An optimization method and system for a biogas combined heat and power supply system in a farm

Abstract

The present invention relates to an optimization method and system for a biogas combined heat and power supply system in a farm. The method includes: constructing a heat supply side heat model and a heat demand model; the heat supply side heat model represents the relationship between the supplied heat and the amount of biogas and feed amount used for biogas power generation; the supplied heat includes: the heat provided by the generator and the heat provided by the boiler; the heat demand model represents the relationship between the heat demand of the anaerobic digester and the feed amount; determining a heat demand constraint model according to the heat supply side heat model and the heat demand model; constructing a heat supply model with the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas amount constraint model for power generation as constraint conditions and the maximum return on investment as the objective function; solving the heat supply model using a genetic algorithm to obtain the optimal individual, and controlling the biogas combined heat and power supply system in the farm according to the optimal individual. The present invention can better achieve the effect of energy conservation and emission reduction.

Description

Technical Field

[0001] The present invention relates to the technical field of energy, and particularly to an optimization method and system for a biogas combined heat and power supply system in a farm. Background Art

[0002] Energy and environmental problems have become important bottlenecks restricting the social development of the world today. Achieving the coordinated development of energy, environmental protection and economy has become an urgent need. On September 22, 2020, China put forward an energy strategic goal at the United Nations General Assembly to strive to peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. At present, the scale and concentration of the breeding industry are getting higher and higher, and the agricultural non-point source pollution is serious. At the same time, the rural energy structure still mainly relies on fossil energy, which is difficult to support the rural revitalization construction and rapid development under the "dual carbon goal". How to rationally utilize energy, effectively improve the energy utilization level, and promote the virtuous cycle of the ecological environment has become the key issue currently studied in the energy field of our country.

[0003] Most of the existing combined cooling, heat and power supply systems mainly use natural gas fossil fuels as the main energy source. Utilizing clean energy such as biogas can better reflect the advantages of energy conservation and emission reduction. However, the existing technical solutions provide a linearization method for the optimal operation of a combined cooling, heat and power supply system. The linearization software for the optimization of a combined cooling, heat and power supply system is compiled using MATLAB to realize the formulation of the optimal operation strategy of the system and conduct operation optimization. The selected linear programming algorithm has a fast calculation speed, can be applied to large-scale power systems, and has strong practicability. However, it does not involve small combined cooling, heat and power supply systems, especially the optimization of combined supply systems of biomass energy, and cannot better achieve the effect of energy conservation and emission reduction. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimization method and system for a biogas combined heat and power supply system in a farm, which can better achieve the effect of energy conservation and emission reduction.

[0005] To achieve the above purpose, the present invention provides the following solutions:

[0006] An optimization method for a biogas combined heat and power supply system in a farm, comprising:

[0007] Constructing an energy supply side heat model and a heat demand model; the energy supply side heat model represents the relationship between the provided heat and the biogas amount and feed amount used for biogas power generation; the provided heat includes: the heat provided by the generator and the heat provided by the boiler; the heat demand model represents the relationship between the heat demand of the anaerobic digester and the feed amount.

[0008] Determining a heat demand constraint model according to the energy supply side heat model and the heat demand model;

[0009] Taking the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation as constraint conditions, a heating model is constructed with the maximum return on investment as the objective function;

[0010] The genetic algorithm is used to solve the heating model to obtain the optimal individual, and the biogas combined heat and power system of the farm is controlled according to the optimal individual.

[0011] Optionally, the step of using the genetic algorithm to solve the heating model to obtain the optimal individual specifically includes:

[0012] Calculate the reference heat demand of the anaerobic digester according to the reference feed amount and the heat demand model;

[0013] At the current iteration number, initialize the population, where the population includes multiple individuals; the individuals include the feed amount, the biogas volume for biogas power generation, the operating power of the generator set, and the operating power of the boiler;

[0014] Decode the chromosomes of the population to obtain the candidate optimal individual;

[0015] According to the reference heat demand, the candidate optimal individual, the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation, judge whether the optimal individual meets the constraint conditions to obtain the first judgment result;

[0016] If the first judgment result is no, then perform the next iteration;

[0017] If the first judgment result is yes, calculate the fitness value of the candidate optimal individual with the objective function as the fitness function, and judge whether the set iteration number is reached to obtain the second judgment result;

[0018] If the second judgment result is yes, determine the candidate optimal individual with the maximum fitness value at all iteration numbers as the optimal individual;

[0019] If the second judgment condition is no, perform selection operation, crossover operation, and mutation operation on the population in sequence to obtain the updated population and return to the step of decoding the chromosomes of the population to obtain the candidate optimal individual.

[0020] Optionally,

[0021] The heat model on the energy supply side is:

[0022]

[0023] Among them, Q A is the heat provided for the generator, Q g,dThe biogas quantity for biogas power generation, q b The calorific value of biogas, η α The ratio of the heat of the cylinder jacket water of the generator set to the total heat of the fuel gas used by the generator set, η β The ratio of the heat recovery of the generator set to the heat of the cylinder jacket water The water content in biogas The hydrogen sulfide content in biogas, m represents the feed quantity, Q B The heat provided by the boiler, η g The heat exchange efficiency of the boiler

[0024] Optionally, the heat demand model is specifically:

[0025]

[0026] Wherein, Q N Is the heat demand of the anaerobic digester, c is the specific heat capacity of the feed liquid, m is the feed quantity, T b Is the temperature of the feed liquid in the anaerobic digester tank, T s Is the temperature of the fresh feed liquid, K i Is the comprehensive heat transfer coefficient of the top, bottom and side tank walls of the anaerobic digester, S i Is the equivalent heat transfer area of the top, bottom and side tank walls of the anaerobic digester, T i Is the comprehensive temperature outside the anaerobic digester, m w Is the mass flow rate of water vapor carried by the biogas flow, H w Is the latent heat of vaporization of water vapor at the fermentation temperature, c w Is the specific heat capacity of water vapor, T a Is the outside air temperature, f is the proportion of the biogas volume in the discharged biogas volume, vb is the effective volume of the anaerobic digester, and γ is the biogas volume production

