Electrolytic aluminum regulation capability evaluation method

Abstract

The embodiment of the invention discloses an electrolytic aluminum regulation capability evaluation method, which comprises the following steps: solving the temperature of an electrolytic bath through production condition parameters, fundamentally avoiding the problem that the temperature of the electrolytic bath is difficult to obtain in the actual production process, and meanwhile, in the regulation capability evaluation process, fully considering the operation condition constraint of the actual production process. It is ensured that the evaluation result better fits the actual production condition, a scientific and accurate method reference can be provided for adjustment performance analysis and adjustment capability evaluation in the process that electrolytic aluminum participates in power grid interaction, development and utilization of load side flexible adjustment resources in the novel power system construction process can be effectively supported, and the development and utilization efficiency is improved. The new energy consumption capability of the power grid is improved, the stability and reliability of the power grid are enhanced, and the sustainable development of a novel power system is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of electrolytic aluminum, and in particular to a method for evaluating the regulation capability of electrolytic aluminum. Background Art

[0002] As new power systems are being built, the proportion of renewable energy access continues to rise. This trend is driving a sharp increase in the grid's demand for flexible regulation resources. The electrolytic aluminum industry, as a typical high-energy-consuming industrial user, holds enormous potential for participating in grid regulation. However, the regulation capacity of electrolytic aluminum is not unrestricted, constrained by various factors, including cell temperature and busbar voltage. In particular, due to the high temperature and highly corrosive environment of the aluminum electrolyte, collecting cell temperature presents numerous challenges, a key factor hindering its effective participation in grid regulation.

[0003] Currently, existing methods for assessing the adjustable capacity of aluminum electrolysis suffer from significant shortcomings. These methods typically rely on a fixed temperature input, employ relatively simple and simplistic evaluation constraints, and lack a scientific and comprehensive quantitative assessment system for regulating capacity. In actual production, accurate measurement of electrolytic cell temperature is difficult, and existing assessment methods fail to fully account for this reality. This results in a significant disconnect between assessment results and actual production needs, failing to provide accurate and effective guidance for aluminum electrolysis's participation in grid interaction. Summary of the Invention

[0004] Based on this, it is necessary to propose a method for evaluating the adjustment capacity of electrolytic aluminum to address the above problems. The electrolytic cell temperature is solved by production condition parameters, which fundamentally avoids the problem of difficulty in obtaining the electrolytic cell temperature in the actual production process. At the same time, in the process of adjusting capacity evaluation, the operating condition constraints of the actual production process are fully considered to ensure that the evaluation results are more in line with the actual production situation.

[0005] To achieve the above objectives, the present invention provides, in a first aspect, a method for evaluating the adjustment capability of electrolytic aluminum, the method comprising:

[0006] Obtain the electrolytic cell power, various production condition parameters and various operating condition parameters of electrolytic aluminum, as well as various operating condition constraints;

[0007] Determine the electrolytic cell temperature based on all production condition parameters;

[0008] Determining a comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints;

[0009] The regulation capability of the electrolytic aluminum is evaluated based on the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature and the electrolytic cell power.

[0010] Optionally, determining a comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints includes:

[0011] Using the grey correlation method, the correlation between each operating condition parameter and the electrolytic cell temperature is determined;

[0012] Determining the electrolytic cell temperature constraint corresponding to each operating condition constraint based on the correlation between each operating condition parameter and the electrolytic cell temperature and the corresponding operating condition constraint;

[0013] The comprehensive electrolytic cell temperature constraint is determined based on all electrolytic cell temperature constraints.

[0014] Optionally, determining a comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints includes:

[0015] Within a preset electrolytic cell power range, the electrolytic cell power of the electrolytic aluminum is modified to the lower limit of the preset electrolytic cell power range, and the electrolytic cell power of the electrolytic aluminum is adjusted incrementally in sequence according to a preset step size, or the electrolytic cell power of the electrolytic aluminum is modified to the upper limit of the preset electrolytic cell power range, and the electrolytic cell power of the electrolytic aluminum is adjusted incrementally in sequence according to the preset step size, and after each adjustment, the step of obtaining the electrolytic cell power of the electrolytic aluminum, various production condition parameters, and various operating condition parameters is returned to be executed to obtain the various operating condition parameters and the electrolytic cell temperature obtained in each adjustment;

[0016] When all the operating condition parameters obtained by each adjustment meet the corresponding operating condition constraints, the electrolytic cell temperature obtained by the corresponding adjustment is used as the target electrolytic cell temperature;

[0017] The comprehensive electrolyzer temperature constraint is determined based on all target electrolyzer temperatures.

[0018] Optionally, the evaluating the regulation capability of the electrolytic aluminum according to the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature, and the electrolytic cell power includes:

[0019] Determining, based on the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature, and the electrolytic cell power, an upper limit and a lower limit of the electrolytic cell power, as well as a first adjustment duration for the electrolytic aluminum electrolytic cell power to be continuously adjusted to the upper limit of the electrolytic cell power and a second adjustment duration for the electrolytic aluminum electrolytic cell power to be continuously adjusted to the lower limit of the electrolytic cell power;

[0020] The adjustment capability of the electrolytic aluminum is evaluated based on the electrolytic cell power upper limit, the electrolytic cell power lower limit, the first adjustment time and the second adjustment time.

[0021] Optionally, determining the upper limit and lower limit of electrolytic cell power according to the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature, and the electrolytic cell power, as well as a first adjustment duration that can be maintained after the electrolytic cell power of the electrolytic aluminum is adjusted to the upper limit of the electrolytic cell power and a second adjustment duration that can be maintained after the electrolytic cell power of the electrolytic aluminum is adjusted to the lower limit of the electrolytic cell power, includes:

[0022] Using the formula Determining the electrolytic cell power upper limit, the electrolytic cell power lower limit, the first adjustment time, and the second adjustment time;

[0023] Among them, P max is the upper limit of the electrolytic cell power, P is the electrolytic cell power, Δt1 is the first adjustment time, m is the electrolyte mass, C p is the specific heat capacity of the electrolyte, T max is the upper limit of the comprehensive electrolytic cell temperature constraint, T fr is the electrolytic cell temperature, Q loss Q is the sum of the electrolyte heat loss and the flue gas heat loss, r It is the sum of the heat absorbed by the electrochemical reaction and the heat absorbed by the material heating, P min is the lower limit of the electrolytic cell power, Δt2 is the second adjustment time, T min is the lower limit of the comprehensive electrolytic cell temperature constraint, and Δt is the adjustment time.

[0024] Optionally, determining the electrolytic cell temperature according to all production condition parameters includes:

[0025] Input all production condition parameters into the preset electrolytic cell temperature prediction model to obtain the predicted electrolytic cell temperature;

[0026] The predicted electrolytic cell temperature is used as the electrolytic cell temperature.

[0027] Optionally, before inputting all production condition parameters into a preset electrolytic cell temperature prediction model to obtain a predicted electrolytic cell temperature, the method further comprises:

[0028] Obtaining various historical production condition parameters of the electrolytic aluminum, as well as historical electrolytic cell temperatures and historical Joule heat sources;

[0029] Each historical production condition parameter is standardized to obtain multiple standard production condition parameters;

[0030] Using the grey correlation method, the correlation between each standard production condition parameter and the historical electrolytic cell temperature is determined;

[0031] According to the comparison result between the correlation degree between each standard production condition parameter and the electrolytic cell temperature and the correlation degree threshold, the standard production condition parameters are eliminated to obtain multiple target production condition parameters;

[0032] Multiple target production condition parameters, as well as the historical electrolytic cell temperature and the historical Joule heat source are input into the initial LSTM model for training to obtain the preset electrolytic cell temperature prediction model.

[0033] Optionally, the loss function of the initial LSTM model during training is:

[0034]

[0035] Wherein, L is the loss function value, α is the first preset proportional coefficient, MSE is the root mean square error between the predicted electrolytic cell temperature obtained during the training process and the historical electrolytic cell temperature, β is the second preset proportional coefficient, L phy is the physical loss, T is the total time, Q j,fr,t is the Joule heat source at the tth moment in the predicted Joule heat source obtained during the training process, Q j,t is the Joule heat source at the tth moment in the historical Joule heat source.

