evaluation objective that combines net benefit and switching cost into one as the search criterion, it achieves optimal scheduling determination in a "one-step" manner. Compared with traditional methods that require multi-stage iteration or alternating optimization, this method has a simple structure and clear logic, and can be directly applied to online or offline scheduling decisions, demonstrating strong adaptability. Simultaneously, the singularity of the evaluation objective reduces the difficulty of parameter tuning and improves the algorithm's engineering sophistication. In practical applications, this innovative strategy ensures that the final scheduling maximizes annual or monthly net energy efficiency while avoiding production fluctuations caused by frequent switching, achieving precise, efficient, and reliable energy utilization optimization.

[0067] 6. This solution is based on the device's heat capacity model, recursively predicting the temperature for each sub-time period in segments, and dynamically limiting the absorbed and released heat energy by combining the highest and lowest temperature boundaries. Subsequently, based on the gap between the actual energy after limiting and the minimum workload requirement, temperature quotas are supplemented within a controllable range. A three-stage closed loop of "prediction—limiting—supplementation" is constructed, balancing both temperature safety and energy efficiency requirements. Unlike relying solely on a single limiting or over-limit alarm measure, this solution immediately limits the temperature upon detecting potential temperature exceedances and restores process requirements through supplementation, ensuring both device safety and production continuity and energy utilization efficiency. Furthermore, the supplementation process is executed sequentially within each segment, ensuring dynamic balance between temperature and energy, enabling the execution strategy to respond to temperature fluctuations in real time, exhibiting good adaptability and robustness.

[0068] 7. To address the issue of localized overheating or uneven energy efficiency caused by large-area continuous heat release, this solution first identifies the maximum continuous heat release section and, under conditions that meet temperature and power boundary requirements, evenly distributes the total heat release energy across the sub-time periods within that section. This innovative strategy effectively suppresses temperature spikes caused by localized short-duration high heat release, improving the balance of energy utilization. Unlike previous methods that simply limited or uniformly adjusted a single segment throughout the entire cycle, the equalization process performs localized optimization on continuous sections, resulting in a smoother temperature curve transition and improved production stability. Simultaneously, by dynamically judging the feasibility of uniform distribution through feasibility variables during the segmentation process, the algorithm ensures consistency with system boundary constraints at all times. This innovation leads to a more uniform heat distribution, reducing equipment fatigue and safety hazards caused by uneven system heating and cooling, and improving overall operational reliability and long-term stability.

[0069] 8. This solution ultimately outputs the optimal scheduling operation state sequence, segmented power commands, and temperature execution trajectories, forming an execution sequence that can be directly sent to the field control system. Compared to traditional scheduling solutions that require manual secondary conversion or post-processing, this step converts all intermediate calculation results into engineering-executable commands and provides a complete time series, greatly simplifying the field implementation process. The output sequence includes not only thermal power commands but also temperature recursion results, facilitating comparison and verification with the field temperature measurement system and achieving closed-loop real-time monitoring. Furthermore, all outputs have undergone boundary and constraint verification during the algorithm design phase, ensuring safety and controllability. This innovative approach has significant practical value in the context of production line automation and the Industrial Internet, and can be quickly integrated into production management systems to achieve intelligent and integrated energy scheduling and temperature control. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] Example, refer to Figure 1 An energy utilization optimization method for a coal-to-methanol production process includes:

[0073] Set the control cycle and divide it into multiple sub-time periods, and establish an operation status sequence in chronological order;

[0074] Record the heat absorption power or heat release power in each sub-time period and convert it into energy, and establish net energy inclusion rules according to the state;

[0075] Accumulate the net energy of the control cycle and count the number of state transitions in adjacent sub-time periods, set an upper limit for unit switching loss, and form an evaluation target;

[0076] Obtain the minimum total heat absorption and the minimum total heat release of the process, and screen the feasible solution set according to the total amount constraint;

[0077] Calculate the evaluation objective within the feasible solution set and select the scheduling sequence with the largest evaluation objective value;

[0078] Obtain the system heat capacity and initial temperature, and perform temperature prediction, boundary limiting and quota supplementation according to the selected schedule to form a limiting temperature sequence;

[0079] Identify the maximum continuous heat release section, and perform continuous heat release section equalization processing and update the power sequence under the condition of satisfying the temperature and power boundaries;

[0080] Output the optimal scheduling, execution power sequence, and final temperature sequence.

