CN113887021B - Optimized adjustment method for anaerobic digestion process parameters - Google Patents
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Abstract
The invention discloses an optimal adjustment method for anaerobic digestion process parameters. The method comprises the steps of (1) setting up a fitting function by using a K nearest neighbor method, wherein the 5 key parameters are independent variables, namely the anaerobic tank temperature T, the anaerobic tank pH, the anaerobic tank slurry quantity L, the anaerobic tank slurry solid content G and the anaerobic tank accumulated stirring duration J; and (3) optimizing and analyzing the gas production function Q, solving the values of all key technological parameters when the energy consumption is lowest and the anaerobic digestion gas production is maximum, and for the problem of optimizing the anaerobic digestion gas production, 3 objective functions, namely Q, T and J, are adopted to construct an evaluation function and 3 constraint conditions, so that the establishment of a mathematical model of the anaerobic digestion gas production multi-objective planning is completed. The invention realizes the automatic optimization and adjustment of the technological parameters, which not only reduces the working intensity of operators, but also greatly improves the working efficiency and the working quality.
Description
Technical Field
The invention relates to the technical field of environmental treatment, in particular to an optimal adjustment method for anaerobic digestion process parameters.
Background
Anaerobic digestion is an important solid waste treatment method, and is a process of putting organic matters (kitchen waste, livestock and poultry manure, organic sludge, agricultural waste and the like) into a closed container (anaerobic tank) for anaerobic fermentation so as to decompose and convert organic matters of organic macromolecules into methane which is mainly methane. Compared with disposal methods such as landfill, incineration, composting and the like, anaerobic digestion is the most widely used recycling disposal method internationally at present due to efficient resource recovery and low environmental impact.
The anaerobic digestion and biogas purification system mainly comprises a mechanical pretreatment system, an anaerobic digestion system (comprising one or a plurality of anaerobic tanks) and a biogas purification system. Generally, the control of the three systems can be integrated together and can be displayed in the same operation interface, and an operator can complete the manual control of the three systems by only operating on one operation interface.
What the operator needs to do is mainly to pay attention to and adjust the following key process parameters in real time: the method comprises the steps of controlling the liquid level of a feeding tank, opening and closing a stirrer of the feeding tank, opening and closing a feeding pump, setting the feeding quantity, controlling the liquid level of each anaerobic tank, controlling the temperature of each anaerobic tank, controlling the pressure of each anaerobic tank, opening and closing the stirrer of each anaerobic tank, opening and closing a material conveying pump between the anaerobic tanks, setting the gas production channel occupancy of each anaerobic tank, setting the ignition pressure of a torch of the anaerobic tank, and setting the gas production flow of a methane purification product. The aim is to ensure that the daily gas yield of the biogas purification product is as high as possible under the condition of low exhaustion.
An operator pays attention to tens of key process parameters in real time, makes a correct and reasonable judgment in a short time, and makes corresponding adjustments. This is not a small pressure for the operator. The efforts of operators are limited after all, and the operators cannot consider the problem and face, and the problem and face are unavoidable. It is not practical to rely on manpower to ensure that the daily gas production of the product gas is as great as possible. The daily gas production achieved by different operators will necessarily be different due to experience and ability differences.
Therefore, an accurate automatic adjustment method is urgently needed at present, and on the premise that the anaerobic digestion raw materials are sufficiently supplied and the components are stable, the control system can automatically coordinate and control the mechanical pretreatment system, the anaerobic digestion system and the biogas purification system, so that the maximization of the daily biogas yield of a biogas purification product is ensured under the condition that the exhaustion is as low as possible.
Disclosure of Invention
The invention aims to provide an optimal adjustment method for anaerobic digestion process parameters, which reduces the working intensity of operators and greatly improves the working efficiency and the working quality, aiming at the defects of the prior art.