[0036] Optionally, the inputting of multiple target production condition parameters, the historical electrolytic cell temperature, and the historical Joule heat source into an initial LSTM model for training to obtain the preset electrolytic cell temperature prediction model includes:

[0037] Obtain historical electrolyte mass, historical electrolyte specific heat capacity, historical heat loss between historical electrolyte heat loss and historical flue gas heat loss, and historical absorbed heat between historical electrochemical reaction heat absorption and historical material heating heat absorption;

[0038] The historical electrolyte mass, the historical electrolyte specific heat capacity, the historical heat loss, the historical absorbed heat, multiple target production condition parameters, the historical electrolytic cell temperature and the historical Joule heat source are input into the initial LSTM model for training to obtain the preset electrolytic cell temperature prediction model.

[0039] Optionally, the method further includes:

[0040] Using formula m t C p,t ΔT t =Q j,fr,t -Q loss,t -Q r,t determining the Joule heat source at time t in the predicted Joule heat source;

[0041] Using the formula Qj,t =(E t -E r,t )I t Δt determines the Joule heat source at the t-th moment in the historical Joule heat source;

[0042] Among them, m t is the electrolyte mass at the tth moment in the historical electrolyte mass, C p,t is the electrolyte specific heat capacity at the tth moment in the electrolyte specific heat capacity, ΔT t Q is the change between the electrolytic cell temperature at the time t-1 and the electrolytic cell temperature at the time t in the predicted electrolytic cell temperature. j,fr,t is the Joule heat source at the tth moment in the predicted Joule heat source, Q loss,t is the heat loss at the tth moment in the historical heat loss, Q r,t is the heat absorbed at the tth moment in the historical heat absorption, Q j,t is the Joule heat source at the tth moment in the historical Joule heat source, E t is the electrolytic cell voltage at time t in the historical electrolytic cell voltage, E r,t is the electrolytic cell back electromotive force at the tth moment in the history of electrolytic cell back electromotive force, I t is the electrolytic cell current at the tth moment in the historical electrolytic cell current, and Δt is the change between the t-1th moment and the tth moment.

[0043] To achieve the above-mentioned object, the present invention provides, in a second aspect, a device for evaluating the adjustment capability of electrolytic aluminum, the device comprising:

[0044] An acquisition module is used to obtain the electrolytic cell power, various production condition parameters and various operating condition parameters of electrolytic aluminum, as well as various operating condition constraints;

[0045] A first determination module is used to determine the temperature of the electrolytic cell according to all production condition parameters;

[0046] A second determination module is used to determine a comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters and all operating condition constraints;

[0047] An evaluation module is used to evaluate the regulation capability of the electrolytic aluminum based on the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature and the electrolytic cell power.

[0048] To achieve the above-mentioned object, the present invention provides, in a third aspect, a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the method as described in any one of the first aspects.

[0049] To achieve the above-mentioned objectives, the present invention provides a computer device in a fourth aspect, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the method as described in any one of the first aspects.

[0050] The embodiment of the present invention has the following beneficial effects: the above method obtains the electrolytic cell power, various production condition parameters and various operating condition parameters, as well as various operating condition constraints of electrolytic aluminum, and then determines the electrolytic cell temperature according to all production condition parameters, and then determines the comprehensive electrolytic cell temperature constraint according to the electrolytic cell temperature, all operating condition parameters and all operating condition constraints, and finally evaluates the regulation capacity of electrolytic aluminum based on the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature and the electrolytic cell power; that is, solving the electrolytic cell temperature by using the production condition parameters fundamentally avoids the problem of difficulty in obtaining the electrolytic cell temperature in the actual production process, and at the same time, in the regulation capacity evaluation process, fully considers the operating condition constraints of the actual production process, ensuring that the evaluation results are more in line with the actual production situation, and can provide a scientific and accurate method reference for the regulation performance analysis and regulation capacity evaluation of electrolytic aluminum in the process of participating in the grid interaction, and can effectively support the development and utilization of load-side flexibility regulation resources in the construction of new power systems, improve the grid's ability to absorb new energy, enhance the stability and reliability of the grid, and promote the sustainable development of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] in:

[0053] Figure 1 Schematic diagram of a method for evaluating the adjustment capability of electrolytic aluminum in an embodiment of the present application;

[0054] Figure 2 This is a schematic diagram of an electrolytic aluminum adjustment capability evaluation device according to an embodiment of the present application;

[0055] Figure 3 1 is a diagram of the internal structure of a computer device in some embodiments. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] As new power systems are being built, the proportion of renewable energy access continues to rise. This trend is driving a sharp increase in the grid's demand for flexible regulation resources. The electrolytic aluminum industry, as a typical high-energy-consuming industrial user, holds enormous potential for participating in grid regulation. However, the regulation capacity of electrolytic aluminum is not unrestricted, constrained by various factors, including cell temperature and busbar voltage. In particular, due to the high temperature and highly corrosive environment of the aluminum electrolyte, collecting cell temperature presents numerous challenges, a key factor hindering its effective participation in grid regulation.

[0058] Currently, existing methods for assessing the adjustable capacity of aluminum electrolysis suffer from significant shortcomings. These methods typically rely on a fixed temperature input, employ relatively simple and simplistic evaluation constraints, and lack a scientific and comprehensive quantitative assessment system for regulating capacity. In actual production, accurate measurement of electrolytic cell temperature is difficult, and existing assessment methods fail to fully account for this reality. This results in a significant disconnect between assessment results and actual production needs, failing to provide accurate and effective guidance for aluminum electrolysis's participation in grid interaction.

[0059] In response to the above problems, this application proposes a method for evaluating the adjustment capacity of electrolytic aluminum. The electrolytic cell temperature is solved by production condition parameters, which fundamentally avoids the problem of difficulty in obtaining the electrolytic cell temperature in the actual production process. At the same time, in the process of adjusting capacity evaluation, the operating condition constraints of the actual production process are fully considered to ensure that the evaluation results are more in line with the actual production situation. The specific implementation principles will be described in detail in the following embodiments.

[0060] In a first aspect, the present application provides a method for evaluating the regulation capability of electrolytic aluminum.

[0061] See also Figure 1 , is a schematic diagram of a method for evaluating the adjustment capability of electrolytic aluminum according to an embodiment of the present application, the method comprising:

[0062] Step 110: Obtain the electrolytic cell power, various production condition parameters and various operating condition parameters of electrolytic aluminum, as well as various operating condition constraints.

[0063] Regarding the method of obtaining various production condition parameters, in some embodiments, various production condition parameters of electrolytic aluminum can be extracted based on the production conditions of the electrolytic aluminum production process; wherein the various production condition parameters include but are not limited to cell voltage, molecular ratio, aluminum liquid level, alumina discharge amount, series current, aluminum output and anode height, etc.

[0064] Regarding the method of obtaining various operating condition parameters, in some embodiments, various operating condition parameters of electrolytic aluminum can be extracted based on the operating conditions of the electrolytic aluminum production process; wherein the various operating condition parameters include but are not limited to current fluctuation value, energy efficiency attenuation value, electrolytic cell voltage, alumina concentration, molecular ratio and electrolyte level, etc.

[0065] Regarding the method of obtaining various operating condition constraints, in some embodiments, various operating condition constraints for electrolytic aluminum can be constructed based on the operating conditions of the electrolytic aluminum production process; wherein the various operating condition constraints include but are not limited to current fluctuation constraints, energy efficiency attenuation constraints, electrolytic cell voltage constraints, alumina concentration constraints, molecular ratio constraints and electrolyte level constraints, etc.

[0066] It should be noted that the constraints mentioned in this application may be a range between an upper limit and a lower limit; for example, for a current fluctuation constraint, it may be a current fluctuation range.

[0067] Step 120: Determine the electrolytic cell temperature based on all production condition parameters.

[0068] Regarding the method of determining the electrolytic cell temperature, in some embodiments, the production condition parameters can be matched in a preset electrolytic cell temperature table based on all production condition parameters to determine the electrolytic cell temperature; wherein, the preset electrolytic cell temperature table can be obtained and pre-set by the operator based on a large amount of experience, experiments or statistics.

[0069] Regarding the method of determining the electrolytic cell temperature, in some embodiments, the electrolytic cell temperature can also be determined based on all production condition parameters and a preset relationship between the production condition parameters and the electrolytic cell temperature; wherein the preset relationship can be obtained and preset by the operator based on a large amount of experience, experiments or statistics.