[0027] Optionally, the heat demand constraint model is:

[0028] Wherein, Q N Is the heat demand of the anaerobic digester, Q g,d Is the biogas quantity for biogas power generation, q b Is the calorific value of biogas, η α Is the ratio of the heat of the cylinder jacket water of the generator set to the total heat of the fuel gas used by the generator set, η β Is the ratio of the heat recovery of the generator set to the heat of the cylinder jacket water Is the water content in biogas Is the hydrogen sulfide content in biogas, m represents the feed quantity, η g Is the heat exchange efficiency of the boiler, Q X Is the lost heat

[0029] The power constraint model of the generator set is as follows:

[0030] Q g,d ×l d / 24 ≤ x A , where Q g,d is the amount of biogas used for biogas power generation, l d is the power generation amount per cubic meter of biogas, and x A is the operating power of the generator;

[0031] The power constraint model of the gas boiler is as follows:

[0032] where x B is the operating power of the boiler;

[0033] The constraint model of the amount of biogas used for power generation is as follows:

[0034]

[0035] An optimization system for a biogas combined heat and power supply system in a farm includes:

[0036] A model construction module for constructing a heat supply side heat model and a heat demand model; the heat supply side heat model represents the relationship between the provided heat, the amount of biogas used for biogas power generation, and the feed amount; the provided heat includes: the heat provided by the generator and the heat provided by the boiler; the heat demand model represents the relationship between the heat demand of the anaerobic digester and the feed amount;

[0037] A heat demand constraint model construction module for determining a heat demand constraint model according to the heat supply side heat model and the heat demand model;

[0038] A heat supply model construction module for constructing a heat supply model with the heat demand constraint model, the power constraint model of the generator set, the power constraint model of the gas boiler, and the constraint model of the amount of biogas used for power generation as constraint conditions and the maximum return on investment as the objective function;

[0039] An optimal feed amount determination module for solving the heat supply model using a genetic algorithm to obtain an optimal individual and controlling the biogas combined heat and power supply system in the farm according to the optimal individual.

[0040] Optionally, the optimal feed amount determination module includes:

[0041] A reference heat demand calculation unit for calculating the reference heat demand of the anaerobic digester according to the reference feed amount and the heat demand model;

[0042] A population initialization unit for initializing a population at the current iteration number, where the population includes multiple individuals; the individuals include feed rate, biogas volume for biogas power generation, operating power of the generator set, and operating power of the boiler.

[0043] A chromosome decoding unit for decoding the population to obtain candidate optimal individuals.

[0044] A constraint judgment unit for judging whether the optimal individual satisfies the constraint conditions according to the reference heat demand, the candidate optimal individual, the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation, to obtain a first judgment result.

[0045] An iteration unit for performing the next iteration if the first judgment result is no.

[0046] A fitness value calculation unit for calculating the fitness value of the candidate optimal individual with the objective function as the fitness function and judging whether the set iteration number is reached to obtain a second judgment result if the first judgment result is yes.

[0047] An optimal feed rate determination unit for determining the candidate optimal individual with the maximum fitness value at all iteration numbers as the optimal individual if the second judgment result is yes.

[0048] An updated population unit for performing a selection operation, a crossover operation, and a mutation operation on the population in sequence to obtain an updated population and returning to the step of decoding the population to obtain candidate optimal individuals if the second judgment condition is no.

[0049] Optionally,

[0050] The heat model on the energy supply side is:

[0051]

[0052] where Q A is the heat provided to the generator, Q g,d is the biogas volume for biogas power generation, q b is the calorific value of biogas, η α is the ratio of the cylinder jacket water heat of the generator set to the total heat of the gas used by the generator set, η β is the ratio of the heat recovery of the generator set to the cylinder jacket water heat, is the water content in biogas, is the hydrogen sulfide content in biogas, m represents the feed rate, Q B is the heat provided by the boiler, η g is the heat transfer efficiency of the boiler.

[0053] Optionally, the heat demand model is specifically:

[0054]

[0055] where Q N is the heat demand of the anaerobic fermentation tank, c is the specific heat capacity of the liquid material, m is the feed rate, T b is the temperature of the liquid material in the anaerobic fermentation tank of biogas, T s is the temperature of the fresh liquid material, K i is the comprehensive heat transfer coefficient of the top, bottom and side tank walls of the anaerobic fermentation tank, S i is the equivalent heat transfer area of the top, bottom and side tank walls of the anaerobic fermentation tank, T i is the comprehensive temperature outside the anaerobic fermentation tank, m w is the mass flow rate of water vapor carried by the biogas flow, H w is the latent heat of vaporization of water vapor at the fermentation temperature, c w is the specific heat capacity of water vapor, T a is the outside air temperature, f is the proportion of biogas volume in the discharged biogas volume, v b is the effective volume of the anaerobic fermentation tank, γ is the volume production of biogas.

