Carbon emission reduction method and device, storage medium and electronic device

Abstract

The present application discloses a method and device for reducing carbon emissions, a storage medium and an electronic device, the method comprising: determining a target output power of renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power; adjusting the working states of different sub-slots in an electrolyzer array according to the target output power, and determining a target amount of hydrogen generated by the electrolyzer array; controlling the target amount of hydrogen to react chemically with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

Description

Technical Field

[0001] The present application relates to the field of energy technology, and in particular to a method and device for reducing carbon emissions, a storage medium and an electronic device. Background Art

[0002] With the growing global demand for clean energy and the pursuit of carbon reduction targets, water electrolysis and methanol synthesis have attracted widespread attention as important green technologies. Water electrolysis hydrogen production technology mainly relies on alkaline electrolyzers, proton exchange membrane (PEM) electrolyzers and solid oxide electrolysis cells (SOEC). Although these technologies can effectively produce hydrogen when the power input is sufficient, in practical applications, they usually operate independently and do not form effective synergy with the downstream methanol synthesis process. In addition, due to the significant volatility of renewable energy such as photovoltaic and wind power generation, the stability and energy efficiency of the hydrogen production system are affected. Methanol synthesis technology, as a key link in carbon capture and utilization (CCU), can convert carbon dioxide in industrial emissions into high-value-added chemicals and realize the recycling of carbon resources. However, most methanol synthesis systems rely on external hydrogen supply, and the source of hydrogen often comes from fossil energy, which limits its potential in reducing carbon emissions. At the same time, the separation of hydrogen production and methanol synthesis means that the efficiency of energy and resource utilization has not reached the optimal level, and there is room for further improvement. Although the existing power prediction system is widely used in the fields of smart grids and energy storage management, its integrated application in the fields of hydrogen energy and methanol synthesis is still in its infancy. Most systems lack deep integration of hydrogen production and methanol synthesis processes and fail to achieve dynamic process control based on prediction, resulting in an imbalance in energy supply and demand and reduced energy efficiency.

[0003] With regard to the problem in related technologies that the dynamic balance of energy utilization cannot be guaranteed when emission reduction is achieved through the reaction between hydrogen and carbon dioxide, no effective solution has yet been proposed.

[0004] Therefore, it is necessary to improve the existing related technologies to overcome the above-mentioned defects in the related technologies and meet the needs of current actual production. Summary of the invention

[0005] The embodiments of the present application provide a method and device for reducing carbon emissions, a storage medium and an electronic device, so as to at least solve the problem in the related art that the dynamic balance of energy utilization cannot be guaranteed when reducing emissions by the reaction between hydrogen and carbon dioxide.

[0006] According to one aspect of an embodiment of the present application, a method for reducing carbon emissions is provided, including: determining a target output power of renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the power prediction model input is weather data, historical data, and real-time monitoring data, and the power prediction model output is the predicted output power; adjusting the working states of different sub-cells in an electrolyzer array according to the target output power, and determining a target amount of hydrogen generated by the electrolyzer array; controlling the target amount of hydrogen to react chemically with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

[0007] In an exemplary embodiment, before controlling a target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere, the method further includes: obtaining the captured amount of carbon dioxide to be reacted; determining the hydrogen supply amount to react with the captured amount from the target amount; calculating the difference between the target amount and the hydrogen supply amount, and calling the difference amount of hydrogen to an energy storage area, wherein the energy storage area is used for liquefied storage of hydrogen.

[0008] In an exemplary embodiment, after determining the target output power of renewable energy through a pre-trained power prediction model, the method further includes: when it is determined that the target output power is less than the electrolysis demand power of the electrolyzer array, sending a first prompt message to the target object, wherein the first prompt message is used to instruct the target object to send an instruction to call for storage of hydrogen to the energy storage area; when it is determined that the target output power is greater than the electrolysis demand power of the electrolyzer array, sending a second prompt message to the target object, wherein the second prompt message is used to instruct the target object to expand the number of sub-electrolyzers of the electrolyzer array.

[0009] In an exemplary embodiment, when it is determined that the target output power is greater than the electrolysis required power of the electrolytic cell array, after sending the second prompt information to the target object, the method further includes: determining an adjustment instruction to be issued to the electrolytic cell array; wherein the adjustment instruction is used to increase the electrolysis intensity of the electrolytic cell array; instructing the electrolytic cell array to increase the current density in each electrolytic cell according to the adjustment instruction; receiving the adjustment result of the current density, and changing the target process conditions of the chemical reaction according to the adjustment result.

