System and method for optimizing configuration of methane storage driven by solar light condensation

Abstract

The invention provides a configuration optimization system and method for biogas storage driven by solar condensation, and relates to the technical field of biogas engineering optimizing.According to the configuration optimization system and method, the total energy of incident solar energy is calculated by calculating the area of a daylighting area, the solar radiation power and the optical efficiency of a condenser; calculating effective heat energy and solar photo-thermal conversion rate based on the heat transfer fluid parameters and the temperature of the heat absorber; the biogas yield is corrected according to the reference fermentation temperature and actual heat supply; the solar photo-thermal conversion rate and the biogas production rate are taken as targets, the specific heat capacity of heat transfer fluid and the quality of fermentation pulp are optimized through a multi-target particle swarm optimization algorithm, and it is ensured that the fermentation temperature is within the strain activity range. The problems that a traditional system is high in energy consumption and poor in stability are solved, and the energy utilization efficiency and the gas production rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biogas project optimization, and specifically provides a solar concentrating-driven biogas gas storage configuration optimization system and method. Background Art

[0002] Traditional biogas generation systems mainly rely on conventional energy sources such as electricity or fossil fuels to maintain the fermentation temperature. This not only results in relatively high operating costs but also causes environmental pollution problems. In recent years, solar energy, as a clean and renewable energy form, has been increasingly widely used. Solar concentrating technology converts solar radiation energy into heat energy by concentrating it, and has become an important means to improve the utilization efficiency of solar energy. However, existing solar-assisted biogas systems often lack the coordinated optimization of the photothermal conversion efficiency and the gas production rate, resulting in low energy utilization efficiency and difficulty in maintaining a stable fermentation temperature under different environmental conditions. These problems limit the economy and sustainability of biogas systems. Therefore, there is an urgent need for a system that can efficiently utilize solar energy and optimize the synergistic relationship between the photothermal conversion efficiency and the biogas production rate to solve the technical problems of energy waste, high costs, and insufficient stability.

[0003] In the prior art, the published patent with the publication number CN115168941A discloses a design method and operation strategy for a biogas fermentation system based on gas production performance prediction. By constructing a gas production performance prediction model, based on the coupling of a biogas heat transfer model and a biogas production model, the device performance parameters of the biogas production system are combined with outdoor environmental parameters to construct a biogas heat transfer model. At the same time, in combination with the system operation plan, a biogas fermentation gas production model is constructed; the operation strategy includes the control of heat preservation, feeding, and gas storage tanks.

[0004] The main problems of the above solution are as follows: The biogas heat transfer model and the gas production model highly depend on environmental parameters, and it is difficult for the system to maintain the optimal fermentation temperature in extreme environments, affecting the gas production stability; the above solution mainly focuses on gas production performance prediction and does not consider key indicators such as energy utilization efficiency, resulting in poor optimization effect of the system in terms of energy consumption. Moreover, it is necessary to dynamically adjust the control of heat preservation, feeding, and gas storage tanks in combination with the operation plan, increasing the system complexity and the risk of failures.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a solar concentrating-driven biogas gas storage configuration optimization system and method to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A solar concentrator-driven biogas gas storage configuration optimization system, specifically including:

[0009] A light measurement module, which is used to measure the lighting area of the concentrator within the monitoring time period, the solar radiation power vertically incident per unit area within the lighting area, and obtain the optical efficiency of the concentrator, and generate the total incident solar energy based on the lighting area, the solar radiation power vertically incident per unit area, and the optical efficiency of the concentrator. The monitoring time period is a natural day;

[0010] A heat conversion module, which is used to transfer heat to the heat transfer fluid through the heat absorber, measure the temperature of the heat transfer fluid entering and leaving the heat absorber, as well as the flow rate and specific heat capacity of the heat transfer fluid, calculate the effective thermal energy, and generate the solar thermal conversion efficiency based on the total incident solar energy and the effective thermal energy;

[0011] A fermentation gas production module, which is used to set the reference fermentation temperature of the fermentation tank, calculate the heat required to maintain the reference fermentation temperature within the monitoring time period and record it as the reference heat, set the reference gas production rate under the reference heat, calculate the actual fermentation temperature based on the effective thermal energy and the reference heat, and correct the reference gas production rate based on the actual fermentation temperature and the reference fermentation temperature to generate the biogas production rate;

[0012] A comprehensive optimization module, which is used to optimize the specific heat capacity of the heat transfer fluid and the mass of the input fermentation slurry within the monitoring time period through a multi-objective particle swarm optimization algorithm, with the flow rate range of the heat transfer fluid, the specific heat capacity range of the heat transfer fluid, the maximum fermentation slurry mass, and the temperature range in which the fermentation bacteria are active as constraint conditions, and the optimization goal is to maximize the solar thermal conversion efficiency and the biogas production rate.

