Green energy utilization system and method for environment-friendly mixture production

Abstract

The invention relates to the technical field of energy management, and discloses a green energy utilization system and method for environment-friendly mixture production. Comprising a renewable energy supply module, an energy storage module, an energy allocation and optimization module, a waste heat recovery module, a carbon capture and conversion module, a fuel characteristic monitoring and optimization module, a waste treatment and regeneration module and a full-process intelligent management module. The renewable energy source supply module is used for obtaining energy from renewable energy sources such as solar energy, wind energy and geothermal energy; and the energy storage module is used for storing redundant renewable energy sources and releasing energy in peak hours. Waste heat is effectively recycled through the high-low temperature waste heat recycling module, and energy consumption is reduced; the carbon capture and conversion module dynamically captures carbon dioxide and converts the carbon dioxide into renewable fuel or industrial raw materials, closed-loop utilization of carbon resources and synergistic effect of waste heat and carbon capture technologies are achieved, energy and carbon resources are recycled to the maximum extent, carbon emission is reduced, meanwhile, recyclable resources are provided, and environmental protection benefits are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a green energy utilization system and method for producing environmentally friendly mixed materials. Background Art

[0002] Environmentally friendly mixture production is an industrial production method with sustainable development as its goal. It aims to achieve a green and efficient mixture production process by reducing dependence on traditional fossil energy, reducing carbon emissions, and optimizing resource utilization. The production process focuses on efficient use of energy and recycling of waste materials, reducing environmental pollution and energy waste, and meeting modern industrial environmental protection requirements. In environmentally friendly mixture production, the use of green energy is its core innovation part. A variety of renewable energy sources, including solar energy, wind energy and geothermal energy, are introduced into the production process. Through solar photovoltaic power generation, wind power generation, geothermal heating and equipment heating, the system provides a stable energy supply for mixture production. In addition, the system manages green energy through intelligent scheduling and energy storage modules to ensure that excess energy is stored during low energy consumption periods and released during high energy consumption periods to meet production needs. The comprehensive application of green energy not only reduces the consumption of fossil fuels, but also reduces carbon footprint, achieving environmental protection and efficient production goals.

[0003] In the production process of mixed materials, although renewable energy is gradually introduced, its intermittent and unstable nature leads to low effective utilization of these green energy in the production process. Traditional energy management systems lack intelligent dispatching means for renewable energy, but they cannot reasonably allocate different types of energy according to production needs, resulting in uneven energy utilization, waste of some renewable energy and still relying on fossil energy supplements. Secondly, the energy dispatching system lacks flexibility, usually only responding to peak demand through energy storage release, lacking comprehensive optimization of multiple renewable energy sources, and failing to give full play to the complementary role of various energy sources. At the same time, it weakens the advantages of green energy in environmental protection and energy efficiency, limiting the sustainability and flexibility of the entire system.

[0004] Secondly, a large amount of high-temperature and low-temperature waste heat, as well as greenhouse gases such as carbon dioxide, are generated during the production of mixed materials. The existing system has low efficiency in recycling and reusing waste heat and carbon dioxide, and waste heat is usually wasted. The application of carbon capture and conversion technology is limited, and carbon dioxide cannot be effectively converted into valuable fuels or industrial raw materials, resulting in high carbon emissions and difficulty in meeting increasingly stringent environmental protection standards.

[0005] Finally, current production systems generally lack intelligent management methods based on big data and machine learning, and are unable to comprehensively optimize energy consumption, carbon capture, waste heat recovery, and waste treatment in production. The industrial waste treatment rate is low, and resources are seriously wasted. In addition, energy utilization, carbon emissions, and waste treatment in the production process cannot be adjusted and optimized in real time, which restricts the comprehensive application of green energy and the realization of environmental protection goals. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a green energy utilization system and method for environmentally friendly mixed material production, which solves the problems of low utilization efficiency and unintelligent scheduling of renewable energy in the mixed material production process.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a green energy utilization system for environmentally friendly mixed material production, including a renewable energy supply module, an energy storage module, an energy allocation and optimization module, a waste heat recovery module, a carbon capture and conversion module, a fuel property monitoring and optimization module, a waste material treatment and regeneration module and a full-process intelligent management module;

[0008] The renewable energy supply module is used to obtain energy from renewable energy sources such as solar energy, wind energy and geothermal energy;

[0009] The energy storage module is used to store excess renewable energy and release energy at peak times;

[0010] The energy allocation and optimization module is used to obtain energy from the external power grid and realize dynamic allocation by combining internal renewable energy and energy storage system;

[0011] The waste heat recovery module is used to recover high-temperature and low-temperature waste heat generated during the production process;

[0012] The carbon capture and conversion module is used to capture carbon dioxide in the production process and convert it into renewable fuel or industrial raw materials;

[0013] The fuel property monitoring and optimization module is used to monitor the composition, humidity and calorific value of the fuel in real time, and optimize the combustion process based on the monitoring data;

[0014] The waste treatment and recycling module is used to sort and treat industrial waste and convert it into reusable raw materials or energy;

[0015] The full-process intelligent management module is used to dynamically optimize energy consumption, carbon capture, waste treatment and waste heat recovery in the production process based on artificial intelligence algorithms.