[0010] In an exemplary embodiment, before controlling a target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere, the method further includes: determining target process conditions for the chemical reaction, and parameter information of the target process conditions; and when the target process conditions are different from historical process conditions of a previous chemical reaction, using the parameter information to adjust equipment parameters for performing the chemical reaction.

[0011] In an exemplary embodiment, after controlling a target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction, the method further includes: recording the correspondence between the target product and the target output power; and fitting multiple correspondences of renewable energy within a target time period to obtain a target emission reduction curve.

[0012] According to another aspect of an embodiment of the present application, a carbon emission reduction device is also provided, including: a first determination module, used to determine the target output power of renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the power prediction model input is weather data, historical data, and real-time monitoring data, and the power prediction model output is the predicted output power; an adjustment module, used to adjust the working status of different sub-tanks in the electrolyzer array according to the target output power, and determine the target amount of hydrogen generated by the electrolyzer array; a control module, used to control the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

[0013] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned carbon emission reduction method when running.

[0014] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the carbon emission reduction method through the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method for reducing carbon emissions is implemented.

[0016] Through this application, the target output power of renewable energy is determined by a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power; the working state of different sub-slots in the electrolyzer array is adjusted according to the target output power, and the target number of hydrogen generated by the electrolyzer array is determined; the target number of hydrogen is controlled to react chemically with carbon dioxide captured from the atmosphere to generate a target product for emission reduction. The above technical solution is adopted to solve the problem that the dynamic balance of energy utilization cannot be guaranteed when emission reduction is performed by the reaction between hydrogen and carbon dioxide. Furthermore, the power of renewable energy is predicted by a pre-trained model, the hydrogen production of the electrolyzer is dynamically adjusted, and it reacts with the captured CO2 to generate emission reduction products, thereby realizing energy optimization and carbon utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for reducing carbon emissions according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for reducing carbon emissions according to an embodiment of the present application;

[0021] Figure 3 It is a logical schematic diagram of a multi-energy hydrogen and methanol production coordinated optimization system for carbon emission reduction according to an embodiment of the present application;

[0022] Figure 4 This is a flow chart of a multi-energy hydrogen and methanol production coordinated optimization method for carbon emission reduction according to an embodiment of the present application;

[0023] Figure 5 It is a structural block diagram of a carbon emission reduction device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal, a mobile terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal according to a method for reducing carbon emissions in an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or N ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, CPU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the adjustment method of the monitoring network point in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or N magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for reducing carbon emissions is provided. Figure 2 is a flow chart of a method for reducing carbon emissions according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S206:

[0030] Step S202: determining the target output power of the renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to the renewable energy, historical data corresponding to the renewable energy, and real-time monitoring data corresponding to the renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power;

[0031] Optionally, a pre-trained power model is a model developed by machine learning or deep learning technology, which is used to predict the output power of renewable energy (such as solar energy, wind energy) power generation systems. The model is trained on weather data, historical power generation data, and real-time monitoring data. Among them, weather data includes light intensity, wind speed, temperature, humidity, etc. Historical power generation data is the actual power generation record of the power generation system in the past period of time. Real-time monitoring data includes information such as current power generation and equipment operation status. Collect the above data and extract useful features, such as the relationship between light intensity and power generation, the relationship between wind speed and power generation, and historical power generation trends. Select a suitable machine learning or deep learning algorithm, such as Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit (GRU), etc., and use the prepared data set for training to let the model learn the relationship between power generation and weather conditions. The trained model is deployed to the actual system, the latest weather forecast and monitoring data are input in real time, and the predicted output power of the power generation system is output, providing data support for the scheduling and optimization of the power generation system.