The invention relates to an optimized adjustment method for anaerobic digestion process parameters, which comprises the following steps:
S1: and (3) establishing a database: recording the temperature T of the anaerobic tank, the pH value of the anaerobic tank, the slurry quantity L of the anaerobic tank, the solid content G of the slurry of the anaerobic tank, the accumulated stirring duration J of the anaerobic tank and the gas yield Q of anaerobic digestion, and establishing a database of the 6 key data;
S2, establishing a mathematical model of anaerobic digestion gas production: the method comprises the steps of selecting anaerobic tank temperature T, anaerobic tank pH, anaerobic tank slurry quantity L, anaerobic tank slurry solid content G and anaerobic tank accumulated stirring duration J in a database, taking 5 key parameters as independent variables, taking anaerobic digestion gas production quantity Q as dependent variables, and establishing a fitting function by a K nearest neighbor method to obtain the fitting function: q=f (T, pH, L, G, J);
S3: establishing a mathematical model of anaerobic digestion gas production multi-objective planning: for each anaerobic tank, in order to realize that the exhaustion is as low as possible, the maximum anaerobic digestion gas yield is ensured, the optimization analysis is required to be carried out on a gas yield function Q, the values of all key process parameters are obtained when the energy consumption is the lowest and the anaerobic digestion gas yield is the maximum, for the anaerobic digestion gas yield optimization problem, 3 target values are provided, namely, the anaerobic digestion gas yield Q, the anaerobic tank temperature T and the anaerobic tank accumulated stirring duration J, an evaluation function is constructed, namely, minQ= - [ omega 1f(T)+ω2f(J)+ω3f(L)+ω4f(pH)+ω5 f (G) ], one of constraint conditions is that the value range of 3 key process parameters is T min≤T≤Tmax between the lowest temperature and the highest temperature necessary for the anaerobic digestion reaction, the anaerobic tank slurry quantity L is L min≤L≤Lmax between the lowest capacity and the highest capacity which can be carried by the anaerobic tank, and the anaerobic tank accumulated stirring duration J is between the lowest accumulated duration J and the highest accumulated duration J min≤J≤Jmax; the second constraint condition is that the pH value of the anaerobic tank is equal to a given pH value, and the solid content G of slurry of the anaerobic tank is equal to a given solid content; omega 1、ω2 and omega 3 are weights of 3 objective functions, omega 4、ω5 is a weight of a non-key parameter, and specific values are assigned according to actual conditions;
the mathematical model of the multi-objective planning of the anaerobic digestion gas production is established by the objective function and constraint conditions, and the values of the key process parameters can be obtained by optimizing the multi-objective planning problem when the possible low anaerobic digestion gas production can be exhausted and the anaerobic digestion gas production is as large as possible.
Further, the temperature adjustment of the anaerobic tank is realized by turning on or off the boiler; increasing the slurry amount L of the anaerobic tank, which is realized by feeding the slurry into the anaerobic tank through a feeding tank or conveying the slurry to the anaerobic tank through other anaerobic tanks, and reducing the slurry amount L of the anaerobic tank, which is realized by outputting the slurry to other anaerobic tanks or directly discharging the slurry to the biogas slurry tank from outside; increasing the anaerobic tank cumulative stirring time period J is realized by starting the anaerobic tank stirrer and increasing the number and the time period of the starting, and decreasing the anaerobic tank cumulative stirring time period J is realized by completely closing the anaerobic tank stirrer or partially closing the anaerobic tank stirrer.
Further, according to the mathematical model, 3 key process parameters, namely the temperature T of the anaerobic tank, the slurry quantity L of the anaerobic tank and the accumulated stirring time length J of the anaerobic tank, reach an optimized value, the pressure of the anaerobic tank changes at the moment, for the pressure of the anaerobic tank, the pressure of the anaerobic tank is required to be within a certain range, the pressure range of the anaerobic tank is divided into 3 areas, namely a high-pressure early warning area, a normal working area and a low-pressure early warning area, and when the pressure of the anaerobic tank fluctuates in the normal working area, the process parameters do not need to be adjusted; when the pressure of the anaerobic tank is in a high-pressure early-warning area or a low-pressure early-warning area, the corresponding decompression method or the corresponding pressurization method is started immediately, namely the process parameters must be adjusted.
Further, the realization method for reducing the pressure of the anaerobic tank is that the stirrer of the anaerobic tank is completely or partially closed, or the gas production flow of the purified biogas product is increased, or the boiler is started until the pressure of the anaerobic tank is restored to a normal working area; the method for realizing the increase of the pressure of the anaerobic tank is to completely or partially open the stirrer of the anaerobic tank, or reduce the gas flow rate of the purified biogas product or close the boiler.
