CN115959933B - Intelligent control method and system for aerobic composting - Google Patents
Intelligent control method and system for aerobic composting Download PDFInfo
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- 238000009264 composting Methods 0.000 title claims abstract description 113
- 238000000034 method Methods 0.000 title claims abstract description 56
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 91
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 91
- 239000001301 oxygen Substances 0.000 claims abstract description 91
- 238000005457 optimization Methods 0.000 claims abstract description 76
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- 230000014509 gene expression Effects 0.000 claims abstract description 54
- 238000013528 artificial neural network Methods 0.000 claims abstract description 53
- 238000010564 aerobic fermentation Methods 0.000 claims abstract description 45
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- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 50
- 238000005265 energy consumption Methods 0.000 claims description 26
- 229910021529 ammonia Inorganic materials 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 8
- 239000007788 liquid Substances 0.000 claims description 6
- 238000005325 percolation Methods 0.000 claims description 6
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/40—Bio-organic fraction processing; Production of fertilisers from the organic fraction of waste or refuse
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Abstract
The invention discloses an intelligent control method and system for aerobic composting, which relate to the technical field of agriculture and forestry solid waste resource utilization, and comprise the following steps: training a first TS fuzzy neural network to determine a ventilation prediction relation; training a second TS fuzzy neural network to determine a blower energy prediction relationship; training a third TS fuzzy neural network to determine a leachate pH prediction relationship; training a fourth TS fuzzy neural network to determine a pile carbon-nitrogen ratio prediction relational expression; establishing an aerobic composting multi-objective optimization model based on a ventilation quantity prediction relational expression, a blower energy prediction relational expression, a percolate pH prediction relational expression and a heap carbon nitrogen ratio prediction relational expression, and solving to obtain a Pareto optimal solution; and inputting the Pareto optimal solution into a PID controller to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank. The invention realizes automatic and accurate regulation and control of aerobic composting.
Description
Technical Field
The invention relates to the technical field of agricultural and forestry solid waste resource utilization, in particular to an intelligent control method and system for aerobic composting.
Background
In recent years, the yield of main agricultural and forestry wastes in China is continuously increased, and crop straws have rich N, P, K and organic matters and nutrients, so that the crop straws are important renewable biomass resources in the agricultural production process. The agriculture and forestry waste recycling gradually forms a development pattern of fertilizer production, feed production and fuel production stable step propulsion, base material production and raw material production auxiliary, and the aerobic composting technology is an important technical means for realizing fertilizer production and matrix production of the agricultural solid waste.
Aerobic composting is generally divided into static stacking fermentation, bar-type turning fermentation, tank-type turning fermentation and stirred container composting fermentation. The standard composting process comprises the links of pretreatment, proportioning, turning and blasting, environmental control and the like, and has large occupied area and complex operation. In the composting process, stack state indexes such as temperature and humidity, pH value, TC (Total Carbon) and the like need to be measured for a long time by a chemical analysis method, and real-time data and enough data volume are difficult to obtain. Because the aeration system has uncertainty and instantaneity in the composting process and physical change, chemical change and microorganism change occur in the reactor at the same time, the reaction changes are interwoven together to enable the aerobic composting process to form a multivariable and strong-coupling complex system, the coupling relation in the complex reaction process is difficult to infer by relying on the traditional manual single-index detection, adjustment and judgment cannot be timely and timely made, and accurate regulation and control on the aerobic composting process are difficult to achieve.
Disclosure of Invention
The invention aims to provide an intelligent control method and system for aerobic composting, which are based on multivariate real-time monitoring to realize automatic and accurate regulation and control of the aerobic composting process.