[0032] Step S204: adjusting the working states of different sub-cells in the electrolyzer array according to the target output power, and determining the target amount of hydrogen generated by the electrolyzer array;

[0033] Optionally, a large electrolyzer array consisting of 10 sub-cells is used to electrolyze water to produce hydrogen. If the pre-trained power prediction model predicts that the output power of the photovoltaic power generation system will fluctuate significantly in the next 24 hours, with the power generation reaching a peak from 10 am to 12 noon, and the output power during this period is expected to be 6000kW. During the predicted peak power generation period, increase the current density of all electrolyzer sub-cells from the current 100mA / cm 2 Increase to 150mA / cm 2 , to increase the rate of hydrogen generation. At the same time, the sub-tanks in the electrolyzer array are adjusted to ensure that all sub-tanks operate simultaneously during this period to achieve maximum hydrogen production. Based on the adjusted electrolyzer operating parameters and the expected photovoltaic power output, the amount of hydrogen that the electrolyzer array is expected to generate during this period is calculated. Through this embodiment, dynamically adjusting the working state of the electrolyzer array can achieve efficient conversion and utilization of energy, while ensuring the stability and flexibility of hydrogen production, which is of great significance to achieving sustainable development goals.

[0034] Step S206: Control the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

[0035] In the above steps, the target output power of renewable energy is determined by a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power; the working state of different sub-slots in the electrolyzer array is adjusted according to the target output power, and the target amount of hydrogen generated by the electrolyzer array is determined; the target amount of hydrogen is controlled to react chemically with carbon dioxide captured from the atmosphere to generate a target product for emission reduction. The above technical solution solves the problem that the dynamic balance of energy utilization cannot be guaranteed when emission reduction is performed by the reaction between hydrogen and carbon dioxide. Furthermore, the power of renewable energy is predicted by a pre-trained model, the hydrogen production of the electrolyzer is dynamically adjusted, and the hydrogen is reacted with the captured CO2 to generate emission reduction products, thereby achieving energy optimization and carbon utilization.

[0036] In an exemplary embodiment, before controlling a target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere, the method further includes: obtaining the captured amount of carbon dioxide to be reacted; determining the hydrogen supply amount to react with the captured amount from the target amount; calculating the difference between the target amount and the hydrogen supply amount, and calling the difference amount of hydrogen to an energy storage area, wherein the energy storage area is used for liquefied storage of hydrogen.

[0037] Optionally, in the above embodiment, if the captured amount of CO2 to be reacted is 1000kg, the hydrogen supply required to react with the captured amount is determined to be 500kg from the target demand hydrogen quantity. The equipment for producing hydrogen by electrolysis of water can generate 600kg of hydrogen, which exceeds the hydrogen supply required for the reaction by 100kg, that is, the difference between the hydrogen supply and the target demand is 100kg. The 100kg difference of hydrogen is converted into liquid hydrogen by a hydrogen liquefaction device and stored in an energy storage area. Through this embodiment, the CO2 capture amount and the hydrogen supply are accurately matched, which can not only improve the reaction efficiency and reduce the waste of resources, but also provide guarantee for future energy demand through the liquefied storage of hydrogen, further enhance the flexibility and economy of the system, and have a positive impact on the environment.

[0038] In an exemplary embodiment, after determining the target output power of renewable energy through a pre-trained power prediction model, the method further includes: when it is determined that the target output power is less than the electrolysis demand power of the electrolyzer array, sending a first prompt message to the target object, wherein the first prompt message is used to instruct the target object to send an instruction to call for storage of hydrogen to the energy storage area; when it is determined that the target output power is greater than the electrolysis demand power of the electrolyzer array, sending a second prompt message to the target object, wherein the second prompt message is used to instruct the target object to expand the number of sub-electrolyzers of the electrolyzer array.

[0039] Optionally, when the output power of wind power generation is lower than the electrolysis power requirement of the electrolyzer array, for example, wind power generation can only provide 2000kW of electricity, while the electrolysis power requirement is 2500kW. At this time, the system sends a first prompt message to the operation center (equivalent to the above-mentioned target object), instructing the operation center to call the stored hydrogen from the energy storage area to make up for the hydrogen generation demand when the power is insufficient. After the operation center receives the message, the instruction to call the stored hydrogen is sent to the energy storage area. When the output power of wind power generation significantly exceeds the electrolysis power requirement of the electrolyzer array, for example, wind power generation can provide 3000kW of electricity, while the electrolysis power requirement is 2000kW. At this time, the system operation center sends a second prompt message, instructing the operation center to expand the number of sub-electrolyzers of the electrolyzer array to make full use of the additional power resources and generate more hydrogen. The operation center expands the electrolyzer array according to the prompt message. For example, start a set of spare electrolyzers to increase the total operating power of the electrolyzer array to 2500kW, thereby making full use of the additional 500kW of electricity and increasing the production of hydrogen. The above embodiments ensure the stability of hydrogen production and the controllability of costs by intelligently calling for hydrogen storage or expanding the number of electrolysis tanks, significantly improve economic and environmental benefits, and promote the sustainable development of green chemistry.