[0056] Optionally, the heat demand constraint model is:

[0057] where Q N is the heat demand of the anaerobic fermentation tank, Q g,d is the biogas volume used for biogas power generation, q b is the biogas calorific value, η α is the ratio of the heat of the cylinder jacket water of the generator set to the total heat of the fuel gas used by the generator set, η β is the ratio of the heat recovery of the generator set to the heat of the cylinder jacket water, is the water content in the biogas, is the hydrogen sulfide content in the biogas, m represents the feed rate, η g is the boiler heat exchange efficiency, Q X is the lost heat;

[0058] The power constraint model of the generator set is:

[0059] Q g,d ×l d / 24 ≤ x A where Q g,d is the biogas volume used for biogas power generation, l d is the power generation per cubic meter of biogas, x A is the operating power of the generator;

[0060] The power constraint model of the gas boiler is as follows:

[0061] where x B is the operating power of the boiler;

[0062] The biogas volume constraint model for power generation is as follows:

[0063]

[0064] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention constructs a heat supply side heat model and a heat demand model; determines a heat demand constraint model according to the heat supply side heat model and the heat demand model; constructs a heat supply model with the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation as constraint conditions and the maximum return on investment as the objective function; uses a genetic algorithm to solve the heat supply model to obtain the optimal individual, and controls the biogas combined heat and power system of the farm according to the optimal individual. It is applicable to small-scale combined cooling, heat and power systems that utilize clean energy such as biogas, and can better achieve the effect of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0066] Figure 1 It is the structure diagram of the biogas combined heat and power system of the farm provided by the embodiment of the present invention;

[0067] Figure 2 It is the flow chart of an optimization method for the biogas combined heat and power system of a farm provided by the embodiment of the present invention;

[0068] Figure 3 It is the fitting diagram of the relationship between the feed amount and the biogas production;

[0069] Figure 4 It is the flow chart of the genetic algorithm provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0071] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] The present invention provides an existing biogas combined heat and power generation system for a farm, as Figure 1 shown in the structure diagram of a biogas combined heat and power generation system for a certain farm in Hebei Province, which mainly consists of a biogas fermentation module, a biogas purification module, an internal combustion engine set power generation and heating module, a utilization module for the waste heat of the generator set, etc. Among them, the short dashed line represents the airflow, the long dashed line represents the heat energy flow, and the dotted dashed line represents the electric energy flow.

[0073] The working process of the system is as follows: First, biomass raw materials such as cow dung are treated and then fed into the anaerobic fermentation tank for fermentation to produce gas. After passing through the purification link to remove impurities such as moisture and hydrogen sulfide, it is used as the fuel for the internal combustion engine. The internal combustion engine generates motive power through combustion and expansion, driving the generator to generate electricity. The cylinder jacket water heat exchanger and the flue gas heat exchanger of the generator recover the waste heat of the generator set, and the two are separately used for heating. Among them, the cylinder jacket water heat exchanger of the generator heats the circulating water of the anaerobic fermentation tank heating system to achieve the purpose of increasing the temperature of the anaerobic fermentation tank; the heat of the flue gas heat exchanger is mainly used to dry the cow bedding.

[0074] Except in winter, the waste heat of the generator is sufficient and can fully meet the heat demand of the anaerobic fermentation tank. In winter, when the outside temperature is low and the heat demand is large, a biogas-fired boiler is added as an auxiliary temperature-increasing device when the waste heat of the generator is insufficient. The remaining fermentation liquid can be separated into biogas slurry and biogas residue through the solid-liquid separation system. The biogas slurry can be used for the cultivation of organic feed, and the biogas residue is used as the raw material for producing cow bedding.

[0075] In the biogas combined heat and power supply system, in winter, the anaerobic fermentation tank mainly obtains the heat required for the heat load from the waste heat of the generator and the biogas boiler. On the energy supply side, the feed rate is an important influencing factor for the biogas production; on the demand side, the feed rate also affects the heat load demand of the anaerobic fermentation tank. The feed rate plays an important hub role in the system. Therefore, it is necessary to consider the uncertainty of the feed rate, analyze the supply-demand relationship, in order to achieve the optimal operating parameters of the energy supply-side equipment and the energy demand of the energy demand side, with a view to improving the economic benefits and energy utilization level.

[0076] In view of the problems of resource treatment of livestock manure and coordinated optimization between energy supply and demand under the dual-carbon goal, the present invention proposes a modeling and optimization method for a biogas combined heat and power supply system in a livestock farm considering the energy supply characteristics of manure treatment facilities and the coordination of dynamic heat demand. First, the relationship between the feed amount and the biogas production amount is obtained by data fitting. Then, considering the influence of the feed amount, fermentation temperature, and external environmental temperature on the heat load demand, a dynamic heat load heat balance model of the anaerobic digester is established. On this basis, considering the uncertainties of factors such as the feed amount and external environmental temperature, an optimized operation model of the combined heat and power supply system is constructed with the maximum system investment return rate as the objective function. This model can realize the optimal operation parameters of energy supply-side equipment and the energy demand on the energy demand side by reasonably allocating the feed amount, and can effectively improve the energy utilization level. As Figure 2 shown, an optimization method for a biogas combined heat and power supply system in a livestock farm provided by an embodiment of the present invention includes:

[0077] Step 101: Construct an energy supply-side heat model and a heat demand model; the energy supply-side heat model represents the relationship between the provided heat and the biogas amount and feed amount used for biogas power generation; the provided heat includes: the heat provided by the generator and the heat provided by the boiler; the heat demand model represents the relationship between the heat demand of the anaerobic digester and the feed amount.