[0070] Regarding the method of determining the electrolytic cell temperature, in some embodiments, the electrolytic cell temperature can also be determined based on all production condition parameters and a pre-trained deep learning model; wherein the pre-trained deep learning model can be obtained by the operator through training according to an existing training method and pre-set.

[0071] Step 130: Determine the comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints.

[0072] Regarding the method for determining the comprehensive electrolytic cell temperature constraint, in some embodiments, multiple electrolytic cell temperature constraints can be determined based on the constraint relationship between each operating condition parameter and the corresponding operating condition constraint, as well as the electrolytic cell temperature, and then the comprehensive electrolytic cell temperature constraint can be determined based on all the electrolytic cell temperature constraints.

[0073] It should be noted that for the comprehensive electrolytic cell temperature constraint, the constraint relationship between all operating condition parameters and the operating condition constraints of the corresponding items must be satisfied. Therefore, in some embodiments, the minimum electrolytic cell temperature constraint among all electrolytic cell temperature constraints can be used as the comprehensive electrolytic cell temperature constraint.

[0074] Step 140: Evaluate the regulation capability of electrolytic aluminum based on the comprehensive electrolytic cell temperature constraint, electrolytic cell temperature, and electrolytic cell power.

[0075] It should be noted that the regulation capacity of electrolytic aluminum refers to the product of the first regulation time that can be sustained after the electrolytic cell power of electrolytic aluminum is adjusted to the upper limit of the electrolytic cell power and the upper limit of the electrolytic cell power, and the product of the second regulation time that can be sustained after the electrolytic cell power of electrolytic aluminum is adjusted to the lower limit of the electrolytic cell power and the lower limit of the electrolytic cell power.

[0076] Regarding the evaluation method of the regulation capability of electrolytic aluminum, in some embodiments, the electrolytic cell power constraint can be determined based on the constraint relationship between the comprehensive electrolytic cell temperature constraint and the electrolytic cell temperature, as well as the electrolytic cell power, and then the first regulation time and the second regulation time can be obtained based on the electrolytic cell power constraint. Finally, the regulation capability of electrolytic aluminum can be evaluated based on the electrolytic cell power constraint, the first regulation time and the second regulation time.

[0077] In the embodiment of the present application, the electrolytic cell temperature is solved by solving the production condition parameters, which fundamentally avoids the problem of difficulty in obtaining the electrolytic cell temperature in the actual production process. At the same time, in the process of regulating capacity evaluation, the operating condition constraints of the actual production process are fully considered to ensure that the evaluation results are more in line with the actual production situation. It can provide a scientific and accurate method reference for the regulation performance analysis and regulation capacity evaluation of electrolytic aluminum in the process of grid interaction, and can effectively support the development and utilization of load-side flexible regulation resources in the construction of new power systems, improve the grid's ability to absorb new energy, enhance the stability and reliability of the grid, and promote the sustainable development of new power systems.

[0078] In addition, in addition to the advantages mentioned above, the electrolytic aluminum regulation capacity evaluation method proposed in this application also has the following advantages: In terms of economic benefits, it reduces production costs: By accurately evaluating the electrolytic aluminum regulation capacity, enterprises can more scientifically arrange production plans and the timing of participating in grid regulation, avoid unstable production processes due to blind adjustment, and reduce equipment loss, raw material waste, and increased defective rates caused by factors such as temperature fluctuations and out-of-control operating conditions, thereby reducing overall production costs; increase additional benefits: In the context of new power system construction, enterprises can obtain corresponding economic compensation by participating in grid regulation. This method can help enterprises accurately grasp the timing and extent of participating in regulation, and participate in grid regulation as much as possible on the premise of ensuring production stability, obtain more additional benefits, and improve corporate economic benefits. Regarding technological innovation and development, it promotes technological progress in the industry: this method provides the electrolytic aluminum industry with a scientific and complete quantitative evaluation system for regulation capabilities, filling the gap in existing technologies for evaluating the regulation capabilities of electrolytic aluminum. Its successful application will guide other companies in the industry to pay attention to and study related technologies for electrolytic aluminum to participate in grid regulation, and promote innovation and development in the field of power regulation technology for the entire electrolytic aluminum industry; it promotes cross-industry technology integration: this method integrates electrolytic aluminum production process knowledge, power system regulation requirements, and advanced data analysis technology. During the implementation process, technologies from different fields collide and merge with each other, providing an opportunity for cross-industry technical exchanges and cooperation, helping to cultivate compound technical talents and promote the coordinated progress of related industry technologies. At the energy and environmental level, optimize energy allocation: the proportion of new energy in the new power system is constantly increasing, and its volatility brings challenges to the stable operation of the power grid. This method enables electrolytic aluminum enterprises to respond to the power grid demand more flexibly according to their own adjustment capabilities, increase electricity load during the peak of new energy power generation, and reduce electricity consumption during the off-peak period, so as to achieve optimal allocation of energy in different time periods and improve energy utilization efficiency; help achieve carbon emission reduction goals: by improving the power grid's ability to absorb new energy, reducing dependence on traditional fossil energy power generation, and indirectly reducing carbon emissions. At the same time, electrolytic aluminum enterprises optimize the production adjustment process, reduce energy waste and additional carbon emissions caused by unstable production, and make positive contributions to the realization of carbon emission reduction goals for the industry and even the entire society.In terms of power grid security and stability, the peak-shaving capacity of the power grid is enhanced: as a high-energy-consuming industrial user, the regulation capacity of electrolytic aluminum plays an important role in the peak-shaving of the power grid. This method can accurately evaluate the regulation capacity of electrolytic aluminum under different operating conditions, so that the power grid dispatching department can more reasonably arrange the peak-shaving tasks of electrolytic aluminum enterprises, give full play to their load regulation potential, effectively alleviate the peak-valley difference problem of the power grid, and enhance the peak-shaving capacity of the power grid; improve the anti-interference ability of the power grid: when the power grid is disturbed by new energy fluctuations, sudden failures, etc., electrolytic aluminum enterprises can respond to the power grid dispatching instructions quickly and accurately with the regulation capacity determined by this evaluation method, adjust their own electricity load, provide the necessary frequency and voltage support for the power grid, enhance the power grid's ability to respond to emergencies, and ensure the safe and stable operation of the power grid. At the policy and market level, it is in line with policy guidance: as the country attaches importance to the construction of new power systems and energy transformation, a series of policies encouraging high-energy-consuming enterprises to participate in grid regulation have been introduced. This method provides technical support for enterprises to respond to policy requirements, helps enterprises better adapt to the policy environment, and achieve sustainable development under policy guidance; promotes the healthy development of the power market: in the power market environment, accurate regulation capacity assessment is the basis of market transactions. This method can provide a scientific basis for electrolytic aluminum enterprises to participate in power market regulation transactions, make market transactions more fair, transparent and efficient, promote the optimal allocation of power market resources, and promote the healthy development of the power market.

[0079] In a feasible implementation, step 130 in the above embodiment determines the comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters and all operating condition constraints, including: using the grey correlation method to determine the correlation between each operating condition parameter and the electrolytic cell temperature; determining the electrolytic cell temperature constraint corresponding to each operating condition constraint based on the correlation between each operating condition parameter and the electrolytic cell temperature, and the corresponding operating condition constraint; and determining the comprehensive electrolytic cell temperature constraint based on all electrolytic cell temperature constraints.

[0080] Regarding the method for determining the electrolytic cell temperature constraint corresponding to each operating condition constraint, in some embodiments, the upper limit of the electrolytic cell temperature constraint corresponding to each operating condition constraint can be determined based on the correlation between each operating condition parameter and the electrolytic cell temperature, and the upper limit of the corresponding operating condition constraint, and the lower limit of the electrolytic cell temperature constraint corresponding to each operating condition constraint can be determined based on the correlation between each operating condition parameter and the electrolytic cell temperature, and the lower limit of the corresponding operating condition constraint.

[0081] Regarding the method for determining the comprehensive electrolytic cell temperature constraint, in some embodiments, multiple electrolytic cell temperature upper limits and multiple electrolytic cell temperature lower limits can be determined based on all electrolytic cell temperature constraints, and then the smallest electrolytic cell temperature upper limit among all electrolytic cell temperature upper limits can be used as the comprehensive electrolytic cell temperature upper limit, and the largest electrolytic cell temperature lower limit among all electrolytic cell temperature lower limits can be used as the comprehensive electrolytic cell temperature lower limit, and finally the comprehensive electrolytic cell temperature constraint is determined based on the comprehensive electrolytic cell temperature upper limit and the comprehensive electrolytic cell temperature lower limit.

