A parameter optimization control method and system for microbial fuel cell

Abstract

The present invention belongs to the technical field of energy control, and provides a parameter optimization control method and system for a microbial fuel cell; wherein the parameter optimization control method for a microbial fuel cell comprises: obtaining a mathematical model of the microbial fuel cell, considering the convergence law, and constructing a fixed-time sliding mode controller for the microbial fuel cell; adopting an improved zebra algorithm to obtain the optimal control parameters of the fixed-time sliding mode controller, and obtain the maximum output voltage of the microbial fuel cell; wherein the improved zebra algorithm adopts a chaotic map with a circular map to initialize the population, introduces an adaptive weight factor based on a tangent transformation to update the position of a pioneer zebra, and dynamically adjusts the search pace until the iteration is completed to obtain the optimal control parameters.

Description

Technical Field

[0001] The invention belongs to the technical field of energy control, and in particular relates to a parameter optimization control method and system for a microbial fuel cell. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of economy, energy and environmental issues have become the most serious challenges facing mankind. Renewable energy is considered to be an ideal choice to replace fossil fuels in the future. Microbial fuel cells, as an emerging clean energy technology, have received widespread attention in recent years.

[0004] Microbial fuel cells are devices that use the metabolic activity of microorganisms to convert the chemical energy of organic matter into electrical energy. Their working principle is: in the anaerobic environment of the anode chamber, the organic substrate is decomposed by microorganisms and releases electrons and protons. Electrons are effectively transferred between the biological components and the anode by appropriate electron transfer mediators, and are transferred to the cathode through an external circuit to form an electric current, while protons are transferred to the cathode through a proton exchange module. The oxidant (usually oxygen) obtains electrons at the cathode and is reduced to combine with protons to form water. The working principle of microbial fuel cells combines microbial metabolism and electrochemical processes. By utilizing the biological activity and electrochemical characteristics of microorganisms, the goal of converting waste organic matter into clean electrical energy is achieved; it has broad application prospects in wastewater treatment, pollutant degradation, and renewable energy generation. Therefore, microbial fuel cells have many advantages such as high efficiency, renewability, low cost, and environmental friendliness, and are an important part of the future sustainable energy system.

[0005] However, the performance of microbial fuel cells is currently far below the ideal state. Factors that restrict battery performance include kinetic factors, internal resistance factors, and transfer factors. In order to optimize the performance and stability of microbial fuel cells, effective control strategies must be adopted. The design of microbial fuel cell controllers plays a vital role in regulating various parameters such as voltage, current, and microbial activity to improve power output and overall efficiency. Summary of the invention

[0006] To solve the above problems, the present invention proposes a parameter optimization control method and system for a microbial fuel cell, which adopts a fixed time sliding mode and an improved zebra algorithm to perform parameter optimization control of the microbial fuel cell, and controls the dilution rate of the input amount of the microbial fuel cell through a fixed time sliding mode controller; the improved zebra algorithm is used to optimize the parameters of the fixed time sliding mode controller, so that the microbial fuel cell can quickly and stably generate a maximum output voltage while meeting the system stability and convergence time. This also greatly avoids the problem that the control theory cannot be combined with practice.

[0007] According to some embodiments, a first solution of the present invention provides a parameter optimization control method for a microbial fuel cell, using the following technical solution:

[0008] A parameter optimization control method for a microbial fuel cell, comprising:

[0009] Obtain mathematical models of microbial fuel cells;

[0010] According to the obtained mathematical model of microbial fuel cell, considering the reaching law, a fixed time sliding mode controller of microbial fuel cell is constructed;

[0011] The improved zebra algorithm is used to optimize the control parameters of the constructed fixed-time sliding mode controller to obtain the optimal control parameters.

[0012] According to the obtained optimal control parameters, the maximum output voltage of the microbial fuel cell is obtained;

[0013] The improved zebra algorithm uses a chaotic map (tent chaotic map) with a circle map to initialize the population, introduces an adaptive weight factor based on tangent transformation to update the position of the pioneer zebra, and dynamically adjusts the search pace until the iteration is completed to obtain the optimal control parameters.

[0014] As a further technical limitation, in the process of population initialization, the chaotic map introduced by the circular map is: Among them, mod is the remainder function, k is a constant that can adjust the mapping effect, and x n+1 is the n+1th individual in the population, x n is the nth individual in the population.