[0040] In an exemplary embodiment, when it is determined that the target output power is greater than the electrolysis required power of the electrolytic cell array, after sending the second prompt information to the target object, the method further includes: determining an adjustment instruction to be issued to the electrolytic cell array; wherein the adjustment instruction is used to increase the electrolysis intensity of the electrolytic cell array; instructing the electrolytic cell array to increase the current density in each electrolytic cell according to the adjustment instruction; receiving the adjustment result of the current density, and changing the target process conditions of the chemical reaction according to the adjustment result.

[0041] Optionally, during a period of sufficient power supply, the intelligent control system detects that the wind farm power output is 4000kW, while the conventional demand of the electrolyzer array is 3000kW. After evaluation, the system issues adjustment instructions to increase the electrolysis intensity of the electrolyzer array to make full use of the excess power. For example, the current density in each electrolyzer is increased from the standard 150mA / cm 2 Increase to 200mA / cm 2 , in order to increase the electrolysis rate and increase the hydrogen production. Based on the feedback information after the current density increases, the intelligent control system further adjusts the target process conditions. For example, considering that the increase in current density may affect the reaction heat, the system decides to increase the cooling cycle rate of the electrolyzer by 15% to ensure that the electrolyzer does not overheat, while optimizing the catalyst flow rate during the reaction to improve the purity and yield of hydrogen. The above technical solutions can not only improve the efficiency of energy utilization, but also ensure the flexibility of production and the optimization of product quality. In practical applications, this dynamic adjustment mechanism can effectively help chemical companies cope with the intermittent and volatile nature of renewable energy, achieve stable, efficient and economical hydrogen production, and contribute to achieving carbon emission reduction goals.

[0042] In an exemplary embodiment, before controlling a target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere, the method further includes: determining target process conditions for the chemical reaction, and parameter information of the target process conditions; and when the target process conditions are different from historical process conditions of a previous chemical reaction, using the parameter information to adjust equipment parameters for performing the chemical reaction.

[0043] Optionally, in the above embodiment, first, the target process conditions of the chemical reaction are determined, including: reaction temperature of 250°C, reaction pressure of 50bar, and catalyst flow rate of 1.2mL / min. The system calls the historical process conditions of the previous chemical reaction for comparison. If the historical process conditions are: reaction temperature of 220°C, reaction pressure of 40bar, and catalyst flow rate of 1.0mL / min. It can be seen that the target process conditions are different from the historical process conditions. At this time, the system will send an adjustment instruction to the equipment control unit based on parameter information such as reaction temperature, reaction pressure, and catalyst flow rate, and increase the reaction temperature from 220°C to 250°C; increase the reaction pressure from 40bar to 50bar; and increase the catalyst flow rate from 1.0mL / min to 1.2mL / min. After the equipment parameter adjustment is completed, the system will continue to monitor the actual operating status of the electrolyzer, including real-time temperature, pressure, current density, and hydrogen production. If any parameter deviates from the target or the reaction efficiency does not meet expectations, the system will automatically make fine adjustments until the optimal process conditions are reached. Through this series of dynamic adjustments, the system successfully utilized the peak period of renewable energy to produce hydrogen efficiently and at low cost, and further combined hydrogen with captured carbon dioxide to synthesize methanol, which not only improved production efficiency and reduced production costs, but also reduced greenhouse gas emissions, demonstrating the advantages of intelligent control and dynamic optimization in modern chemical production.

[0044] In an exemplary embodiment, after controlling a target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction, the method further includes: recording the correspondence between the target product and the target output power; and fitting multiple correspondences of renewable energy within a target time period to obtain a target emission reduction curve.