[0082] In the embodiment of the present application, the comprehensive electrolytic cell temperature constraints are determined by adopting the grey correlation method, which improves the scientificity and accuracy of the evaluation, is more in line with the actual production situation, and enhances the feasibility and effectiveness of electrolytic aluminum participating in grid regulation.

[0083] It can be understood that the scientific nature of the evaluation is enhanced: the grey correlation method can more objectively reflect the correlation between each parameter and temperature by quantitatively analyzing the correlation between the operating condition parameters and the electrolytic cell temperature, and can make the determined electrolytic cell temperature constraints more scientific, thereby improving the scientific nature of the entire regulation capacity evaluation; improving the accuracy of the evaluation: according to the correlation between each operating condition parameter and the electrolytic cell temperature, and the corresponding operating condition constraints, the electrolytic cell temperature constraints corresponding to each operating condition constraint are determined. This method fully considers the different effects of different operating conditions on the electrolytic cell temperature. By separately determining the temperature constraints corresponding to each constraint, and then comprehensively deriving the comprehensive electrolytic cell temperature constraints, it can more accurately characterize the temperature variation range of the electrolytic cell under different operating conditions, thereby improving the accuracy of the evaluation; in line with actual production conditions: in actual During production, the operating conditions of electrolytic aluminum are complex and changeable, and the various parameters influence and restrict each other. The gray correlation method is used to determine the comprehensive electrolytic cell temperature constraints, which can fully consider these actual factors and make the evaluation results closer to the actual production situation. This helps enterprises to more reasonably arrange production plans and the timing of participating in grid regulation according to actual production conditions, thereby improving production efficiency and economic benefits; enhancing the feasibility of regulation: by determining the comprehensive electrolytic cell temperature constraints more scientifically and accurately, enterprises can have a clearer understanding of their own regulation capabilities under different operating conditions, which helps enterprises to formulate more reasonable regulation strategies when participating in grid regulation, and avoid adverse effects on production due to excessive or insufficient regulation. At the same time, it also enhances the trust of the grid dispatching department in the regulation capabilities of electrolytic aluminum enterprises, and improves the feasibility and effectiveness of electrolytic aluminum participating in grid regulation.

[0084] In a feasible implementation, step 130 in the above embodiment determines the comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters and all operating condition constraints, including: within the preset electrolytic cell power range, modifying the electrolytic cell power of electrolytic aluminum to the lower limit of the preset electrolytic cell power range, and adjusting the electrolytic cell power of electrolytic aluminum in sequence and increment according to the preset step size, or modifying the electrolytic cell power of electrolytic aluminum to the upper limit of the preset electrolytic cell power range, and adjusting the electrolytic cell power of electrolytic aluminum in sequence and decrement according to the preset step size, and after each adjustment, returning to execute the step of obtaining the electrolytic cell power of electrolytic aluminum, various production condition parameters and various operating condition parameters to obtain various operating condition parameters and electrolytic cell temperature obtained in each adjustment; when various operating condition parameters obtained in each adjustment meet the corresponding operating condition constraints, the electrolytic cell temperature obtained in the corresponding adjustment is used as the target electrolytic cell temperature; and determining the comprehensive electrolytic cell temperature constraint based on all target electrolytic cell temperatures.

[0085] The preset electrolytic cell power range and the preset step size can be obtained and pre-set by the operator based on a large amount of experience, experiments or statistics. Of course, they can also be pre-set by the operator based on actual needs.

[0086] Regarding the method for determining the comprehensive electrolytic cell temperature constraint, in some embodiments, the maximum target electrolytic cell temperature among all target electrolytic cell temperatures can be used as the upper limit of the comprehensive electrolytic cell temperature, and the minimum target electrolytic cell temperature can be used as the lower limit of the comprehensive electrolytic cell temperature. Then, the comprehensive electrolytic cell temperature constraint is determined based on the upper limit and the lower limit of the comprehensive electrolytic cell temperature.

[0087] In an embodiment of the present application, the comprehensive electrolytic cell temperature constraint is determined by dynamically adjusting the electrolytic cell power and verifying the operating condition constraints, thereby improving the comprehensiveness and reliability of the evaluation and enhancing the adaptability and stability of electrolytic aluminum in participating in grid regulation.

[0088] It is understandable that the comprehensiveness of the evaluation is improved: by dynamically adjusting the electrolytic cell power within the preset electrolytic cell power range and obtaining the operating condition parameters and electrolytic cell temperature after each adjustment, the operating status of electrolytic aluminum at different powers can be fully simulated, ensuring that the evaluation results cover a wider range of operating scenarios and avoiding the limitations caused by single power point evaluation; enhancing the reliability of the evaluation: after each adjustment, by verifying whether the various operating condition parameters meet the corresponding operating condition constraints, only the electrolytic cell temperature that meets the constraints is used as the target electrolytic cell temperature, ensuring that the evaluation results are based on actual feasible operating conditions, avoiding evaluation deviations caused by violation of constraints, and improving the reliability of the evaluation. Reliability; Enhanced regulation adaptability: By dynamically adjusting the electrolytic cell power and verifying the operating condition constraints, the temperature variation range of electrolytic aluminum at different powers can be more accurately characterized, enabling enterprises to more flexibly adjust the electrolytic cell power according to actual production conditions and grid demand, thereby enhancing the adaptability of electrolytic aluminum in grid regulation; Ensured regulation stability: By determining the comprehensive electrolytic cell temperature constraints, enterprises can clearly define the safe temperature range under different operating conditions, avoid production instability or equipment damage caused by excessive temperature fluctuations, and ensure the stability of electrolytic aluminum in grid regulation. At the same time, it also improves the grid's ability to absorb new energy and enhances the stability and reliability of the grid.

[0089] In a feasible implementation, step 140 in the above embodiment evaluates the regulation capability of electrolytic aluminum based on the comprehensive electrolytic cell temperature constraints, electrolytic cell temperature and electrolytic cell power, including: determining the upper limit and lower limit of electrolytic cell power, as well as the first regulation time that can be maintained after the electrolytic cell power of electrolytic aluminum is adjusted to the upper limit of electrolytic cell power and the second regulation time that can be maintained after the electrolytic cell power of electrolytic aluminum is adjusted to the lower limit of electrolytic cell power based on the comprehensive electrolytic cell temperature constraints, electrolytic cell temperature and electrolytic cell power; evaluating the regulation capability of electrolytic aluminum based on the upper limit of electrolytic cell power, the lower limit of electrolytic cell power, the first regulation time and the second regulation time.

[0090] Regarding the evaluation method of the regulation capability of electrolytic aluminum, in some embodiments, the product between the upper limit of the electrolytic cell power and the first regulation time, and the product between the lower limit of the electrolytic cell power and the second regulation time can be used as the regulation capability of the electrolytic aluminum to be evaluated.

[0091] In the embodiments of the present application, by comprehensively considering the comprehensive electrolytic cell temperature constraints, electrolytic cell temperature and electrolytic cell power, the accuracy and practicality of the electrolytic aluminum regulation capacity assessment are improved, providing a scientific and quantitative basis for electrolytic aluminum to participate in grid regulation, and enhancing the feasibility and effectiveness of regulation.

[0092] It is understandable that the evaluation accuracy is improved: by comprehensively considering the comprehensive electrolytic cell temperature constraint, electrolytic cell temperature and electrolytic cell power, this method can more accurately determine the upper and lower limits of the electrolytic cell power, as well as the adjustment time, thereby ensuring the accuracy of the adjustment capacity evaluation results. This evaluation method avoids the evaluation deviation caused by a single factor, making the evaluation results closer to the actual production situation; Enhance the practicality of adjustment: This method not only evaluates the adjustment capacity of electrolytic aluminum, but also provides specific information on the upper and lower limits of the electrolytic cell power and the adjustment time. This information has important practical value for enterprises, which can help enterprises arrange production plans and participate in grid adjustment more scientifically, and improve the adjustment efficiency. Feasibility and effectiveness; Support stable operation of the power grid: Accurate regulation capacity assessment helps the power grid dispatching department to arrange the peak-shaving tasks of electrolytic aluminum enterprises more reasonably and give full play to their load regulation potential, which helps to alleviate the peak-to-valley problem of the power grid and enhance the peak-shaving capacity of the power grid. At the same time, it improves the ability of the power grid to respond to emergencies and ensures the safe and stable operation of the power grid; Promote optimal energy allocation: By evaluating the regulation capacity of electrolytic aluminum, this method helps to achieve optimal allocation of energy in different time periods. During the peak of new energy power generation, electrolytic aluminum enterprises can increase their electricity load, and during the valley, they can reduce electricity consumption. This optimized configuration improves energy utilization efficiency and helps promote the sustainable development of new power systems.