[0016] Preferably, the renewable energy supply module includes a solar photovoltaic power generation unit, a wind power generation unit and a geothermal energy utilization unit;

[0017] The solar photovoltaic power generation unit is used to convert solar energy into electrical energy;

[0018] The wind power generation unit is used to convert wind energy into electrical energy;

[0019] The geothermal energy utilization unit is used for utilizing geothermal energy for heating of factory areas or heating of production equipment.

[0020] Preferably, the energy storage module includes a battery energy storage unit and a thermal energy storage unit;

[0021] The battery energy storage unit is used to store excess electrical energy and release it during peak demand;

[0022] The thermal energy storage unit stores high-temperature and low-temperature waste heat through phase change materials and releases heat according to production needs.

[0023] Preferably, the energy allocation and optimization module includes a smart grid interface unit and an energy load prediction algorithm unit;

[0024] The smart grid interface unit is used to obtain power from an external power grid;

[0025] The energy load forecasting algorithm unit is used to forecast future energy load demand based on historical data and real-time production demand, and its formula is:

[0026] P t =α1P t-1 +α2P t-2 +…+α n P t-n +∈ t

[0027] Among them, P t is the energy load forecast value at time t; α1, α2, …, α n is the weight of historical load demand; P t-1 ,P t-2 ,…,P t-n is the load demand value at the historical moment; ∈ t is a random error term.

[0028] Preferably, the waste heat recovery module includes a high-temperature heat exchange unit and a low-temperature waste heat recovery unit;

[0029] The high temperature heat exchange unit is used to recover high temperature waste heat above 300°C and use it in production equipment or other high energy consumption processes;

[0030] The low-temperature waste heat recovery unit is used to recover waste heat below 150° C. and use it for plant heating or low-temperature production processes.

[0031] Preferably, the carbon capture and conversion module comprises an intelligent carbon capture unit and a carbon conversion unit;

[0032] The intelligent carbon capture unit is used to capture carbon dioxide according to the carbon dioxide concentration in the gas and the production process parameters;

[0033] The efficiency formula of the smart carbon capture unit is:

[0034]

[0035] in, is the carbon dioxide capture efficiency; m captured is the mass of captured carbon dioxide; m input is the mass of carbon dioxide entering the capture system; T is the capture temperature; P is the capture pressure; is the concentration of carbon dioxide in the gas;

[0036] The carbon conversion unit converts the captured carbon dioxide into methanol, synthesis gas or other industrial raw materials through electrocatalysis or chemical reaction;

[0037] The carbon conversion unit conversion efficiency formula is:

[0038]

[0039] Among them, E conversion is the carbon conversion efficiency; m product E is the mass of fuel or chemical raw material generated; input is the input electrical energy or thermal energy; Q is the amount of catalyst; T is the reaction temperature; C is the carbon dioxide concentration.

[0040] Preferably, the fuel property monitoring and optimization module includes a component analysis unit and a combustion optimization control unit;

[0041] The component analysis unit is used to analyze the chemical composition and humidity of the fuel;

[0042] The combustion optimization control unit dynamically adjusts the combustion temperature and oxygen supply based on the combustion parameter prediction formula and in combination with the combustion efficiency and the characteristic data of the fuel;

[0043] The combustion parameter prediction formula is:

[0044] T opt =f(X1,X2,...,X n )

[0045] Among them, T opt is the optimal combustion temperature; X1, X2, ..., X n is the fuel characteristic parameter;

[0046] The calculation formula of the combustion efficiency is:

[0047]

[0048] Among them, η combustion is the combustion efficiency; Q in The heat input of the fuel; Q loss The heat lost due to combustion.

[0049] Preferably, the waste processing and regeneration module includes a waste sorting unit and a waste regeneration unit;

[0050] The waste sorting unit automatically sorts industrial waste by using image recognition technology;

[0051] The waste recycling unit converts waste into reusable raw materials or energy through pyrolysis or catalytic conversion technology.

[0052] Preferably, the full-process intelligent management module includes a data acquisition unit and an intelligent optimization algorithm unit;

[0053] The data collection unit is used to collect energy consumption, carbon capture, waste heat recovery and waste material treatment data in each link of the production process;

[0054] The intelligent optimization algorithm unit performs multi-objective optimization on the system based on machine learning and big data analysis, and adopts a non-dominated sorting genetic algorithm for multi-objective optimization. The optimization steps include:

[0055] S1. Data collection: collect real-time data such as energy consumption, carbon emissions, waste heat recovery, and waste treatment efficiency from the system;

[0056] S2. Machine learning model training: Based on historical production data and real-time data, energy consumption, carbon emissions and waste heat recovery efficiency are predicted through random forests;

[0057] S3. Objective function definition: Balance multiple objectives through the following multi-objective optimization formula:

[0058] min(W1F1(x)+W2F2(x)+…+W n F n (x)

[0059] Among them, F1(x) is the total energy consumption; F2(x) is the carbon emission; F3(x) is the waste heat recovery efficiency; F4(x) is the waste treatment efficiency; W1, W2, …, W n is the weight of each goal;

[0060] S4, NSGA-II optimization: Through genetic algorithm crossover, mutation and other operations, it optimizes multiple objectives of the system and outputs the Pareto frontier solution;

[0061] S5. Dynamic adjustment through reinforcement learning: Using reinforcement learning algorithms in real-time systems to adjust optimization strategies based on feedback;

[0062] The target optimization formula is:

[0063] min(W1F1+W2F2+…+W n F n )

[0064] Among them, F1, F2, …, F n are the energy consumption, emission and waste heat recovery efficiency of each objective function; W1, W2, …, W n is the weight of each objective function.