[0081] By finely dividing a continuous operating cycle into multiple sub-time periods and clearly defining the heat absorption or release state for each period, the problem of accurately measuring energy consumption and efficiency during each period under the traditional "overall cycle" perspective is solved. Subsequently, by recording thermal power and converting it to energy within each period, a "net energy inclusion rule" is established, uniformly converting heat absorption and release data into positive and negative energy efficiency quantities. This avoids the computational chaos caused by multiple data sources and unit conversions in existing technologies, achieving integrated data processing and evaluation. Next, by accumulating the net energy of the entire cycle and statistically analyzing the number of switching between adjacent time periods, and introducing an upper limit for unit switching loss, a comprehensive evaluation index is constructed. This index pursues maximum net energy efficiency while suppressing additional energy consumption and equipment fatigue caused by high-frequency switching, differing from traditional solutions that only focus on a single energy efficiency or single energy-saving mode. Based on the minimum requirements of the production process for the total amount of heat absorption and release, a feasible solution set is selected through dual constraints, unifying process safety and energy efficiency optimization, providing process feasibility assurance for subsequent searches. The optimal scheduler directly searches for the state vector that maximizes the comprehensive evaluation objective within this set, further ensuring algorithm efficiency and engineering feasibility. Temperature prediction and limiting compensation closed-loop solutions address the challenges of equipment safety risks and process interruptions caused by excessively high or low temperatures. Meanwhile, continuous heat release section equalization mitigates the risks of localized overheating and concentrated energy release, ensuring a smooth transition of the temperature curve throughout the entire cycle. Finally, the system outputs executable scheduling, power, and temperature sequences for the field, achieving integration from algorithm to execution. Compared to existing technologies, this approach couples energy efficiency, switching costs, and temperature safety across multiple dimensions, and through fine-grained segmentation and dynamic compensation mechanisms, achieves safe, stable, and efficient comprehensive energy scheduling optimization.

[0082] The setting of the control cycle and its division into multiple sub-time periods, and the establishment of an operation state sequence in chronological order, specifically includes:

[0083] Set the total duration of the control cycle;

[0084] The control cycle is divided into several sub-time periods, and each sub-time period is assigned a unique number.

[0085] Set an operation status flag for each sub-time period, with values ​​of either heat absorption or heat release, which are mutually exclusive.

[0086] The operation status of each sub-time period is arranged in numerical order to form an operation status sequence.

[0087] Further specific implementation steps include:

[0088] The total control cycle duration for the coal-to-methanol production unit under low-load operation is set as follows: Hour;

[0089] Divide the entire period evenly into There are 1 sub-time period, and the length of each sub-time period is 1. Hour, sub-time period number is ;

[0090] Set operation status variables for each sub-time period Its value is ;in, For the first Operation status within a sub-time period;

[0091] when When, it indicates the first The section is in the endothermic operation stage;

[0092] when When, it indicates the first The section is in the heat release operation phase;

[0093] Construct the operation state vector as follows ;in, For the state of each time period The column vector formed by these.

[0094] By finely dividing the time frame, each small time window becomes the smallest unit of execution that can be evaluated and controlled independently, enabling energy scheduling and temperature control to be carried out synchronously and efficiently at the microscale. It also provides reliable time-series coordinates for subsequent net energy input, switching penalty calculation, and temperature limiting compensation, making the algorithm no longer a black-box global search, but a segmented, reusable module.

[0095] The process of recording the heat absorption or release power in each sub-time period and converting it into energy, and establishing net energy inclusion rules based on state, specifically includes:

[0096] Record the heat absorption power in the sub-time periods marked as heat absorption, and convert it into heat absorption energy according to the duration of the corresponding sub-time period.

[0097] Record the heat release power in the sub-time periods marked as heat release, and convert it into heat release energy according to the duration of the corresponding sub-time period;

[0098] Establish rules for net energy inclusion: include exothermic energy in exothermic sub-periods and include endothermic energy as a deduction item in net energy in endothermic sub-periods.

[0099] Further specific implementation steps include:

[0100] For the The heat absorption power is denoted as segment . kilowatts, corresponding to the heat absorption is ;in, For the first The heat power absorbed by the system from the outside during each sub-time period; For the first The total amount of heat absorbed during a given time period;

[0101] For the The heat release power is denoted as segment . kilowatts, corresponding to heat release is ;in, For the first The heat power released by the system to the outside during each sub-time period; For the first The total amount of heat released during each time period;

[0102] Construct the net useful energy function: ;in, In the first Within a specific time period, the energy that can be included in the net benefit, depending on whether that period is endothermic or exothermic; this function in A positive value indicates that usable energy can be output; in A negative value indicates the energy required to achieve the next stage of the process.