Further, when the anaerobic tank pressure is in the high-pressure early warning area or the low-pressure early warning area, the anaerobic tank pressure is regulated and then is restored to the normal working area, but at the moment, the anaerobic tank temperature T, the anaerobic tank slurry quantity L and the anaerobic tank accumulated stirring time J possibly deviate from the current optimized values, when the anaerobic tank pressure deviates from the current optimized values, the anaerobic tank temperature T, the anaerobic tank pH value, the anaerobic tank slurry quantity L, the solid content G of the anaerobic tank slurry and the anaerobic tank accumulated stirring time J are regulated according to the current 5 technological parameter values, so that the multi-objective planning is carried out according to the fitting function Q, the optimized values of the anaerobic tank temperature T, the anaerobic tank slurry quantity L and the anaerobic tank accumulated stirring time J in the given time period can be obtained, then the anaerobic tank temperature T, the anaerobic tank slurry quantity L and the anaerobic tank accumulated stirring time J are regulated according to the optimized values, and thus the change of the anaerobic tank pressure can be caused, and if the anaerobic tank pressure fluctuates in the normal working area, the technological parameter is not regulated; if the pressure of the anaerobic tank is in the high-pressure early warning area or the low-pressure early warning area, the corresponding decompression method or the corresponding pressurization method is started immediately, namely the process parameters must be adjusted, and the process parameters are cyclically repeated, so that the whole process parameter system is maintained in a dynamic balance.
According to the invention, through the establishment of the anaerobic digestion gas production mathematical model and the multi-objective planning of key process parameters, the automatic optimization and adjustment of the process parameters are realized, so that the working strength of operators is reduced, and the working efficiency and the working quality are greatly improved.
Detailed Description
The following are specific examples of the present invention, and the technical solutions of the present invention are further described, but the present invention is not limited to these examples.
The anaerobic digestion process parameters are not independent, but are interrelated, and some parameters even are in a loop-to-loop relationship.
For the liquid level of the feeding pool, the liquid level is required to be within a certain range and is too high, so that raw materials can overflow the feeding pool, and environmental pollution and raw material waste are caused; too low, the feed tank stirrer would be suspended and disadvantageous to the stirrer. For a feed tank agitator, it does not require 24 hours to operate, but only when the trailer is unloaded or the feed pump is on. For the feed pump, it is only turned on when the anaerobic tank needs to be fed or when the feed tank level is too high, and is normally turned off. For the feeding quantity, the size is determined according to the liquid level height of the feeding tank and the pressure condition of the anaerobic tank, if the pressure of the anaerobic tank is too low, a feeding pump is started, and the proper feeding quantity is set, so that the pressure of the anaerobic tank can be increased; however, if the feed amount is too large, the feed tank liquid level height and the anaerobic tank liquid level height are seriously affected, and the anaerobic tank pressure is too high.
For the liquid level of the anaerobic tank, the liquid level must be within a certain range, and too high, there is no gas storage space at all, and the anaerobic tank pressure is forced to be too high; too low, most of the anaerobic tank stirrer is empty, which is unfavorable to the stirrer, and too low liquid level can cause unstable pressure of the anaerobic tank, so that pressure maintaining is difficult. For the anaerobic tank temperature, the anaerobic tank temperature must be within a certain range, and methanogens in the tank can only survive within a certain temperature range. In addition, the anaerobic tank is heated by a boiler generally, and the energy of the boiler comes from methane generated by the anaerobic tank, so that the methane in the anaerobic tank is consumed inevitably once the tank body needs to be heated, and the pressure of the anaerobic tank is reduced. For the pressure of the anaerobic tank, the pressure must be within a certain range, too high, the top film of the anaerobic tank is in a high-pressure tightening state, and the top film is unfavorable, and the anaerobic tank is provided with a safety relief valve which can be opened automatically immediately once the pressure exceeds a critical value, so that the gas in the tank is discharged forcibly, thereby forcing the anaerobic tank to be depressurized, and the step is generally not achieved, because the torch ignition pressure of the anaerobic tank is also provided before the pressure reaches the critical value, the setting of the torch ignition pressure is also taught, and if the pressure is too low, the frequent overpressure ignition of the anaerobic tank can be caused; setting too high is disadvantageous for the top film. If the anaerobic tank pressure is too low, this will cause the anaerobic tank to suck back, which is absolutely impermissible. For an anaerobic tank stirrer, it does not need to be operated for 24 hours, only when the anaerobic tank pressure is reduced. When the stirrer works, slurry in the tank is uniform, anaerobic biochemical reaction can be quickened, gas in the slurry overflows rapidly, and accordingly pressure of the anaerobic tank is increased. In addition, more than one stirrer is arranged in one anaerobic tank, and all the stirrers work simultaneously and a single stirrer works independently, so that the effects are different.