[0013] Further, the formula for generating the total incident solar energy is:

[0014] E = S × F × η × t

[0015] Wherein, E represents the total incident solar energy, S represents the lighting area, F represents the average value of the solar radiation power vertically incident per unit area within the monitoring time period, η represents the optical efficiency of the concentrator, and t represents the length of the monitoring time period.

[0016] Further, the principle for generating the solar thermal conversion efficiency is:

[0017] The formula for generating the effective thermal energy is:

[0018] Q = L × C × (T out -T in ) × t

[0019] Wherein, Q represents the effective thermal energy, L represents the average flow rate of the heat transfer fluid within the monitoring time period, C represents the specific heat capacity of the heat transfer fluid, Tout Denotes the temperature at which the heat transfer fluid leaves the heat absorber, T in Denotes the temperature at which the heat transfer fluid enters the heat absorber;

[0020] The formula for generating the solar thermal conversion efficiency is:

[0021]

[0022] where σ denotes the solar thermal conversion efficiency.

[0023] Furthermore, the principle for generating the biogas production rate is:

[0024] The formula for generating the reference heat is:

[0025] Q0 = L × C × (T0 - T env ) × t

[0026] where Q0 denotes the reference heat, T0 denotes the reference fermentation temperature, and T env denotes the ambient temperature;

[0027] The formula for generating the biogas production rate is:

[0028]

[0029] where T denotes the actual fermentation temperature, m s,d denotes the mass of the fermentation slurry input during the monitoring period, G denotes the biogas production rate, G0 denotes the reference production rate, and k denotes the temperature coefficient, and k = 0.069.

[0030] Furthermore, the principle for optimizing the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry input during the monitoring period is:

[0031] Based on the deep learning network, a model is constructed with the heat transfer fluid flow rate, specific heat capacity, and total incident solar energy as inputs, and the temperatures at which the heat transfer fluid enters and leaves the heat absorber as labels to train the temperature prediction model;

[0032] Each particle represents a set (C i , m i,s,d ), and C i ∈[C min , C max , m i,s,d ∈[0, m max,d ;

[0033] where C i denotes the specific heat capacity of the heat transfer fluid corresponding to the i-th particle, m i,s,d denotes the mass of the fermentation slurry input during the monitoring period corresponding to the i-th particle, and S minDenote the minimum specific heat capacity as S max Denote the maximum specific heat capacity as m max,d Denote the maximum mass of the fermentation slurry that the fermenter can hold;

[0034] Randomly select a group of particles (C i , m i,s,d ), and randomly initialize the velocity of the particles. Input the corresponding specific heat capacity of the heat transfer fluid, the flow rate of the heat transfer fluid, and the total incident solar energy into the temperature prediction model to generate the temperatures of the heat transfer fluid entering and leaving the solar receiver. Then, calculate the solar thermal conversion efficiency and the biogas production rate corresponding to the particles respectively:

[0035]

[0036] Among them, σ i Denote the solar thermal conversion efficiency of the i-th particle as Q i Denote the effective thermal energy of the i-th particle as E i Denote the total incident solar energy of the i-th particle as G i Denote the biogas production rate of the i-th particle as T i Denote the actual fermentation temperature of the i-th particle;

[0037] Set the maximum solar thermal conversion efficiency as the ideal solar thermal conversion efficiency, and the maximum biogas production rate as the ideal biogas production rate. Combine the ideal solar thermal conversion efficiency and the ideal biogas production rate to generate an ideal point, and calculate the Euclidean distance between the particle and the ideal point:

[0038]

[0039] Among them, d i Denote the Euclidean distance from the i-th particle to the ideal point as σ max Denote the ideal solar thermal conversion efficiency as G max Denote the ideal biogas production rate;

[0040] For each particle, update the individual optimal position. If the Euclidean distance from the particle's current position to the ideal point is better than the individual historical optimal position, then set the current position as the individual optimal position. Select the particle with the smallest d i among all individuals to update the global optimal position; Update the particle velocity and position every time d i is calculated. When the maximum number of iterations is reached, output the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry corresponding to the particle at the global optimal position, which are the optimal specific heat capacity of the heat transfer fluid and the optimal mass of the fermentation slurry.