[0078] In practical applications, biogas production is affected by various factors, such as feed amount, fermentation temperature, external environmental temperature, etc. The existing control system of this project has controlled the temperature in the anaerobic digester within a certain suitable range. Next, the SPSS software is used to compare and analyze the influence degree of measured data such as feed amount, fermentation temperature, and external environmental temperature on biogas production. It is found that the factor of feed amount has a significant impact on biogas production. The feed concentration also affects the biogas production. In summer, due to the high temperature, water needs to be sprayed on cows to reduce the body surface temperature. The dilution of the components caused by the mixing of water in the feed liquid and the rainwater problem in other seasons will reduce the feed concentration, thus affecting the analysis results. By checking the precipitation data of the local meteorological station, it is found that there is no precipitation day in January 2021. Therefore, the measured data in January 2021 is selected to fit the relationship between the feed amount and biogas production. Four anaerobic digesters of the same model are selected for this project to analyze the relationship between the feed amount and biogas production in this month. The fitting relationship curve is obtained, which can represent the relationship between the feed amount and biogas production of each tank in this month. The fitting relationship curves of multiple models are as Figure 3 shown, Figure 3 which is the fitting diagram of the relationship between the feed amount and biogas production.

[0079] From the comparison of the curve fitting relationships of multiple models, it can be seen that when the feed amount and biogas production satisfy a linear relationship, R 2= 0.904 > 0.8, with a relatively high goodness of fit and strong significance, indicating an obvious linear correlation between the feedstock amount and biogas production in that month. The fitting relationship can be expressed by Equation (1).

[0080] Q g = 14.240m + 772.415 (1)

[0081] In the formula: Q g is the biogas production, with the unit of m 3 / d; m is the feedstock amount, with the unit of ton.

[0082] Biogas is mainly composed of methane, carbon dioxide, and small amounts of nitrogen, hydrogen, oxygen, ammonia, carbon monoxide, and hydrogen sulfide gases. Biogas needs to be purified (dried, deodorized, and desulfurized of H2S) before reuse, generally mainly removing impurities such as water vapor and hydrogen sulfide. Biogas passes through a biological desulfurization system before entering the generator and then undergoes dry desulfurization treatment, with low operating costs and high desulfurization efficiency.

[0083] The purified biogas production is shown in Equation (2).

[0084]

[0085] In the formula: Q' g is the purified biogas production, with the unit of m 3 / d, is the content of H2O in biogas, is the content of H2S in biogas.

[0086] The heat provided by the energy supply side includes the waste heat Q A from the internal combustion generator set for power generation and the heat Q B provided by the boiler, which is the sum of the two parts.

[0087] Q A = Q g,d q b η α η β (3)

[0088] Q B = (Q' g - Q g,d )q b η g (4)

[0089] In summary: The heat model of the energy supply side is as follows:

[0090] Among them, Q A is the heat provided by the generator, with the unit of MJ, and Q g,d is the biogas volume used for biogas power generation, with the unit of m3 / d, q b is the calorific value of biogas, with the unit of MJ / m 3 , η α is the ratio of the heat of the cylinder jacket water of the generator set to the total heat of the gas used by the generator set, η β is the ratio of the heat recovered by the generator set to the heat of the cylinder jacket water, is the water content in biogas, is the hydrogen sulfide content in biogas, m represents the feed rate, Q B is the heat provided by the boiler, with the unit of MJ, η g is the heat exchange efficiency of the boiler.

[0091] In practical applications, the heat load of the present invention is mainly the heat preservation of the anaerobic fermentation tank and the drying of the cattle bedding. The required heat comes from the cylinder jacket water heat exchanger and the flue gas at the generator outlet respectively. Since the flue gas still remains at 50 - 80 °C after drying the cattle bedding, it fully meets the heat demand, and the heat required for drying the bedding is basically constant, so it is assumed that its load demand is a fixed value; while due to its influence by various factors such as the feed rate, fermentation temperature, and ambient temperature, the heat load of the anaerobic fermentation tank is dynamically changing. Therefore, the present invention only considers establishing a dynamic heat load model of the anaerobic fermentation tank for heat balance analysis of the fermentation tank.

[0092] For the biogas project, according to the law of conservation of energy, the output (lost) energy and the input (acquired) energy should be equal. The energy lost by the biogas project every day consists of the heat required for the newly added feed every day, the heat dissipation of the anaerobic fermentation tank body and pipelines, and the heat carried away by the discharged water vapor and discharged biogas. Among them, the feed loss and the heat dissipation of the tank body are the main parts.

[0093] The formula for calculating the total heat required to heat the anaerobic fermentation tank is shown in formula (6):

[0094] Q N = Q1 + Q2 + Q3 + Q4 (6)

[0095] In the formula, Q N is the heat demand of the anaerobic fermentation tank, that is, the total heat required to heat the anaerobic fermentation tank, with the unit of MJ / d, Q1 is the heat required to heat the fermentation broth, with the unit of MJ / d, Q2 is the heat dissipation of the fermentation tank body, with the unit of MJ / d, Q3 is the heat carried away by the discharged water vapor, with the unit of MJ / d, and Q4 is the heat carried away by the discharged biogas, with the unit of MJ / d.

[0096] The heat Q1 required to heat the fermentation broth is the heat required to heat the fermentation broth from the feed temperature to the fermentation temperature, as shown in formula (7). The heat loss of the anaerobic fermentation tank during feeding is affected by the feed rate, fermentation temperature, and feed temperature.

[0097]

[0098] Among them, c is the specific heat capacity of the feed liquid, with the unit of kJ / (kg·℃), m is the daily feed volume into the biogas digester, with the unit of ton, T b is the temperature of the feed liquid in the biogas fermentation tank, with the unit of ℃, T s is the temperature of the fresh feed liquid, approximately the ambient temperature, with the unit of ℃, and TS is the solid content rate of the feed liquid, with the unit of %.

[0099] The heat dissipation Q2 of the fermentation tank body consists of the loads of the tank top, tank wall and tank bottom, as shown in formula (8), and it is affected by the fermentation temperature and the ambient temperature.