In order to achieve the above object, the present invention provides the following solutions:
the invention provides an intelligent control method for aerobic composting, which is applied to an aerobic composting device, wherein the aerobic composting device comprises an aerobic fermentation tank, a percolation liquid tank, a blower and a PID controller;
the intelligent control method for the aerobic composting comprises the following steps:
Acquiring a historical ventilation quantity data set, a historical blower energy data set, a historical percolate pH data set, a historical oxygen concentration data set, a historical ammonia gas concentration data set, a historical temperature data set and a historical pile carbon nitrogen ratio data set in the aerobic fermentation tank;
Training a first TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical ventilation dataset to determine a ventilation prediction relationship; training a second TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical blower energy consumption dataset to determine a blower energy consumption prediction relationship; training a third TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, the historical ammonia concentration dataset, and the historical leachate pH dataset to determine a leachate pH prediction relationship; training a fourth TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set, the historical ammonia concentration data set and the historical stack carbon-nitrogen ratio data set to determine a stack carbon-nitrogen ratio prediction relational expression;
Establishing an aerobic composting multi-objective optimization model based on the ventilation quantity prediction relational expression, the blower energy prediction relational expression, the percolate pH prediction relational expression and the heap carbon nitrogen ratio prediction relational expression;
Solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution; the Pareto optimal solution comprises an oxygen concentration optimization set value and a temperature optimization set value in the aerobic fermentation tank;
and inputting the Pareto optimal solution to the PID controller to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
Optionally, the first TS fuzzy neural network includes an input layer, a fuzzy rule calculation layer, and an output layer;
The input layer is used for inputting the historical oxygen concentration data set, the historical temperature data set and the historical ventilation volume data set; the historical oxygen concentration dataset and the historical temperature dataset form an optimization variable;
the fuzzy layer is used for carrying out fuzzification processing on the optimized variable through a membership generating function so as to obtain fuzzy membership;
the fuzzy rule calculation layer is used for calculating the fuzzy rule of the fuzzy membership;
The output layer is used for receiving all data output by the fuzzy rule calculation layer, correcting the received data according to the historical ventilation quantity data corresponding to the optimized variable, and determining a ventilation quantity predicted value.
Optionally, solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution, which specifically comprises:
Processing the constraint function by adopting a punishment function method;
solving the aerobic composting multi-objective optimization model processed by a penalty function method by adopting an NSGA-II algorithm to obtain a group of Pareto optimization solutions;
Based on a preset decision, a Pareto optimal solution is selected from a group of Pareto optimal solutions.
Optionally, the aerobic composting device further comprises a stirrer; the stirrer is arranged in the aerobic fermentation tank;
the intelligent control method for the aerobic composting further comprises the following steps:
and adjusting the rotating speed of the air blower and the rotating speed of the stirrer based on the Pareto optimal solution by adopting the PID controller so as to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
The invention also provides an intelligent control system for the aerobic composting, which is applied to an aerobic composting device, wherein the aerobic composting device comprises an aerobic fermentation tank, a percolation liquid tank, a blower and a PID controller;
The intelligent control system for aerobic composting comprises:
the data acquisition module is used for acquiring a historical ventilation volume data set, a historical blower energy data set, a historical percolate pH data set, a historical oxygen concentration data set, a historical ammonia gas concentration data set, a historical temperature data set and a historical pile carbon-nitrogen ratio data set in the aerobic fermentation tank;
The relation determining module is used for training the first TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set and the historical ventilation volume data set so as to determine a ventilation volume prediction relation; training a second TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical blower energy consumption dataset to determine a blower energy consumption prediction relationship; training a third TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, the historical ammonia concentration dataset, and the historical leachate pH dataset to determine a leachate pH prediction relationship; training a fourth TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set, the historical ammonia concentration data set and the historical stack carbon-nitrogen ratio data set to determine a stack carbon-nitrogen ratio prediction relational expression;
the multi-objective optimization model construction module is used for constructing an aerobic composting multi-objective optimization model based on the ventilation quantity prediction relational expression, the blower energy prediction relational expression, the percolate pH prediction relational expression and the heap carbon nitrogen ratio prediction relational expression;
The multi-objective optimization model solving module is used for solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution; the Pareto optimal solution comprises an oxygen concentration optimization set value and a temperature optimization set value in the aerobic fermentation tank;
and the PID control module is used for inputting the Pareto optimal solution into the PID controller so as to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