[0103] By recording the heat absorption and release power in each sub-time period and converting them into heat absorption and release, and then uniformly measuring them according to the net energy inclusion rule based on the state, the energy flow of the device at different stages is accurately quantified into directly comparable net benefits, effectively solving the problem of scattered and difficult-to-compare energy statistics across multiple segments. Heat release is directly included in the benefits, while heat absorption is included as a deduction in the cost, forming a one-dimensional, positive-or-negative net energy evaluation index, providing a unified evaluation basis for comprehensive optimization. Furthermore, this index has its own sign meaning, marked as positive in the heat release stage and negative in the heat absorption stage, making the calculation and judgment logic very intuitive, eliminating the need for additional normalization or weight settings, and simplifying the algorithm complexity.

[0104] The cumulative net energy of the control cycle and the number of state transitions in adjacent sub-time periods are counted. An upper limit for unit switching loss is set to form an evaluation target, which specifically includes:

[0105] The net energy of each sub-time period is accumulated within the control period to obtain the net energy of the control period;

[0106] Compare the operation status of two adjacent sub-time periods and count the number of status transitions;

[0107] Within the control cycle, the maximum values ​​of the heat absorption energy and the heat release energy of a single sub-time period are retrieved, and the larger one is taken as the upper limit of unit switching loss.

[0108] The evaluation objective is constructed based on the net energy of the control cycle, minus the product of the upper limit of unit switching loss and the number of state switching.

[0109] Further specific implementation steps include:

[0110] The total net usable energy across all time periods within a complete cycle is calculated as follows: ;

[0111] The number of inter-segment handovers is calculated as follows: , ;in, For the first Section to the Did a heat absorption or heat release switch occur between the segments?

[0112] like ,but , indicates no switching;

[0113] like ,but This indicates that a switch has occurred;

[0114] Calculate the sum of the number of handovers that occur throughout the entire cycle. ;

[0115] The maximum energy loss for switching between construction units is: ;in, It is the larger of the maximum values ​​of heat absorbed and heat released throughout all time periods of the entire cycle;

[0116] The objective function is constructed as follows: ;in, Let be the objective function value, representing the total net energy over the entire cycle minus the maximum possible losses due to switching; under the premise of satisfying process constraints, by maximizing This achieves the comprehensive optimization goal of pursuing both high net energy efficiency and limiting frequent switching.

[0117] By accumulating the net energy of all sub-time periods within the entire control cycle and counting the number of state transitions between adjacent sub-time periods, and then deducting the number of transitions multiplied by the upper limit of unit transition loss from the accumulated net energy, a comprehensive evaluation target is formed. This invention innovatively couples transition costs and energy efficiency benefits into the same evaluation system. By setting the upper limit of unit transition loss to the larger of the maximum heat absorption and release values ​​throughout the entire cycle, the rationality and adaptability of the transition penalty are ensured. This penalty mechanism automatically adjusts with changes in device operating conditions, eliminating the need for manual setting of transition cost coefficients and avoiding optimization deviations caused by inaccurate empirical coefficients.

[0118] The process of obtaining the minimum total heat absorption and minimum total heat release is used to screen feasible solution sets based on total heat constraints, specifically including:

[0119] To obtain the minimum total heat absorption specified by the process;

[0120] Under a given scheduling scheme, the heat absorption energy of each heat absorption sub-time period is accumulated to form the actual total heat absorption, and the actual total heat absorption is set to be no less than the minimum total heat absorption.

[0121] To obtain the minimum total heat release specified by the process;

[0122] Under a given scheduling scheme, the heat release energy of each heat release sub-time period is accumulated to form the actual total heat release, and the actual total heat release is set to be no less than the minimum total heat release.

[0123] All scheduling schemes that simultaneously meet the minimum total heat absorption and minimum total heat release limits are aggregated into a feasible solution set.

[0124] Further specific implementation steps include:

[0125] The minimum total heat absorption required for the production process is denoted as . ;

[0126] Calculate the total amount of heat absorbed that is actually completed under the given schedule. and satisfy constraints ;

[0127] The minimum total heat release during the entire cycle is denoted as . ;

[0128] Calculate the actual total heat release under the scheduling scheme. ;

[0129] Constructing a set of feasible solutions Specifically: ;in, Let represent the set of all scheduling vectors that satisfy the total heat absorption and release constraints, which is the set of feasible solutions.