[0015] As a further technical limitation, in order to balance the relationship between local development and global search, the inertia weight factor based on tangent change is used to update the position of the pioneer zebra, that is, the inertia weight factor based on tangent change is Among them, ω max is the initial inertia weight, ω min is the inertia weight at the end, T is the maximum number of iterations, and t is the number of iterations.

[0016] As a further technical limitation, the mathematical model of the microbial fuel cell obtained is Among them, x1 represents substrate concentration, x2 represents biomass concentration, x3 represents acetate concentration, x4 represents hydrogen ion concentration, Y represents biomass growth, K s represents the half-saturation constant, S o represents the substrate concentration, K d represents the bacterial decay coefficient, qmax is the maximum substrate utilization, μ max is the maximum growth rate and u is the control input.

[0017] As a further technical qualification, the reaching law considered is Among them, ν1, ν3>1, 0<ν2, ν4<1, k1, k2, k3, k4>0 are all normal numbers.

[0018] Further, according to According to the considered approach rate, the control input u, is solved and the fixed-time sliding mode controller of the microbial fuel cell is obtained as

[0019] According to some embodiments, the second solution of the present invention provides a parameter optimization control system for a microbial fuel cell, which adopts the following technical solution:

[0020] A parameter optimization control system for a microbial fuel cell, comprising:

[0021] an acquisition module configured to acquire a mathematical model of a microbial fuel cell;

[0022] A construction module is configured to construct a fixed-time sliding mode controller of the microbial fuel cell according to the obtained mathematical model of the microbial fuel cell and taking into account the reaching law;

[0023] An optimization module is configured to optimize the control parameters of the constructed fixed-time sliding mode controller by using an improved zebra algorithm to obtain optimal control parameters; and obtain a maximum output voltage of the microbial fuel cell according to the obtained optimal control parameters;

[0024] The improved zebra algorithm uses a chaotic map with a circular map to initialize the population, introduces an adaptive weight factor based on a tangent transformation to update the position of the pioneer zebra, and dynamically adjusts the search pace until the iteration is completed to obtain the optimal control parameters.

[0025] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0026] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in the parameter optimization control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0027] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0028] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the parameter optimization control method of a microbial fuel cell as described in the first embodiment of the present invention are implemented.

[0029] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0030] A computer program product includes software codes, wherein the program in the software codes executes the steps in the parameter optimization control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention adopts fixed-time sliding mode and improved zebra algorithm to perform parameter optimization control of microbial fuel cells, and controls the dilution rate of the input amount of the microbial fuel cell through a fixed-time sliding mode controller; adopts the improved zebra algorithm to optimize the parameters of the fixed-time sliding mode controller, so that the microbial fuel cell can quickly and stably generate the maximum output voltage under the premise of meeting the system stability and convergence time, which also greatly avoids the problem that the control theory cannot be combined with practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0034] Figure 1 This is a flow chart of a parameter optimization control method for a microbial fuel cell in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0038] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0039] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0040] Embodiment 1

[0041] Embodiment 1 of the present invention introduces a parameter optimization control method for a microbial fuel cell.

[0042] like Figure 1 As shown, this embodiment controls the dilution rate of the input amount of the microbial fuel cell through a fixed-time sliding mode controller; uses an improved zebra algorithm to optimize the parameters of the fixed-time sliding mode controller to ensure that the microbial fuel cell can quickly and stably generate the maximum output voltage while meeting the system stability and convergence time; and avoids the problem that the control theory cannot be combined with practice.

[0043] The mathematical model of the microbial fuel cell obtained in this embodiment is Among them, x1 represents substrate concentration, x2 represents biomass concentration, x3 represents acetate concentration, x4 represents hydrogen ion concentration, Y represents biomass growth, K s represents the half-saturation constant, S o represents the substrate concentration, K d represents the bacterial decay coefficient (in this embodiment, K d >0), q max is the maximum substrate utilization, μ max is the maximum growth rate and u is the control input.

[0044] When the microbial fuel cell is open circuit, the anode potential V a and cathode potential V c It can be expressed as Where a and c represent the anode and cathode of the microbial fuel cell, respectively; E a and E c Represent the internal voltage of the anode and cathode of the microbial fuel cell respectively; E Loss,a and E Loss,c They represent the internal voltage losses of the anode and cathode of the microbial fuel cell, which are 0.15 V and 1.019 V, respectively, verified in the laboratory. 0a and E 0crepresents the standard potential of the anode and cathode respectively; F represents the Faraday constant, which is 96485C / mol; R represents the ideal gas constant, which is 8.3144J / (mol·k); n1 and n2 represent the electron transfer numbers, which are 8 and 4 respectively; T o is the temperature; the fuel cell potential is V MFC =V a +V c .