[0041] The present invention also provides a method for optimizing the solar concentrating driven biogas gas storage configuration. The method is executed by the above-mentioned solar concentrating driven biogas gas storage configuration optimization system. The specific steps include:

[0042] Step 1: Measure the lighting area of the concentrator and the solar radiation power vertically incident per unit area within the lighting area during the monitoring period, and obtain the optical efficiency of the concentrator. Generate the total incident solar energy based on the lighting area, the solar radiation power vertically incident per unit area, and the optical efficiency of the concentrator. The monitoring period is a natural day;

[0043] Step 2: Transfer heat to the heat transfer fluid through the heat absorber. Measure the temperature of the heat transfer fluid entering and leaving the heat absorber, as well as the flow rate and specific heat capacity of the heat transfer fluid. Calculate the effective thermal energy. Generate the solar thermal conversion rate based on the total incident solar energy and the effective thermal energy;

[0044] Step 3: Set the reference fermentation temperature of the fermenter, calculate the heat required to maintain the reference fermentation temperature during the monitoring period and record it as the reference heat. Set the reference gas production rate under the reference heat. Calculate the actual fermentation temperature based on the effective thermal energy and the reference heat. Correct the reference gas production rate based on the actual fermentation temperature and the reference fermentation temperature to generate the biogas production rate;

[0045] Step 4: Take the flow rate range of the heat transfer fluid, the specific heat capacity range of the heat transfer fluid, the maximum mass of the fermentation slurry, and the temperature range in which the fermentation strain is active as the constraint conditions. The optimization objective is to maximize the solar thermal conversion rate and the biogas production rate. Optimize the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry input during the monitoring period through the multi-objective particle swarm optimization algorithm.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] The present invention measures the synergistic effect of four modules and realizes the optimization of the solar energy-driven biogas system. In the light measurement module, the solar energy input is accurately quantified, the solar radiation power and the light efficiency are monitored in real time, which is applicable to the calculation of solar energy input in different scenarios and provides a reliable energy benchmark; in the heat conversion module, the effective thermal energy and the thermal conversion rate are dynamically calculated based on the parameters of the heat transfer fluid. Taking the thermal conversion rate as the key index, it solves the problem of fuzzy thermal energy utilization rate in the traditional system, clarifies the conversion relationship between solar energy and available thermal energy, and provides an efficient and stable energy input for subsequent fermentation and optimization.

[0048] The present invention also dynamically corrects the fermentation temperature and gas production rate by comparing the reference heat with the actual heat energy, realizes temperature adaptive gas production optimization, avoids overheating and insufficient heating, accurately adjusts the heat distribution, improves the resource utilization rate, and realizes the efficient matching of heat supply and fermentation demand. Taking the photothermal conversion rate and gas production rate as optimization objectives, optimizing through the multi-objective particle swarm algorithm, and optimizing the photothermal conversion rate and gas production rate by changing the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry under constraint conditions, avoiding the performance imbalance caused by single-objective optimization, and determining the optimization result by calculating the particle closest to the ideal point, improving the accuracy of multi-objective optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the system module of the embodiment of the present invention;

[0050] Figure 2 It is a schematic diagram of the method flow of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0052] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0053] Embodiment:

[0054] Please refer to Figure 1 , the present invention provides a technical solution:

[0055] A solar concentrating drive biogas gas storage configuration optimization system, specifically including:

[0056] A light measurement module is used to measure the area of the sunlight collection area of the concentrator during the monitoring time period, as well as the solar radiation power vertically incident per unit area within the sunlight collection area, and obtain the optical efficiency of the concentrator. Based on the area of the sunlight collection area, the solar radiation power vertically incident per unit area, and the optical efficiency of the concentrator, the total incident solar energy is generated. The monitoring time period is one natural day;

[0057] In this embodiment, the formula for generating the total incident solar energy is:

[0058] E = S × F × η × t

[0059] Wherein, E represents the total incident solar energy, S represents the area of the sunlight collection area, F represents the average value of the solar radiation power vertically incident per unit area during the monitoring time period, η represents the optical efficiency of the concentrator, and t represents the length of the monitoring time period.