[0130] By strictly introducing dual constraints of minimum total heat absorption and minimum total heat release during the optimization process, this invention ensures that all candidate scheduling schemes not only perform well in terms of energy efficiency but also meet the lower limits of heat input and output required by the process. This solves the risk of decreased product quality or process stability caused by focusing solely on energy saving in previous methods. By calculating the actual total heat absorption and release under the current scheduling scheme in real time and comparing it with the lower limits specified by the process, only scheduling vectors that simultaneously meet both lower limits are retained to construct a feasible solution set. Unlike the traditional method of first globally enumerating and then individually verifying process constraints, this method integrates constraints into the feasible solution generation process, reducing the computational workload of invalid combinations and improving algorithm efficiency.

[0131] The step of calculating the evaluation objective within the feasible solution set and selecting the scheduling sequence with the largest evaluation objective value specifically includes:

[0132] Enumerate the sequence of operation states with the same length as the control cycle in the feasible solution set;

[0133] For each operational state sequence, the net energy of the control cycle and the evaluation target are calculated based on the net energy inclusion rule and the switching penalty model.

[0134] The optimal schedule is selected from the sequence of operation states that maximizes the evaluation objective.

[0135] Further specific implementation steps include:

[0136] In the set of feasible solutions In the middle, enumerate all lengths of sequence: , ;

[0137] For each feasible schedule Calculate item by item ;

[0138] Calculate its net benefit function value ;

[0139] Find the scheduling vector that maximizes the objective function: ;in, In the feasible set make the objective function The scheduling vector that achieves the maximum value.

[0140] By enumerating all possible operational state sequences within a pre-selected set of feasible solutions, and directly calculating the comprehensive evaluation target value based on the net energy inclusion rule and switching penalty model, this invention constructs a complete "one-search, ultimate decision" process, solving the challenges of weight allocation and iterative convergence in multi-objective optimization. It integrates these two aspects, using a single evaluation target to uniformly measure energy efficiency and stability; enumeration ensures the availability of the global optimal solution; and pre-selection of the feasible solution set reduces the enumeration scale, achieving a balance between computational complexity and stability.

[0141] The process of acquiring the system's heat capacity and initial temperature, performing temperature prediction, boundary limiting, and quota compensation according to the selected schedule, and forming a limiting temperature sequence specifically includes:

[0142] Obtain the system heat capacity and initial temperature, and set the minimum and maximum temperature boundaries;

[0143] The heat absorption and release energy of each sub-time period is initialized to the original value, a limited temperature sequence is established, and the first term is set as the initial temperature;

[0144] By recursively extrapolating the energy balance over time, a predicted temperature sequence is obtained.

[0145] At the beginning of each sub-time period, the temperature at the beginning of the sub-time period is used as the current limiting temperature. The maximum energy increase and the maximum energy decrease allowed up to the highest temperature boundary and the lowest temperature boundary are calculated.

[0146] When the current limiting temperature is higher than the highest temperature boundary and the current sub-time period is in the state of heat absorption, the heat absorption energy of the current sub-time period is set to zero; when the current limiting temperature is lower than the lowest temperature boundary and the current sub-time period is in the state of heat release, the heat release energy of the current sub-time period is set to zero.

[0147] When the predicted temperature is higher than the maximum temperature boundary and the current sub-time period is in the state of heat absorption, the heat absorption energy of the current sub-time period is limited according to the allowable value of the maximum temperature boundary; when the predicted temperature is lower than the minimum temperature boundary and the current sub-time period is in the state of heat release, the heat release energy of the current sub-time period is limited according to the allowable value of the minimum temperature boundary; when the predicted temperature is between the two temperature boundaries, the energy of the current sub-time period is kept below the corresponding upper limit of the original heat absorption capacity or the original heat release capacity of the equipment.

[0148] Update the temperature value at the end of the current sub-time period in the limited temperature sequence using the adjusted energy;

[0149] The total heat absorbed and released after the statistical limit are compared with the minimum total heat absorbed and the minimum total heat released to obtain the heat absorption gap and the heat release gap, respectively.

[0150] The starting temperature for the quota replenishment process is set to the current limit temperature. Replenishment is carried out in each sub-time period in chronological order: In the sub-time period marked as heat absorption, heat absorption exceeding the heat absorption gap is replenished, provided that the original heat absorption capacity of the equipment is not exceeded and the maximum temperature boundary is not exceeded; In the sub-time period marked as heat release, heat release exceeding the heat release gap is replenished, provided that the temperature is not lower than the minimum temperature boundary and the original heat release capacity of the equipment is not exceeded; After each sub-time period is completed, the temperature at the end of the corresponding sub-time period is updated and the process is rolled over to the next sub-time period.