The embodiment of the invention discloses an intelligent control method and system for aerobic composting, which are applied to an aerobic composting device, wherein the method comprises the following steps: based on the collected historical data, training is carried out on a plurality of TS fuzzy neural networks respectively to obtain a corresponding ventilation quantity prediction relational expression, a blower energy prediction relational expression, a percolate pH prediction relational expression and a heap carbon nitrogen ratio prediction relational expression, so that performance index optimization is realized based on the TS fuzzy neural networks, and a more accurate prediction relational expression in the aerobic composting process is obtained. Then, an aerobic composting multi-objective optimization model is established based on a ventilation quantity prediction relational expression, a blower energy prediction relational expression, a percolate pH prediction relational expression and a heap carbon nitrogen ratio prediction relational expression, and a Pareto optimal solution is obtained by model solving, so that optimization control based on multiple variables is realized; wherein the Pareto optimal solution comprises an oxygen concentration optimization set value and a temperature optimization set value in the aerobic fermentation tank. And finally, inputting the Pareto optimal solution into a PID controller to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank, thereby realizing automatic and accurate regulation and control of the aerobic composting process. The oxygen concentration and the temperature in the aerobic fermentation tank can be determined by the energy consumption of the air blower and the ventilation quantity generated by the air blower, and the accurate control of the oxygen concentration and the temperature is the optimal control of the energy ventilation quantity of the air blower, so that the problem of low energy and resource utilization rate caused by the ventilation quantity in the aerobic composting process can be solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an intelligent control method for aerobic composting;
FIG. 2 is a schematic diagram of the structure of the TS fuzzy neural network according to the present invention;
FIG. 3 is a schematic diagram of a PID control architecture according to the present invention;
FIG. 4 is a schematic structural diagram of the intelligent aerobic composting control system of the invention;
FIG. 5 is a general control structure diagram of an intelligent control system for aerobic composting in the example of the invention;
FIG. 6 is a schematic diagram of the overall architecture of the intelligent control system for aerobic composting in the example of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides an intelligent control method and system for aerobic composting, which are characterized in that a blower energy prediction model, a heap C/N ratio function model and the like are established based on a TS fuzzy neural network, and performance indexes are optimized; and obtaining a Pareto optimal solution according to the multi-objective optimization algorithm model, and optimizing oxygen concentration and a temperature set value by intelligent decision to realize dynamic optimizing and tracking control of the blower rotating speed and the stirrer rotating speed set value, thereby constructing the multi-objective optimization intelligent control method taking energy consumption and ventilation as optimization objectives.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in figure 1, the invention provides an intelligent control method for aerobic composting, which is applied to an aerobic composting device, wherein the aerobic composting device comprises an aerobic fermentation tank, a percolation liquid tank, a blower and a PID controller.
The principle of the invention is as follows: degrading organic matters by means of the metabolism process of aerobic microorganisms under the conditions of good ventilation and sufficient oxygen to form new humus. In the initial stage of the composting reaction, the decomposition of substances is carried out by mesophilic bacteria (the optimum growth temperature is 30-40 ℃), the temperature of the compost is gradually replaced by thermophilic bacteria with the optimum temperature of 45-65 ℃, and after a period of time, most of organic matters are degraded, and the temperature of the compost begins to drop. The composting stage and effect are directly related to the composition ratio and concentration of CH 4、O2、CO2、NH3 generated in the reactor. The aerobic composting device is a closed reaction device, gas components and concentration parameters in the composting device and an air outlet are collected to judge the composting reaction stage and the composting effect, meanwhile, parameters such as pH, EC, turbidity and the like of percolate are monitored, and the ventilation quantity is intelligently controlled by regulating and controlling the rotation speed of a stirrer and the rotation speed of a blower, so that the energy consumption is saved, and the composting effect is improved.
Specifically, the intelligent control method for the aerobic composting comprises the following steps:
Step 100, a historical ventilation quantity data set, a historical blower energy data set, a historical percolate pH data set, a historical oxygen concentration data set, a historical ammonia concentration data set, a historical temperature data set and a historical pile carbon nitrogen ratio data set in the aerobic fermentation tank are obtained.
Specifically, oxygen concentration data, ammonia concentration data and temperature data are monitored in real time by using a sensor arranged in the aerobic fermentation tank, and an oxygen sensor, an ammonia sensor and a temperature sensor are correspondingly adopted; in addition, a methane sensor can be arranged to detect the methane concentration at the air outlet of the aerobic fermentation tank, and a carbon dioxide sensor is arranged to detect the carbon dioxide concentration at the air outlet of the aerobic fermentation tank, so that the gas component detection at the air outlet of the aerobic fermentation tank and the measurement of the concentration parameters of each component are realized.
The pH value, EC and turbidity parameters of the percolate in the percolate tank are monitored by an online water quality analyzer. The air mass flowmeter is used for measuring the air quantity blown into the aerobic fermentation tank by the air blower, namely the ventilation quantity, and the ventilation quantity can influence the oxygen concentration and the temperature in the aerobic fermentation tank. Further, the amount of gas blown into the aerobic fermentation tank by the blower is related to the energy consumption of the blower, and thus the blower energy consumption data needs to be measured. That is, the set value of a certain gas mass flowmeter can correspond to different blower speeds, and the minimum energy consumption is selected through a model. For example, the oxygen concentration is 21%, the temperature is 60 ℃, 100 oxygen is needed, the gas mass flowmeter is set to 100, and the blower can rotate 2000-4000 revolutions, but the minimum energy consumption of 2500 revolutions is possible by comprehensively considering various resistances.