[0045] Defining Systematic Error By taking the derivatives of the above equations, we can get:

[0046]

[0047] Defining nonlinear functions The sliding surface obtained in the sliding mode control design is s1(t)=e1(t), s2(t)=e2(t), and according to the characteristics of the controller to be designed, this embodiment proposes a new reaching law, namely according to Solve the reaching law and obtain the control input of the sliding mode controller

[0048] Select a suitable Lyapunov function V and design a sliding mode controller u to ensure is always negative, i.e. in, Taking the derivative of the Lyapunov function, we get Defining the Adaptation Rate Can get

[0049]

[0050] Therefore, the designed controller ensures the stability of the microbial fuel cell system, and the system convergence time meets: Where 0<ν<1.

[0051] This embodiment uses an improved zebra algorithm to optimize the designed microbial fuel cell system controller parameter ν; that is, the population is divided into 4 sub-populations of equal size, and the 4 populations are optimized in different ways. Among them, population one is optimized in the normal zebra optimization method; population two introduces a new golden sine algorithm in the pioneer zebra position formula and the defense resistance strategy formula; population three uses circle-tent chaotic mapping to initialize the population and introduces a new adaptive weight factor based on tangent change; population four sets the random generation probability X, and when X takes different values, different methods are used for optimization. When the maximum number of iterations is reached, the 4 sub-populations are merged. The normal optimization operation of the zebra algorithm is continued for this population to find the optimal value of the parameter ν, so that the system state quantity can quickly stabilize to the set value, and under the premise of ensuring system stability and convergence time, the output voltage of the microbial fuel cell reaches the maximum.

[0052] The traditional zebra optimization algorithm consists of two stages:

[0053] Phase 1: Foraging behavior, update the position of the zebra in the foraging phase to

[0054] in, The new position of the i-th zebra during the foraging phase, The j-dimensional value of the first zebra; The new objective function value, PZ refers to the pioneer zebra, PZ j Refers to the j-dimensional value of the Pioneer Zebra; r is a random number between [0,1]; I = round(1+rand), rand is a random number between [0,1].

[0055] Phase 2: In response to the defense behaviors of different predators, the zebra optimization algorithm simulates the defense strategies of zebras when facing predator attacks to update the positions of zebra population members in the search space; Among them, T is the maximum number of iterations; t is the number of iterations

[0056] times; t is a random number in [0,1]; R is a constant of 0.01; is the objective function value of the i-th zebra after updating in the second stage. is the new position of the i-th zebra; is the new position of the i-th zebra in the j-th dimension, AZ j is the j-th dimension position of the attacked zebra, P S is the probability of encountering two defensive behaviors.

[0057] This embodiment uses binary numbers to encode the fixed-time sliding mode controller parameters, and divides the initial zebra population into four sub-populations of equal size, named as population 1, population 2, population 3 and population 4, respectively. The four sub-populations use different optimization methods;

[0058] Population 1: Without considering weight, the pioneer zebra position update formula and defense resistance strategy in the normal zebra algorithm are used to find the optimal solution.

[0059] Population 2: Considering the adaptive weight, we get the adaptive optimization algorithm. We introduce a new golden sine algorithm into the position update formula and the defense resistance strategy formula. By combining the golden ratio and the sine function, the position update formula of the pioneer zebra in the zebra algorithm is: Defense resistance strategy updated to This greatly enhances the local search and global optimization capabilities of the zebra algorithm; among them, the golden angle φ φ g It's the golden ratio

[0060] Population 3: Design a new improved tent chaotic mapping to initialize the population, namely circle-tent mapping, which is a combination of traditional tent chaotic mapping and circle mapping.

[0061] The traditional tent mapping is Among them, x n+1 is the n+1th individual in the population, x n is the nth individual in the population, a is a constant in the interval (0,1), and a=0.5 is taken.

[0062] After combining the traditional tent mapping with the circle mapping, the resulting circle-tent chaotic mapping is: Among them, mod is the modulo function, k is a constant, and its value can be adjusted to change the mapping effect.