[0060] The total incident solar energy reflects the total amount of solar radiation energy captured by the concentrator during the monitoring time period. The area of the sunlight collection area represents the actual physical area where the concentrator receives solar radiation. The larger the area of the sunlight collection area, the more solar energy is captured. The concentrator usually only utilizes direct sunlight and ignores scattered light. During the monitoring time period, the solar radiation power vertically incident per unit area is collected at 1-hour intervals by a pyrheliometer, and the average value of the solar radiation power vertically incident per unit area during the monitoring time period is calculated. The optical efficiency of the concentrator is used to measure the ability of the concentrator to transfer solar radiation energy to the heat absorber; the total incident solar energy is proportional to the area of the sunlight collection area, the solar radiation power, and the optical efficiency of the concentrator; the higher the total incident solar energy, the more solar energy the concentrator can provide for the heat absorber.

[0061] A heat conversion module is used to transfer heat to the heat transfer fluid through the heat absorber, measure the temperatures of the heat transfer fluid entering and leaving the heat absorber, as well as the flow rate and specific heat capacity of the heat transfer fluid, calculate the effective thermal energy, and generate the solar thermal conversion efficiency based on the total incident solar energy and the effective thermal energy;

[0062] In this embodiment, the principle for generating the solar thermal conversion efficiency is:

[0063] The formula for generating the effective thermal energy is:

[0064] Q = L × C × (T out -T in ) × t

[0065] Wherein, Q represents the effective thermal energy, L represents the average flow rate of the heat transfer fluid during the monitoring time period, C represents the specific heat capacity of the heat transfer fluid, T out represents the temperature of the heat transfer fluid leaving the heat absorber, and T in represents the temperature of the heat transfer fluid entering the heat absorber;

[0066] The effective thermal energy reflects the available heat actually transferred by the heat absorber to the heat transfer fluid, that is, the net heat absorbed by the heat transfer fluid when flowing through the heat absorber. The effective thermal energy is calculated based on the principles of energy conservation and heat transfer. That is, the net heat absorbed by the heat transfer fluid is equal to the energy carried by the fluid due to temperature change. The flow rate L determines the total amount of fluid that can carry heat. The larger the flow rate, the larger the total amount of fluid carrying heat, and the higher the effective thermal energy carried away. The effective thermal energy is proportional to the flow rate of the heat transfer fluid. The specific heat capacity C represents the heat required for the fluid to increase by 1°C, reflecting the heat storage capacity of the fluid. The higher the specific heat capacity of the heat transfer fluid, the higher the heat contained in the heat transfer fluid, and the higher the effective thermal energy that can be carried away. The effective thermal energy is proportional to the flow rate of the heat transfer fluid; T out -T in represents the temperature change of the fluid after passing through the heat absorber, that is, the temperature rise, which is the temperature increase of the heat transfer fluid after passing through the heat absorber. The higher the temperature rise, the higher the thermal energy carried away by the heat transfer fluid. L×C reflects the heat required for the fluid to increase by 1°C. The heat received by the surface of the heat absorber may be dissipated through radiation, convection and other channels. Only the part carried away by the heat transfer fluid is available.

[0067] The formula for generating the solar thermal conversion rate is:

[0068]

[0069] Among them, σ represents the solar thermal conversion rate.

[0070] The solar thermal conversion rate represents the proportion of the solar energy captured by the concentrator that is actually utilized, measuring the efficiency of the system in converting solar energy into available thermal energy. The actually utilized part of the solar energy is the effective thermal energy. The higher the conversion rate, the less energy loss in the system, the better the heat transfer effect, and it directly affects the heating capacity of the subsequent fermentation process. A high conversion rate means more heat is used to maintain the fermentation dimension, thus increasing the biogas production. The solar thermal conversion rate is proportional to the effective thermal energy and inversely proportional to the total incident solar energy.