In general, in aerobic composting, it is desirable to achieve two goals: the energy consumption of the air blower is the lowest and the composting effect is the best. Because of the complex nonlinearity of the composting process, it is difficult to build accurate blower energy and composting effect models. The energy consumption of the blower and the composting effect (composting effect is represented by the composting ratio C/N, pH) are thus modeled by means of the fuzzy neural network. According to correlation analysis, the input variables of the blower rotating speed prediction model are temperature, humidity, oxygen concentration and gas mass flowmeter set values, and the temperature and the oxygen concentration can be controlled by adjusting the blower rotating speed and the stirrer rotating speed as the humidity is continuously changed under the influence of the temperature and the ventilation quantity, so that the multi-target optimization problem is the optimization problem of two targets. The decision variables are temperature and oxygen concentration, and the targets are blower energy and stack C/N ratio. In the reaction process of the reactor, the oxygen concentration and the temperature are detected in real time, when the environment changes, the set values of the temperature and the oxygen concentration are optimized, and then the set values are tracked and controlled by a fuzzy controller.
Based on this, step 200 is: training a first TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical ventilation dataset to determine a ventilation prediction relationship; training a second TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical blower energy consumption dataset to determine a blower energy consumption prediction relationship; training a third TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, the historical ammonia concentration dataset, and the historical leachate pH dataset to determine a leachate pH prediction relationship; and training a fourth TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set, the historical ammonia concentration data set and the historical stack carbon-nitrogen ratio data set to determine a stack carbon-nitrogen ratio prediction relational expression.
The first TS fuzzy neural network, the second TS fuzzy neural network, the third TS fuzzy neural network and the fourth TS fuzzy neural network have the same structure and comprise an input layer, a fuzzy rule calculation layer and an output layer, as shown in figure 2.
For a first TS fuzzy neural network, the input layer is for inputting the historical oxygen concentration dataset, the historical temperature dataset, and the historical ventilation dataset; the historical oxygen concentration dataset and the historical temperature dataset constitute an optimization variable. I.e. z (k) = [ x 1(k) x2(k)]T ].
For a second TS fuzzy neural network, the input layer is configured to input the historical oxygen concentration dataset, the historical temperature dataset, and the historical blower energy dataset; the historical oxygen concentration dataset and the historical temperature dataset constitute an optimization variable. I.e. z (k) = [ x 1(k) x2(k)]T ].
For a third TS fuzzy neural network, the input layer is configured to input the historical oxygen concentration dataset, the historical temperature dataset, the historical ammonia concentration dataset, and the historical leachate pH dataset; the historical oxygen concentration dataset, the historical temperature dataset and the historical ammonia concentration dataset constitute an optimization variable. I.e. z (k) = [ x 1(k) x2(k) x3(k)]T ].
For a fourth TS fuzzy neural network, the input layer is used for inputting the historical oxygen concentration data set, the historical temperature data set, the historical ammonia concentration data set and the historical pile carbon-nitrogen ratio data set; the historical oxygen concentration dataset, the historical temperature dataset and the historical ammonia concentration dataset constitute an optimization variable. I.e. z (k) = [ x 1(k) x2(k) x3(k)]T ].
Wherein x 1 (k) is oxygen concentration data at time k, x 2 (k) is temperature data at time k, and x 3 (k) is ammonia concentration data at time k.
In one embodiment, the input layer is connected to the input vector, each node is connected to the input component z i, and the number of network input nodes is set to 4.
For the four TS fuzzy neural networks, the fuzzy layer is used for carrying out fuzzification processing on the optimized variable through a membership generating function so as to obtain fuzzy membership; specifically, parameters affecting the rotation speed of the blower are selected, and the parameters are subjected to fuzzification processing through a membership generating function (Gaussian function) to obtain the fuzzy membership of each input component. Parameters affecting blower speed include: the temperature, humidity, oxygen concentration at the stack and the outlet of the aerobic fermentation tank and the set value of the gas mass flowmeter.
The fuzzy rule calculation layer is used for calculating the fuzzy rule of the fuzzy membership; specifically, connecting each node in the neural network with a circuit to form a corresponding j rule; multiplication is used.