[0065] Preferably, the green energy utilization method for producing an environmentally friendly mixed material comprises the following steps:

[0066] S1. Renewable energy acquisition and dispatch: Obtain the energy required for production through green energy such as solar energy, wind energy and geothermal energy, and store excess renewable energy in the energy storage system. When production demand peaks, dispatch the stored energy to meet production demand and ensure energy supply;

[0067] S2. External energy allocation and comprehensive management: Based on the load forecast and energy demand in the production process, additional energy is obtained from the external power grid through the intelligent allocation module, and combined with the internal energy storage system for comprehensive scheduling to ensure the balance of energy supply and demand in the production process;

[0068] S3. Waste heat recovery and utilization: The waste heat generated during the production process is recovered through high-temperature and low-temperature waste heat recovery modules, and used for heating the plant area or for energy supplementation in the production process, reducing energy waste;

[0069] S4. Intelligent carbon capture and conversion: During the production process, carbon dioxide is captured in stages through intelligent carbon capture modules, and the captured carbon dioxide is centrally processed and converted into renewable fuels or industrial raw materials through chemical catalysis or electrocatalysis to achieve the reuse of carbon resources;

[0070] S5. Fuel optimization and control: Real-time monitoring of fuel characteristics such as composition, humidity and calorific value used in mixture production, and adjustment of combustion conditions through combustion optimization algorithms to ensure efficient combustion of fuel, reduce emissions caused by incomplete combustion, and improve fuel utilization efficiency;

[0071] S6. Industrial waste treatment and full-process intelligent management: The industrial waste generated in the production process is sorted and processed through the waste treatment module, and converted into recyclable raw materials or energy. The full-process intelligent management module is used to achieve dynamic optimization and adaptive adjustment of the entire system.

[0072] The present invention provides a green energy utilization system and method for environmentally friendly mixed material production. It has the following beneficial effects:

[0073] 1. The present invention effectively recovers waste heat through high-temperature and low-temperature waste heat recovery modules to reduce fresh energy consumption. At the same time, the carbon capture and conversion module dynamically captures carbon dioxide and converts it into renewable fuel or industrial raw materials to achieve closed-loop utilization of carbon resources. The waste heat and carbon capture technologies work synergistically to maximize the recovery of energy and carbon resources, which not only reduces carbon emissions, but also provides reusable resources for production, significantly improving environmental benefits.

[0074] 2. The present invention integrates solar energy, wind energy and geothermal energy, and intelligently allocates renewable energy through energy storage modules to ensure energy supply and demand balance and reduce dependence on fossil energy. Diversified energy supply and intelligent scheduling improve energy utilization efficiency and reduce carbon footprint. Combined with energy load forecasting algorithms and external power grids, energy management is further optimized to make the system more efficient and flexible, ensuring the green and sustainable development of the production process.

[0075] 3. The present invention uses data collection and intelligent optimization algorithms to monitor energy consumption, carbon capture, waste heat recovery and waste treatment in real time, and combines machine learning for multi-objective optimization to improve overall energy, carbon emissions and waste treatment efficiency. The waste treatment module uses image recognition technology to regenerate waste and reduce resource waste. The reinforcement learning algorithm adaptively adjusts the optimization strategy to ensure efficient, green and intelligent operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a system module distribution diagram of the present invention;

[0077] Figure 2 A schematic diagram of a renewable energy supply module of the present invention;

[0078] Figure 3 It is a schematic diagram of the energy storage module of the present invention;

[0079] Figure 4 It is a schematic diagram of the energy allocation and optimization module of the present invention;

[0080] Figure 5 It is a schematic diagram of the waste heat recovery module of the present invention;

[0081] Figure 6 A schematic diagram of a carbon capture and conversion module of the present invention;

[0082] Figure 7 It is a schematic diagram of the fuel property monitoring and optimization module of the present invention;

[0083] Figure 8 It is a schematic diagram of the waste treatment and regeneration module of the present invention;

[0084] Fig. 9 This is a schematic diagram of the full-process intelligent management module of the present invention;

[0085] Fig.10 It is a flow chart of the optimization steps of the present invention;

[0086] Fig.11 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] Embodiment 1:

[0089] Please refer to the attached Figure 1 , an embodiment of the present invention provides a green energy utilization system for environmentally friendly mixed material production, including a renewable energy supply module, an energy storage module, an energy allocation and optimization module, a waste heat recovery module, a carbon capture and conversion module, a fuel property monitoring and optimization module, a waste material treatment and regeneration module and a full-process intelligent management module;

[0090] The renewable energy supply module is used to obtain energy from renewable energy sources such as solar energy, wind energy and geothermal energy;

[0091] The energy storage module is used to store excess renewable energy and release energy at peak times;

[0092] The energy allocation and optimization module is used to obtain energy from the external power grid and realize dynamic allocation by combining internal renewable energy and energy storage system;

[0093] The waste heat recovery module is used to recover high-temperature and low-temperature waste heat generated during the production process;

[0094] The carbon capture and conversion module is used to capture carbon dioxide in the production process and convert it into renewable fuel or industrial raw materials;

[0095] The fuel property monitoring and optimization module is used to monitor the composition, humidity and calorific value of the fuel in real time, and optimize the combustion process based on the monitoring data;

[0096] The waste treatment and recycling module is used to sort and treat industrial waste and convert it into reusable raw materials or energy;

[0097] The full-process intelligent management module is used to dynamically optimize energy consumption, carbon capture, waste treatment and waste heat recovery in the production process based on artificial intelligence algorithms.