[0063] It should be noted that the parameters of the circle-tent mapping, tent mapping and circle mapping in this embodiment can be adjusted independently. For example, the frequency parameter in the circle mapping can control the rotation speed of the system, and the piecewise linear characteristics of the tent mapping can control the degree of chaos of the system. This flexibility makes the composite mapping suitable for studying the impact of parameter changes on system behavior. The improved circle-tent chaotic mapping combines the nonlinear characteristics of the tent mapping and the periodic characteristics of the circle mapping. The circle-tent chaotic mapping can oscillate and fluctuate continuously, so that it has a more uniform distribution. Using it for population initialization can provide a high-quality search space for the algorithm, thereby helping to improve the convergence accuracy of the algorithm.

[0064] In order to better balance the relationship between local development and global search, this embodiment introduces an inertia weight factor based on tangent change to update the position of the pioneer zebra; that is, the inertia weight factor based on tangent change is Among them, ω max is the initial inertia weight, ω min is the inertia weight at the end.

[0065] Introducing adaptive inertia weight into the pioneer zebra position update formula and defense resistance strategy formula, we get

[0066]

[0067] The inertia weight factor is usually used to adjust the search behavior of the algorithm. By introducing the inertia weight based on tangent change, a larger weight can be maintained in the early stage, thereby enhancing the global search capability and exploring a wider solution space. As the iteration proceeds, the weight gradually decreases, enhancing the local search capability and helping to find the optimal solution more finely. The inertia weight factor based on the tangent function can dynamically adjust the search pace, so that the algorithm quickly converges to a better area in the early stage, and then gradually reduces the pace and refines the search. This change mode can effectively prevent the algorithm from falling into the local optimum, thereby improving the overall convergence efficiency.

[0068] Population Four: A random probability X is generated. When X < 0.25, the pioneer zebras move randomly to find the optimal solution. When 0.25 < X < 0.5, the optimization method of the population is used for optimization. When 0.5 < X < 0.75, the optimization method of Population Two is used for optimization. When 0.5 < X < 0.75, the optimization method of Population Three is used for optimization. This enables different sub-populations to search in parallel in different regions of the search space, thereby improving the search efficiency and coverage. In this way, the optimal solution or a solution close to the optimal solution can be found more quickly. During the optimization process, information exchange and cooperation can occur among the sub-populations. The synergistic effect of different optimization strategies can integrate their respective advantages and further improve the performance of the algorithm.

[0069] When all four sub-populations reach the maximum number of iterations, the four sub-populations are merged; using the output voltage value of the microbial fuel cell as the cost function, the zebras re-optimize to find the optimal value of the parameter ν, so that the system state variables x1, x2, x3, and x4 quickly converge to the set values under the control of the designed controller, and maximize the output voltage of the system; when the maximum number of iterations is reached, the optimal solution Y is output; at this time, the microbial fuel cell outputs the maximum voltage.

[0070] In this embodiment, a fixed-time sliding mode and an improved zebra algorithm are used for parameter optimization control of the microbial fuel cell. The dilution rate of the input quantity of the microbial fuel cell is controlled by a fixed-time sliding mode controller; the parameters of the fixed-time sliding mode controller are optimized using the improved zebra algorithm. On the premise of meeting the system stability and convergence time, the microbial fuel cell can quickly and stably generate the maximum output voltage, which also greatly avoids the problem that control theory cannot be combined with practice.

[0071] Embodiment Two

[0072] Embodiment Two of the present invention introduces a parameter optimization control system for a microbial fuel cell.

[0073] A parameter optimization control system for a microbial fuel cell, comprising:

[0074] An acquisition module configured to acquire the mathematical model of the microbial fuel cell;

[0075] A construction module configured to construct a fixed-time sliding mode controller for the microbial fuel cell according to the acquired mathematical model of the microbial fuel cell, considering the reaching law;

[0076] An optimization module configured to optimize the control parameters of the constructed fixed-time sliding mode controller using the improved zebra algorithm to obtain the optimal control parameters; and obtain the maximum output voltage of the microbial fuel cell according to the obtained optimal control parameters;

[0077] The improved zebra algorithm uses a chaotic map with a circular map to initialize the population, introduces an adaptive weight factor based on a tangent transformation to update the position of the pioneer zebra, and dynamically adjusts the search pace until the iteration is completed to obtain the optimal control parameters.