[0093] In a feasible implementation, the above embodiment determines the upper limit and lower limit of the electrolytic cell power, as well as the first adjustment time that can be maintained after the electrolytic cell power of the electrolytic aluminum is adjusted to the upper limit of the electrolytic cell power and the second adjustment time that can be maintained after the electrolytic cell power of the electrolytic aluminum is adjusted to the lower limit of the electrolytic cell power according to the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature and the electrolytic cell power, including:

[0094] Using the formula Determine the upper limit of electrolytic cell power, the lower limit of electrolytic cell power, the first adjustment time, and the second adjustment time;

[0095] Among them, P max is the upper limit of electrolytic cell power, P is the electrolytic cell power, Δt1 is the first adjustment time, m is the electrolyte mass, C p is the specific heat capacity of the electrolyte, T max is the upper limit of the comprehensive electrolytic cell temperature constraint, T fr is the electrolytic cell temperature, Q loss Q is the sum of the electrolyte heat loss and the flue gas heat loss, r It is the sum of the heat absorbed by the electrochemical reaction and the heat absorbed by the material heating, P min is the lower limit of electrolytic cell power, Δt2 is the second adjustment time, T min is the lower limit of the comprehensive electrolytic cell temperature constraint, and Δt is the adjustment time.

[0096] In the embodiments of the present application, the upper and lower limits of the electrolytic cell power and the adjustment time are accurately calculated through formulas, which significantly improves the accuracy and scientific nature of the adjustment capacity assessment and provides solid quantitative support for electrolytic aluminum to participate in grid regulation.

[0097] It is understandable that the accuracy of the assessment is improved: using a formula that includes key parameters such as electrolyte mass, specific heat capacity, temperature constraints, and heat dissipation losses, the upper and lower limits of the electrolytic cell power and the adjustment time can be accurately calculated, avoiding errors caused by subjective judgment and empirical estimation, and ensuring the accuracy of the assessment results; enhancing the scientific nature of the assessment: all parameters in the formula are based on the physical and chemical principles of the electrolytic aluminum production process and determined through scientific methods, making the assessment process more rigorous and scientific, helping enterprises to more accurately understand their own adjustment capabilities and providing a scientific basis for formulating reasonable production plans and participating in grid adjustment strategies; providing quantitative support: the upper and lower limits of the electrolytic cell power and the adjustment time calculated by the formula provide specific quantitative indicators for electrolytic aluminum to participate in grid adjustment, helping grid dispatching departments to more reasonably arrange the peak adjustment tasks of electrolytic aluminum enterprises, fully tap their load adjustment potential, and enhance the peak adjustment capacity and stability of the grid; supporting decision-making: accurate assessment results help enterprises make more informed decisions on when to increase or decrease electricity load to respond to grid demand, achieve optimal energy allocation, improve energy utilization efficiency, while reducing production costs and increasing additional profits.

[0098] In a feasible implementation, step 120 in the above embodiment determines the electrolytic cell temperature based on all production condition parameters, including: inputting all production condition parameters into a preset electrolytic cell temperature prediction model to obtain a predicted electrolytic cell temperature; and using the predicted electrolytic cell temperature as the electrolytic cell temperature.

[0099] The preset electrolytic cell temperature prediction model here refers to a deep learning model that has been trained and can be directly used to predict the output of the electrolytic cell temperature based on all input production condition parameters.

[0100] Regarding the training method of the preset electrolytic cell temperature prediction model, in some embodiments, a large number of all production condition parameters and the corresponding electrolytic cell temperatures can be obtained, and then all the production condition parameters and the corresponding electrolytic cell temperatures can be trained in the initial deep learning model. After training to a certain extent, a trained preset electrolytic cell temperature prediction model can be obtained; wherein, the corresponding electrolytic cell temperature can be used as the true value of the initial deep learning model, that is, by comparing the true value with the predicted electrolytic cell temperature output during the training process one by one, it can be determined whether the initial deep learning model is well trained and has met the expected requirements.

[0101] In an embodiment of the present application, by inputting all production condition parameters into a preset electrolytic cell temperature prediction model, the predicted electrolytic cell temperature is obtained, which can improve the efficiency and accuracy of determining the electrolytic cell temperature and provide a reliable basis for regulating capacity evaluation.

[0102] It can be understood that efficiency is improved: by inputting all production condition parameters into the preset electrolytic cell temperature prediction model, the predicted electrolytic cell temperature can be quickly obtained, avoiding complex calculations or tedious search processes, greatly shortening the time to determine the electrolytic cell temperature, and improving the overall efficiency of the evaluation process; enhancing accuracy: the preset electrolytic cell temperature prediction model is trained based on a large number of production condition parameters and corresponding electrolytic cell temperature data. It can learn the complex nonlinear relationship between production condition parameters and electrolytic cell temperature. Compared with traditional methods, the prediction results are more accurate, reducing the evaluation error caused by inaccurate temperature, and providing a reliable data basis for subsequent adjustment capability evaluation; adapting to complex working conditions: in actual production, the production conditions of electrolytic aluminum are complex and changeable. The preset electrolytic cell temperature prediction model can flexibly and accurately predict the electrolytic cell temperature according to different production condition parameter combinations, adapt to various actual working conditions, and make the evaluation results more practical and valuable for reference.

[0103] In a feasible implementation, all the production condition parameters in the above embodiment are input into the preset electrolytic cell temperature prediction model to obtain the predicted electrolytic cell temperature. The method also includes: obtaining various historical production condition parameters of electrolytic aluminum, as well as historical electrolytic cell temperatures and historical Joule heat sources; standardizing each historical production condition parameter to obtain multiple standard production condition parameters; using the grey correlation method to determine the correlation between each standard production condition parameter and the historical electrolytic cell temperature; based on the comparison result between the correlation between each standard production condition parameter and the electrolytic cell temperature and the correlation threshold, the standard production condition parameters are eliminated to obtain multiple target production condition parameters; the multiple target production condition parameters, as well as the historical electrolytic cell temperature and the historical Joule heat source are input into the initial LSTM model for training to obtain a preset electrolytic cell temperature prediction model.

[0104] Among them, the standardization process can also be called normalization process, and its purpose is to eliminate the dimension effect; the correlation threshold can be obtained and pre-set by the operator based on a lot of experience, experiments or statistics. Of course, it can also be pre-set by the operator according to actual needs.

[0105] Regarding the method of obtaining various historical production condition parameters, in some embodiments, various historical production condition parameters of electrolytic aluminum can be extracted based on the production conditions of the historical production process of electrolytic aluminum; wherein the various historical production condition parameters include but are not limited to cell voltage, molecular ratio, aluminum liquid level, alumina discharge amount, series current, aluminum output and anode height, etc.

[0106] It should be noted that, when the historical production condition parameters include cell voltage, molecular ratio, aluminum liquid level, alumina feed rate, series current, aluminum output and anode height, and after elimination processing, if the series current is eliminated, that is, multiple target production condition parameters include cell voltage, molecular ratio, aluminum liquid level, alumina feed rate, aluminum output and anode height, then correspondingly, step 110 in the above embodiment, its various production condition parameters should also include cell voltage, molecular ratio, aluminum liquid level, alumina feed rate, aluminum output and anode height, that is, the production condition parameters of the training stage and the prediction stage must be the same, and the production condition parameters of the two stages are highly correlated.

[0107] In the embodiment of the present application, by optimizing the model input data, the prediction accuracy and efficiency of the preset electrolytic cell temperature prediction model are improved, providing more reliable data support for the evaluation of the electrolytic aluminum regulation capacity.