[0098] Specifically, the renewable energy supply module provides basic energy for the entire system through various forms of renewable energy such as wind energy, solar energy, geothermal energy, etc., and realizes coordinated energy supply with the external power grid. When there is an oversupply of renewable energy, the energy storage module stores the excess energy and releases the stored energy during peak production energy consumption hours to balance the overall energy demand of the system. The energy allocation and optimization module monitors the energy consumption of the entire system in real time, and dynamically allocates energy obtained from the external power grid, internal renewable energy and energy storage modules to ensure the optimal state of system energy utilization. During the production process, the waste heat recovery module will capture and recycle the high-temperature and low-temperature waste heat generated by the system, and provide energy support for other production links or energy storage modules through the secondary use of waste heat, thereby reducing energy waste. The carbon capture and conversion module extracts carbon dioxide emitted during the production process through advanced capture technology, and further converts it into renewable fuels or chemical raw materials. The fuel property monitoring and optimization module monitors the composition, humidity, calorific value and other parameters of the fuel used in the production process of the mixture in real time, and feeds the data back to the intelligent management module for calculation and analysis, optimizes the combustion process of the fuel, and ensures that the combustion efficiency and emission control reach the best level; the waste treatment and regeneration module intelligently sorts and processes the waste generated in the production process, maximizes the conversion of waste into renewable raw materials or combustible energy materials, and reduces the consumption of raw materials and the discharge of waste in the production process; the full-process intelligent management module, as the core of the system, integrates data from various modules through artificial intelligence algorithms, and optimizes the management of various parameters such as energy consumption, carbon emissions, waste heat recovery, and waste treatment in real time, making the entire production process greener and more efficient. Finally, through intelligent collaborative operation, each module achieves the green energy utilization and environmental protection goals of mixture production.

[0099] Please refer to the attached Figure 2 -Attached Figure 4 , the renewable energy supply module includes a solar photovoltaic power generation unit, a wind power generation unit and a geothermal energy utilization unit;

[0100] The solar photovoltaic power generation unit is used to convert solar energy into electrical energy;

[0101] The wind power generation unit is used to convert wind energy into electrical energy;

[0102] The geothermal energy utilization unit is used to utilize geothermal energy for heating the factory area or heating production equipment;

[0103] The energy storage module includes a battery energy storage unit and a thermal energy storage unit;

[0104] The battery energy storage unit is used to store excess electrical energy and release it during peak demand;

[0105] The thermal energy storage unit stores high-temperature and low-temperature waste heat through phase change materials and releases heat according to production needs;

[0106] The energy allocation and optimization module includes a smart grid interface unit and an energy load prediction algorithm unit;

[0107] The smart grid interface unit is used to obtain power from an external power grid;

[0108] The energy load forecasting algorithm unit is used to forecast future energy load demand based on historical data and real-time production demand, and its formula is:

[0109] P t =α1P t-1 +α2P t-2 +…+α n P t-n +∈ t

[0110] Among them, P t is the energy load forecast value at time t; α1, α2, …, α n is the weight of historical load demand; P t-1 ,P t-2 ,…,P t-n is the load demand value at the historical moment; ∈ t is a random error term.

[0111] Specifically, the solar photovoltaic power generation unit, wind power generation unit and geothermal energy utilization unit each provide different forms of renewable energy, which are stored and released through the battery energy storage unit or thermal energy storage unit. The energy allocation and optimization module obtains electricity from the external power grid through the smart grid interface unit when renewable energy is insufficient. At the same time, the energy load prediction algorithm unit predicts future load demand based on historical load data and real-time production demand, and dynamically adjusts the energy allocation strategy according to the formula. In the prediction formula, the weights of historical load demand α1, α2,…, α n With the random error term ∈ t Ensure the accuracy of the prediction, the predicted value P t It determines the distribution ratio of energy obtained from the energy storage unit or the external power grid. The coordinated operation of each unit ensures a smooth and efficient energy supply and avoids the imbalance between supply and demand.

