An anaerobic reactor self-adaptive temperature control system and method

Abstract

This invention relates to the field of environmental protection and automation control technology. It discloses an adaptive temperature control system and method for an anaerobic reactor. The system includes temperature detection, fuzzification, fuzzy inference, defuzzification, PID control, an actuator, and an adaptive adjustment module. Through a fuzzy PID control algorithm, the temperature regulation parameters are dynamically adjusted to eliminate the impact of temperature fluctuations on the activity of methanogenic bacteria, maintaining reaction temperature fluctuations ≤ ±0.5℃. This invention is primarily designed to address the nonlinear and large hysteresis characteristics of anaerobic reactors. Combined with adaptive optimization, it improves control accuracy and robustness, while simultaneously increasing gas production efficiency by 40%, reducing energy consumption, and extending equipment lifespan. It is applicable to biogas projects, wastewater treatment, and other scenarios, demonstrating significant economic and environmental benefits.

Description

Technical Field

[0001] This invention relates to the field of environmental protection and automation control technology, and in particular to an adaptive temperature control system and method for anaerobic reactors. Background Technology

[0002] Anaerobic reactors, as a core biological treatment device, are widely used in various fields such as wastewater treatment, organic waste resource utilization, and biogas production, playing a vital role in promoting sustainable development and the circular economy. In wastewater treatment plants, anaerobic reactors degrade organic pollutants, reduce sludge production, and lower treatment costs. In agriculture and animal husbandry, they treat livestock manure and straw waste, producing biogas and organic fertilizer, achieving energy recovery and pollution control. In industrial sectors such as food processing, brewing, and papermaking, anaerobic reactors treat high-concentration organic wastewater while simultaneously generating renewable energy. Furthermore, with the global energy transition, anaerobic digestion technology has become an important source of biomass energy, supplementing the power grid with green electricity or serving as vehicle fuel, reducing dependence on fossil fuels.

[0003] In these applications, temperature is a critical control parameter in the anaerobic digestion process. Methanogens, as the core microbial community in anaerobic reactions, are highly dependent on temperature stability for their activity. Mesophilic anaerobic reactions are typically maintained at 35-37°C, while hyperthermic reactions are maintained at 55-60°C. Temperature fluctuations directly affect the metabolic rate of the microbial community, substrate degradation efficiency, and biogas yield. For example, in large-scale biogas projects, reactor volumes can reach thousands of cubic meters. Uneven or fluctuating temperatures can lead to localized rancidity, decreased methane production, and even system collapse. Therefore, precise temperature control is not only a means to improve gas production efficiency but also a necessary condition for ensuring stable process operation, extending equipment life, and reducing operational risks. Currently, most applications rely on basic temperature control systems, but their performance is often unsatisfactory under complex operating conditions.

[0004] In practical operation, anaerobic reactor temperature control faces multiple technical challenges, severely impacting its application effectiveness and economic efficiency. Anaerobic reactors exhibit significant nonlinear, time-varying, and large hysteresis characteristics. Fluctuations in feed load (such as changes in organic matter concentration), seasonal differences in ambient temperature, and uneven exothermic reactions all introduce random disturbances, leading to complex dynamic temperature changes. Traditional temperature control methods, such as simple on / off control or conventional PID control, are ill-suited to these characteristics. On / off control regulates temperature by starting and stopping the heater, but its response is sluggish, prone to overshoot and oscillation, with temperature fluctuations often exceeding ±2°C. This not only wastes energy but also repeatedly impacts methanogenic bacteria, causing activity inhibition.

[0005] While conventional PID control can provide continuous regulation, its parameters (K) p K i Kd Typically fixed and tuned based on linear models, anaerobic reactors cannot handle nonlinear dynamics. For example, when the feed load suddenly increases, the exothermic reaction increases, and the PID controller may not respond sufficiently, leading to an excessively rapid temperature rise; conversely, when the load decreases, the temperature drop lags. Furthermore, the high thermal inertia of anaerobic reactors and the delay between temperature sensors and heating / cooling actuators further exacerbate the control difficulty. Studies have shown that temperature fluctuations exceeding ±1°C can reduce methanogen activity by more than 20% and biogas production by 15-30%. Simultaneously, existing systems lack adaptive capabilities and cannot optimize control strategies based on long-term operational data, resulting in low energy efficiency and frequent maintenance.