[0100] Q2 = Q t + Q m + Q b = ∑K i S i (T b - T i ) (8)

[0101] Among them, Q t is the heat dissipation of the tank top, Q m is the heat dissipation of the tank wall, Q b is the heat dissipation of the tank bottom, K i is the overall heat transfer coefficient of each part, with the unit of W / (m 2 ·℃), which depends on the heat transfer coefficients of the inner and outer surfaces of the fermentation tank and the heat conduction coefficient of the tank wall, S i is the equivalent heat transfer area of each part, with the unit of m 2 , T i is the overall temperature outside the fermentation tank of each part, with the unit of ℃.

[0102] The heat Q3 carried away by the discharged water vapor is caused by the water vapor accompanying the discharged biogas, and it is affected by parameters such as the water vapor content carried by the discharged biogas, the latent heat of vaporization of water at the corresponding fermentation temperature, the effective volume of the fermentation tank, and the volumetric gas production rate of the system, as shown in formula (9).

[0103]

[0104] m w is the mass flow rate of water vapor carried by the biogas flow, with the unit of kg / d, H w is the latent heat of vaporization of water vapor at the fermentation temperature, with the unit of MJ / kg, c w is the specific heat capacity of water vapor, with the unit of kJ / (kg·℃), v b is the effective volume of the fermentation tank, with the unit of m 3 , γ is the volume production of biogas, with the unit of kg / (m 3 , T ais the outside air temperature, with the unit of °C, f is the proportion of biogas volume in the discharged biogas volume, and ζ w is the number of water molecules in biogas.

[0105] Biogas is mainly composed of CH4 and CO2. The heat Q4 carried away by the discharged biogas is mainly the sum of the sensible heats of CH4 and CO2, as shown in formula (10).

[0106]

[0107] In summary, the heat demand model is specifically:

[0108]

[0109] Among them, Q N is the heat demand of the anaerobic digester, c is the specific heat capacity of the liquid manure, m is the feed rate, and T b is the temperature of the liquid manure in the anaerobic digester tank, and T s is the temperature of the fresh liquid manure, K i is the comprehensive heat transfer coefficient of the top, bottom, and side tank walls of the anaerobic digester, and S i is the equivalent heat transfer area of the top, bottom, and side tank walls of the anaerobic digester, and T i is the comprehensive temperature outside the anaerobic digester, and m w is the mass flow rate of water vapor carried by the biogas flow, and H w is the latent heat of vaporization of water vapor at the fermentation temperature, and c w is the specific heat capacity of water vapor, and T a is the outside air temperature, f is the proportion of biogas volume in the discharged biogas volume, and v b is the effective volume of the anaerobic digester, and γ is the volume production of biogas.

[0110] Step 102: Determine the heat demand constraint model according to the energy supply side heat model and the heat demand model.

[0111] Step 103: Construct a heat supply model with the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation as constraint conditions, and with the maximum return on investment as the objective function.

[0112] In practical applications, the heat demand constraint model is:

[0113]

[0114] (12), where Q N is the heat demand of the anaerobic digester, and Q A is the heat provided by the generator; Q B is the heat provided by the boiler;X is the heat loss.

[0115] The power constraint model of the generator set is:

[0116] Q g,d ×l d / 24 ≤ x A (13), where Q g,d is the amount of biogas used for biogas power generation, l d is the power generation per cubic meter of biogas, and x A is the operating power of the generator.

[0117] The power constraint model of the gas boiler is:

[0118] where x B is the operating power of the boiler.

[0119] The constraint model of the amount of biogas used for power generation is:

[0120]

[0121] In practical applications, taking the maximum return on investment rate R as the objective function, as shown in formula (16).

[0122] maxR = {(P - I) / I} (16)

[0123] In the formula, P is the daily net income, in yuan, and I is the average daily initial investment amount in the initial investment, in yuan.

[0124] The average daily initial investment amount I in the initial investment includes the investment in equipment such as generator sets and gas boilers, the renovation cost of the anaerobic reactor, and the cost of the cattle bed renewable system equipment. Therefore

[0125]

[0126] α is the investment in the generator set, in yuan; β is the investment in the gas boiler, in yuan; Γ is the renovation cost of the anaerobic reactor, in yuan, and λ is the cost of the cattle bed renewable system equipment, in yuan. P A is the unit price of the generator, in yuan / kW; x A is the operating power of the generator, in kW; P B is the unit price of the boiler, in yuan / kW; x B is the operating power of the boiler, in kW.

[0127] The daily net income P includes the income generated by the power generation of the generator set minus the electricity consumption costs of the ranch and the plant area, the income generated by the biogas boiler providing heat for the anaerobic fermentation tank converted into the coal cost of using a coal-fired boiler to provide the same amount of heat, and the sum of the income generated by the cattle bedding, minus the operation costs. Therefore

[0128]

[0129] In the formula, A is the net income from power generation, B is the income generated by the biogas boiler providing heat for the anaerobic fermentation tank, with the unit of yuan; C is the income generated from the production of cattle bedding, with the unit of yuan; E is the annual operation cost of the system, including labor costs and equipment maintenance costs, etc., with the unit of yuan, A1 is the income generated by the power generation of the generator set, with the unit of yuan; A2 is the cost generated by the electricity consumption of the plant area and the ranch, with the unit of yuan; P d is the electricity price, with the unit of yuan / kWh, P coal is the price of coal, with the unit of yuan / ton; q coal is the calorific value of coal, with the unit of MJ / kg; η coal is the combustion efficiency of coal; V is the daily production volume of cattle bedding, with the unit of m 3 ; P cow is the unit price of cattle bedding, with the unit of yuan / m 2 .

[0130] Step 104: In order to solve for the maximum initial investment return rate, the genetic algorithm is used to solve the heating model to obtain the optimal individual, and the biogas combined heat and power system of the farm is controlled according to the optimal individual.