[0071] The fermentation gas production module is used to set the reference fermentation temperature of the fermentation tank, calculate the heat required to maintain the reference fermentation temperature during the monitoring period and record it as the reference heat, set the reference gas production rate under the reference heat, calculate the actual fermentation temperature based on the effective thermal energy and the reference heat, and correct the reference gas production rate based on the actual fermentation temperature and the reference fermentation temperature to generate the biogas production rate;

[0072] In this embodiment, the principle for generating the biogas production rate is:

[0073] The formula for generating the reference heat is:

[0074] Q0=L×C×(T0-Tenv )×t

[0075] Among them, Q0 represents the reference heat, T0 represents the reference fermentation temperature, and T env represents the ambient temperature.

[0076] The reference heat represents the minimum heat required to maintain the fermenter at the reference fermentation temperature, and is used to judge whether the actual heat supply is sufficient. C×(T0 - T env ) reflects the heat required for a unit mass of heat transfer fluid to raise the temperature from the ambient temperature to the reference fermentation temperature. The flow rate reflects the total amount of fluid used for heat transfer. The longer the monitoring time period, the more heat is required for long-term heating and heat preservation. The reference fermentation temperature depends on the type of fermentation strain and refers to the temperature at which the fermentation strain works most actively; the reference heat reflects the minimum heat supply requirement during fermentation. By comparing the reference heat and the effective thermal energy provided by solar energy, it is judged whether the heat supply is sufficient during the fermentation process.

[0077] The reference gas production rate represents the volume of biogas produced per unit mass of fermentation slurry per unit time at the reference fermentation temperature. When other parameters remain unchanged, each fermentation temperature corresponds to a gas production rate. When the temperature is controlled to be maintained at the reference fermentation temperature, the gas production rate at this time is the reference gas production rate.

[0078] The formula for generating the biogas production rate is as follows:

[0079]

[0080] Among them, T represents the actual fermentation temperature, m s,d represents the mass of the fermentation slurry input during the monitoring time period, G represents the biogas production rate, G0 represents the reference gas production rate, and k represents the temperature coefficient, and k = 0.069.

[0081] The biogas production rate represents the actual gas production rate after correcting the reference gas production rate according to the actual fermentation temperature; the temperature is calculated in sections according to whether the effective thermal energy can meet the reference heat. When Q ≥ Q0, it means that the temperature meets the standard and the reference fermentation temperature can be maintained all the time. When Q < Q0, it is impossible to maintain the reference fermentation temperature, and T env is the lowest limit of the fermentation system. When the heat supply is completely insufficient, the temperature in the fermenter approaches the ambient temperature. Q is the effective thermal energy actually provided by solar energy. The effective thermal energy supplies heat to two places at the same time: the heat loss of the fermenter and heating the fermentation slurry. U×A reflects the rate of heat dissipation of the fermenter to the environment, and m s,d ×C s reflects the heat capacity of the fermentation slurry, that is, how much heat is required to raise its temperature. U×A + m s,d ×C s represents the total thermal resistance of the fermentation system, It reflects the heat that can be provided for heating the fermentation slurry after the effective heat energy offsets the total thermal resistance; the temperature coefficient k is taken as 0.069. Based on the Arrhenius equation, it means that when the temperature rises by 1 °C, the gas production rate increases by about 7%. When T < T0, the activity of the fermentation bacteria decreases, reducing the biogas production rate. G represents the actual biogas rate after correcting the reference gas production rate G0 through the fermentation temperature. When Q ≥ Q0, T = T0, and it can be maintained at the reference fermentation temperature. At this time, the biogas production rate is equal to the reference gas production rate. When Q < Q0, T < T0, and the weakening degree of the biogas production rate relative to the reference gas production rate is determined according to the temperature difference. Moreover, the larger T is, the lower the weakening degree is. This relationship is reflected by an exponential function.

[0082] The comprehensive optimization module is used to optimize the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry input during the monitoring period with the flow range of the heat transfer fluid, the specific heat capacity range of the heat transfer fluid, the maximum mass of the fermentation slurry, and the temperature range in which the fermentation bacteria are active as the constraint conditions, and the optimization goal is to maximize the solar thermal conversion efficiency and the biogas production rate. The multi-objective particle swarm optimization algorithm is used to optimize the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry input.

[0083] In this embodiment, the principle for optimizing the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry input during the monitoring period is as follows:

[0084] Based on the deep learning network, a model is constructed with the flow rate of the heat transfer fluid, the specific heat capacity, and the total incident solar energy as the inputs, and the temperatures of the heat transfer fluid entering and leaving the heat absorber as the labels to train the temperature prediction model;

[0085] The flow rate of the heat transfer fluid and the total incident solar energy are known parameters that can be calculated. Input a specific heat capacity of the heat transfer fluid, which respectively has corresponding flow rates of the heat transfer fluid and total incident solar energy. These three parameters are used as inputs to obtain the temperatures of the heat transfer fluid entering and leaving the heat absorber.