The output layer is used for receiving all data output by the fuzzy rule calculation layer, and correcting the received data according to the historical ventilation quantity data/the historical blower energy data/the historical ammonia concentration data/the historical leachate pH data corresponding to the optimized variable so as to determine a ventilation quantity predicted value/a blower energy predicted value/an ammonia concentration predicted value/a leachate pH predicted value. Specifically, the number of output nodes is 1, the network structure is determined to be 4-9-1, the prediction result correction is carried out, the data is normalized, the average value of parameter variables is set to be 0, and the standard deviation value is set to be 1.0, so that the deviation of different characteristic factors or calculation dimensions on the prediction result of the rotating speed of the blower is eliminated.
For the first TS fuzzy neural network, the ventilation quantity prediction relation is as follows:
hj(z,θj)＝[1 zT]·θj；
Wherein, The air quantity predicted value is used; n is the number of fuzzy rules,/>Outputting a fuzzy layer corresponding to the jth rule, and outputting a back part corresponding to the jth rule by h j; z= [ z 1 z2z3 ... zr]x ] is the input of the first TS fuzzy neural network, namely the input of the ventilation prediction relation, r is the number of input optimization variables, θ j is the back-piece parameter of the network, j=1, 2, n; a jk (z (k)) represents a membership matrix obtained by blurring a network input z (k), a gaussian function is adopted, z (k) = [ x 1(k) x2(k)]T,x1 (k) is oxygen concentration data at k moment, x 2 (k) is temperature data at k moment, z (k) is the optimization variable, and z (k) is simply denoted as z k.
Similarly, the prediction relational expressions corresponding to the other three TS fuzzy neural networks can be obtained.
In the training process of the fuzzy neural network, 75% of the historical data set corresponding to each neural network is collected as a training sample, and the other 25% is collected as a test sample. The training parameters are set as follows, training times: 100 times; the activation mode is as follows: relu-Identity activation function, network learning rate: 0.25; training attenuation coefficient: 0.90 to 0.95; regularization coefficient: 0.005 to finally obtain a high-precision prediction model or prediction relation.
In the composting process, the pH and the C/N ratio can be used for representing the humification degree of the pile body, the pH and the C/N ratio are used as constraint conditions of the humification characteristics of the pile body, a functional relation between an optimization set value and an optimization performance index is established, and a model between the energy consumption of the air blower, the humification index of the compost and the optimization set value is obtained, so that the prediction and evaluation of the performance index are obtained.
Based on this, step 300 is: establishing an aerobic composting multi-objective optimization model based on the ventilation quantity prediction relational expression, the blower energy prediction relational expression, the percolate pH prediction relational expression and the heap carbon nitrogen ratio prediction relational expression; the aerobic composting multi-objective optimization model comprises an objective function and a constraint function.
The objective function is:
MinF(x)＝{fae(x),fle(x)}；
The constraint function is:
Wherein MinF (x) is a function f ae (x) and f le (x) are both minimum, f ae (x) is a fan energy consumption prediction relational expression, f le (x) is a ventilation quantity prediction relational expression, b 1、b2、b3 is a preset constant threshold value, g 1 (x) is a percolate pH prediction relational expression, g 2 (x) is a stack carbon nitrogen ratio prediction relational expression, x 1 (k) is oxygen concentration data at time k, x 2 (k) is temperature data at time k, x' 1、x′2 is set lower limits of oxygen concentration data and temperature data respectively, Upper limits are set for oxygen concentration data and temperature data, respectively.
Step 400, solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution; the Pareto optimal solution comprises an oxygen concentration optimization set value and a temperature optimization set value in the aerobic fermentation tank.
Step 400 specifically includes:
1) Processing the constraint function by adopting a punishment function method; specifically, the following penalty function is constructed:
G(x)＝F(x)+Cφ(x)
wherein, C is penalty factor, when all are feasible solutions, C is 0; c takes a relatively large value when the feasible solutions are relatively small; phi (x) is a penalty term.
After adding the penalty term:
2) And solving the aerobic composting multi-objective optimization model processed by a penalty function method by adopting an NSGA-II algorithm to obtain a group of Pareto optimization solutions.
3) Based on a preset decision, a Pareto optimal solution is selected from a group of Pareto optimal solutions. Wherein the preset decision is the oxygen concentration and temperature value required in the actual composting treatment.
Further, solving the aerobic composting multi-objective optimization model processed by a punishment function method by adopting an NSGA-II algorithm, and specifically comprises the following steps:
1) Initializing a population P (0), setting a population number N, an evolution algebra Gen=0, a maximum evolution algebra MAX, generating an initial population consisting of individuals with the population number of 1.5 times by parameters, and solving fitness of the individuals.
2) And performing non-dominant ranking on the initial population, if the non-dominant ranks are the same, selecting excellent individuals with large crowding coefficients, and performing subsequent iteration on the selected population number, namely generating a parent population.