[0006] Another prominent issue is the trade-off between control accuracy and cost. High-precision temperature control systems (such as model predictive control) require complex mathematical models and high-speed processors, resulting in high costs and making them unsuitable for small and medium-sized applications; while low-cost solutions offer poor control performance and struggle to meet the requirements of industrial production. Therefore, developing a temperature control system that is both accurate and economical, and capable of adaptive changes, has become an urgent need for the industry.

[0007] In view of the above problems, those skilled in the art urgently need a temperature control method specifically for anaerobic reactors that combines fuzzy PID control and adaptive optimization. Summary of the Invention

[0008] The purpose of this invention is to solve the above-mentioned problems by designing an adaptive temperature control system and method for anaerobic reactors. The bottleneck of temperature control in anaerobic reactors lies in the inability of traditional methods to balance accuracy, adaptability, and cost. Fuzzy PID control, as an intelligent control technology, combines the robustness of fuzzy logic with the reliability of PID control. It can handle nonlinear systems without the need for precise mathematical models, and its computational load is moderate, making it suitable for real-time control. By designing fuzzy rules and adaptive mechanisms tailored to the characteristics of anaerobic reactors, this system is expected to achieve stable temperature control of ±0.5°C, thereby directly improving the activity of methanogens and increasing gas production efficiency.

[0009] In addition, in terms of economic costs, this application can significantly shorten the investment payback period and enhance the economic feasibility of the project by increasing gas production efficiency by 40%; at the same time, the adaptive adjustment function can reduce manual intervention and energy consumption, further saving operating costs.

[0010] At the environmental and social level, stable and efficient anaerobic treatment helps reduce greenhouse gas emissions (such as methane escape due to uncontrolled temperature), promotes the resource utilization of organic waste, and is in line with the carbon neutrality policy orientation.

[0011] The technical solution of the present invention to achieve the above objectives is an adaptive temperature control system for an anaerobic reactor, comprising a temperature detection module, a fuzzification module, a fuzzy inference module, a defuzzification module, a PID control module, an actuator module, and an adaptive adjustment module. These modules work together to achieve adaptive temperature control of the anaerobic reactor.

[0012] The temperature detection module monitors the temperature of the anaerobic reactor in real time and calculates the temperature error and the rate of change of error.

[0013] The fuzzification module converts temperature error and error change rate into fuzzy quantities;

[0014] The fuzzy inference module outputs fuzzy control quantities based on the received fuzzy quantities and according to the fuzzy rule base.

[0015] The defuzzification module converts fuzzy control quantities into precise control quantities;

[0016] The PID control module adjusts the PID parameters and generates control signals according to the precise control quantity.

[0017] The actuator module adjusts the temperature in response to control signals;

[0018] The adaptive adjustment module optimizes fuzzy rules or PID parameters online based on temperature fluctuations and gas production efficiency feedback.

[0019] The fuzzification module uses triangular membership functions to map the temperature error e(t) and the rate of change of error ec(t) to the fuzzy set {NB, NM, NS, ZO, PS, PM, PB}, where e(t) = T set -T actual , among which, T set To set the temperature, T actual For the actual temperature, ec(t) = de(t) / dt.

[0020] The fuzzy rule base of the fuzzy inference module contains 49 rules in the form of "if e is A and ec is B, then u is C", where A, B, and C are fuzzy sets. The rules are formulated based on the experience of experts in anaerobic reactor temperature control.

[0021] The defuzzification module uses the centroid method to output a precise control quantity u=∑(α) j *c j ) / ∑α j , where α j For the rule activation strength, c j To output the center value of the fuzzy set, It is a precise control quantity used for parameter adjustment.

[0022] The dynamic parameter adjustment formula of the PID control module is: K p =K p0 +ΔK p K i =K i0 +ΔK i K d =K d0 +ΔK d K p0 K i0 K d0 Let ΔK be the initial parameter. p ΔK i ΔK d The precise control quantity output by fuzzy inference is the control signal u(t) = K. p e(t)+K i ∫e(t)dt+K d ec(t).