In order to keep the material balance among different anaerobic tanks, a slurry conveying pump between the anaerobic tanks needs to be started from time to time, the anaerobic tank with high liquid level conveys slurry to the anaerobic tank with low liquid level, at the moment, the pressure of the anaerobic tank with high liquid level can be reduced along with the discharge of the slurry, and the pressure of the anaerobic tank with low liquid level can be increased along with the input of the slurry. However, it is not necessary to ensure that the slurry between the different anaerobic tanks is absolutely equal, and the slurry balance between the different anaerobic tanks is a delicate balance which is required to properly reduce the liquid level difference and simultaneously ensure that the influence of the pressure of the anaerobic tanks is minimized.
The gas production channels of all anaerobic tanks are finally gathered into a main pipe, and the main pipe is finally led to biogas purification equipment. In order to balance the exhaust flow of each anaerobic tank, a regulating valve is arranged at the tail end of the gas production channel of each anaerobic tank, and the opening of the branch pipe can be regulated by the regulating valve, so that the occupancy of the gas production channel of each anaerobic tank is changed. When the opening of one of the anaerobic tank regulating valves is reduced, the pressure of the anaerobic tank is increased, and conversely, the pressure of the anaerobic tank is reduced. Through the adjustment, the purpose of adjusting the pressure of the anaerobic tank is achieved.
The gas flow rate of the biogas purification product must be within a certain range, and certainly, the higher the biogas purification product is, the better the biogas purification product is, but the higher the biogas purification product is, the pressure of the anaerobic tank is greatly reduced; too low can cause a substantial increase in anaerobic tank pressure. This necessitates a dynamic balance.
Because anaerobic digestion involves various complex reactions such as biology, chemistry, physics and the like, it is difficult to describe the reactions by using accurate mathematical models, but in order to quantify the effect of various parameter changes on the anaerobic digestion gas yield, a function with certain process parameters as independent variables and the anaerobic digestion gas yield as dependent variables must be constructed. This function can only be fitted with an approximation method, so that the error between the fitted function and the actual function is as small as possible. Factors affecting the anaerobic digestion system, such as anaerobic tank temperature, anaerobic environment, anaerobic tank pH, fatty acid, anaerobic tank slurry amount, anaerobic tank slurry solids content, anaerobic tank agitation effect, etc., are very numerous, and many of them are essential for establishing mathematical models, but in practical engineering, a large amount of data is not available. By means of modern mathematical numerical simulation technology and data mining technology, we do not need to master all parameters, only one fitting function can be constructed by using the existing parameters, and the error accuracy can be controlled.
Therefore, 5 key parameters of anaerobic tank temperature (DEG C), anaerobic tank pH value, anaerobic tank slurry quantity (m 3/min), anaerobic tank slurry solid content and anaerobic tank accumulated stirring time (min) are selected as independent variables, anaerobic digestion gas production quantity (m 3/min) is used as dependent variables, and a K nearest neighbor method is used for establishing a fitting function.
The KNN (K-Nearest Neighbor) method, namely the K Nearest Neighbor method, was originally proposed by Cover and Hart in 1968, is a theoretical and mature method, and is also one of the simplest machine learning algorithms. The method has the advantages of simple and visual idea: if a sample belongs to a class for the majority of the K most similar (i.e., nearest neighbor) samples in the feature space, then the sample also belongs to that class. The method only determines the category to which the sample to be classified belongs according to the category of one or more samples which are nearest to each other in the classification decision. The KNN method is only relevant to a very small number of adjacent samples when making a class decision. The KNN method is more suitable than other methods for the sample set to be divided with more cross or overlap of class domains because the class is determined mainly by surrounding limited adjacent samples rather than by a method for distinguishing the class domains.
The KNN classification algorithm includes the following 4 steps:
① Data is prepared and preprocessed.
② The distance of the test sample point (i.e., the point to be classified) to each of the other sample points is calculated.
③ Each distance is ordered and then K points with the smallest distance are selected.