3) And carrying out crossover and mutation operation on the generated parent population to generate a child population.
4) The parent and offspring are combined into a new temporary population.
5) The new population is subjected to a rapid non-dominant ranking.
6) The dynamic crowding degree and each performance index value of the new temporary population are calculated.
7) The best N individuals are selected as the next generation evolution population by adopting a best-keeping strategy.
8) Repeating the steps (4) - (8) until the evolution algebra reaches the maximum evolution algebra MAX.
Specifically, the basic parameters of the multi-objective optimization algorithm are set as follows: the population scale N=60, the maximum evolution algebra MAX=300, the basic crossover probability is 0.9, the basic mutation probability is 0.2, and the energy performance index of the blower is calculated by utilizing the functional relation between the performance index established by the fuzzy neural network and the optimization variable, and corresponds to the oxygen concentration optimization set value and the temperature optimization set value in the aerobic fermentation tank.
And 500, inputting the Pareto optimal solution to the PID controller to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
Preferably, the aerobic composting device further comprises a stirrer; the stirrer is arranged in the aerobic fermentation tank. The intelligent control method for the aerobic composting further comprises the following steps: and the PID controller is adopted, the rotating speed of the air blower and the rotating speed of the stirrer are adaptively adjusted based on the Pareto optimal solution, so that the tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank is realized, the closed-loop control of the ventilation quantity is also realized, the ventilation quantity is kept in an optimal state all the time, and the optimal composting effect is realized.
As shown in fig. 3, 2 optimized setting values are obtained as input variables according to the aerobic composting optimization model; setting an input variable through a parameter correction module to obtain an input variable proportion coefficient K P, an integral coefficient K i and a differential coefficient K d of a PID control module; and then the gas mass flowmeter and the stirrer rotating speed are respectively tracked and controlled through the PID control module, and the gas mass flowmeter can adjust the rotating speed of the blower so as to realize real-time tracking and controlling. In fig. 3, e c (t) represents the blower rotational speed error variation, and e (t) represents the blower rotational speed error.
In a specific embodiment, the intelligent control method for the aerobic composting is realized based on APP DESIGNER in Matlab, and an intelligent control visual interface for the aerobic composting is constructed. When the Matlab design graphical monitoring interface is adopted to preprocess and model data, the method comprises the following steps: initialization, sensor data acquisition, real-time data display, prediction data display, storage and transportation, and the method specifically comprises the following steps:
after the system initialization is completed, the system enters a main control interface, data acquired by each sensor are calculated according to a designated parameter reading mode and stored in a database, and the data are displayed on an upper computer interface through a communication protocol and a wireless serial port module, so that real-time monitoring of each index and a certain data volume are realized.
The interface design comprises controls such as a selection button, a sliding block, an editing frame, a coordinate axis frame and the like. The specific design comprises the following steps: the data display graphics area, control panel and parameter settings. The data display graph area comprises temperature change curve drawing, humidity change curve drawing, oxygen concentration change curve drawing, pH value change curve drawing, EC change curve drawing and turbidity change curve drawing. The temperature and humidity change curves are divided into an upper sensor, a middle sensor and a lower sensor, 2 coordinate axis frames respectively display temperature and humidity, oxygen concentration, pH, EC and turbidity change curves, and one coordinate axis frame of each index is displayed. The control panel is provided with 6 buttons for selecting real-time recording, stopping recording, emptying, copying pictures, saving pictures and setting parameters, 2 pull-down menus are set, scanning intervals are selected, and data types are selected.
The embodiment runs the layout of a Graphic User Interface (GUI) visual component in MATLAB and the programming of app behavior to construct a model interface of the aerobic fermentation intelligent system of the compost, so that the compost operation index parameters and the model are expressed more simply and intuitively, the method is also suitable for experimental teaching, and the method is high in integration level and simple and convenient to operate.
Example two
As shown in fig. 4, to achieve the effect of the technical solution in the first embodiment, the present embodiment further provides an aerobic composting intelligent control system, which is applied to an aerobic composting device, where the aerobic composting device includes an aerobic fermentation tank, a percolation liquid tank, a blower, and a PID controller.
The intelligent control system for aerobic composting comprises:
the data acquisition module 101 is used for acquiring a historical ventilation volume data set, a historical blower energy data set, a historical percolate pH data set, a historical oxygen concentration data set, a historical ammonia gas concentration data set, a historical temperature data set and a historical pile carbon nitrogen ratio data set in the aerobic fermentation tank.