[0086] Each particle represents a group (C i , m i,s,d ), and C i ∈ [C min , C max , m i,s,d ∈ [0, m max,d ;

[0087] Among them, C i represents the specific heat capacity of the heat transfer fluid corresponding to the i-th particle, m i,s,d represents the mass of the fermentation slurry input during the monitoring period corresponding to the i-th particle, S min represents the minimum specific heat capacity, S max represents the maximum specific heat capacity, and m max,d represents the maximum mass of the fermentation slurry that the fermentation tank can accommodate;

[0088] The specific heat capacity of the heat transfer fluid is changed by changing the type of the heat transfer fluid.

[0089] Randomly select a group of particles (C i , m i,s,d ), and randomly initialize the velocities of the particles. Input the corresponding specific heat capacity of the heat transfer fluid, the flow rate of the heat transfer fluid, and the total incident solar energy into the temperature prediction model to generate the corresponding temperatures of the heat transfer fluid entering and leaving the heat absorber, and then calculate the corresponding solar energy photothermal conversion rate and biogas production rate of the particles respectively:

[0090]

[0091] Among them, σ i represents the solar energy photothermal conversion rate of the i-th particle, Q i represents the effective thermal energy of the i-th particle, E i represents the total incident solar energy of the i-th particle, G i represents the biogas production rate of the i-th particle, and T i represents the actual fermentation temperature of the i-th particle;

[0092] Set the maximum solar energy photothermal conversion rate as the ideal solar energy photothermal conversion rate, and the maximum biogas production rate as the ideal biogas production rate. Combine the ideal solar energy photothermal conversion rate and the ideal biogas production rate to generate an ideal point, and calculate the Euclidean distance between the particle and the ideal point:

[0093]

[0094] Among them, d i represents the Euclidean distance from the i-th particle to the ideal point, v max represents the ideal solar energy photothermal conversion rate, and G max represents the ideal biogas production rate;

[0095] For each particle, update the individual optimal position. If the Euclidean distance from the current position of the particle to the ideal point is better than the individual historical optimal position, then set the current position as the individual optimal position, select the particle with the smallest d i among all individuals, and update the global optimal position; update the particle velocity and position each time d i is calculated. When the maximum number of iterations is reached, output the specific heat capacity of the heat transfer fluid and the fermentation slurry mass corresponding to the particle at the global optimal position, which are the optimal specific heat capacity of the heat transfer fluid and the fermentation slurry mass.

[0096] The ideal point represents the theoretical optimal value when each objective function is optimized separately. The ideal specific heat capacity of the heat transfer fluid represents the specific heat capacity of the heat transfer fluid that maximizes the solar thermal conversion efficiency when the solar thermal conversion efficiency is optimized separately. The ideal mass of the fermentation slurry represents the mass of the fermentation slurry that maximizes the biogas production rate when the biogas production rate is optimized separately. In the case of multi-objective optimization, the actual solution cannot reach the two theoretical optimal values simultaneously but can approach them gradually. The smaller the Euclidean distance between the particle and the ideal point, the closer the particle is to the ideal point, and the better the optimization scheme corresponding to the particle.

[0097] Please refer to Figure 2 , the present invention also provides a method for optimizing the configuration of a solar concentrating-driven biogas storage, which is executed by the above-mentioned solar concentrating-driven biogas storage configuration optimization system. The specific steps include:

[0098] Step 1: Measure the area of the light-collecting area of the concentrator and the solar radiation power vertically incident per unit area within the light-collecting area during the monitoring period, and obtain the optical efficiency of the concentrator. Generate the total incident solar energy based on the area of the light-collecting area, the solar radiation power vertically incident per unit area, and the optical efficiency of the concentrator. The monitoring period is a natural day;

[0099] Step 2: Transfer heat to the heat transfer fluid through the heat absorber, measure the temperatures of the heat transfer fluid entering and leaving the heat absorber, as well as the flow rate and specific heat capacity of the heat transfer fluid, calculate the effective thermal energy, and generate the solar thermal conversion efficiency based on the total incident solar energy and the effective thermal energy;