④ And comparing the categories of the K points, and classifying the test sample points into the category with the highest proportion among the K points according to the principle of minority compliance and majority compliance.
The data are the parameter data recorded before, namely the anaerobic tank temperature T (DEG C), the pH value of the anaerobic tank, the slurry quantity L of the anaerobic tank (m 3/min), the solid content G of the slurry of the anaerobic tank, the accumulated stirring duration J (min) of the anaerobic tank and the gas yield Q (m 3/min) of the anaerobic digestion, and as long as samples are enough, the proper K value is set, the fitting function error obtained finally is smaller and more accurate.
Finally, a fitting function can be obtained:
Q＝F(T、pH、L、G、J)。
The fitting function Q is established for a single anaerobic tank, and the premise is that the slurry components are fixed, and obviously, the parameter data of the slurry components are different for different types of slurries such as kitchen waste, livestock manure and organic sludge, so that the gas generating function fitted according to the parameter data is necessarily different. In addition, the formula is considered in terms of "minutes" which, if considered in terms of "hours" or "days", would make the time interval too long for the next optimization operation. And when parameter data acquisition is performed in the earlier stage, the parameter data are correspondingly measured in time units of minutes.
For each anaerobic tank, in order to ensure that the maximum anaerobic digestion gas production is achieved under the condition that the exhaustion is as low as possible, optimization analysis is required to be carried out on the gas production function Q, and the values of the key process parameters are obtained when the energy consumption is the lowest and the anaerobic digestion gas production is the maximum.
This is a multi-objective planning problem, which is a branch of mathematical planning. More than one optimization of the objective function over a given area, also known as multi-objective optimization, is studied. In many practical problems, such as the fields of economy, management, military, science and engineering, it is often difficult to measure the quality of a solution using an index, and multiple targets are needed for comparison, and these targets are sometimes not even coordinated or even contradictory. Any multi-objective planning problem consists of two basic components: (1) two or more objective functions; (2) a number of constraints.
Since the maximum problem can be translated into the minimum problem, the general form of the multi-objective planning problem is:
min[f1(x),f2(x),…,fp(x)]T,p＞1
s.t.gi(x)≥0,i＝1,2,…,m
hi(x)＝0,i＝1,2,…,n
The most basic method for solving the multi-objective planning problem is an evaluation function method, and the basic method is as follows: by means of visual background in geometry or application, constructing an evaluation function, converting the multi-objective optimization problem into a single-objective optimization problem, then solving an optimal solution by utilizing a solving method of the single-objective optimization problem, and taking the optimal solution as an optimal solution of the multi-objective optimization problem.
In an optimization problem with multiple indices, one always wants to give larger weight coefficients to those of relative importance, thereby converting the multi-objective vector problem into a weighted sum scalar problem for all objectives. Based on this reality, the following evaluation functions are constructed, namely
minF(x)＝ω1f1(x)+ω2f2(x)+…+ωifi(x),i＝1,2,…,p
Wherein ω i is a weighting factor, and many methods are selected, such as expert scoring and tolerance.
For the problem of optimizing the anaerobic digestion gas production, the target values are 3, namely the anaerobic digestion gas production Q, the anaerobic tank temperature T and the anaerobic tank accumulated stirring time length J, the anaerobic digestion gas production Q is as large as possible, the anaerobic tank temperature T and the anaerobic tank accumulated stirring time length J are as small as possible, but the weights of the 3 target values are different, and obviously the anaerobic digestion gas production Q is maximum, and the weight of the anaerobic tank temperature T is minimum. As to how large the weights of the 3 objective functions are, the comprehensive judgment is required according to the actual situation.
For the anaerobic digestion gas production optimization problem, the evaluation function is as follows:
minQ＝-[ω1f(T)+ω2f(J)+ω3f(L)+ω4f(pH)+ω5f(G)]
one of the constraint conditions of the anaerobic digestion gas production optimization problem is that the value range of 3 key process parameters is that the temperature T of the anaerobic tank is required to be between the minimum temperature and the maximum temperature required by the anaerobic digestion reaction, the slurry quantity L of the anaerobic tank is required to be between the minimum capacity and the maximum capacity which can be borne by the anaerobic tank, and the accumulated stirring duration J of the anaerobic tank is required to be between the minimum accumulated duration and the maximum accumulated duration.