[0151] When both types of gaps are zero, it is determined that the minimum total heat absorption and minimum total heat release constraints are met; if gaps still exist, it is determined that the current parameter set is not feasible, and the parameters are reset by one or more of the following methods: increasing the maximum temperature boundary, decreasing the minimum temperature boundary, reducing the minimum total heat absorption, reducing the original heat release capacity of the equipment, or increasing the number of segments.

[0152] Further specific implementation steps include:

[0153] The heat capacity of the coal-to-methanol unit is denoted as . The unit is ;

[0154] Set the initial temperature to The unit is ;

[0155] Temperature is predicted iteratively piecewise using energy changes, with the initial value of the limited temperature sequence set to [value missing]. Predicting temperature sequences The following formula is used to derive: , ;in, Before limiting the heat absorption and release energy, based on the original , and optimal scheduling The calculated first Predicted temperature, unit: ; For the limiting temperature sequence used for boundary management after considering temperature boundary constraints, the first... item; For the optimal scheduling variable, for the th The optimal choice of heat absorption and release states;

[0156] The minimum and maximum system temperatures allowed by the process are denoted as follows: and ;

[0157] First, give the initial value of the adjustment: , ;in, After adjustments such as temperature boundary constraints and quota replenishment, in the first The heat absorption energy actually used in the segment is initially set to the original value. The adjusted heat release energy in response is set to an initial value of 1. ;

[0158] Based on the first segment of the limiting temperature sequence, set the maximum allowable energy change with strictly non-negative upper and lower bounds: , ;in, To ensure that the temperature does not exceed the upper boundary temperature Under the premise of current temperature limit The starting point, the maximum allowable increase in positive energy; To ensure that the temperature is not lower than the lower boundary temperature Under the premise of [condition], the absolute value of the maximum allowable negative energy change;

[0159] Apply non-deteriorating constraints:

[0160] like and ,but ;

[0161] like and ,but ;

[0162] Based on this, unidirectional amplitude limiting is applied according to the three mutually exclusive branches of the predicted boundary overflow, specifically as follows:

[0163] S601, Upper boundary check, if :

[0164] Only when Amplitude limiting is required, so ;

[0165] S602, Lower boundary check, if :

[0166] Only when Amplitude limiting is required, so ;

[0167] S603, No boundary crossing determination, if :

[0168] make , ;

[0169] The final value of this segment of the limiting temperature sequence is then updated using the adjusted energy, specifically: ;

[0170] First, calculate the total amount and the gap after the limit is set, specifically: , ;in, This represents the total heat absorbed after temperature limiting. This represents the total heat release after temperature limiting. , ;in, This represents the gap between the current total heat absorption and the minimum heat absorption requirement; This represents the gap between the current total heat release and the minimum heat release requirement;

[0171] Set the initial value of the temperature recursion during the compensation process to... ;

[0172] right Sequential recursion, specifically:

[0173] S604, if Set the maximum number of segments that can be supplemented to: ;in, For the first Each heat absorption section can supplement the maximum amount of heat absorption, provided that it does not exceed the original heat absorption capacity of the equipment and the current upper temperature limit.

[0174] The actual heat absorption compensation for this section is set as follows: , , ;in, In the first The actual amount of heat absorbed in the heat absorption section;

[0175] S605, if The maximum length that can be supplemented in this section is: ;in, In the first The maximum amount of heat that can be additionally released in the heat-releasing section;

[0176] Set the actual heat release compensation for this section: , , ;in, In the first The actual amount of heat released in the heat release section is supplemented.

[0177] S606. Update the end temperature of this segment with the replenished energy and use it for the starting point of the next segment: ;

[0178] Within each segment, the upper limit is calculated and supplemented using the latest starting temperature, thus maintaining: , ;

[0179] After the sequence is completed, such as and The minimum task constraint is then satisfied after the limit is applied; if any gap remains positive, the current parameter set is deemed infeasible, and the parameters need to be reset. One or more of the following methods can be used in combination:

[0180] Increase , reduce , reduce , reduce Increase .

[0181] By acquiring the device's heat capacity and initial temperature, and iteratively predicting the temperature trajectory based on the optimal scheduling vector, and then combining this with upper and lower temperature limits for energy limiting and replenishment, this invention innovatively constructs a "temperature safety closed loop." On the one hand, it uses a device heat capacity model to accurately reflect the real-time impact of energy changes on temperature; on the other hand, it sets strict upper and lower limits and non-deteriorating constraints to ensure that the temperature does not exceed the safe range; and then, after limiting and adjusting, it statistically analyzes the gap and replenishes energy sequentially to ensure that the minimum heat requirement is met, thereby improving the response speed to abnormal temperature fluctuations.