A relation determination module 201, configured to train the first TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical ventilation dataset, to determine a ventilation prediction relation; training a second TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical blower energy consumption dataset to determine a blower energy consumption prediction relationship; training a third TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, the historical ammonia concentration dataset, and the historical leachate pH dataset to determine a leachate pH prediction relationship; and training a fourth TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set, the historical ammonia concentration data set and the historical stack carbon-nitrogen ratio data set to determine a stack carbon-nitrogen ratio prediction relational expression.
The multi-objective optimization model construction module 301 is configured to establish an aerobic composting multi-objective optimization model based on the ventilation volume prediction relational expression, the blower energy consumption prediction relational expression, the percolate pH prediction relational expression, and the heap carbon nitrogen ratio prediction relational expression.
The multi-objective optimization model solving module 401 is configured to solve the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution; the Pareto optimal solution comprises an oxygen concentration optimization set value and a temperature optimization set value in the aerobic fermentation tank.
And the PID control module 501 is used for inputting the Pareto optimal solution into the PID controller so as to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
In one embodiment, as shown in FIG. 5, the aerobic composting intelligent control system includes a multi-objective optimization module, a floor tracking control module, and a composting process. In the multi-objective optimization module, constructing a neural network through the data acquired by the off-line data acquisition sub-module; carrying out online optimization by combining the real-time data and NSGAII to obtain a ventilation quantity predicted value, a blower energy consumption predicted value, a percolate pH predicted value and a stack carbon nitrogen ratio predicted value; and then the oxygen concentration and the temperature in the air blower and the aerobic fermentation tank are optimally controlled by the corresponding PID controllers, so as to realize high-precision aerobic composting control. And FIG. 6 is a schematic diagram of the overall architecture of the intelligent control system for aerobic composting.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer 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 points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (7)
1. The intelligent control method for the aerobic composting is characterized by being applied to an aerobic composting device, wherein the aerobic composting device comprises an aerobic fermentation tank, a percolation liquid tank, a blower and a PID controller;
the intelligent control method for the aerobic composting comprises the following steps:
Acquiring a historical ventilation quantity data set, a historical blower energy data set, a historical percolate pH data set, a historical oxygen concentration data set, a historical ammonia gas concentration data set, a historical temperature data set and a historical pile carbon nitrogen ratio data set in the aerobic fermentation tank;
Training a first TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical ventilation dataset to determine a ventilation prediction relationship; training a second TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical blower energy consumption dataset to determine a blower energy consumption prediction relationship; training a third TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, the historical ammonia concentration dataset, and the historical leachate pH dataset to determine a leachate pH prediction relationship; training a fourth TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set, the historical ammonia concentration data set and the historical stack carbon-nitrogen ratio data set to determine a stack carbon-nitrogen ratio prediction relational expression;
Establishing an aerobic composting multi-objective optimization model based on the ventilation quantity prediction relational expression, the blower energy prediction relational expression, the percolate pH prediction relational expression and the heap carbon nitrogen ratio prediction relational expression;
solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution; the aerobic composting multi-objective optimization model comprises an objective function and a constraint function;
Solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution, which comprises the following steps: processing the constraint function by adopting a punishment function method; solving the aerobic composting multi-objective optimization model processed by a penalty function method by adopting an NSGA-II algorithm to obtain a group of Pareto optimization solutions; based on a preset decision, selecting a Pareto optimal solution from a group of Pareto optimal solutions; the preset decision is the oxygen concentration and temperature value required in the actual composting treatment; the Pareto optimal solution comprises an oxygen concentration optimization set value and a temperature optimization set value in the aerobic fermentation tank;
and inputting the Pareto optimal solution to the PID controller to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
2. The intelligent control method for aerobic composting according to claim 1, wherein the first TS fuzzy neural network comprises an input layer, a fuzzy rule calculation layer and an output layer;
The input layer is used for inputting the historical oxygen concentration data set, the historical temperature data set and the historical ventilation volume data set; the historical oxygen concentration dataset and the historical temperature dataset form an optimization variable;
the fuzzy layer is used for carrying out fuzzification processing on the optimized variable through a membership generating function so as to obtain fuzzy membership;
the fuzzy rule calculation layer is used for calculating the fuzzy rule of the fuzzy membership;
The output layer is used for receiving all data output by the fuzzy rule calculation layer, correcting the received data according to the historical ventilation quantity data corresponding to the optimized variable, and determining a ventilation quantity predicted value.