[0078] The detailed steps are the same as those of the parameter optimization control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0079] Embodiment 3

[0080] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0081] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the parameter optimization control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0082] The detailed steps are the same as those of the parameter optimization control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0083] Embodiment 4

[0084] A fourth embodiment of the present invention provides an electronic device.

[0085] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the parameter optimization control method of a microbial fuel cell as described in the first embodiment of the present invention are implemented.

[0086] The detailed steps are the same as those of the parameter optimization control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0087] Embodiment 5

[0088] Embodiment 5 of the present invention provides a computer program product.

[0089] A computer program product includes software codes, wherein the program in the software codes executes the steps in the parameter optimization control method for a microbial fuel cell as described in the first embodiment of the present invention.

[0090] The detailed steps are the same as those of the parameter optimization control method for the microbial fuel cell provided in Example 1, and will not be repeated here.

[0091] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A parameter optimization control method for a microbial fuel cell, characterized in that: include: Obtain mathematical models of microbial fuel cells; According to the obtained mathematical model of microbial fuel cell, considering the reaching law, a fixed time sliding mode controller of microbial fuel cell is constructed; The improved zebra algorithm is used to optimize the control parameters of the constructed fixed-time sliding mode controller to obtain the optimal control parameters. According to the obtained optimal control parameters, the maximum output voltage of the microbial fuel cell is obtained; The improved zebra algorithm uses a chaotic map with a circular map to initialize the population, introduces an adaptive weight factor based on a tangent transformation to update the position of the pioneer zebra, and dynamically adjusts the search pace until the iteration is completed to obtain the optimal control parameters; In the process of population initialization, the chaotic map introduced by the circular map is: ; where mod is the remainder function, k is a constant that can adjust the mapping effect. x n+1 For the population n +1 individual, x n For the population n Individual.

2. A parameter optimization control method for a microbial fuel cell as claimed in claim 1, characterized in that: In order to balance the relationship between local development and global search, an adaptive weight factor based on tangent change is used to update the position of the pioneer zebra, that is, the inertia weight factor based on tangent change is ;in, is the initial inertia weight, is the inertia weight at the end, T is the maximum number of iterations; t is the number of iterations.

3. A parameter optimization control method for a microbial fuel cell as claimed in claim 1, characterized in that: The mathematical model of the microbial fuel cell obtained is ; in, x 1 represents the substrate concentration, x 2 represents the biomass concentration, x 3 represents the acetate concentration, x 4 represents the hydrogen ion concentration, Y represents the biomass growth, K s represents the half-saturation constant, S o represents the substrate concentration, K d represents the bacterial decay coefficient, q max is the maximum substrate utilization, μ max is the maximum growth rate, u For control input.

4. A parameter optimization control method for a microbial fuel cell as claimed in claim 1, characterized in that: The reaching law considered is ;in, , , k 1, k 2, k 3, k 4>0 are all normal numbers, , is the system error.

5. A parameter optimization control method for a microbial fuel cell as claimed in claim 4, characterized in that according to , according to the approach rate considered, solve the control input u , the fixed-time sliding mode controller of the microbial fuel cell is obtained as ,in, and A nonlinear function defined when solving a fixed-time controller.

6. A parameter optimization control system for a microbial fuel cell, characterized in that: include: an acquisition module configured to acquire a mathematical model of a microbial fuel cell; A construction module is configured to construct a fixed-time sliding mode controller of the microbial fuel cell according to the obtained mathematical model of the microbial fuel cell and taking into account the reaching law; An optimization module is configured to optimize the control parameters of the constructed fixed-time sliding mode controller by using an improved zebra algorithm to obtain optimal control parameters; and obtain a maximum output voltage of the microbial fuel cell according to the obtained optimal control parameters; The improved zebra algorithm uses a chaotic map with a circular map to initialize the population, introduces an adaptive weight factor based on a tangent transformation to update the position of the pioneer zebra, and dynamically adjusts the search pace until the iteration is completed to obtain the optimal control parameters; In the process of population initialization, the chaotic map introduced by the circular map is: ; where mod is the remainder function, k is a constant that can adjust the mapping effect. x n+1 For the population n +1 individual, x n For the population n Individual.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the parameter optimization control method for a microbial fuel cell according to any one of claims 1 to 5 are implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the parameter optimization control method for the microbial fuel cell according to any one of claims 1 to 5 are implemented.

9. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the parameter optimization control method for a microbial fuel cell according to any one of claims 1 to 5.

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