[0045] Optionally, a photovoltaic and wind power-powered water electrolysis hydrogen production system combines carbon capture and methanol synthesis processes to achieve carbon emission reduction. First, record the output power of photovoltaic and wind power at different time periods every day, as well as the corresponding hydrogen production and methanol synthesis. For example, when the total output power of photovoltaic and wind power reaches its peak at 10 am, the recorded hydrogen production is 120kg / h and the methanol synthesis is 60kg / h; at 8 pm, when the total output power is low, the hydrogen production and methanol synthesis drop to 80kg / h and 40kg / h respectively. Then, the above data are fitted and analyzed by establishing a model to predict the changes in hydrogen production and methanol synthesis under different renewable energy output powers. Based on the results of the analysis, a target emission reduction curve is drawn to show the carbon emission reduction effect of the system at different power levels. For example, when the total output power reaches 200kW, the carbon emission reduction effect achieved by the system is 500kg CO2 / h. Thus, a more effective production plan can be formulated based on the target emission reduction curve. Through the above steps, not only can production efficiency be improved, but also carbon emissions can be significantly reduced, achieving green production and sustainable development goals.

[0046] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0047] The optional embodiment of the present application provides a multi-energy hydrogen production and methanol production coordinated optimization method for carbon emission reduction to solve the problem that the dynamic balance of energy utilization cannot be guaranteed when using the reaction between hydrogen and carbon dioxide for emission reduction. The method constructs a multi-energy hydrogen production and methanol production coordinated optimization system, which organically combines renewable energy sources such as photovoltaics and wind power with hydrogen production and methanol synthesis processes, and uses power prediction and dynamic process control technology to improve the production efficiency of hydrogen and methanol. It can not only effectively capture and utilize carbon dioxide to provide raw materials for methanol synthesis, but also significantly reduce greenhouse gas emissions and support the realization of carbon neutrality goals. In addition, the flexibility of the system enables it to adapt to different regions and environmental conditions, and is widely used in chemical, energy, transportation and other fields, promoting the optimal allocation of resources and bringing significant economic benefits. While being environmentally friendly, it meets the market demand for hydrogen energy and clean fuels, and makes important contributions to achieving sustainable development goals and responding to global climate change.

[0048] As an optional implementation, Figure 3 It is a logical schematic diagram of a multi-energy hydrogen and methanol production coordinated optimization system for carbon emission reduction according to an embodiment of the present application. The above system includes at least: renewable energy input 32, water electrolysis hydrogen production unit 34, methanol synthesis and carbon capture unit 36, and system control architecture 38.

[0049] Optionally, renewable energy input 32. Integrate multiple renewable energy inputs, such as photovoltaic power generation systems and wind power generation systems. Since the power generation of photovoltaic and wind power is volatile, the system introduces an intelligent power prediction model to predict the power output of these renewable energy sources in real time. The power prediction model is based on weather forecasts, historical data, and real-time monitoring data, and uses machine learning or big data analysis technology to make short-term and medium-term predictions of future photovoltaic and wind power generation. These predictions provide a reliable energy supply plan for hydrogen production and methanol synthesis, allowing the system to reasonably allocate power under different energy supply conditions to ensure the continuity and efficiency of the production process.

[0050] Optionally, a water electrolysis hydrogen production unit 34. The water electrolysis hydrogen production unit uses renewable electricity from photovoltaic and wind power to produce hydrogen through a water electrolysis process. The system uses dynamic process control technology to monitor key parameters in the electrolysis process (such as current density, temperature, pressure, etc.) in real time, and dynamically adjusts the working state of the electrolyzer according to the power prediction results. For example, when photovoltaic or wind power generation is sufficient, the system can increase the current density of electrolysis to maximize hydrogen production; when power generation decreases, the system reduces the intensity of electrolysis to avoid energy waste. In addition, the system can temporarily store excess hydrogen produced through a hydrogen storage device to cope with future energy fluctuations.

[0051] Optionally, a methanol synthesis and carbon capture unit 36. The methanol synthesis unit uses the hydrogen produced by the hydrogen production unit and the carbon dioxide captured by the carbon capture unit to synthesize methanol. The carbon capture unit can capture carbon dioxide from industrial emissions or the atmosphere, and purify and treat it before inputting it into the methanol synthesis unit. During the methanol synthesis process, the system adopts dynamic process control to dynamically adjust the reaction conditions (such as temperature, pressure, catalyst flow rate, etc.) according to the results of real-time hydrogen supply, carbon dioxide capture and power prediction. Through this dynamic regulation, the system can optimize the synthesis reaction conditions when the hydrogen supply is sufficient to increase the methanol production; when the hydrogen supply is insufficient, the system appropriately slows down the reaction rate to maintain the stability of the process.