[0112] Please refer to the attached Figure 5 -Attached Figure 7 , the waste heat recovery module includes a high-temperature heat exchange unit and a low-temperature waste heat recovery unit;

[0113] The high temperature heat exchange unit is used to recover high temperature waste heat above 300°C and use it in production equipment or other high energy consumption processes;

[0114] The low-temperature waste heat recovery unit is used to recover waste heat below 150°C and use it for plant heating or low-temperature production processes;

[0115] The carbon capture and conversion module includes an intelligent carbon capture unit and a carbon conversion unit;

[0116] The intelligent carbon capture unit is used to capture carbon dioxide according to the carbon dioxide concentration in the gas and the production process parameters;

[0117] The efficiency formula of the smart carbon capture unit is:

[0118]

[0119] in, is the carbon dioxide capture efficiency; m captured is the mass of captured carbon dioxide; m input is the mass of carbon dioxide entering the capture system; T is the capture temperature; P is the capture pressure; is the concentration of carbon dioxide in the gas;

[0120] The carbon conversion unit converts the captured carbon dioxide into methanol, synthesis gas or other industrial raw materials through electrocatalysis or chemical reaction;

[0121] The carbon conversion unit conversion efficiency formula is:

[0122]

[0123] Among them, E conversion is the carbon conversion efficiency; m product E is the mass of fuel or chemical raw material generated; input is the input electrical energy or thermal energy; Q is the amount of catalyst; T is the reaction temperature; C is the carbon dioxide concentration;

[0124] The fuel property monitoring and optimization module includes a component analysis unit and a combustion optimization control unit;

[0125] The component analysis unit is used to analyze the chemical composition and humidity of the fuel;

[0126] The combustion optimization control unit dynamically adjusts the combustion temperature and oxygen supply based on the combustion parameter prediction formula and in combination with the combustion efficiency and the characteristic data of the fuel;

[0127] The combustion parameter prediction formula is:

[0128] T opt =f(X1,X2,...,Xn )

[0129] Among them, T opt is the optimal combustion temperature; X1, X2, ..., X n is the fuel characteristic parameter;

[0130] The calculation formula of the combustion efficiency is:

[0131]

[0132] Among them, η combustion is the combustion efficiency; Q in The heat input of the fuel; Q loss The heat lost due to combustion.

[0133] Specifically, the high-temperature heat exchange unit and the low-temperature waste heat recovery unit in the waste heat recovery module process waste heat at different temperatures respectively. The high-temperature waste heat is directly reused in high-energy-consuming processes, while the low-temperature waste heat is used for plant heating or low-temperature production processes. The two work together to ensure that waste heat in different temperature ranges can be effectively utilized, reducing energy waste; the intelligent carbon capture unit dynamically adjusts the capture efficiency by real-time monitoring of carbon dioxide concentration and process parameters. The captured carbon dioxide will be transmitted to the carbon conversion unit. The carbon conversion unit converts the captured carbon dioxide into industrial raw materials such as methanol and synthesis gas through electrocatalysis or chemical reactions according to production needs, thereby realizing the recycling of carbon resources. The seamless connection between the capture and conversion units ensures an efficient process from carbon dioxide capture to conversion, reducing carbon dioxide. The fuel characteristics monitoring and optimization module reduces emissions and increases the output of useful products; the component analysis unit in the fuel characteristics monitoring and optimization module continuously monitors the chemical composition and moisture of the fuel. These data are transmitted to the combustion optimization control unit, which dynamically adjusts the temperature and oxygen supply during the combustion process based on the combustion parameters and prediction formulas to maximize combustion efficiency and reduce fuel losses. Through real-time monitoring and adjustment, the combustion optimization control unit works in conjunction with the waste heat recovery module to ensure that the heat energy generated by combustion is recovered and reused by the waste heat recovery unit as much as possible; the coordination between the module units is achieved through the intelligent management system, and the waste heat recovery, carbon capture and conversion, and combustion optimization links are optimized through data sharing and dynamic adjustment to ensure efficient use of energy, minimization of carbon emissions and the overall environmental protection of the production process.

[0134] Please refer to the attached Figure 8 -Attached Fig.10 , the waste processing and regeneration module includes a waste sorting unit and a waste regeneration unit;

[0135] The waste sorting unit automatically sorts industrial waste by using image recognition technology;

[0136] The waste recycling unit converts waste into reusable raw materials or energy through pyrolysis or catalytic conversion technology;

[0137] The full-process intelligent management module includes a data acquisition unit and an intelligent optimization algorithm unit;

[0138] The data collection unit is used to collect energy consumption, carbon capture, waste heat recovery and waste material treatment data in each link of the production process;

[0139] The intelligent optimization algorithm unit performs multi-objective optimization on the system based on machine learning and big data analysis, and adopts a non-dominated sorting genetic algorithm for multi-objective optimization. The optimization steps include:

[0140] S1. Data collection: collect real-time data such as energy consumption, carbon emissions, waste heat recovery, and waste treatment efficiency from the system;

[0141] S2. Machine learning model training: Based on historical production data and real-time data, energy consumption, carbon emissions and waste heat recovery efficiency are predicted through random forests;

[0142] S3. Objective function definition: Balance multiple objectives through the following multi-objective optimization formula:

[0143] min(W1F1(x)+W2F2(x)+…+W n F n (x)

[0144] Among them, F1(x) is the total energy consumption; F2(x) is the carbon emission; F3(x) is the waste heat recovery efficiency; F4(x) is the waste treatment efficiency; W1, W2, …, W n is the weight of each goal;

[0145] S4, NSGA-II optimization: Through genetic algorithm crossover, mutation and other operations, it optimizes multiple objectives of the system and outputs the Pareto frontier solution;

[0146] S5. Dynamic adjustment through reinforcement learning: Using reinforcement learning algorithms in real-time systems to adjust optimization strategies based on feedback;

[0147] The target optimization formula is:

[0148] min(W1F1+W2F2+…+W n F n )

[0149] Among them, F1, F2, …, F n are the energy consumption, emission and waste heat recovery efficiency of each objective function; W1, W2, …, W n is the weight of each objective function.