[0182] The process of identifying the maximum continuous heat release segment, performing continuous heat release segment equalization processing and updating the power sequence under the condition of satisfying the temperature and power boundaries, specifically includes:

[0183] Identify each maximum continuous exothermic zone, record the start and end indices, and build an index set;

[0184] Establish coverage and mutual exclusion relationships: all exothermic sub-time periods are completely covered by the index set, and no two index sets overlap;

[0185] The total heat release of each continuous heat release segment is obtained by accumulating the heat release energy after limiting and quota supplementation, and the number of sub-time periods contained in the continuous heat release segment is calculated.

[0186] Within the continuous heat release section, the total heat release candidate is evenly distributed to each sub-time period, and the hypothetical temperature trajectory of the continuous heat release section is constructed based on the current limiting temperature at the start of the quota replenishment phase.

[0187] Feasibility assessment: If the assumed temperature trajectory is not lower than the minimum temperature boundary and the candidate heat release does not exceed the original heat release capacity of the equipment, uniform distribution is deemed feasible.

[0188] Where feasible, the final heat release of each sub-time period within the continuous heat release section is set to an average value, and the new heat release power is calculated accordingly; where not feasible, the heat release energy and power of the continuous heat release section are kept constant.

[0189] Further specific implementation steps include:

[0190] Set the first Start and end indices of the maximum continuous exothermic interval satisfy , And if , ,like , Let the index set of this segment be , ;in, This refers to the sequence number of the continuous heat release section; The first The start and end time period indices of a continuous heat release interval; For the first The set of indexes for all time periods contained in a maximum continuous exothermic zone; The total number of maximally continuous heat-releasing zones;

[0191] And it satisfies the covering and mutual exclusion relationships: , , ;in, To and Indices for the start and end time periods of different continuous heat release intervals;

[0192] The total energy of this segment is accumulated by adding the adjusted heat release energy: ;in, For the first The sum of all exothermic energy released within a continuous heat release zone after temperature limiting and replenishment;

[0193] Calculate the first The length of each heat-generating section ;

[0194] The energy of a candidate uniformly distributed single segment is: ;in, In the first Within a segment, after the total exothermic energy is evenly distributed to each time period, the candidate exothermic energy for each segment is determined.

[0195] Based on the temperature during the quota replenishment phase, construct a uniformly distributed temperature trajectory within the segment: , , ;in, Assuming the heat release energy is uniformly distributed throughout the entire section In this case, the hypothetical temperature trajectory is obtained by sequential recursion; initial value This indicates that the hypothetical trajectory for this segment starts from the temperature at the corresponding time point during the quota replenishment phase;

[0196] And construct a segment determination indicator, specifically: ;in, For the first An indicator variable for whether a uniform distribution scheme for each heat-generating section is feasible;

[0197] when This indicates that assuming uniform distribution will not cause the temperature to drop below a certain level. And the energy is uniformly distributed in a single segment. No more than the original heat release capacity of each segment ;

[0198] when This is considered infeasible;

[0199] Based on this, the final exothermic energy sequence is given: ;in, In the first The heat release energy that is ultimately determined over the time period;

[0200] And in The heat release power after uniform distribution within the set section is: , ;in, The first step, determined after the uniform distribution treatment in the section, is to meet the requirements of the new exothermic energy distribution. Heat release power per segment, in units of ;

[0201] like Then keep , .

[0202] By identifying the maximum continuous heat release segment and, under the condition of satisfying temperature and power boundary conditions, uniformly distributing the total heat release energy within the segment to each sub-time period, this invention proposes an innovative method for local energy smoothing to address the risk of temperature spikes and localized overheating of equipment caused by long-term continuous heat release. Through a two-step judgment process of candidate uniform distribution and hypothetical temperature trajectory verification, it ensures that the heat release of each segment meets production requirements without exceeding the lower temperature limit, while simultaneously smoothing out the cumulative effect of multiple segments and reducing the amplitude of temperature fluctuations.

[0203] The output optimal scheduling, execution power sequence, and final temperature sequence specifically include: the output optimal scheduling operation state sequence;

[0204] Segment-by-segment generation of execution power: within sub-time periods marked as heat absorption, the power is calculated by converting the final heat absorption energy with the duration of the corresponding sub-time period; within sub-time periods marked as heat release, the new heat release power is obtained after equalization processing.

[0205] The starting point of the final execution temperature sequence is set as the initial temperature, and the final temperature sequence is obtained by recursively calculating the energy balance segment by segment.