[0131] In practical applications, the use of the genetic algorithm to solve the heating model to obtain the optimal individual specifically includes:[[]]

[0132] Calculate the reference heat demand of the anaerobic fermentation tank according to the reference feed volume and the heat demand model.

[0133] At the current iteration number, initialize the population, and the population includes multiple individuals; each individual includes the feed volume, the biogas volume used for biogas power generation, the operating power of the generator set, and the operating power of the boiler.

[0134] Decode the chromosomes of the population to obtain the candidate optimal individual.

[0135] Judge whether the optimal individual meets the constraint conditions according to the reference heat demand, the candidate optimal individual, the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation, and obtain the first judgment result.

[0136] If the first judgment result is negative, perform the next iteration.

[0137] If the first judgment result is yes, calculate the fitness value of the candidate optimal individual with the objective function as the fitness function, and determine whether the set number of iterations is reached to obtain a second judgment result.

[0138] If the second judgment result is yes, determine the candidate optimal individual with the maximum fitness value at all iteration times as the optimal individual.

[0139] If the second judgment condition is no, perform selection operation, crossover operation and mutation operation on the population in sequence to obtain an updated population, and return to the step of decoding the chromosomes of the population to obtain the candidate optimal individual.

[0140] In practical applications, judging whether the optimal individual meets the constraint conditions according to the reference heat demand, the candidate optimal individual, the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model and the biogas volume constraint model for power generation specifically includes:

[0141] Calculate Q according to the candidate optimal individual A +Q B Taking the reference heat demand as Q in the heat demand constraint model N , it can be judged whether the heat demand constraint model is satisfied; according to the biogas volume for biogas power generation and the generator operating power in the candidate optimal individual, it can be determined whether the generator set power constraint model is satisfied; according to the boiler operating power, feed amount and biogas volume for biogas power generation in the candidate optimal individual, it can be determined whether the gas boiler power constraint model is satisfied, and according to the biogas volume for biogas power generation and the feed amount in the candidate optimal individual, it can be determined whether the biogas volume constraint model for power generation is satisfied.

[0142] As Figure 4 shown, the embodiments of the present invention provide more specific steps of the genetic algorithm:

[0143] ① Parameter setting: First, calculate the reference heat demand of the anaerobic digester according to the reference feed amount, input the equipment operation parameters and cost parameters of the cogeneration system, and set genetic parameters such as population size, number of variables, crossover probability, mutation probability, and number of iterations.

[0144] ② Set the fitness function: Define the maximization of the initial investment return rate as the fitness function to facilitate the calculation of the fitness value.

[0145] ③ Set the constraint conditions: Use the daily heat demand not less than the total heat provided by the equipment minus the heat loss, and the maximum operating power of the generator set and the biogas boiler, that is, formulas (12) to (15) as inequality constraint conditions.

[0146] ④ Parameter initialization: Randomly generate an initial population (feed rate, biogas volume for biogas power generation, operating power of the generator set, operating power of the boiler).

[0147] ⑤ Chromosome decoding: Calculate the optimal individual based on the initial population.

[0148] ⑥ Determine whether the optimal individual meets the heat load demand (constraint condition). If it meets, go to step ⑦; otherwise, return to ④.

[0149] ⑦ Calculate the fitness of the optimal individual and record the optimal value.

[0150] ⑧ Determine whether the termination iteration number of the genetic algorithm is reached. If it is reached, go to step ⑩; otherwise, go to step ⑨.

[0151] ⑨ Form the next generation population through selection, crossover, and mutation, and return to step ⑤.

[0152] ⑩ Output the optimal individual and the corresponding initial investment return rate of the optimal individual, reasonably allocate the feed rate according to the optimal individual to adjust the biogas production volume, and achieve the best ratio of the biogas production volume supplied to the generator and the boiler, so that the equipment is in the best power operation state, and the investment return rate can be maximized.

[0153] The embodiment of the present invention provides an optimized system for a biogas combined heat and power supply system in a farm corresponding to the above optimization method, including:

[0154] A model construction module, configured to construct an energy supply side heat model and a heat demand model; the energy supply side heat model represents the relationship between the provided heat, the biogas volume for biogas power generation, and the feed rate; the provided heat includes: the heat provided by the generator and the heat provided by the boiler; the heat demand model represents the relationship between the heat demand of the anaerobic digester and the feed rate.

[0155] A heat demand constraint model construction module, configured to determine a heat demand constraint model according to the energy supply side heat model and the heat demand model.

[0156] A heat supply model construction module, configured to construct a heat supply model with the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation as constraint conditions and the maximum investment return rate as the objective function.

[0157] An optimal feed rate determination module, configured to solve the heat supply model using a genetic algorithm to obtain the optimal individual, and control the biogas combined heat and power supply system in the farm according to the optimal individual.

[0158] As an optional implementation manner, the optimal feed rate determination module includes:

[0159] A reference heat demand calculation unit for calculating the reference heat demand of the anaerobic fermentation tank according to the reference feed rate and the heat demand model.

[0160] A population initialization unit for initializing a population at the current iteration number, where the population includes multiple individuals; each individual includes a feed rate, the amount of biogas used for biogas power generation, the operating power of the generator set, and the operating power of the boiler.

[0161] A chromosome decoding unit for decoding the population to obtain candidate optimal individuals.

[0162] A constraint judgment unit for judging whether the optimal individual meets the constraint conditions according to the reference heat demand, the candidate optimal individual, the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas amount constraint model for power generation, to obtain a first judgment result.

[0163] An iteration unit for performing the next iteration if the first judgment result is no.