[0108] It is understandable that the prediction accuracy is improved: by standardizing the historical production condition parameters, the dimensional influence between different parameters is eliminated, making the data more comparable, and the gray correlation method is used to screen out the target production condition parameters with a high correlation with the historical electrolytic cell temperature, eliminating irrelevant or weakly correlated parameters with low correlation, reducing noise interference, and allowing the model to focus on factors that have a significant impact on the electrolytic cell temperature, thereby significantly improving the prediction accuracy of the preset electrolytic cell temperature prediction model; improving model efficiency: after eliminating the standard production condition parameters with low correlation, the data dimension input to the initial LSTM model for training is reduced, the model calculation amount is reduced, and the model training speed is accelerated. At the same time, more streamlined input data also helps the model converge faster, improves training efficiency, and makes the preset electrolytic cell temperature prediction The model can be put into practical application more quickly; Enhance model stability: Training is based on multiple screened target production condition parameters as well as historical electrolytic cell temperatures and historical Joule heat sources, so that the features learned by the model are more representative and stable, avoiding the impact of irrelevant parameter fluctuations on the model prediction results, and enhancing the stability and generalization ability of the preset electrolytic cell temperature prediction model under different production conditions, ensuring the reliability of the prediction results; Provide reliable data support: Accurate and reliable prediction of electrolytic cell temperature provides a solid data foundation for the subsequent evaluation of the electrolytic aluminum regulation capacity, which helps to more scientifically evaluate the regulation capacity of electrolytic aluminum, enable enterprises to better participate in grid regulation, optimize production plans, reduce production costs, and increase additional profits, while also providing strong support for the stable operation of the grid and the construction of new power systems.

[0109] In a feasible implementation, the loss function of the initial LSTM model in the above embodiment during training is:

[0110]

[0111] Wherein, L is the loss function value, α is the first preset proportional coefficient, MSE is the root mean square error between the predicted electrolytic cell temperature and the historical electrolytic cell temperature obtained during the training process, β is the second preset proportional coefficient, L phy is the physical loss, T is the total time, Q j,fr,t is the Joule heat source at the tth moment in the predicted Joule heat source obtained during the training process, Q j,t is the Joule heat source at the tth moment in the historical Joule heat source.

[0112] In the embodiment of the present application, the root mean square error between the predicted electrolytic cell temperature and the historical electrolytic cell temperature and the physical loss are comprehensively considered through the loss function, which can optimize the model training process, improve the prediction accuracy and physical rationality of the preset electrolytic cell temperature prediction model, and provide more accurate data support for the evaluation of the electrolytic aluminum regulation capability.

[0113] It is understandable that the prediction accuracy is improved: the loss function comprehensively considers the root mean square error (RMSE) between the predicted electrolytic cell temperature and the historical electrolytic cell temperature and the physical loss. The root mean square error directly reflects the degree of deviation between the predicted value and the true value. By minimizing the root mean square error, the model can learn a more accurate mapping relationship between the production condition parameters and the electrolytic cell temperature, thereby significantly improving the accuracy of predicting the electrolytic cell temperature; enhance physical rationality: introduce physical loss as part of the loss function to ensure that the model prediction results are not only numerically close to the true value, but also conform to the physical laws of the electrolytic aluminum production process. The physical loss is calculated based on the difference between the predicted Joule heat source and the historical Joule heat source. The Joule heat source is an important physical quantity in the electrolytic aluminum production process and is closely related to the electrolytic cell temperature. By minimizing physical losses, the model can better capture the physical essence of the production process and enhance the physical rationality of the prediction results; improve the generalization ability of the model: the loss function design that comprehensively considers the root mean square error and physical loss makes the model focus not only on numerical fitting during training, but also on the compliance with physical laws. This training method helps the model learn more generalizable feature representations, avoids the occurrence of overfitting, and enables the model to maintain good prediction performance under different production conditions; support precise regulation capability evaluation: accurate and reliable prediction of electrolytic cell temperature provides a solid data foundation for the subsequent evaluation of electrolytic aluminum regulation capability. Based on high-precision prediction results, it can more scientifically evaluate the regulation capability of electrolytic aluminum under different production conditions, provide strong decision-making support for enterprises to participate in grid regulation, optimize production plans, reduce production costs, increase additional profits, and also provide strong guarantees for the stable operation of the grid and the construction of new power systems.

[0114] In a feasible implementation method, in the above embodiment, multiple target production condition parameters, as well as historical electrolytic cell temperature and historical Joule heat source are input into the initial LSTM model for training to obtain a preset electrolytic cell temperature prediction model, including: obtaining historical electrolyte mass, historical electrolyte specific heat capacity, historical heat loss between historical electrolyte heat loss and historical flue gas heat loss, and historical absorbed heat between heat absorbed by historical electrochemical reactions and heat absorbed by historical material heating; historical electrolyte mass, historical electrolyte specific heat capacity, historical heat loss, historical absorbed heat, multiple target production condition parameters, as well as historical electrolytic cell temperature and historical Joule heat source are input into the initial LSTM model for training to obtain a preset electrolytic cell temperature prediction model.

[0115] In the embodiments of the present application, by introducing key physical parameters such as historical electrolyte mass, historical electrolyte specific heat capacity, historical heat loss, and historical absorbed heat, the integrity of the model training data can be enhanced, the prediction accuracy and reliability of the preset electrolytic cell temperature prediction model can be improved, and a more solid data foundation can be provided for the evaluation of the electrolytic aluminum regulation capability.

[0116] It is understandable that the prediction accuracy is improved: by introducing key physical parameters such as historical electrolyte mass, historical electrolyte specific heat capacity, historical heat loss and historical absorbed heat, the model can more comprehensively capture the physical changes in the electrolytic aluminum production process. These parameters are closely related to the electrolytic cell temperature. Incorporating them into the model training data can enable the model to learn more accurate temperature prediction relationships, thereby significantly improving the accuracy of predicting the electrolytic cell temperature; enhancing model reliability: the added physical parameters provide the model with richer information, which helps the model better understand the complex relationships in the production process, which enables the model to maintain good prediction performance under different production conditions, reduces prediction deviations caused by insufficient or one-sided data, and enhances the reliability of the model. and stability; optimize the model training process: incorporating more relevant parameters into the training data helps the model converge to the optimal solution more quickly, thereby improving training efficiency. At the same time, these parameters also provide more constraints for the model, enabling the model to fit the real data more accurately during training, further optimizing the model training process; support precise regulation capability evaluation: based on the high-precision prediction of the electrolytic cell temperature, the regulation capability of electrolytic aluminum under different production conditions can be evaluated more scientifically, which provides strong decision-making support for enterprises to participate in grid regulation, helps enterprises optimize production plans, reduce production costs, and increase additional revenue. At the same time, it also provides strong guarantees for the stable operation of the grid and the construction of new power systems, and promotes the efficient use of energy and sustainable development.

[0117] In a feasible implementation, the method in the above embodiment further includes:

[0118] Using formula m t C p,t ΔT t =Q j,fr,t -Q loss,t -Q r,t Determine the Joule heat source at time t in the predicted Joule heat source;

[0119] Using the formula Q j,t =(E t -E r,t )I t Δt determines the Joule heat source at time t in the historical Joule heat source;

[0120] Among them, m t is the electrolyte mass at the tth moment in the historical electrolyte mass, C p,t is the electrolyte specific heat capacity at the tth moment, ΔT t To predict the change in the electrolytic cell temperature between the electrolytic cell temperature at time t-1 and the electrolytic cell temperature at time t, Q j,fr,tTo predict the Joule heat source at the tth moment, Q loss,t is the heat dissipation loss at the tth moment in the historical heat dissipation loss, Q r,t is the heat absorbed at the tth moment in the historical heat absorption, Q j,t is the Joule heat source at the tth moment in the historical Joule heat source, E t is the electrolytic cell voltage at time t in the historical electrolytic cell voltage, E r,t is the electrolytic cell back electromotive force at the tth moment in the history of electrolytic cell back electromotive force, I t is the electrolytic cell current at the tth moment in the historical electrolytic cell current, and Δt is the change between the t-1th moment and the tth moment.

[0121] In the embodiments of the present application, by enhancing the accuracy of Joule heat source calculation, the scientificity and reliability of electrolytic cell temperature prediction and regulation capacity evaluation are improved, providing a more accurate decision-making basis for electrolytic aluminum enterprises to participate in grid regulation.