[0150] Specifically, the waste treatment and recycling module works together through the waste sorting unit and the waste regeneration unit. The waste sorting unit uses image recognition technology to automatically classify industrial waste to improve sorting efficiency and ensure that different types of waste enter the corresponding recycling process. Subsequently, the waste regeneration unit converts these wastes into reusable raw materials or energy through pyrolysis or catalytic conversion technology. This process not only reduces waste discharge, but also provides new raw materials and energy for the system, further reducing overall resource consumption; the full-process intelligent management module obtains energy consumption, carbon capture, waste heat recovery and waste treatment data in the production process in real time through the data acquisition unit. The collected data is transmitted to the intelligent optimization algorithm unit, which performs multi-objective optimization of the system based on machine learning and big data analysis. The machine learning model uses the random forest algorithm to use historical production data and real-time data to predict the system's energy consumption, carbon emissions and waste heat recovery efficiency; during the optimization process, the system defines multiple The target optimization formula is used to balance energy consumption, carbon emissions, waste heat recovery efficiency and waste treatment efficiency. The NSGA-II genetic algorithm searches for Pareto frontier solutions through crossover and mutation operations, and outputs a combination with the lowest energy consumption, the lowest carbon emissions, the highest waste heat recovery rate and the highest waste treatment efficiency. During the production process, the reinforcement learning algorithm dynamically adjusts the optimization strategy through feedback to ensure real-time adjustment of the operation of each link in the system to improve the overall optimization effect. During the whole process, the waste treatment and regeneration module and the full-process intelligent management module work closely together through data sharing. Waste treatment efficiency, as one of the optimization goals, will directly affect the decision-making of the intelligent optimization algorithm. The data acquisition unit collects waste treatment efficiency in real time. The intelligent optimization algorithm continuously adjusts the waste treatment strategy based on these data to ensure that the overall energy consumption and carbon emission levels of the system are optimized while improving the regeneration efficiency. Each module is interconnected through multi-objective optimization and real-time feedback to form a highly automated and intelligent green production system.

[0151] Embodiment 2:

[0152] Please refer to the attached Fig.11 The green energy utilization method for environmentally friendly mixed material production comprises the following steps:

[0153] S1. Renewable energy acquisition and dispatch: Obtain the energy required for production through green energy such as solar energy, wind energy and geothermal energy, and store excess renewable energy in the energy storage system. When production demand peaks, dispatch the stored energy to meet production demand and ensure energy supply;

[0154] S2. External energy allocation and comprehensive management: Based on the load forecast and energy demand in the production process, additional energy is obtained from the external power grid through the intelligent allocation module, and combined with the internal energy storage system for comprehensive scheduling to ensure the balance of energy supply and demand in the production process;

[0155] S3. Waste heat recovery and utilization: The waste heat generated during the production process is recovered through high-temperature and low-temperature waste heat recovery modules, and used for heating the plant area or for energy supplementation in the production process, reducing energy waste;

[0156] S4. Intelligent carbon capture and conversion: During the production process, carbon dioxide is captured in stages through intelligent carbon capture modules, and the captured carbon dioxide is centrally processed and converted into renewable fuels or industrial raw materials through chemical catalysis or electrocatalysis to achieve the reuse of carbon resources;

[0157] S5. Fuel optimization and control: Real-time monitoring of fuel characteristics such as composition, humidity and calorific value used in mixture production, and adjustment of combustion conditions through combustion optimization algorithms to ensure efficient combustion of fuel, reduce emissions caused by incomplete combustion, and improve fuel utilization efficiency;

[0158] S6. Industrial waste treatment and full-process intelligent management: The industrial waste generated in the production process is sorted and processed through the waste treatment module, and converted into recyclable raw materials or energy. The full-process intelligent management module is used to achieve dynamic optimization and adaptive adjustment of the entire system.