The second constraint is that the pH value of the anaerobic tank is equal to a given pH value, and the solid content G of slurry of the anaerobic tank is equal to a given solid content.
Thus, the establishment of a mathematical model of the anaerobic digestion gas production multi-objective planning is completed by the target value and the constraint condition.
Omega 1、ω2 and omega 3 are weights of 3 objective functions, omega 4、ω5 is a weight of a non-key parameter, and specific values are assigned according to actual conditions; this weight is an empirical value summarized at the time of data accumulation in the early stage, and here, according to actual conditions, refers to conditions according to the operation of the early stage system.
By solving the multi-objective programming problem, the values of the key process parameters can be obtained when the possible exhaustion is low and the anaerobic digestion gas yield is as high as possible.
Through anaerobic digestion gas production optimization calculation, the optimized values of 3 key process parameters (anaerobic tank temperature T, anaerobic tank slurry quantity L and anaerobic tank accumulated stirring duration J) in a given time period can be obtained, and a conclusion can be obtained according to the comparison between the optimized values and the current actual values: in order to optimize the anaerobic digestion gas production, it is necessary to increase or decrease the anaerobic tank temperature T, increase or decrease the anaerobic tank slurry amount L, and increase or decrease the anaerobic tank cumulative agitation time period J during the present period (1 minute).
The temperature T of the anaerobic tank is increased by starting the boiler; the temperature T of the anaerobic tank is reduced by turning off the boiler. This causes a change in the pressure of the anaerobic tank. When the temperature T of the anaerobic tank is increased, the pressure of the anaerobic tank is reduced; when the anaerobic tank temperature T is lowered, the anaerobic tank pressure will increase.
Increasing the slurry amount L of the anaerobic tank is realized by feeding the anaerobic tank through a feeding tank or conveying the slurry to the anaerobic tank through other anaerobic tanks; the reduction of the slurry amount L of the anaerobic tank is realized by outputting slurry to other anaerobic tanks or directly discharging the slurry to the biogas slurry tank from outside.
The accumulated stirring time J of the anaerobic tank is increased by starting an anaerobic tank stirrer and increasing the number and the time of starting; the reduction of the anaerobic tank cumulative agitation time period J is achieved by completely turning off the anaerobic tank agitator, or by partially turning off. This causes a change in the pressure of the anaerobic tank. When the accumulated stirring time J of the anaerobic tank is increased, the pressure of the anaerobic tank is increased; when the anaerobic tank cumulative agitation period J is reduced, the anaerobic tank pressure is reduced.
The system enables 3 key process parameters (the temperature T of the anaerobic tank, the slurry quantity L of the anaerobic tank and the accumulated stirring time length J of the anaerobic tank) to reach an optimal value through automatic adjustment, and the pressure of the anaerobic tank changes accordingly. As described above, for the anaerobic tank pressure, the pressure must be within a certain range, and for this purpose, the anaerobic tank pressure range is divided into 3 areas, namely, a high-pressure early warning area, a normal working area, and a low-pressure early warning area. When the pressure of the anaerobic tank fluctuates in the normal working area, the process parameters do not need to be adjusted; when the pressure of the anaerobic tank is in a high-pressure early-warning area or a low-pressure early-warning area, the corresponding decompression method or the corresponding pressurization method is started immediately, namely the process parameters must be adjusted.
The method for reducing the pressure of the anaerobic tank is realized by completely or partially closing the stirrer of the anaerobic tank, or increasing the gas production flow of the biogas purification product or starting the boiler. Until the anaerobic tank pressure returns to the normal working area.
The method for increasing the pressure of the anaerobic tank is realized by completely or partially opening an agitator of the anaerobic tank, or reducing the gas production flow of the biogas purification product, or closing a boiler, or feeding the anaerobic tank through a feeding tank, or conveying slurry to the anaerobic tank through other anaerobic tanks. Until the anaerobic tank pressure returns to the normal working area.