[0122] It is understandable that the accuracy of Joule heat source calculation is enhanced: the Joule heat source at the tth moment in the predicted Joule heat source and the historical Joule heat source are calculated separately through clear formulas, which fully considers key factors such as electrolyte quality, specific heat capacity, electrolytic cell temperature change, heat dissipation loss, absorbed heat, electrolytic cell voltage, back electromotive force and current, and can more accurately characterize the Joule heat source changes in the electrolytic aluminum production process, avoid the errors caused by traditional estimation methods, and provide an accurate data basis for subsequent electrolytic cell temperature prediction and regulation capacity evaluation; improve the scientific nature of electrolytic cell temperature prediction: accurate Joule heat source Calculation is the key link in the prediction of electrolytic cell temperature. Based on accurate Joule heat source data, the preset electrolytic cell temperature prediction model can more accurately learn the mapping relationship between production condition parameters and electrolytic cell temperature, thereby improving the scientificity and accuracy of temperature prediction. This helps enterprises to more accurately grasp the changes in electrolytic cell temperature and provide strong support for the optimization and control of the production process; Enhance the reliability of regulation capacity evaluation: The regulation capacity evaluation of electrolytic aluminum depends on accurate electrolytic cell temperature prediction and Joule heat source calculation. By optimizing the calculation method of Joule heat source, it is possible to more scientifically evaluate the regulation capacity of electrolytic aluminum in different The regulation capability under the same production conditions ensures that the assessment results are closer to actual production conditions, which provides a reliable decision-making basis for enterprises participating in grid regulation, helps them formulate more reasonable regulation strategies, and improves the feasibility and effectiveness of regulation. Supporting precise decision-making: Accurate Joule heat source calculation and electrolytic cell temperature prediction results enable enterprises to more clearly understand their energy consumption and regulation potential under different production conditions. This helps enterprises make more informed decisions when participating in grid regulation, such as when to increase or decrease electricity load to respond to grid demand, achieve energy optimization, improve energy utilization efficiency, while reducing production costs and increasing additional profits. Promoting the construction of new power systems: By improving the accuracy and reliability of electrolytic aluminum regulation capability assessment, this method helps promote the development and utilization of load-side flexible regulation resources during the construction of new power systems. As high-energy-consuming industrial users, the effective utilization of the regulation capability of electrolytic aluminum enterprises is of great significance to improving the grid's ability to absorb new energy and enhancing the grid's stability and reliability. The application of this method will promote in-depth interaction between electrolytic aluminum enterprises and the grid, and jointly promote the sustainable development of new power systems.

[0123] In a second aspect, the present application provides an electrolytic aluminum regulation capability evaluation device.

[0124] See also Figure 2 , is a schematic diagram of an electrolytic aluminum adjustment capability evaluation device according to an embodiment of the present application, wherein the device 210 includes:

[0125] An acquisition module 211 is used to obtain the electrolytic cell power, various production condition parameters and various operating condition parameters of electrolytic aluminum, as well as various operating condition constraints;

[0126] A first determination module 212 is used to determine the electrolytic cell temperature based on all production condition parameters;

[0127] A second determination module 213 is configured to determine a comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints;

[0128] The evaluation module 214 is used to evaluate the regulation capability of electrolytic aluminum based on the comprehensive electrolytic cell temperature constraint, electrolytic cell temperature and electrolytic cell power.

[0129] In the embodiment of the present application, the relevant contents of the acquisition module 211, the first determination module 212, the second determination module 213 and the evaluation module 214 can be found in Figure 1 The contents of the illustrated embodiments are not described in detail here.

[0130] It should be noted that the device 210 of the present application also includes some other modules. It can be understood that the method of the present application and the device 210 have a one-to-one correspondence. Therefore, the other modules of the device 210 of the present application are the contents corresponding to the method of the present application in the above-mentioned embodiment.

[0131] In the embodiment of the present application, the electrolytic cell temperature is solved by solving the production condition parameters, which fundamentally avoids the problem of difficulty in obtaining the electrolytic cell temperature in the actual production process. At the same time, in the process of regulating capacity evaluation, the operating condition constraints of the actual production process are fully considered to ensure that the evaluation results are more in line with the actual production situation. It can provide a scientific and accurate device reference for the regulation performance analysis and regulation capacity evaluation of electrolytic aluminum in the process of grid interaction, and can effectively support the development and utilization of load-side flexible regulation resources in the construction of new power systems, improve the grid's ability to absorb new energy, enhance the stability and reliability of the grid, and promote the sustainable development of new power systems.

[0132] In addition, in addition to the advantages mentioned above, the electrolytic aluminum regulation capacity evaluation device proposed in this application also has the following advantages: In terms of economic benefits, it reduces production costs: by accurately evaluating the electrolytic aluminum regulation capacity, enterprises can more scientifically arrange production plans and the timing of participating in grid regulation, avoid unstable production processes due to blind adjustment, and reduce equipment loss, raw material waste, and increased defective rates caused by factors such as temperature fluctuations and out-of-control operating conditions, thereby reducing overall production costs; increase additional benefits: in the context of new power system construction, enterprises can obtain corresponding economic compensation by participating in grid regulation. The device can help enterprises accurately grasp the timing and magnitude of participating in regulation, and participate in grid regulation as much as possible on the premise of ensuring production stability, obtain more additional benefits, and improve the economic benefits of the enterprise. In terms of technological innovation and development, it promotes technological progress in the industry: the device provides the electrolytic aluminum industry with a scientific and complete quantitative evaluation system for regulation capabilities, filling the gap in existing technologies in the evaluation of electrolytic aluminum regulation capabilities. Its successful application will guide other companies in the industry to pay attention to and study related technologies for electrolytic aluminum to participate in grid regulation, and promote innovation and development in the field of power regulation technology for the entire electrolytic aluminum industry; promote cross-industry technology integration: the device integrates electrolytic aluminum production process knowledge, power system regulation requirements and advanced data analysis technology. During the implementation process, technologies from different fields collide and merge with each other, providing an opportunity for cross-industry technical exchanges and cooperation, helping to cultivate compound technical talents and promote the coordinated progress of related industry technologies. At the energy and environmental level, it optimizes energy allocation: the proportion of new energy in the new power system is constantly increasing, and its volatility brings challenges to the stable operation of the power grid. This device enables electrolytic aluminum enterprises to respond more flexibly to the needs of the power grid according to their own adjustment capabilities, increase electricity load during the peak of new energy power generation, and reduce electricity consumption during the low period, so as to achieve optimal allocation of energy in different time periods and improve energy utilization efficiency; it helps achieve carbon emission reduction goals: by improving the power grid's ability to absorb new energy, reducing dependence on traditional fossil energy power generation, and indirectly reducing carbon emissions. At the same time, electrolytic aluminum enterprises optimize the production adjustment process, reduce energy waste and additional carbon emissions caused by unstable production, and make positive contributions to the realization of carbon emission reduction goals for the industry and even the entire society.In terms of power grid security and stability, the peak-shaving capacity of the power grid is enhanced: as a high-energy-consuming industrial user, the regulation capacity of electrolytic aluminum plays an important role in the peak-shaving of the power grid. The device can accurately evaluate the regulation capacity of electrolytic aluminum under different operating conditions, so that the power grid dispatching department can more reasonably arrange the peak-shaving tasks of electrolytic aluminum enterprises, give full play to their load regulation potential, effectively alleviate the peak-valley difference problem of the power grid, and enhance the peak-shaving capacity of the power grid; improve the anti-interference ability of the power grid: when the power grid is disturbed by new energy fluctuations, sudden failures, etc., electrolytic aluminum enterprises can respond to the power grid dispatching instructions quickly and accurately with the regulation capacity determined by this evaluation device, adjust their own electricity load, provide the necessary frequency and voltage support for the power grid, enhance the power grid's ability to respond to emergencies, and ensure the safe and stable operation of the power grid. At the policy and market levels, it complies with policy guidance: As the country attaches importance to the construction of new power systems and energy transformation, a series of policies have been introduced to encourage high-energy-consuming enterprises to participate in grid regulation. The device provides technical support for enterprises to respond to policy requirements, helping enterprises to better adapt to the policy environment and achieve sustainable development under policy guidance; promoting the healthy development of the power market: in the power market environment, accurate regulation capacity assessment is the basis of market transactions. The device can provide a scientific basis for electrolytic aluminum enterprises to participate in power market regulation transactions, making market transactions more fair, transparent and efficient, promoting the optimal allocation of power market resources, and promoting the healthy development of the power market.