[0052] Optionally, a system control architecture 38. This architecture connects photovoltaic power generation, wind power generation, hydrogen production, methanol synthesis, carbon capture, power prediction and process control modules through a central control system. The central control system coordinates the operation of each module based on real-time data, prediction results and optimization algorithms to ensure that each link achieves the best match in energy and material flow. Through this systematic integration and coordinated control, the present invention can achieve stable and efficient hydrogen production and methanol synthesis under renewable energy fluctuation conditions.

[0053] Optional, Figure 4This is a flow chart of a multi-energy hydrogen production and methanol production coordinated optimization method for carbon emission reduction according to an embodiment of the present application, which specifically includes the following steps:

[0054] Step 1: Integration of multiple energy inputs and power forecasting. By integrating multiple renewable energy inputs (such as photovoltaic and wind power generation), and using intelligent power forecasting models to predict power generation output in real time. Power forecasting technology combines weather forecasts, historical data, and real-time sensor data, and uses big data analysis and machine learning algorithms to accurately predict future power output. This prediction provides a reliable basis for dynamic process control of hydrogen production and methanol synthesis, ensuring efficient use of energy.

[0055] Step 2: Dynamic process control and real-time adjustment. According to the volatility of renewable energy and power forecast results, the working status of hydrogen production and methanol synthesis processes is adjusted in real time. The water electrolysis hydrogen production unit increases the current density to increase hydrogen production when power is surplus, and automatically lowers the electrolysis intensity when power decreases to avoid energy waste. Similarly, the methanol synthesis unit dynamically adjusts the reaction conditions (temperature, pressure, catalyst flow rate, etc.) according to changes in hydrogen supply and carbon capture to ensure the stability and efficiency of the reaction. Through real-time power prediction and changes in process conditions, the working parameters of the hydrogen production and methanol synthesis processes are automatically adjusted to ensure the optimal allocation of energy and materials and achieve efficient production.

[0056] Step 3: Hydrogen storage and peak-shaving and valley-filling strategies. In the case of large fluctuations in renewable energy generation, hydrogen storage devices play a role in peak-shaving and valley-filling. By storing hydrogen when energy is sufficient and releasing the stored hydrogen to continue to supply the methanol synthesis unit when power generation is insufficient, efficient energy scheduling is achieved, energy waste is avoided, and process continuity is ensured. Through intelligent hydrogen storage and release mechanisms, this technology can effectively respond to power fluctuations, ensure stable operation of the production process, and optimize energy utilization efficiency.

[0057] Step 4: Linking carbon capture with methanol synthesis. Combine carbon dioxide capture with methanol synthesis. The carbon capture unit captures carbon dioxide from industrial emissions or the atmosphere, and reacts it with hydrogen produced by the hydrogen production unit to synthesize methanol. This technology realizes the recycling of carbon resources, not only reduces carbon emissions, but also produces chemicals (methanol) with economic value, forming a green production cycle with both environmental and economic benefits, and has broad industrial application prospects.

[0058] Step 5: System integration and coordinated optimization. The entire system is coordinated and managed through a central control architecture, integrating multiple subsystems such as power prediction, hydrogen production, methanol synthesis, carbon capture, and hydrogen storage. The central control system comprehensively optimizes the operating parameters of each subsystem based on real-time data, prediction results, and optimization algorithms, thereby achieving the optimal balance in terms of energy efficiency, production efficiency, and carbon emission reduction.

[0059] It should be noted that dynamic process control needs to accurately adjust the process parameters of water electrolysis for hydrogen production, such as current density, temperature and pressure, according to the power prediction results. In this process, attention should be paid to the safe operation of the electrolyzer to avoid equipment overload or failure due to improper parameter adjustment. At the same time, the selection and design of hydrogen storage technology must ensure safety to prevent the risk of hydrogen leakage or explosion. Parameters such as temperature, pressure and catalyst flow rate in the methanol synthesis process need to be dynamically matched with the hydrogen supply and carbon dioxide capture to maintain the efficiency and stability of the reaction. The selection of carbon capture technology should consider the CO2 capture efficiency and subsequent processing costs, while ensuring that the purity of the captured CO2 meets the requirements of methanol synthesis. During the design and operation process, safety regulations and environmental protection laws and regulations must be strictly observed to ensure that the operation of the system will not cause harm to operators and the environment. For example, hydrogen storage and carbon capture units need to be regularly inspected for safety to prevent potential leakage and pollution problems.