[0023] The actuator module includes an electric heater and a circulating cooler, and adjusts the power according to the control signal u(t), with a power adjustment range of 0-100%.

[0024] The adaptive adjustment module periodically analyzes the standard deviation of temperature fluctuations and gas production efficiency data. When the fluctuation exceeds 0.5°C or the efficiency decreases, the gradient descent method is used to optimize the membership function parameters and fuzzy rules.

[0025] An adaptive temperature control method for an anaerobic reactor, using the aforementioned system, includes the following steps:

[0026] Step 1: System initialization and parameter setting;

[0027] Step two: Real-time temperature detection and calculation of error and error change rate;

[0028] Step 3: Blur processing;

[0029] Step four: perform fuzzy inference and generate control variables;

[0030] Step 5: Deblurring to obtain precise control values;

[0031] Step 6: Adjust the PID parameters and output the control signal;

[0032] Step 7: Adjust the temperature.

[0033] Step 8, Adaptive Optimization;

[0034] Step nine: Cyclic operation and monitoring.

[0035] An anaerobic reactor, integrated with the aforementioned system, is used to treat organic waste or wastewater and produce biogas.

[0036] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method.

[0037] Compared with existing technologies, this system has the following non-obvious technical features:

[0038] First, this system uses a fuzzy PID controller. The fuzzy rule base and membership function are customized for the temperature sensitivity of methanogenic bacteria in the anaerobic reactor. For example, the rules focus on suppressing overshoot and achieving rapid stabilization, rather than general control. This requires a deep understanding of bioreaction dynamics.

[0039] Second, this system adopts a multi-module collaborative adaptive mechanism, integrating temperature detection, fuzzy inference, PID control and adaptive adjustment modules to form a closed-loop learning system, which can optimize control parameters online based on gas production efficiency feedback, while existing systems usually only rely on temperature feedback.

[0040] Third, this system achieves high-precision temperature fluctuation control. By dynamically adjusting the PID parameters through fuzzy logic, it achieves temperature fluctuation ≤ ±0.5℃. This level of precision is difficult to achieve by conventional methods in the large hysteresis environment of anaerobic reactors.

[0041] Fourth, this system combines mathematical models with biological processes, integrates the output of fuzzy reasoning with the PID algorithm, and derives mathematical expressions for parameter adjustment to ensure that the control response matches the activity change curve of methanogens and improves the scientific nature of the control.

[0042] Fifth, this system adopts a modular design, which makes it easy to embed into existing reactors, eliminates the need for expensive hardware, improves performance through software algorithms, and lowers the deployment threshold.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. This invention can effectively improve the temperature control accuracy, maintain temperature fluctuation ≤ ±0.5℃, provide the optimal environment for methanogens, and improve stability by more than 50% compared with traditional methods (fluctuation ±1-2°C);

[0045] 2. This invention can significantly improve biogas production efficiency. By stabilizing the temperature, the activity of methanogenic bacteria is maximized, and the biogas production rate is increased by 40%, directly enhancing energy recovery efficiency.

[0046] 3. This invention features low energy consumption; adaptive adjustment can avoid unnecessary heating / cooling actions, improving energy utilization by approximately 20% and reducing operating costs.

[0047] 4. The system of the present invention has strong robustness. It can adapt to disturbances such as feed load and ambient temperature through fuzzy PID control, reduce manual intervention and improve the level of automation.

[0048] 5. This invention can effectively extend the service life of equipment, stabilize temperature control to reduce thermal stress damage to the reactor structure, extend maintenance intervals by 30%, and effectively improve the service life of equipment.