[0133] In a third aspect, the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes a method for evaluating the electrolytic aluminum regulation capability in the above-mentioned method embodiment.

[0134] In a fourth aspect, the present application further provides a computer device including a memory and a processor, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes a method for evaluating the adjustment capacity of electrolytic aluminum in the above-mentioned method embodiment.

[0135] Figure 3 The internal structure diagram of the computer device in some embodiments is shown. The computer device can be a terminal, a server, or a gateway. Figure 3 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus.

[0136] The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. It will be understood by those skilled in the art that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0137] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0138] Among them, any reference to memory, storage, database or other media used in the various embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchl ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0139] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for evaluating the adjustment capability of electrolytic aluminum, characterized in that: The method comprises: Obtain the electrolytic cell power, various production condition parameters and various operating condition parameters of electrolytic aluminum, as well as various operating condition constraints; Determine the electrolytic cell temperature based on all production condition parameters; Determining a comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints; The regulation capability of the electrolytic aluminum is evaluated based on the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature and the electrolytic cell power.

2. The method according to claim 1, characterized in that Determining the comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints includes: Using the grey correlation method, the correlation between each operating condition parameter and the electrolytic cell temperature is determined; Determining the electrolytic cell temperature constraint corresponding to each operating condition constraint based on the correlation between each operating condition parameter and the electrolytic cell temperature and the corresponding operating condition constraint; The comprehensive electrolytic cell temperature constraint is determined based on all electrolytic cell temperature constraints.

3. The method according to claim 1, characterized in that Determining the comprehensive electrolytic cell temperature constraint based on the electrolytic cell temperature, all operating condition parameters, and all operating condition constraints includes: Within a preset electrolytic cell power range, the electrolytic cell power of the electrolytic aluminum is modified to the lower limit of the preset electrolytic cell power range, and the electrolytic cell power of the electrolytic aluminum is adjusted incrementally in sequence according to a preset step size, or the electrolytic cell power of the electrolytic aluminum is modified to the upper limit of the preset electrolytic cell power range, and the electrolytic cell power of the electrolytic aluminum is adjusted incrementally in sequence according to the preset step size, and after each adjustment, the step of obtaining the electrolytic cell power of the electrolytic aluminum, various production condition parameters, and various operating condition parameters is returned to be executed to obtain the various operating condition parameters and the electrolytic cell temperature obtained in each adjustment; When all the operating condition parameters obtained by each adjustment meet the corresponding operating condition constraints, the electrolytic cell temperature obtained by the corresponding adjustment is used as the target electrolytic cell temperature; The comprehensive electrolyzer temperature constraint is determined based on all target electrolyzer temperatures.

4. The method according to claim 1, wherein The step of evaluating the regulation capability of the electrolytic aluminum according to the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature, and the electrolytic cell power includes: Determining, based on the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature, and the electrolytic cell power, an upper limit and a lower limit of the electrolytic cell power, as well as a first adjustment duration for the electrolytic aluminum electrolytic cell power to be continuously adjusted to the upper limit of the electrolytic cell power and a second adjustment duration for the electrolytic aluminum electrolytic cell power to be continuously adjusted to the lower limit of the electrolytic cell power; The adjustment capability of the electrolytic aluminum is evaluated based on the electrolytic cell power upper limit, the electrolytic cell power lower limit, the first adjustment time and the second adjustment time.

5. The method according to claim 4, characterized in that The determining, based on the comprehensive electrolytic cell temperature constraint, the electrolytic cell temperature, and the electrolytic cell power, an upper limit and a lower limit of the electrolytic cell power, as well as a first adjustment duration that can be maintained after the electrolytic aluminum electrolytic cell power is adjusted to the upper limit of the electrolytic cell power and a second adjustment duration that can be maintained after the electrolytic aluminum electrolytic cell power is adjusted to the lower limit of the electrolytic cell power, includes: Using the formula Determining the electrolytic cell power upper limit, the electrolytic cell power lower limit, the first adjustment time, and the second adjustment time; Among them, P max is the upper limit of the electrolytic cell power, P is the electrolytic cell power, Δt1 is the first adjustment time, m is the electrolyte mass, C p is the specific heat capacity of the electrolyte, T max is the upper limit of the comprehensive electrolytic cell temperature constraint, T fr is the electrolytic cell temperature, Q loss Q is the sum of the electrolyte heat loss and the flue gas heat loss, r It is the sum of the heat absorbed by the electrochemical reaction and the heat absorbed by the material heating, P min is the lower limit of the electrolytic cell power, Δt2 is the second adjustment time, T min is the lower limit of the comprehensive electrolytic cell temperature constraint, and Δt is the adjustment time.

6. The method according to claim 1, characterized in that The electrolytic cell temperature is determined based on all production condition parameters, including: Input all production condition parameters into the preset electrolytic cell temperature prediction model to obtain the predicted electrolytic cell temperature; The predicted electrolytic cell temperature is used as the electrolytic cell temperature.

7. The method according to claim 6, characterized in that Before inputting all production condition parameters into the preset electrolytic cell temperature prediction model to obtain the predicted electrolytic cell temperature, the method further includes: Obtaining various historical production condition parameters of the electrolytic aluminum, as well as historical electrolytic cell temperatures and historical Joule heat sources; Each historical production condition parameter is standardized to obtain multiple standard production condition parameters; Using the grey correlation method, the correlation between each standard production condition parameter and the historical electrolytic cell temperature is determined; According to the comparison result between the correlation degree between each standard production condition parameter and the electrolytic cell temperature and the correlation degree threshold, the standard production condition parameters are eliminated to obtain multiple target production condition parameters; Multiple target production condition parameters, as well as the historical electrolytic cell temperature and the historical Joule heat source are input into the initial LSTM model for training to obtain the preset electrolytic cell temperature prediction model.

8. The method according to claim 7, characterized in that The loss function of the initial LSTM model during training is: Wherein, L is the loss function value, α is the first preset proportional coefficient, MSE is the root mean square error between the predicted electrolytic cell temperature obtained during the training process and the historical electrolytic cell temperature, β is the second preset proportional coefficient, L phy is the physical loss, T is the total time, Q j,fr,t is the Joule heat source at the tth moment in the predicted Joule heat source obtained during the training process, Q j,t is the Joule heat source at the tth moment in the historical Joule heat source.

9. The method according to claim 8, characterized in that The multiple target production condition parameters, the historical electrolytic cell temperature, and the historical Joule heat source are input into the initial LSTM model for training to obtain the preset electrolytic cell temperature prediction model, including: Obtain historical electrolyte mass, historical electrolyte specific heat capacity, historical heat loss between historical electrolyte heat loss and historical flue gas heat loss, and historical absorbed heat between historical electrochemical reaction heat absorption and historical material heating heat absorption; The historical electrolyte mass, the historical electrolyte specific heat capacity, the historical heat loss, the historical absorbed heat, multiple target production condition parameters, the historical electrolytic cell temperature and the historical Joule heat source are input into the initial LSTM model for training to obtain the preset electrolytic cell temperature prediction model.

10. The method according to claim 9, characterized in that The method further comprises: Using formula m t C p,t ΔT t =Q j,fr,t -Q loss,t -Q r,t determining the Joule heat source at time t in the predicted Joule heat source; Using the formula Q j,t =(E t -E r,t )I t Δt determines the Joule heat source at the t-th moment in the historical Joule heat source; Among them, m t is the electrolyte mass at the tth moment in the historical electrolyte mass, C p,t is the electrolyte specific heat capacity at the tth moment in the electrolyte specific heat capacity, ΔT t Q is the change between the electrolytic cell temperature at the time t-1 and the electrolytic cell temperature at the time t in the predicted electrolytic cell temperature. j,fr,t is the Joule heat source at the tth moment in the predicted Joule heat source, Q loss,t is the heat loss at the tth moment in the historical heat loss, Q r,t is the heat absorbed at the tth moment in the historical heat absorption, Q j,t is the Joule heat source at the tth moment in the historical Joule heat source, E t is the electrolytic cell voltage at time t in the historical electrolytic cell voltage, E r,t is the electrolytic cell back electromotive force at the tth moment in the history of electrolytic cell back electromotive force, I t is the electrolytic cell current at the tth moment in the historical electrolytic cell current, and Δt is the change between the t-1th moment and the tth moment.

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