[0206] Further specific implementation steps include:

[0207] Output the optimal scheduling scheme: ;

[0208] The execution power for each time period is calculated using the final quantity: ;in, In actual control, the first The thermal power command to be issued and executed within the specified time period is in units of ;

[0209] Set the starting value for the final executed temperature sequence. ;

[0210] Final execution temperature sequence Calculate using the following formula: ; .

[0211] By outputting the optimal scheduling vector, the power sequence, and the final temperature sequence simultaneously and directly converting them into control commands, this invention solves the problem of the disconnect between "scheme-execution" and forms an integrated decision-making-execution closed loop. By outputting the complete time series and parameters, intermediate conversion steps are eliminated, shortening the response time from optimization to implementation. Simultaneously, the output temperature sequence can be directly used for on-site monitoring and verification, allowing for real-time comparison of predicted and actual temperatures, further enhancing the system's monitoring and scheduling accuracy.

[0212] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0213] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing energy utilization in a coal-to-methanol production process, characterized in that, The method comprises the following steps: a control period is set and divided into multiple sub-time periods, and an operation state sequence is established in time sequence; heat absorption power or heat release power is recorded in each sub-time period, and energy is converted, and net energy input rules are established according to states; net energy in the control period is accumulated, and the number of state switches between adjacent sub-time periods is counted, and an upper limit of unit switching loss is set to form an evaluation target; the lowest total heat absorption and the lowest total heat release of the process are obtained, and a feasible solution set is screened according to the total amount constraint; the evaluation target is calculated in the feasible solution set, and the scheduling sequence with the maximum evaluation target value is selected; the system heat capacity and the initial temperature are obtained, the temperature prediction, the boundary limiting and the quota supplement are performed according to the selected scheduling, and a limited temperature sequence is formed; a maximum continuous heat release section is identified, and the continuous heat release section equalization processing is performed under the condition of meeting the temperature and power boundary, and the power sequence is updated; the optimal scheduling, the execution power sequence and the final temperature sequence are output.

2. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 1, wherein, The control period is set and divided into multiple sub-time periods, and an operation state sequence is established in time sequence, specifically comprising: the total length of the control period is set; the control period is divided into a plurality of sub-time periods, and each sub-time period is uniquely numbered; an operation state flag is set for each sub-time period, and the value is heat absorption or heat release, which are mutually exclusive; the operation states of each sub-time period are arranged in order of numbering to form an operation state sequence.

3. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 2, wherein, The heat absorption power or heat release power is recorded in each sub-time period, and the energy is converted, and the net energy input rules are established according to the states, specifically comprising: the heat absorption power is recorded in the sub-time period marked as heat absorption, and the heat absorption energy is converted according to the time length of the corresponding sub-time period; the heat release power is recorded in the sub-time period marked as heat release, and the heat release energy is converted according to the time length of the corresponding sub-time period; the net energy input rules are established: the heat release energy is input in the heat release sub-time period, and the heat absorption energy is used as a deduction item to input the net energy in the heat absorption sub-time period.

4. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 3, wherein, The net energy in the control period is accumulated, and the number of state switches between adjacent sub-time periods is counted, and an upper limit of unit switching loss is set to form an evaluation target, specifically comprising: the net energy of each sub-time period in the control period is accumulated to obtain the net energy in the control period; the operation states of adjacent two sub-time periods are compared, and the number of state switches is counted; the maximum value of the heat absorption energy of a single sub-time period and the heat release energy of a single sub-time period in the control period is retrieved respectively, and the larger one is taken as the upper limit of unit switching loss; the evaluation target is constructed: based on the net energy in the control period, the product of the upper limit of unit switching loss and the number of state switches is deducted.

5. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 4, wherein, The lowest total heat absorption and the lowest total heat release of the process are obtained, and a feasible solution set is screened according to the total amount constraint, specifically comprising: the lowest total heat absorption specified by the process is obtained; the heat absorption energy of each heat absorption sub-time period is accumulated under the given scheduling scheme to form the actual total heat absorption, and the actual total heat absorption is set to be not less than the lowest total heat absorption; the lowest total heat release specified by the process is obtained; the heat release energy of each heat release sub-time period is accumulated under the given scheduling scheme to form the actual total heat release, and the actual total heat release is set to be not less than the lowest total heat release; all scheduling schemes that meet the lower limit of the lowest total heat absorption and the lowest total heat release are collected as the feasible solution set.

6. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 5, wherein, The method comprises the following steps: Enumerate operation state sequences consistent with the control cycle length in the feasible solution set; For each operation state sequence, calculate the control cycle net energy and evaluation target according to the net energy charging rule and switching penalty modeling; Select the operation state sequence with the maximum evaluation target as the optimal scheduling.

7. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 6, wherein, The method comprises the following steps: Obtain the system heat capacity and initial temperature, and set the minimum temperature boundary and maximum temperature boundary; Initialize the heat absorption and release energy of each sub-time period to the original value, establish the amplitude-limited temperature sequence, and set the first item as the initial temperature; Recursively calculate the energy balance in time sequence to obtain the predicted temperature sequence; At the beginning of each sub-time period, take the sub-time period starting temperature as the current amplitude-limited temperature, and calculate the maximum energy increase and the maximum energy decrease allowed by the maximum temperature boundary and the minimum temperature boundary; When the current amplitude-limited temperature is higher than the maximum temperature boundary and the current sub-time period is in heat absorption state, set the heat absorption energy of the current sub-time period to zero; when the current amplitude-limited temperature is lower than the minimum temperature boundary and the current sub-time period is in heat release state, set the heat release energy of the current sub-time period to zero; When the predicted temperature is higher than the maximum temperature boundary and the current sub-time period is in heat absorption state, limit the heat absorption energy of the current sub-time period according to the maximum temperature boundary allowed value; when the predicted temperature is lower than the minimum temperature boundary and the current sub-time period is in heat release state, limit the heat release energy of the current sub-time period according to the minimum temperature boundary allowed value; when the predicted temperature is between the two temperature boundaries, keep the energy of the current sub-time period not exceeding the corresponding upper limit of the original heat absorption capacity or the original heat release capacity of the device; Update the temperature value at the end of the current sub-time period in the amplitude-limited temperature sequence using the adjusted energy; Statistically compare the heat absorption total amount and the heat release total amount after amplitude limiting with the minimum heat absorption total amount and the minimum heat release total amount to obtain the heat absorption gap and the heat release gap; Set the starting temperature of the quota supplement process as the current amplitude-limited temperature, and supplement in each sub-time period in time sequence: in the sub-time period marked as heat absorption, supplement the heat absorption amount not exceeding the heat absorption gap under the condition of not exceeding the original heat absorption capacity of the device and not exceeding the maximum temperature boundary; in the sub-time period marked as heat release, supplement the heat release amount not exceeding the heat release gap under the condition of not lower than the minimum temperature boundary and not exceeding the original heat release capacity of the device; after completing each sub-time period, update the temperature at the end of the corresponding sub-time period and recursively to the next sub-time period; When both types of gaps are zero, it is determined that the minimum heat absorption total amount and the minimum heat release total amount constraints are satisfied; if there are still gaps, it is determined that the current parameter set is not feasible, and the parameters are reset by one or more of the following ways: increasing the maximum temperature boundary, decreasing the minimum temperature boundary, lowering the minimum heat absorption total amount, lowering the original heat release capacity of the device, or increasing the number of segments.

8. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 7, wherein, The identification of the maximum continuous exothermic section, under the condition of meeting the temperature and power boundary, performs a continuous exothermic section equalization processing and updates the power sequence, specifically comprising: Identify each maximum continuous exothermic section, record the starting and ending indexes and establish an index set; Establish an overlapping and mutually exclusive relationship: all exothermic sub-periods are completely covered by the index set, and any two index sets do not overlap; For each continuous exothermic section, accumulate the exothermic energy after limiting and quota supplement to obtain the total exothermic amount of the continuous exothermic section, and calculate the number of sub-periods contained in the continuous exothermic section; Within the continuous exothermic section, the total exothermic amount candidate is uniformly distributed to each sub-period, and the hypothetical temperature trajectory of the continuous exothermic section is constructed based on the current limiting temperature at the beginning of the quota supplement stage; Perform a feasibility determination: when the hypothetical temperature trajectory is not lower than the minimum temperature boundary and the candidate exothermic amount does not exceed the original exothermic capacity of the device, determine that the uniform distribution is feasible; When feasible, set the final exothermic amount of each sub-period within the continuous exothermic section to the uniform distribution value, and accordingly back-calculate the new exothermic power; when not feasible, keep the exothermic energy and power of the continuous exothermic section unchanged.

9. The method for optimizing energy utilization of a coal-to-methanol production process according to claim 8, wherein, The output optimal scheduling, execution power sequence and final temperature sequence, specifically comprising: Output the optimal scheduling operation state sequence; Generate execution power by section: in the sub-periods marked as endothermic, convert the final endothermic energy and the duration of the corresponding sub-period to obtain; in the sub-periods marked as exothermic, use the new exothermic power obtained after equalization processing; Set the starting point of the final execution temperature sequence as the initial temperature, and obtain the final temperature sequence by energy balance recursive by section.

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