[0100] Step 3: Set the reference fermentation temperature of the fermentation tank, calculate the heat required to maintain the reference fermentation temperature during the monitoring period and record it as the reference heat. Set the reference biogas production rate under the reference heat, calculate the actual fermentation temperature based on the effective thermal energy and the reference heat, and correct the reference biogas production rate based on the actual fermentation temperature and the reference fermentation temperature to generate the biogas production rate;

[0101] Step 4: With the flow rate range of the heat transfer fluid, the specific heat capacity range of the heat transfer fluid, the maximum mass of the fermentation slurry, and the temperature range in which the fermentation strain is active as constraints, and the optimization objective of maximizing the solar thermal conversion efficiency and the biogas production rate, optimize the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry input during the monitoring period through a multi-objective particle swarm optimization algorithm.

[0102] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0103] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application.

Claims

1. A solar concentrator-driven biogas gas storage configuration optimization system, characterized in that, Specifically, it includes: A light measurement module, which is used to measure the daylighting area of the concentrator within a monitoring time period, as well as the solar radiation power vertically incident per unit area within the daylighting area, and obtain the optical efficiency of the concentrator, and generate the total incident solar energy based on the daylighting area, the solar radiation power vertically incident per unit area, and the optical efficiency of the concentrator. The monitoring time period is a natural day; A heat conversion module, which is used to transfer heat to the heat transfer fluid through the heat absorber, measure the temperature of the heat transfer fluid entering and leaving the heat absorber, as well as the flow rate and specific heat capacity of the heat transfer fluid, calculate the effective thermal energy, and generate the solar thermal conversion rate based on the total incident solar energy and the effective thermal energy; A fermentation gas production module, which is used to set the reference fermentation temperature of the fermentation tank, calculate the heat required to maintain the reference fermentation temperature within the monitoring time period and record it as the reference heat, set the reference gas production rate under the reference heat, calculate the actual fermentation temperature based on the effective thermal energy and the reference heat, and correct the reference gas production rate based on the actual fermentation temperature and the reference fermentation temperature to generate the biogas production rate; A comprehensive optimization module, which takes the flow rate range of the heat transfer fluid, the specific heat capacity range of the heat transfer fluid, the maximum fermentation slurry mass, and the temperature range in which the fermentation bacteria are active as constraint conditions, with the optimization goal of maximizing the solar thermal conversion rate and the biogas production rate, and optimizes the specific heat capacity of the heat transfer fluid and the mass of the input fermentation slurry within the monitoring time period through a multi-objective particle swarm optimization algorithm.

2. The optimized system for solar energy concentrating drive biogas gas storage configuration according to claim 1, wherein: The formula for generating the total incident solar energy in the light measurement module is: E = S×F×η×t Where, E represents the total incident solar energy, S represents the daylighting area, F represents the average value of the solar radiation power vertically incident per unit area within the monitoring time period, η represents the optical efficiency of the concentrator, and t represents the length of the monitoring time period.

3. The optimized system for solar concentrator-driven biogas gas storage configuration according to claim 2, characterized in that: The principle for generating the solar thermal conversion rate in the heat conversion module is: The formula for generating the effective thermal energy is: Q = L×C×(T out - T in )×t Where Q represents the effective heat energy, L represents the average heat transfer fluid flow rate during the monitoring period, C represents the specific heat capacity of the heat transfer fluid, T out represents the temperature of the heat transfer fluid leaving the heat absorber, and T in represents the temperature of the heat transfer fluid entering the heat absorber; The formula for generating the solar thermal conversion rate is: Where, σ represents the solar thermal conversion rate.

4. A solar concentrator-driven biogas gas storage configuration optimization system according to claim 3, characterized in that: The principle for generating the biogas production rate in the fermentation gas production module is: The formula for generating the reference heat is: Q0 = L × C × (T0 - T env ) × t Among them, Q0 represents the reference heat, T0 represents the reference fermentation temperature, and T env represents the ambient temperature; The formula for generating the biogas production rate is: Among them, T represents the actual fermentation temperature, and m s,d represents the mass of the fermentation slurry input during the monitoring period, G represents the biogas production rate, G0 represents the reference production rate, k represents the temperature coefficient, and k = 0.