3. The intelligent control method for aerobic composting according to claim 2, wherein the ventilation quantity prediction relational expression is:
hj(z,θj)＝[1zT]·θj；
Wherein, The air quantity predicted value is used; n is the number of fuzzy rules,/>Outputting a fuzzy layer corresponding to the jth rule, and outputting a back part corresponding to the jth rule by h j; z= [ z 1 z2 z3 … zr]T ] is the input of the first TS fuzzy neural network, namely the input of the ventilation quantity prediction relation, r is the input optimized variable number, θ j is the back-piece parameter of the network, j=1, 2, … and n; a jk (z (k)) represents a membership matrix obtained by blurring a network input z (k), wherein z (k) = [ x 1(k) x2(k)]T,x1 (k) is k-moment oxygen concentration data, x 2 (k) is k-moment temperature data, z (k) is the optimization variable, and z (k) is simply denoted as z k.
4. The intelligent control method for aerobic composting according to claim 1, wherein the objective function is:
MinF(x)＝{fae(x),fle(x)}；
The constraint function is:
Wherein MinF (x) is a function f ae (x) and f le (x) are both minimum, f ae (x) is a fan energy consumption prediction relational expression, f le (x) is a ventilation quantity prediction relational expression, b 1、b2、b3 is a preset constant threshold value, g 1 (x) is a percolate pH prediction relational expression, g 2 (x) is a stack carbon nitrogen ratio prediction relational expression, x 1 (k) is oxygen concentration data at time k, x 2 (k) is temperature data at time k, x' 1、x′2 is set lower limits of oxygen concentration data and temperature data respectively, Upper limits are set for oxygen concentration data and temperature data, respectively.
5. The intelligent control method for aerobic composting according to claim 1, wherein the aerobic composting apparatus further comprises a stirrer; the stirrer is arranged in the aerobic fermentation tank;
the intelligent control method for the aerobic composting further comprises the following steps:
and adjusting the rotating speed of the air blower and the rotating speed of the stirrer based on the Pareto optimal solution by adopting the PID controller so as to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
6. The intelligent control method for aerobic composting according to claim 1, wherein the intelligent control method for aerobic composting is realized based on APP DESIGNER in Matlab, and an intelligent control visual interface for aerobic composting is constructed.
7. The intelligent control system for the aerobic composting is characterized by being applied to an aerobic composting device, wherein the aerobic composting device comprises an aerobic fermentation tank, a percolation liquid tank, a blower and a PID controller;
The intelligent control system for aerobic composting comprises:
the data acquisition module is used for acquiring a historical ventilation volume data set, a historical blower energy data set, a historical percolate pH data set, a historical oxygen concentration data set, a historical ammonia gas concentration data set, a historical temperature data set and a historical pile carbon-nitrogen ratio data set in the aerobic fermentation tank;
The relation determining module is used for training the first TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set and the historical ventilation volume data set so as to determine a ventilation volume prediction relation; training a second TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, and the historical blower energy consumption dataset to determine a blower energy consumption prediction relationship; training a third TS fuzzy neural network based on the historical oxygen concentration dataset, the historical temperature dataset, the historical ammonia concentration dataset, and the historical leachate pH dataset to determine a leachate pH prediction relationship; training a fourth TS fuzzy neural network based on the historical oxygen concentration data set, the historical temperature data set, the historical ammonia concentration data set and the historical stack carbon-nitrogen ratio data set to determine a stack carbon-nitrogen ratio prediction relational expression;
the multi-objective optimization model construction module is used for constructing an aerobic composting multi-objective optimization model based on the ventilation quantity prediction relational expression, the blower energy prediction relational expression, the percolate pH prediction relational expression and the heap carbon nitrogen ratio prediction relational expression;
the multi-objective optimization model solving module is used for solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution; the aerobic composting multi-objective optimization model comprises an objective function and a constraint function;
Solving the aerobic composting multi-objective optimization model to obtain a Pareto optimal solution, which comprises the following steps: processing the constraint function by adopting a punishment function method; solving the aerobic composting multi-objective optimization model processed by a penalty function method by adopting an NSGA-II algorithm to obtain a group of Pareto optimization solutions; based on a preset decision, selecting a Pareto optimal solution from a group of Pareto optimal solutions; the preset decision is the oxygen concentration and temperature value required in the actual composting treatment; the Pareto optimal solution comprises an oxygen concentration optimization set value and a temperature optimization set value in the aerobic fermentation tank;
and the PID control module is used for inputting the Pareto optimal solution into the PID controller so as to realize tracking control of the oxygen concentration and the temperature in the aerobic fermentation tank.
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