[0159] Specifically, in step S1, the renewable energy acquisition stage combines solar photovoltaic, wind power generation and geothermal energy utilization systems, which can not only achieve the acquisition of electricity required for production, but also intelligently manage energy flow through the energy storage system. During low energy consumption periods, excess electricity is stored in battery storage units and thermal energy storage units, and energy is released during peak energy consumption periods. In this way, by making full use of renewable energy, dependence on fossil fuels is reduced, and energy supply is effectively balanced through the energy storage system to avoid energy waste. In step S2, based on load forecasting and real-time production needs, the system obtains supplementary power from the external power grid through the intelligent scheduling module, and combines the internal energy storage system and renewable energy supply for comprehensive management to ensure stable energy supply and load forecasting. The measurement algorithm adjusts the amount of external electricity introduced through historical energy consumption data and real-time demand to avoid power shortages or overloads, thereby optimizing the combined use of internal renewable energy and external energy, ensuring the balance of energy supply and demand in the production process, and reducing the waste of external electricity; in step S3, the high-temperature (>300℃) waste heat generated in the production process is recovered through the high-temperature heat exchange unit and used for high-energy-consuming processes; low-temperature waste heat (<150℃) is used for plant heating or low-temperature processes through the low-temperature waste heat recovery unit. The energy of each heat source is stored in the heat storage unit and released on demand according to the production process requirements, thereby effectively reducing energy waste and improving production efficiency. The reuse of waste heat reduces the demand for fresh energy and saves overall energy consumption; step S4 In step S5, the carbon capture module adjusts the capture efficiency of carbon dioxide according to real-time process parameters, and the captured carbon dioxide is converted into reusable industrial raw materials such as methanol and synthesis gas through electrocatalysis or chemical reaction. The temperature, pressure and catalyst use in this process are dynamically controlled to ensure the conversion efficiency. In this way, the captured carbon dioxide is converted into valuable fuels and raw materials, which realizes the closed-loop utilization of carbon resources, reduces greenhouse gas emissions, and promotes the development of a low-carbon economy. In step S5, the fuel property monitoring module monitors the composition, humidity and calorific value of the fuel in real time, and the data is transmitted to the combustion optimization control module. The combustion conditions, including oxygen supply and combustion temperature, are dynamically adjusted through algorithms to ensure complete combustion of the fuel, reduce energy consumption and emissions from incomplete combustion. release, thereby improving fuel utilization, reducing pollutant emissions, ensuring that the combustion process is efficient and environmentally friendly, and optimizing the overall energy efficiency of the combustion system; in step S6, the waste treatment module uses image recognition technology to sort the waste, and converts the waste into reusable energy or raw materials through pyrolysis or catalytic conversion technology. The full-process intelligent management module monitors all aspects of the production process in real time, including energy consumption, carbon capture, waste heat recovery and waste treatment data, and uses machine learning and big data analysis for optimization and adjustment. Through intelligent waste management and regeneration technology, the recycling of industrial waste is realized, resource waste is reduced, and at the same time, the full-process management improves the system's adaptability, making production more efficient, green and intelligent.

[0160] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A green energy utilization system for environmentally friendly mixed material production, characterized in that: It includes renewable energy supply module, energy storage module, energy allocation and optimization module, waste heat recovery module, carbon capture and conversion module, fuel characteristics monitoring and optimization module, waste treatment and regeneration module and full-process intelligent management module; The renewable energy supply module is used to obtain energy from renewable energy sources such as solar energy, wind energy and geothermal energy; The energy storage module is used to store excess renewable energy and release energy at peak times; The energy allocation and optimization module is used to obtain energy from the external power grid and realize dynamic allocation by combining internal renewable energy and energy storage system; The waste heat recovery module is used to recover high-temperature and low-temperature waste heat generated during the production process; The carbon capture and conversion module is used to capture carbon dioxide in the production process and convert it into renewable fuel or industrial raw materials; The fuel property monitoring and optimization module is used to monitor the composition, humidity and calorific value of the fuel in real time, and optimize the combustion process based on the monitoring data; The waste treatment and recycling module is used to sort and treat industrial waste and convert it into reusable raw materials or energy; The full-process intelligent management module is used to dynamically optimize energy consumption, carbon capture, waste treatment and waste heat recovery in the production process based on artificial intelligence algorithms.

2. The green energy utilization system for environmentally friendly mixed material production according to claim 1 is characterized in that: The renewable energy supply module includes a solar photovoltaic power generation unit, a wind power generation unit and a geothermal energy utilization unit; The solar photovoltaic power generation unit is used to convert solar energy into electrical energy; The wind power generation unit is used to convert wind energy into electrical energy; The geothermal energy utilization unit is used for utilizing geothermal energy for heating of factory areas or heating of production equipment.

3. The green energy utilization system for environmentally friendly mixed material production according to claim 1 is characterized in that: The energy storage module includes a battery energy storage unit and a thermal energy storage unit; The battery energy storage unit is used to store excess electrical energy and release it during peak demand; The thermal energy storage unit stores high-temperature and low-temperature waste heat through phase change materials and releases heat according to production needs.

4. The green energy utilization system for environmentally friendly mixed material production according to claim 1, characterized in that: The energy allocation and optimization module includes a smart grid interface unit and an energy load prediction algorithm unit; The smart grid interface unit is used to obtain power from an external power grid; The energy load forecasting algorithm unit is used to forecast future energy load demand based on historical data and real-time production demand, and its formula is: P t =α1P t-1 +α2P t-2 +…+a n P t-n +∈ t Among them, P t is the energy load forecast value at time t; α1, α2, …, α n is the weight of historical load demand; P t-1 ,P t-2 ,…,P t-n is the load demand value at the historical moment; ∈ t is a random error term.

5. The green energy utilization system for environmentally friendly mixed material production according to claim 1, characterized in that: The waste heat recovery module includes a high-temperature heat exchange unit and a low-temperature waste heat recovery unit; The high temperature heat exchange unit is used to recover high temperature waste heat above 300°C and use it in production equipment or other high energy consumption processes; The low-temperature waste heat recovery unit is used to recover waste heat below 150° C. and use it for plant heating or low-temperature production processes.