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5. The optimized system for solar concentrator-driven biogas gas storage configuration according to claim 4, wherein: The principle for optimizing the specific heat capacity of the heat transfer fluid and the mass of the input fermentation slurry within the monitoring time period in the comprehensive optimization module is: Construct a model based on a deep learning network, use the flow rate of the heat transfer fluid, the specific heat capacity, and the total incident solar energy as inputs, and the temperature of the heat transfer fluid entering and leaving the heat absorber as labels to train the temperature prediction model; Each particle represents a set (C i , m i,s,d ), and C i ∈ [C min , C max , m i,s,d ∈ [0, m max,d ; Among them, C i represents the specific heat capacity of the heat transfer fluid corresponding to the i-th particle, m i,s,d represents the mass of the fermentation slurry input during the monitoring period corresponding to the i-th particle, S min represents the minimum specific heat capacity, S max represents the maximum specific heat capacity, m max,d represents the maximum mass of the fermentation slurry that the fermenter can hold; Randomly select a group of particles (C i , m i,s,d ), and randomly initialize the velocity of the particles. Input the corresponding specific heat capacity of the heat transfer fluid, the flow rate of the heat transfer fluid, and the total incident solar energy into the temperature prediction model to generate the temperatures of the heat transfer fluid entering and leaving the absorber. Then, calculate the corresponding solar thermal conversion efficiency and biogas production rate of the particles respectively: Among them, σ i represents the solar photothermal conversion rate of the i-th particle, Q i represents the effective thermal energy of the i-th particle, E i represents the total incident solar energy of the i-th particle, G i represents the biogas production rate of the i-th particle, T i represents the actual fermentation temperature of the i-th particle; Set the maximum solar thermal conversion rate as the ideal solar thermal conversion rate, set the maximum biogas production rate as the ideal biogas production rate, generate an ideal point by combining the ideal solar thermal conversion rate and the ideal biogas production rate, and calculate the Euclidean distance between the particle and the ideal point: Among them, d i represents the Euclidean distance from the i-th particle to the ideal point, and σ max represents the ideal solar energy photothermal conversion rate, and G max represents the ideal biogas production rate; For each particle, update the individual optimal position. If the Euclidean distance from the current position of the particle to the ideal point is better than the individual historical optimal position, then set the current position as the individual optimal position, and select the particle with the smallest d i among all individuals to update the global optimal position; update the particle velocity and position each time d i is calculated. When the maximum number of iterations is reached, output the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry corresponding to the particle at the global optimal position, which are the optimal specific heat capacity of the heat transfer fluid and the optimal mass of the fermentation slurry.

6. A method for optimizing the configuration of a solar concentrating-driven biogas gas storage, characterized in that: The method is executed by the solar concentrator-driven biogas storage configuration optimization system according to any one of claims 1-5: Step 1: Measure the light-collecting area of the concentrator and the solar radiation power vertically incident per unit area within the light-collecting area during the monitoring time period, and obtain the optical efficiency of the concentrator. Generate the total incident solar energy based on the light-collecting area, the solar radiation power vertically incident per unit area, and the optical efficiency of the concentrator. The monitoring time period is one natural day; Step 2: Transfer heat to the heat transfer fluid through the heat absorber, measure the temperatures of the heat transfer fluid entering and leaving the heat absorber, as well as the flow rate and specific heat capacity of the heat transfer fluid, calculate the effective thermal energy, and generate the solar-to-thermal conversion efficiency based on the total incident solar energy and the effective thermal energy; Step 3: Set the reference fermentation temperature of the fermenter, calculate the heat required to maintain the reference fermentation temperature during the monitoring time period and denote it as the reference heat, set the reference gas production rate under the reference heat, calculate the actual fermentation temperature based on the effective thermal energy and the reference heat, and correct the reference gas production rate based on the actual fermentation temperature and the reference fermentation temperature to generate the biogas production rate; Step 4: With the flow rate range of the heat transfer fluid, the specific heat capacity range of the heat transfer fluid, the maximum mass of the fermentation slurry, and the temperature range in which the fermentation bacteria are active as constraints, and with the goal of maximizing the solar-to-thermal conversion efficiency and the biogas production rate, optimize the specific heat capacity of the heat transfer fluid and the mass of the fermentation slurry input during the monitoring time period through the multi-objective particle swarm optimization algorithm.

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