[0049] 6. This invention can improve biogas production and stability, thereby replacing fossil fuels, reducing greenhouse gas emissions, and supporting a green circular economy. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of an adaptive temperature control system for an anaerobic reactor as described in Embodiment 1 of the present invention;

[0051] Figure 2 This is the operating parameter table of an anaerobic reactor adaptive temperature control system as described in Embodiment 1 of the present invention;

[0052] Figure 3 This is a comparison table of the technical effects of the adaptive temperature control system for an anaerobic reactor described in Embodiment 1 of the present invention;

[0053] Figure 4 This is a flowchart of an adaptive temperature control method for an anaerobic reactor as described in Embodiment 2 of the present invention. Detailed Implementation

[0054] The present invention will now be described in detail with reference to the accompanying drawings;

[0055] Example 1;

[0056] An adaptive temperature control system for an anaerobic reactor, such as Figure 1 As shown, the system includes the following functional modules:

[0057] The temperature detection module monitors the temperature inside the anaerobic reactor in real time, calculates the temperature error e(t) (the difference between the set temperature and the actual temperature) and the error change rate ec(t) (i.e., de / dt), and transmits the data to the fuzzification module; the function of this module is to provide feedback input for the entire system.

[0058] The fuzzification module converts the two precise values, temperature error e(t) and error change rate ec(t), into fuzzy quantities, and maps them to fuzzy sets (such as {NB, NM, NS, ZO, PS, PM, PB}, representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively) through membership functions. The main function of this module is to prepare inputs for fuzzy inference.

[0059] The fuzzy inference module, based on the input fuzzy set, performs inference according to the preset fuzzy rule base and outputs fuzzy control quantity. The rule form is "if e is A and ec is B, then u is C", where A, B, and C are fuzzy sets. The main function of this module is to simulate expert experience and generate adaptive control decisions.

[0060] The defuzzification module uses the centroid method to convert the fuzzy control quantities output by fuzzy inference into precise control quantities; the main function of this module is to provide precise control signals for subsequent control and execution.

[0061] The PID control module receives the defuzzified control signal and dynamically adjusts the PID parameters (e.g., K). p , K i , K d This module generates a control signal u(t); its main function is to achieve precise temperature regulation.

[0062] The actuator module, including the heater and cooler, adjusts the temperature of the anaerobic reactor according to the control signal u(t); the main function of this module is to directly execute temperature regulation actions based on the control signal.

[0063] The adaptive adjustment module optimizes the fuzzy rule base or PID parameters online based on historical temperature fluctuation data and gas production efficiency feedback; the main function of this module is to improve the long-term adaptability and robustness of the system.

[0064] In the technical solution of this application, the temperature detection module outputs e(t) and ec(t) to the fuzzification module; the fuzzification module outputs fuzzy quantities to the fuzzy inference module; the fuzzy inference module outputs fuzzy control quantities to the defuzzification module; the defuzzification module outputs precise control quantities to the PID control module; the PID control module outputs control signal u(t) to the actuator module; and the adaptive adjustment module is connected to all modules to realize online parameter adjustment. The system forms a closed-loop control.

[0065] In the technical solution of this application, the membership function of the fuzzy set (taking the triangle membership function as an example) is:

[0066]

[0067] Where x is the input variable (e.g., e or ec), a and b are boundary values, m is the peak point; A represents a fuzzy set (e.g., NB, ZO, etc.).

[0068] The process of fuzzy rule inference output (taking Mamdani inference as an example) is as follows:

[0069] For the i-th rule: if e is A i And ec is B i Then u is C iGiven inputs e0 and ec0, the rule activation strength The membership function of the output fuzzy set is: , where ∧ represents the smaller operation.

[0070] In the technical solution of this application, the mathematical expression for deblurring (centroid method) is:

[0071]

[0072] Where, α j Let c be the activation strength of the j-th rule. j To output the fuzzy set C j The central value, It is a precise control quantity used for parameter adjustment.

[0073] It should be noted that the mathematical expression of the PID control algorithm in this application is as follows:

[0074]

[0075] Among them, K p K i K d This is a dynamically adjusted parameter, where e(t) represents the temperature error, and the unit of e(t) is °C. It is the value of the set temperature T. set With actual temperature T actual The difference, i.e., e(t) = T set -T actual ec(t) is the rate of change of error, in °C / s, ec(t) = de(t) / dt, K p K i K d These are the proportional, integral, and derivative parameters of the PID control, dynamically adjusted by fuzzy logic. u(t) is the control output, expressed in W or %, used to drive the actuator. A (x) is the membership function, representing the degree to which x belongs to the fuzzy set A, α i The activation strength of the rule is unitless, c. j To output the center value of the fuzzy set, the unit is consistent with u(t).