[0060] In summary, the optional embodiments of the present application, through intelligent power prediction, dynamic process control and multiple energy integration, the system can optimize energy distribution and resource utilization in real time according to the supply of renewable energy, the demand for hydrogen production and methanol synthesis. For example, the system stores excess hydrogen when the power generation is high, and uses the stored hydrogen to continue to produce methanol when the power generation is low, thereby smoothing power fluctuations and avoiding energy waste. Through this optimization mechanism, the system can not only realize power scheduling and peak shaving and valley filling, but also ensure the maximum energy efficiency and stable operation of the production process under different energy supply conditions. In summary, the present application enhances the utilization of renewable energy through power prediction and dynamic regulation, especially for the intermittent problems of photovoltaic and wind power; by integrating hydrogen production, methanol synthesis and carbon capture processes, efficient recycling of resources is achieved, and carbon emissions in the production process are reduced; by converting carbon dioxide into valuable methanol, the environmental carbon load is effectively reduced. While reducing carbon emissions, the energy utilization efficiency is maximized, and it has a wide range of industrial application prospects.

[0061] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0062] In this embodiment, a device for reducing carbon emissions is also provided, which is used to implement the above embodiments and preferred implementations, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0063] Figure 5 : is a structural block diagram of a carbon emission reduction device according to an embodiment of the present application, the device comprising:

[0064] The first determination module 52 is used to determine the target output power of the renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to the renewable energy, historical data corresponding to the renewable energy, and real-time monitoring data corresponding to the renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power;

[0065] An adjustment module 54, configured to adjust the working states of different sub-cells in the electrolyzer array according to the target output power, and determine a target amount of hydrogen generated by the electrolyzer array;

[0066] The control module 56 is used to control the target amount of hydrogen to chemically react with the carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

[0067] The above device determines the target output power of renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power; the working state of different sub-slots in the electrolyzer array is adjusted according to the target output power, and the target amount of hydrogen generated by the electrolyzer array is determined; the target amount of hydrogen is controlled to react chemically with carbon dioxide captured from the atmosphere to generate a target product for emission reduction. The above technical solution solves the problem that the dynamic balance of energy utilization cannot be guaranteed when emission reduction is performed by the reaction between hydrogen and carbon dioxide. Furthermore, the power of renewable energy is predicted by the pre-trained model, the hydrogen production of the electrolyzer is dynamically adjusted, and the hydrogen reacts with the captured CO2 to generate emission reduction products, thereby achieving energy optimization and carbon utilization.

[0068] In an exemplary embodiment, the above-mentioned device also includes: an acquisition module, which is used to control the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere before obtaining the captured amount of carbon dioxide to participate in the reaction; determine the hydrogen supply amount that reacts with the captured amount from the target amount; calculate the difference between the target amount and the hydrogen supply amount, and call the difference amount of hydrogen to an energy storage area, wherein the energy storage area is used to liquefy and store hydrogen.

[0069] In an exemplary embodiment, the above-mentioned device also includes: a sending module, which is used to send a first prompt message to the target object after determining the target output power of renewable energy through a pre-trained power prediction model, when it is determined that the target output power is less than the electrolysis demand power of the electrolyzer array, wherein the first prompt message is used to instruct the target object to send an instruction to call for storage of hydrogen to the energy storage area; when it is determined that the target output power is greater than the electrolysis demand power of the electrolyzer array, send a second prompt message to the target object, wherein the second prompt message is used to instruct the target object to expand the number of sub-electrolyzers of the electrolyzer array.

[0070] In an exemplary embodiment, the sending module further includes: a determination unit for determining an adjustment instruction to be issued to the electrolytic cell array after sending a second prompt message to the target object when it is determined that the target output power is greater than the electrolysis required power of the electrolytic cell array; wherein the adjustment instruction is used to increase the electrolysis intensity of the electrolytic cell array; instruct the electrolytic cell array to increase the current density in each electrolytic cell according to the adjustment instruction; receive the adjustment result of the current density, and change the target process conditions of the chemical reaction according to the adjustment result.

[0071] In an exemplary embodiment, the above-mentioned device also includes: a second determination module, which is used to determine the target process conditions of the chemical reaction and parameter information of the target process conditions before controlling the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere; when the target process conditions are different from the historical process conditions of the previous chemical reaction, the parameter information is used to adjust the equipment parameters for performing the chemical reaction.