When the system automatically adjusts the anaerobic tank pressure, the anaerobic tank pressure is restored to a normal working area, but at the moment, 3 key process parameters (anaerobic tank temperature T, anaerobic tank slurry amount L and anaerobic tank accumulated stirring time length J) possibly deviate from the current optimized value, and then the system automatically selects the 3 key process parameters (anaerobic tank temperature T, anaerobic tank slurry amount L and anaerobic tank accumulated stirring time length J) according to the current 5 process parameter values (anaerobic tank temperature T, anaerobic tank pH value, anaerobic tank slurry amount L, anaerobic tank slurry solid content G and anaerobic tank accumulated stirring time length J) so as to carry out multi-objective planning on an anaerobic digestion gas production function Q Gas production , so that the optimized value of 3 key process parameters (anaerobic tank temperature T, anaerobic tank slurry amount L and anaerobic tank accumulated stirring time length J) in a given time period can be obtained, and then the 3 key process parameters (anaerobic tank temperature T, anaerobic tank slurry amount L and anaerobic tank accumulated stirring time length J) can be automatically selected according to the optimized value, so that the change of the anaerobic tank pressure can be caused, and if the anaerobic tank pressure does not need to be adjusted when the anaerobic tank pressure fluctuates in the normal working area; if the pressure of the anaerobic tank is in a high-pressure early-warning area or a low-pressure early-warning area, the corresponding decompression method or the corresponding pressurization method is started immediately, namely the process parameters must be adjusted. Then the pressure of the anaerobic tank is restored to the normal working area, the 3 key process parameters (the temperature T of the anaerobic tank, the slurry quantity L of the anaerobic tank and the accumulated stirring time length J of the anaerobic tank) can deviate from the current optimized value, and then a new round of multi-objective planning of the anaerobic digestion gas production function Q is performed, so that the cycle is repeated, and the whole process parameter system is maintained in a dynamic balance.
The automatic optimization technology is realized by means of a modern control system, wherein the establishment and correction of an anaerobic digestion gas production mathematical model, the multi-objective planning of key process parameters and the establishment and expansion of a database are all independent of a computer.
The above is not relevant and is applicable to the prior art.
While certain specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the foregoing examples are provided for the purpose of illustration only and are not intended to limit the scope of the invention, and that various modifications or additions and substitutions to the described specific embodiments may be made by those skilled in the art without departing from the scope of the invention or exceeding the scope of the invention as defined in the accompanying claims. It should be understood by those skilled in the art that any modification, equivalent substitution, improvement, etc. made to the above embodiments according to the technical substance of the present invention should be included in the scope of protection of the present invention.
Claims (5)
1. An optimized adjustment method for anaerobic digestion process parameters is characterized in that: the method comprises the following steps:
S1: and (3) establishing a database: recording the temperature T of the anaerobic tank, the pH value of the anaerobic tank, the slurry quantity L of the anaerobic tank, the solid content G of the slurry of the anaerobic tank, the accumulated stirring duration J of the anaerobic tank and the gas yield Q of anaerobic digestion, and establishing a database of the 6 key data;
S2, establishing a mathematical model of anaerobic digestion gas production: the method comprises the steps of selecting anaerobic tank temperature T, anaerobic tank pH, anaerobic tank slurry quantity L, anaerobic tank slurry solid content G and anaerobic tank accumulated stirring duration J in a database, taking 5 key parameters as independent variables, taking anaerobic digestion gas production quantity Q as dependent variables, and establishing a fitting function by a K nearest neighbor method to obtain the fitting function: q=f (T, pH, L, G, J);
S3: establishing a mathematical model of anaerobic digestion gas production multi-objective planning: for each anaerobic tank, in order to realize that the exhaustion is as low as possible, the maximum anaerobic digestion gas yield is ensured, the optimization analysis is required to be carried out on a gas yield function Q, the values of all key process parameters are obtained when the energy consumption is the lowest and the anaerobic digestion gas yield is the maximum, for the anaerobic digestion gas yield optimization problem, 3 target values are provided, namely, the anaerobic digestion gas yield Q, the anaerobic tank temperature T and the anaerobic tank accumulated stirring duration J, an evaluation function is constructed, namely, minQ= - [ omega 1f(T)+ω2f(J)+ω3f(L)+ω4f(pH)+ω5 f (G) ], one of constraint conditions is that the value range of 3 key process parameters is T min≤T≤Tmax between the lowest temperature and the highest temperature necessary for the anaerobic digestion reaction, the anaerobic tank slurry quantity L is L min≤L≤Lmax between the lowest capacity and the highest capacity which can be carried by the anaerobic tank, and the anaerobic tank accumulated stirring duration J is between the lowest accumulated duration J and the highest accumulated duration J min≤J≤Jmax; the second constraint condition is that the pH value of the anaerobic tank is equal to a given pH value, and the solid content G of slurry of the anaerobic tank is equal to a given solid content; omega 1、ω2 and omega 3 are weights of 3 objective functions, omega 4、ω5 is a weight of a non-key parameter, and specific values are assigned according to actual conditions;
the mathematical model of the multi-objective planning of the anaerobic digestion gas production is established by the objective function and constraint conditions, and the values of the key process parameters can be obtained by optimizing the multi-objective planning problem when the possible low anaerobic digestion gas production can be exhausted and the anaerobic digestion gas production is as large as possible.