[0076] In the technical solution of this application, after the system starts up, it operates according to the following steps:

[0077] Temperature detection: The temperature detection module monitors the temperature T inside the anaerobic reactor in real time. actual Calculate and set temperature T set The error e(t) and the rate of change of error ec(t);

[0078] Fuzzification: The fuzzification module converts e(t) and ec(t) into fuzzy quantities through membership functions and maps them to linguistic variables (such as NB, ZO, etc.).

[0079] Fuzzy reasoning: The fuzzy reasoning module performs reasoning based on a preset fuzzy rule base (based on expert experience or experimental data) to generate fuzzy control quantities; for example, if e is negative and ec is positive, the output control quantity is positive.

[0080] Defuzzification: The defuzzification module uses the centroid method to convert fuzzy control quantities into precise control quantities, which are then used as PID parameter adjustment quantities or direct control signals.

[0081] PID control: The PID control module dynamically adjusts K based on the defuzzified output. p K i K d And calculate the control signal u(t); for example, u(t) = K p e(t)+K i ∫e(t)dt+K d ec(t).

[0082] Execution adjustment: The actuator module (heater / cooler) receives u(t), adjusts the power, and changes the reactor temperature.

[0083] Adaptive adjustment: The adaptive adjustment module periodically analyzes temperature fluctuation data (such as standard deviation) and gas production efficiency feedback. If the fluctuation exceeds the threshold or the efficiency decreases, the fuzzy rules or PID parameters are optimized, such as by fine-tuning the membership function parameters through gradient descent.

[0084] Cyclic operation: The above process is carried out continuously, with multiple samples per second to ensure that the temperature fluctuation is ≤±0.5℃, and adaptive adjustments are made to cope with changes in feed load, ambient temperature disturbances, etc.

[0085] The mathematical model used in this application takes the temperature error e(t) and the rate of change of error ec(t) as independent variables, and the control output u(t) and the precise control quantity ΔK used for PID parameter adjustment. p ΔK i ΔK d As dependent variables, the inputs are e(t) and ec(t), and the output is u(t) or ΔK. p ΔK i ΔK d The specific construction process of the above mathematical model is as follows:

[0086] First, design fuzzy sets: define 7 fuzzy sets {NB, NM, NS, ZO, PS, PM, PB} for e and ec, and specify the universe of discourse (e.g., e∈[-2°C, 2°C], ec∈[-0.5°C / s, 0.5°C / s]). Select the triangular membership function, and calibrate the parameters through experiments.

[0087] Secondly, a rule base was established: based on experience in anaerobic reactor temperature control, 49 rules (7×7) were formulated, such as: "If e is NB and ec is PS, then ΔK..." p It is a PM; the rule base is stored in matrix form;

[0088] Third, the reasoning method was selected: Mamdani fuzzy reasoning was adopted because it is easy to implement and suitable for control applications.

[0089] Fourth, select a deblurring method: use the centroid method for deblurring, which has the characteristics of smooth output and stable calculation;

[0090] Finally, integrated PID control: the fuzzy output is used as the adjustment variable for the PID parameter, i.e., K. p =K p0 +ΔK p K i =K i0 +ΔK i K d =K d0 +ΔK d K p0 K i0 K d0 The initial parameters are set using the Ziegler-Nichols method.