[0072] In an exemplary embodiment, the above-mentioned device also includes: a recording module, which is used to control the chemical reaction of a target amount of hydrogen with carbon dioxide captured from the atmosphere to generate a target product for emission reduction, and then record the correspondence between the target product and the target output power; and fit multiple corresponding relationships of renewable energy within a target time period to obtain a target emission reduction curve.

[0073] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0074] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0075] S1. Determine the target output power of renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power;

[0076] S2, adjusting the working states of different sub-cells in the electrolyzer array according to the target output power, and determining the target amount of hydrogen generated by the electrolyzer array;

[0077] S3. Control the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

[0078] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0079] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0080] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are performed.

[0081] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0082] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0083] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0084] S1. Determine the target output power of renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power;

[0085] S2, adjusting the working states of different sub-cells in the electrolyzer array according to the target output power, and determining the target amount of hydrogen generated by the electrolyzer array;

[0086] S3. Control the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

[0087] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0088] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0089] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of N computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or N of the modules or steps can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0090] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for reducing carbon emissions, characterized in that: include: Determine the target output power of the renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to the renewable energy, historical data corresponding to the renewable energy, and real-time monitoring data corresponding to the renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power; Adjusting the working states of different sub-cells in the electrolyzer array according to the target output power, and determining the target amount of hydrogen generated by the electrolyzer array; The target amount of hydrogen is controlled to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

2. The method for reducing carbon emissions according to claim 1, characterized in that: Before controlling the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere, the method further comprises: Obtaining the captured amount of carbon dioxide to be reacted; determining, from the target amount, a hydrogen supply amount corresponding to the captured amount; The difference between the target quantity and the hydrogen supply is calculated, and the hydrogen of the difference is transferred to an energy storage area, wherein the energy storage area is used for liquefied storage of hydrogen.

3. The method for reducing carbon emissions according to claim 1, characterized in that: After determining the target output power of the renewable energy source by using the pre-trained power prediction model, the method further includes: When it is determined that the target output power is less than the electrolysis required power of the electrolyzer array, a first prompt message is sent to the target object, wherein the first prompt message is used to instruct the target object to send an instruction to call for storage of hydrogen to the energy storage area; When it is determined that the target output power is greater than the electrolysis required power of the electrolytic cell array, a second prompt message is sent to the target object, wherein the second prompt message is used to instruct the target object to expand the number of sub-electrolytic cells of the electrolytic cell array.

4. The method for reducing carbon emissions according to claim 3, characterized in that: When it is determined that the target output power is greater than the electrolysis required power of the electrolytic cell array, after sending the second prompt information to the target object, the method further includes: Determining an adjustment instruction to be issued to the electrolytic cell array; wherein the adjustment instruction is used to increase the electrolysis intensity of the electrolytic cell array; instructing the electrolytic cell array to increase the current density in each electrolytic cell according to the adjustment instruction; The adjustment result of the current density is received, and the target process condition of the chemical reaction is changed according to the adjustment result.

5. The method for reducing carbon emissions according to claim 1, characterized in that: Before controlling the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere, the method further comprises: Determining target process conditions of the chemical reaction and parameter information of the target process conditions; In the case where the target process conditions are different from the historical process conditions of the previous chemical reaction, the parameter information is used to adjust the parameters of the equipment performing the chemical reaction.

6. The method for reducing carbon emissions according to claim 1, characterized in that: After controlling the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction, the method further includes: Recording the corresponding relationship between the target product and the target output power; The target emission reduction curve is obtained by fitting multiple corresponding relationships of renewable energy within the target time period.

7. A device for reducing carbon emissions, characterized in that: include: A first determination module is used to determine the target output power of renewable energy through a pre-trained power prediction model; wherein the pre-trained power prediction model is a model for predicting output power trained by machine learning or big data analysis technology using weather data corresponding to renewable energy, historical data corresponding to renewable energy, and real-time monitoring data corresponding to renewable energy, the input of the power prediction model is weather data, historical data, and real-time monitoring data, and the output of the power prediction model is the predicted output power; An adjustment module, used to adjust the working states of different sub-cells in the electrolyzer array according to the target output power, and determine the target amount of hydrogen generated by the electrolyzer array; The control module is used to control the target amount of hydrogen to chemically react with carbon dioxide captured from the atmosphere to generate a target product for emission reduction.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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