6. The green energy utilization system for environmentally friendly mixed material production according to claim 1, characterized in that: The carbon capture and conversion module includes an intelligent carbon capture unit and a carbon conversion unit; The intelligent carbon capture unit is used to capture carbon dioxide according to the carbon dioxide concentration in the gas and the production process parameters; The efficiency formula of the smart carbon capture unit is: in, is the carbon dioxide capture efficiency; m captured is the mass of captured carbon dioxide; m input is the mass of carbon dioxide entering the capture system; T is the capture temperature; P is the capture pressure; is the concentration of carbon dioxide in the gas; The carbon conversion unit converts the captured carbon dioxide into methanol, synthesis gas or other industrial raw materials through electrocatalysis or chemical reaction; The carbon conversion unit conversion efficiency formula is: Among them, E conversion is the carbon conversion efficiency; m product E is the mass of fuel or chemical raw material generated; input is the input electrical energy or thermal energy; Q is the amount of catalyst; T is the reaction temperature; C is the carbon dioxide concentration.

7. The green energy utilization system for environmentally friendly mixed material production according to claim 1, characterized in that: The fuel property monitoring and optimization module includes a component analysis unit and a combustion optimization control unit; The component analysis unit is used to analyze the chemical composition and humidity of the fuel; The combustion optimization control unit dynamically adjusts the combustion temperature and oxygen supply based on the combustion parameter prediction formula and in combination with the combustion efficiency and the characteristic data of the fuel; The combustion parameter prediction formula is: T opt =f(X1,X2,…,X n ) Among them, T opt is the optimal combustion temperature; X1, X2, ..., X n is the fuel characteristic parameter; The calculation formula of the combustion efficiency is: Among them, η combustion is the combustion efficiency; Q in The heat input of the fuel; Q loss The heat lost due to combustion.

8. The green energy utilization system for environmentally friendly mixed material production according to claim 1, characterized in that: The waste processing and regeneration module includes a waste sorting unit and a waste regeneration unit; The waste sorting unit automatically sorts industrial waste by using image recognition technology; The waste recycling unit converts waste into reusable raw materials or energy through pyrolysis or catalytic conversion technology.

9. The green energy utilization system for environmentally friendly mixed material production according to claim 1, characterized in that: The full-process intelligent management module includes a data acquisition unit and an intelligent optimization algorithm unit; The data collection unit is used to collect energy consumption, carbon capture, waste heat recovery and waste material treatment data in each link of the production process; The intelligent optimization algorithm unit performs multi-objective optimization on the system based on machine learning and big data analysis, and adopts a non-dominated sorting genetic algorithm for multi-objective optimization. The optimization steps include: S1. Data collection: collect real-time data such as energy consumption, carbon emissions, waste heat recovery, and waste treatment efficiency from the system; S2. Machine learning model training: Based on historical production data and real-time data, energy consumption, carbon emissions and waste heat recovery efficiency are predicted through random forests; S3. Objective function definition: Balance multiple objectives through the following multi-objective optimization formula: min(W1F1(x)+W2F2(x)+…+W n F n (x)) Among them, F1(x) is the total energy consumption; F2(x) is the carbon emission; F3(x) is the waste heat recovery efficiency; F4(x) is the waste treatment efficiency; W1, W2, …, W n is the weight of each goal; S4, NSGA-II optimization: Through genetic algorithm crossover, mutation and other operations, optimize multiple objectives of the system and output the Pareto frontier solution; S5. Dynamic adjustment through reinforcement learning: Using reinforcement learning algorithms in real-time systems to adjust optimization strategies based on feedback; The target optimization formula is: min(W1F1+W2F2+…+W n F n ) Among them, F1, F2, …, F n are the energy consumption, emission and waste heat recovery efficiency of each objective function; W1, W2, …, W n is the weight of each objective function.

10. A method for utilizing green energy in the production of environmentally friendly mixed materials, a system for utilizing green energy in the production of environmentally friendly mixed materials according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Renewable energy acquisition and dispatch: Obtain the energy required for production through green energy such as solar energy, wind energy and geothermal energy, and store excess renewable energy in the energy storage system. When production demand peaks, dispatch the stored energy to meet production demand and ensure energy supply; S2. External energy allocation and comprehensive management: Based on the load forecast and energy demand in the production process, additional energy is obtained from the external power grid through the intelligent allocation module, and combined with the internal energy storage system for comprehensive scheduling to ensure the balance of energy supply and demand in the production process; S3. Waste heat recovery and utilization: The waste heat generated during the production process is recovered through high-temperature and low-temperature waste heat recovery modules, and used for heating the plant area or for energy supplementation in the production process, reducing energy waste; S4. Intelligent carbon capture and conversion: During the production process, carbon dioxide is captured in stages through intelligent carbon capture modules, and the captured carbon dioxide is centrally processed and converted into renewable fuels or industrial raw materials through chemical catalysis or electrocatalysis to achieve the reuse of carbon resources; S5. Fuel optimization and control: Real-time monitoring of fuel characteristics such as composition, humidity and calorific value used in mixture production, and adjustment of combustion conditions through combustion optimization algorithms to ensure efficient combustion of fuel, reduce emissions caused by incomplete combustion, and improve fuel utilization efficiency; S6. Industrial waste treatment and full-process intelligent management: The industrial waste generated in the production process is sorted and processed through the waste treatment module, and converted into recyclable raw materials or energy. The full-process intelligent management module is used to achieve dynamic optimization and adaptive adjustment of the entire system.

Images