[0091] It should be noted that the above mathematical model needs to follow the process in practical applications:

[0092] Step 1: Fuzzification. Input e0 and ec0, calculate membership degrees. For triangular membership functions, (e0) is calculated by the formula, for example, μ NB (e0)=max(0,(e0-a) / (ma));

[0093] Step 2: Rule activation. For the i-th rule, the activation strength α is... i =min( (e0), (ec0));

[0094] Step 3: Output fuzzy sets, each rule outputs a fuzzy set C. i by α i Cut off, get (u)=min(α i , (u));

[0095] Step 4: Aggregate the output, aggregating all rule outputs: μ C' (u)=max i (u);

[0096] Step 5: Deblurring using the centroid method: ΔK p =∑(α j *c j ) / ∑α j , where c j For ΔK p The corresponding fuzzy set center value (e.g., the center of PB is +0.5); similarly, ΔK is calculated in a similar way. i and ΔK d ;

[0097] Step 6: PID control output, substitute the adjusted parameters into the PID algorithm: u(t) = (K p0 +ΔK p )e(t)+(K i0 +ΔK i )∫e(t)dt+(K d0 +ΔK d )ec(t);

[0098] The above process is based on the ability of fuzzy logic to approximate nonlinear functions, and optimizes parameters by minimizing the integral square index of temperature error.

[0099] The system operating parameters in this embodiment are as follows: Figure 2 As shown, the technical effects of the system implementation are as follows: Figure 3 As shown.

[0100] Example 2;

[0101] An adaptive temperature control method for anaerobic reactors, such as Figure 4 As shown, this method is implemented based on the system described in Embodiment 1, and includes the following steps:

[0102] Step 1: System initialization and parameter setting;

[0103] Set the target temperature T for the anaerobic reactor set (e.g., 35°C), load initial PID parameters (K) p0 =8.0,K i0 =0.05,K d0=2.0), load the preset fuzzy rule base (49 rules) and triangle membership function parameters, and start the temperature detection module, actuator module and adaptive adjustment module;

[0104] Step 2: Real-time temperature detection and error calculation;

[0105] The temperature detection module collects the average temperature from multiple points inside the reactor at a cycle of 1 to 5 seconds as T. actual Calculate the temperature error e(t) = T set -T actual Calculate the error change rate ec(t) = [e(t) - e(t-1)] / Δt (Δt = 1 second);

[0106] Step 3: Blur processing;

[0107] The precise values ​​e(t) and ec(t) are mapped to fuzzy sets {NB, NM, NS, ZO, PS, PM, PB} using seven triangular membership functions. For example, if e(t) = -0.3°C, its membership degree to NS is 0.7 and its membership degree to ZO is 0.3.

[0108] Step four, fuzzy reasoning;

[0109] Mamdani inference is performed based on a fuzzy rule base. For example, the activation rule: "If e is NS and ec is PS, then ΔK..." p For PM;

[0110] Calculate the activation strength of each rule ;

[0111] Aggregate the output fuzzy set of all activated rules;

[0112] Step 5: Deblurring;

[0113] The center of gravity method is used to calculate the precise control quantity ΔK. p ΔK i ΔK d For example: ΔK p =∑(α j *c j ) / ∑α j ;

[0114] Where c j This corresponds to the center value of the output fuzzy set (e.g., the center value of PM is +0.4).

[0115] Step 6: Online adjustment of PID parameters and control output;

[0116] Update PID parameter: K p =K p0 +ΔKp K i =K i0 +ΔK i K d =K d0 +ΔK d ;

[0117] Calculate the control signal u(t): u(t) = K p e(t)+K i ∫e(t)dt+K d ec(t)

[0118] Convert u(t) to actuator power percentage (0%-100%).

[0119] Step 7: Adjust the temperature.

[0120] If u(t)>0, start the heater, power = u(t) × heater rated power;

[0121] If u(t) < 0, start the cooler, power = |u(t)| × cooler rated power;

[0122] By using PID control, the temperature can be quickly brought close to the set value and kept stable.

[0123] Step 8, Adaptive Optimization;

[0124] The adaptive adjustment module analyzes the operating data every 24 hours.

[0125] Calculate the standard deviation of temperature fluctuation σ. If σ > 0.5°C, then trigger optimization.

[0126] Comparison of gas production efficiency (unit: If efficiency drops by more than 5%, optimization will be triggered.

[0127] The gradient descent method is adopted to minimize the sum of squared temperature errors, and the boundary values ​​(a, b, m) of the membership function and the weights of the fuzzy rules are fine-tuned.

[0128] The updated parameters take effect immediately, and the system enters a new control cycle;

[0129] Step nine: Cyclic operation and monitoring;

[0130] Steps 2 to 8 above are executed continuously in a loop, forming a closed-loop control.