[0164] A fitness value calculation unit for calculating the fitness value of the candidate optimal individual with the objective function as the fitness function and judging whether the set iteration number is reached if the first judgment result is yes, to obtain a second judgment result.

[0165] An optimal feed rate determination unit for determining the candidate optimal individual with the maximum fitness value at all iteration numbers as the optimal individual if the second judgment result is yes.

[0166] A population update unit for performing a selection operation, a crossover operation, and a mutation operation on the population in sequence to obtain an updated population and returning to the step of decoding the population to obtain candidate optimal individuals if the second judgment condition is no.

[0167] As an optional implementation manner, the energy supply side heat model is Formula (5).

[0168] As an optional implementation manner, the heat demand model is Formula (11).

[0169] As an optional implementation manner, the heat demand constraint model is Formula (12).

[0170] The generator set power constraint model is Formula (13).

[0171] The gas boiler power constraint model is Formula (14).

[0172] The biogas amount constraint model for power generation is Formula (15).

[0173] The present invention has the following beneficial effects:

[0174] (1) A relationship model between the feed rate and the biogas production rate is obtained by fitting the measured data. By reasonably coordinating the relationship among the feed rate, the ambient temperature, and the fermentation temperature, it is beneficial to increase the biogas production.

[0175] (2) Considering the influence of multiple factors such as the feed rate, the fermentation temperature, and the external ambient temperature, a heat demand model is established, which reflects the dynamic characteristics of the heat load. Under the condition of meeting the heat demand, the balance of the supply-demand relationship is achieved, the waste phenomenon of energy can be avoided, and the energy utilization level can be effectively improved.

[0176] (3) The genetic algorithm is used to reasonably control the feed rate to adjust the biogas production, reasonably allocate the biogas production of each device, and optimize the operating power of the generator and the boiler to maximize the initial investment return rate, which is significantly higher than the original return rate and has good economic benefits.

[0177] (4) The prior art provides a biogas production modeling calculation method and device based on an optimized BP neural network. The data of biogas production, sewage flow rate, influent organic matter concentration, and effluent organic matter concentration are selected, and the average biogas production rate is calculated by a formula and used as the learning rate of the BP neural network to train the BP neural network. Finally, a biogas production modeling model is generated. This method can be used to predict biogas production. During the training convergence process, the selection of the learning efficiency has an important impact on the entire learning convergence. If the value is too small, the convergence speed is too slow or even non-convergent; if the value is too large, the learning is inaccurate and the accuracy is affected. It is necessary to obtain the calculation formula through a large number of practices, and the process is relatively complex, resulting in a relatively complex established model. The present invention obtains the relationship between biogas production and feed rate through polynomial regression fitting of measured data, and the established biogas production model is simpler and has a high prediction accuracy.

[0178] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0179] Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An optimization method for a biogas combined heat and power supply system in a farm, characterized in that, Including: Constructing an energy supply side heat model and a heat demand model; The energy supply side heat model represents the relationship between the supplied heat, the amount of biogas used for biogas power generation, and the feed amount; The supplied heat includes: the heat provided by the generator and the heat provided by the boiler; the heat demand model represents the relationship between the heat demand of the anaerobic digester and the feed amount; Determining a heat demand constraint model according to the energy supply side heat model and the heat demand model; Taking the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas amount constraint model for power generation as constraint conditions, and constructing a heat supply model with the maximum return on investment as the objective function; Using a genetic algorithm to solve the heat supply model to obtain an optimal individual, and controlling the biogas combined heat and power supply system of the farm according to the optimal individual; Using a genetic algorithm to solve the heat supply model to obtain an optimal individual, specifically including: Calculating the reference heat demand of the anaerobic digester according to the reference feed amount and the heat demand model; At the current iteration number, initializing a population, the population includes multiple individuals; the individual includes the feed amount, the amount of biogas used for biogas power generation, the operating power of the generator set, and the operating power of the boiler; Performing chromosome decoding on the population to obtain a candidate optimal individual; Judging whether the optimal individual meets the constraint conditions according to the reference heat demand, the candidate optimal individual, the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas amount constraint model for power generation, and obtaining a first judgment result; If the first judgment result is no, then perform the next iteration; If the first judgment result is yes, then calculate the fitness value of the candidate optimal individual with the objective function as the fitness function, and judge whether the set iteration number is reached, and obtain a second judgment result; If the second judgment result is yes, then determine the candidate optimal individual with the maximum fitness value at all iteration numbers as the optimal individual; If the second judgment condition is no, then perform selection operation, crossover operation, and mutation operation on the population in sequence to obtain an updated population and return to the step of performing chromosome decoding on the population to obtain a candidate optimal individual; The energy supply side heat model is: Among them, Q A is the heat provided to the generator, Q g,d is the amount of biogas used for biogas power generation, q b is the calorific value of biogas, η α is the ratio of the heat of the cylinder jacket water of the generator set to the total heat of the fuel gas used by the generator set, η β is the ratio of the heat recovery amount of the generator set to the heat of the cylinder jacket water, is the water content in biogas, is the hydrogen sulfide content in biogas, m represents the feed rate, Q B is the heat provided by the boiler, η g is the heat exchange efficiency of the boiler; The heat demand model, specifically: Among them, Q N is the heat demand of the anaerobic fermentation tank, c is the specific heat capacity of the feed liquid, m is the feed rate, T b is the temperature of the feed liquid in the anaerobic fermentation tank of biogas, T s is the temperature of the fresh feed liquid, K i is the comprehensive heat transfer coefficient of the top, bottom and side tank walls of the anaerobic fermentation tank, S i is the equivalent heat transfer area of the top, bottom and side tank walls of the anaerobic fermentation tank, T i is the comprehensive temperature outside the anaerobic fermentation tank, m w is the mass flow rate of water vapor carried by the biogas flow, H w is the latent heat of vaporization of water vapor at the fermentation temperature, c w is the specific heat capacity of water vapor, T a is the outside air temperature, f is the proportion of biogas volume in the discharged biogas volume, v b is the effective volume of the anaerobic fermentation tank, γ is the volume yield of biogas; The heat demand constraint model is: Among them, Q N is the heat demand of the anaerobic fermentation tank, Q g,d is the amount of biogas used for biogas power generation, q b is the calorific value of biogas, η α is the ratio of the heat of the cylinder jacket water of the generator set to the total heat of the fuel gas used by the generator set, η β is the ratio of the heat recovery amount of the generator set to the heat of the cylinder jacket water, is the water content in biogas, is the hydrogen sulfide content in biogas, m represents the feed rate, η g is the heat exchange efficiency of the boiler, Q X is the lost heat; The generator set power constraint model is: Q g,d ×e d / 24 ≤ x A where Q g,d is the amount of biogas for biogas power generation, e d is the power generation per cubic meter of biogas, and x A is the operating power of the generator; The gas boiler power constraint model is: where x B is the operating power of the boiler; The biogas amount constraint model for power generation is:

2. An optimization system for a biogas combined heat and power supply system in a farm, characterized in that, Including: A model construction module for constructing an energy supply side heat model and a heat demand model; The energy supply side heat model represents the relationship between the supplied heat, the amount of biogas used for biogas power generation, and the feed amount; The supplied heat includes: the heat provided by the generator and the heat provided by the boiler; the heat demand model represents the relationship between the heat demand of the anaerobic digester and the feed amount; A heat demand constraint model construction module for determining a heat demand constraint model according to the energy supply side heat model and the heat demand model; The heating model construction module is used to construct a heating model with the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation as constraint conditions and the maximum return on investment as the objective function; The optimal feed quantity determination module is used to solve the heating model by using the genetic algorithm to obtain the optimal individual, and control the biogas combined heat and power system of the farm according to the optimal individual; The optimal feed quantity determination module includes: The reference heat demand calculation unit is used to calculate the reference heat demand of the anaerobic digester according to the reference feed quantity and the heat demand model; The population initialization unit is used to initialize the population at the current iteration number, and the population includes multiple individuals; each individual includes the feed quantity, the biogas volume for biogas power generation, the operating power of the generator set, and the operating power of the boiler; The chromosome decoding unit is used to decode the population to obtain the candidate optimal individual; The constraint judgment unit is used to judge whether the optimal individual meets the constraint conditions according to the reference heat demand, the candidate optimal individual, the heat demand constraint model, the generator set power constraint model, the gas boiler power constraint model, and the biogas volume constraint model for power generation, and obtain the first judgment result; The iteration unit is used to perform the next iteration if the first judgment result is no; The fitness value calculation unit is used to calculate the fitness value of the candidate optimal individual with the objective function as the fitness function and judge whether the set iteration number is reached if the first judgment result is yes, and obtain the second judgment result; The optimal feed quantity determination unit is used to determine the candidate optimal individual with the maximum fitness value at all iteration numbers as the optimal individual if the second judgment result is yes; The population update unit is used to perform selection operation, crossover operation, and mutation operation on the population in sequence to obtain the updated population and return to the step of decoding the population to obtain the candidate optimal individual if the second judgment condition is no; The heat model on the energy supply side is: Among them, Q A is the heat provided to the generator, Q g,d is the amount of biogas used for biogas power generation, q b is the calorific value of biogas, η α is the ratio of the heat of the cylinder jacket water of the generator set to the total heat of the gas used by the generator set, η β is the ratio of the heat recovery amount of the generator set to the heat of the cylinder jacket water, is the water content in biogas, is the hydrogen sulfide content in biogas, m represents the feed rate, Q B is the heat provided by the boiler, η g is the heat exchange efficiency of the boiler; The heat demand model is specifically: Among them, Q N is the heat demand of the anaerobic fermentation tank, c is the specific heat capacity of the feed liquid, m is the feed rate, and T b is the temperature of the feed liquid in the anaerobic fermentation tank, and T s is the temperature of the fresh feed liquid, K i is the combined heat transfer coefficient of the top, bottom, and side walls of the anaerobic fermentation tank, and S i is the equivalent heat transfer area of the top, bottom, and side walls of the anaerobic fermentation tank, and T i is the combined temperature outside the anaerobic fermentation tank, m w is the mass flow rate of water vapor carried by the biogas flow, and H w is the latent heat of vaporization of water vapor at the fermentation temperature, and c w is the specific heat capacity of water vapor, and T a is the outside air temperature, f is the proportion of biogas volume in the discharged biogas volume, and v b is the effective volume of the anaerobic fermentation tank, and γ is the volume production of biogas; The heat demand constraint model is: Among them, Q N is the heat demand of the anaerobic fermentation tank, Q g,d is the amount of biogas used for biogas power generation, q b is the calorific value of biogas, η α is the ratio of the heat of the cylinder jacket water of the generator set to the total heat of the gas used by the generator set, η β is the ratio of the heat recovery of the generator set to the heat of the cylinder jacket water, is the water content in biogas, is the hydrogen sulfide content in biogas, m represents the feed rate, η g is the heat exchange efficiency of the boiler, Q X is the lost heat; The generator set power constraint model is: Q g,d ×l d / 24 ≤ x A , where Q g,d is the amount of biogas used for biogas power generation, l d is the power generation per cubic meter of biogas, and x A is the operating power of the generator; The gas boiler power constraint model is: where x B is the operating power of the boiler; The biogas volume constraint model for power generation is:

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