2. The method for optimally adjusting anaerobic digestion process parameters according to claim 1, wherein: the temperature regulation of the anaerobic tank is realized by turning on or off the boiler; increasing the slurry amount L of the anaerobic tank, which is realized by feeding the slurry into the anaerobic tank through a feeding tank or conveying the slurry to the anaerobic tank through other anaerobic tanks, and reducing the slurry amount L of the anaerobic tank, which is realized by outputting the slurry to other anaerobic tanks or directly discharging the slurry to the biogas slurry tank from outside; increasing the anaerobic tank cumulative stirring time period J is realized by starting the anaerobic tank stirrer and increasing the number and the time period of the starting, and decreasing the anaerobic tank cumulative stirring time period J is realized by completely closing the anaerobic tank stirrer or partially closing the anaerobic tank stirrer.
3. The method for optimally adjusting anaerobic digestion process parameters according to claim 1, wherein: according to the mathematical model, 3 key process parameters, namely the temperature T of the anaerobic tank, the slurry quantity L of the anaerobic tank and the accumulated stirring time length J of the anaerobic tank, reach an optimized value, the pressure of the anaerobic tank changes at the moment, the pressure of the anaerobic tank is required to be within a certain range, the pressure range of the anaerobic tank is divided into 3 areas, namely a high-pressure early warning area, a normal working area and a low-pressure early warning area, and when the pressure of the anaerobic tank fluctuates in the normal working area, the process parameters do not need to be adjusted; when the pressure of the anaerobic tank is in a high-pressure early-warning area or a low-pressure early-warning area, the corresponding decompression method or the corresponding pressurization method is started immediately, namely the process parameters must be adjusted.
4. A method for the optimal adjustment of anaerobic digestion process parameters according to claim 3, wherein: the realization method for reducing the pressure of the anaerobic tank is that the stirrer of the anaerobic tank is completely or partially closed, or the gas production flow of the purified biogas product is increased, or the boiler is started until the pressure of the anaerobic tank is restored to a normal working area; the method for realizing the increase of the pressure of the anaerobic tank is to completely or partially open the stirrer of the anaerobic tank, or reduce the gas flow rate of the purified biogas product or close the boiler.
5. A method for the optimal adjustment of anaerobic digestion process parameters according to claim 3, wherein: when the anaerobic tank pressure is in the high-pressure early warning area or the low-pressure early warning area, the anaerobic tank pressure is regulated and then returns to the normal working area, but at the moment, the anaerobic tank temperature T, the anaerobic tank slurry quantity L and the anaerobic tank accumulated stirring time J possibly deviate from the current optimized values, when the anaerobic tank pressure deviates from the current optimized values, the anaerobic tank temperature T, the anaerobic tank pH value, the anaerobic tank slurry quantity L, the solid content G of the anaerobic tank slurry and the anaerobic tank accumulated stirring time J are changed according to the current 5 technological parameter values, so that the multi-objective planning is carried out according to the fitting function Q, the optimized values of the anaerobic tank temperature T, the anaerobic tank slurry quantity L and the anaerobic tank accumulated stirring time J in the given time period can be obtained, then the anaerobic tank temperature T, the anaerobic tank slurry quantity L and the anaerobic tank accumulated stirring time J are regulated according to the optimized values, and the change of the anaerobic tank pressure is caused, and if the anaerobic tank pressure fluctuates in the normal working area, the technological parameter is not regulated; if the pressure of the anaerobic tank is in the high-pressure early warning area or the low-pressure early warning area, the corresponding decompression method or the corresponding pressurization method is started immediately, namely the process parameters must be adjusted, and the process parameters are cyclically repeated, so that the whole process parameter system is maintained in a dynamic balance.
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