[0131] Real-time display of temperature curves, fluctuation statistics, gas production efficiency, and energy consumption data;

[0132] If the system detects an anomaly (such as a persistently excessive temperature), it will issue an alarm and enter safe mode.

[0133] Through the above steps, this embodiment operates continuously for 30 days in a 1000m³ mesophilic anaerobic reactor, with the standard deviation of temperature fluctuation remaining within 0.3°C. The biogas yield is increased by 42% compared to traditional PID control, and the energy consumption per unit of biogas production is reduced by 22%. The system requires no manual parameter tuning throughout the process, demonstrating excellent adaptability and stability.

[0134] Example 3;

[0135] An anaerobic reactor, which integrates the system described in Example 1, is primarily used to treat organic waste or wastewater and generate biogas.

[0136] Example 4;

[0137] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in Embodiment 2.

[0138] The above technical solutions only embody the preferred technical solutions of the present invention. Any modifications that may be made by those skilled in the art to certain parts thereof embody the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. An adaptive temperature control system for an anaerobic reactor, characterized in that, The system includes a temperature detection module, a fuzzification module, a fuzzy inference module, a defuzzification module, a PID control module, an actuator module, and an adaptive adjustment module. These modules work together to achieve adaptive temperature control of the anaerobic reactor. The temperature detection module monitors the temperature of the anaerobic reactor in real time and calculates the temperature error and the rate of change of error. The fuzzification module converts temperature error and error change rate into fuzzy quantities; The fuzzy inference module outputs fuzzy control quantities based on the received fuzzy quantities and according to the fuzzy rule base. The defuzzification module converts fuzzy control quantities into precise control quantities; The PID control module adjusts the PID parameters and generates control signals according to the precise control quantity. The actuator module adjusts the temperature in response to control signals; The adaptive adjustment module optimizes fuzzy rules or PID parameters online based on temperature fluctuations and gas production efficiency feedback.

2. The method according to claim 1, characterized in that, The fuzzification module uses membership functions to map the temperature error e(t) and the rate of change of error ec(t) to a fuzzy set.

3. The method according to claim 1, characterized in that, The fuzzy rule base of the fuzzy inference module contains 49 rules, which are formulated based on the experience of experts in anaerobic reactor temperature control.

4. The method according to claim 1, characterized in that, The defuzzification module uses the centroid method to output a precise control quantity u = ∑(α) j *c j ) / ∑α j , where α j For the rule activation strength, c j To output the center value of the fuzzy set.

5. The method according to claim 1, characterized in that, The dynamic parameter adjustment formula of the PID control module is: K p = K p0 + ΔK p K i = K i0 + ΔK i K d = K d0 + ΔK d K p0 K i0 K d0 Let ΔK be the initial parameter. p ΔK i ΔK d These are the precise control quantities output by the fuzzy inference, and the control signal is u(t) = K. p e(t)+K i ∫e(t)dt+K d ec(t).

6. The method according to claim 1, characterized in that, The actuator module includes an electric heater and a circulating cooler, and adjusts the power according to the control signal u(t), with a power adjustment range of 0-100%.

7. The method according to claim 1, characterized in that, The adaptive adjustment module periodically analyzes the standard deviation of temperature fluctuations and gas production efficiency data, and uses the gradient descent method to optimize the membership function parameters and fuzzy rules.

8. An adaptive temperature control method for an anaerobic reactor, using the system according to any one of claims 1-7, characterized in that, Includes the following steps: Step 1: System initialization and parameter setting; Step two: Real-time temperature detection and calculation of error and error change rate; Step 3: Blur processing; Step four: perform fuzzy inference and generate control variables; Step 5: Defuzzify to obtain precise control values; Step 6: Adjust the PID parameters and output the control signal; Step 7: Adjust the temperature. Step 8, Adaptive Optimization; Step nine: Cyclic operation and monitoring.

9. An anaerobic reactor, characterized in that, The system is integrated with any one of claims 1-7 for treating organic waste or wastewater and generating biogas.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 8.

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