Modularized CSTR fermentation simulation equipment and self-adaptive cooperative control method

Abstract

The invention discloses modular CSTR fermentation simulation equipment and a self-adaptive control method, and relates to the field of biological fermentation and process control. The equipment is composed of a detachable fermentation unit, a gas monitoring unit, a data acquisition unit and other modules, and a multi-parameter sensor and a compensation algorithm are integrated to realize closed-loop control. According to the control method, a double-layer cascade architecture is adopted; a lower-level controller adjusts the feeding speed through a dynamic PID to stabilize the pH value; and the superior controller dynamically optimizes the pH set value based on the gas production deviation, and forms feedback regulation and control in combination with an extreme value search model. And the expert system periodically analyzes the gas production deviation, detects the load limit through micro-disturbance, and dynamically adjusts target parameters to realize organic load optimization. According to the method, multi-parameter collaborative monitoring and an intelligent control model are fused, key parameters such as OLR and HRT are calculated in real time, the processing efficiency is improved while the system stability is maintained, the hysteresis defect of traditional manual regulation and control is broken through, and a self-adaptive solution is provided for the anaerobic fermentation process.

Description

Technical Field

[0001] The present invention relates to the fields of biological fermentation and process control, and particularly to a modular CSTR fermentation simulation equipment and an adaptive collaborative control method. Background Art

[0002] Anaerobic fermentation is a process in which waste produces biogas stably through the metabolic activities of microorganisms under anaerobic conditions, having significant environmental benefits and economic value. In traditional anaerobic fermentation experiments, usually two modes of batch experiments or continuous experiments are adopted. Batch experiments are simple to operate and have a fixed cycle, but they cannot achieve real-time dynamic monitoring of the fermentation process; while continuous experiments, although having the potential for long-term stable operation, their process control mostly relies on manual experience to manually adjust key process parameters such as the feeding rate and feeding amount. This regulation method relying on manual judgment not only consumes manpower, but also often leads to poor system operation stability due to problems such as adjustment lag and inaccurate control, thus affecting the accuracy and repeatability of experimental results.

[0003] The prior art has optimized the feeding control and some fermentation parameters to a certain extent. For example, the automatic feeding anaerobic fermentation system developed by Swedish BIOPROCESS company (publication number: WO2012005667A1) controls the material in and out through differential pressure or pumps, and adopts the sequence of "first out and then in" to prevent the direct outflow of newly added materials. This system relies on the signal of an electronic balance to adjust the valve and can achieve basic control of the in and out material amounts, but it does not involve real-time monitoring and feedback regulation of key process parameters (such as pH, alkalinity, gas components and gas production, etc.) during the fermentation process. Another example is an online monitoring and control system based on a cloud platform (publication number: WO2014070099A1), which includes a fermentation unit, a data acquisition unit and a cloud computing unit with data processing and user interface, and can upload the online data of multiple fermentation processes to the cloud for users to view. However, this system still has problems such as insufficient monitoring parameters, lack of online sensing functions for core process indicators such as gas components and flow rates, and cannot achieve the deduction and control of key operation parameters such as the organic loading rate (OLR) and hydraulic retention time (HRT).

[0004] In the field of anaerobic gas production assessment, for example, Chinese utility model patent CN207215791U discloses an anaerobic fermentation gas assessment device, which can continuously and real-time monitor the gas concentration and emission amount, and is used to ensure the integrity and effectiveness of experimental data. However, such devices still do not integrate modules such as feeding control, gas information processing and process feedback control, and are difficult to meet the high standards of real-time regulation requirements for continuous anaerobic fermentation.

[0005] In summary, although the existing technology has made certain progress in feed control, gas monitoring and data processing, there are still many problems that need to be solved. First of all, the functional layout of most systems is scattered, and there is a lack of effective linkage between the modules, which makes it difficult to form a synergistic effect, resulting in low overall system operation efficiency. Secondly, the parameters that can be monitored by existing equipment are relatively limited, and often only focus on single indicators such as gas production or material quality, which cannot fully reflect the complex dynamic behavior and microbial reaction state in the fermentation process. At the same time, the process lacks an effective feedback control mechanism, and the operating parameters cannot be dynamically adjusted based on real-time monitoring data, so that intelligent control in the true sense cannot be achieved. More importantly, the existing system lacks unified integration in parameter integration, model calculation and process evaluation, and it is difficult to meet the current anaerobic fermentation process for precise control, data analysis and automatic optimization. Comprehensive needs. Summary of the invention

[0006] Based on this, in order to solve the problems existing in the existing continuous experiments, the present invention proposes a comprehensive system integrating multi-parameter real-time monitoring, instant feedback control and precise process control functions. The feed amount is dynamically adjusted in real time through the two core parameters of gas production and gas production change and pH or alkalinity as auxiliary parameters, thereby improving the stability of continuous experiments and the accuracy of data, reducing the labor intensity of manual operations, providing more reliable technical support for the large-scale application of biogas projects, and further promoting research and application in the field of renewable energy.

[0007] A modular CSTR fermentation simulation equipment and an adaptive collaborative control method, comprising a plurality of independently disassembled and assembled functional modules, wherein the functional modules at least include a fermentation unit, a gas component monitoring unit, a gas flow metering unit, a data acquisition unit and an inlet and outlet control unit; The fermentation unit includes a bioreactor, a stirring device and a temperature control system, and each fermentation unit has a modular design, which is convenient for flexible expansion and combination; The fermentation unit is connected to the data acquisition unit, and is used to transmit at least one item of online real-time monitoring data during the fermentation process to the inlet and outlet control unit, so as to realize remote visual viewing and automatic regulation; The gas component monitoring unit is arranged between the fermentation unit and the gas flow metering unit, and is used to perform component analysis on the fermentation gas and transmit the analysis data to the data acquisition unit, and at the same time, transport the gas to the gas flow metering unit; The gas flow metering unit is used to measure the gas production flow and send the measurement data to the data acquisition unit to achieve full process data monitoring and closed-loop control support.

[0008] The modular CSTR fermentation simulation equipment is set up with multiple channels, so usually more than one fermentation or gas component and gas flow metering unit is required.

[0009] Furthermore, the bioreactor is equipped with a feeding unit and a discharging unit for realizing the quantitative addition of reactants and the automatic discharge of fermentation products; the equipment supports the parallel operation of no less than 6 fermentation units and can be used for simultaneous comparative tests of multiple reactors.

[0010] Furthermore, the stirring device in the fermentation unit includes a stirring shaft driven by a motor, which can mix the materials in the reactor evenly.

[0011] Even further, the gas flow measuring device is used to realize automatic real-time gas standardization, and the measuring factors include temperature, pressure, water vapor content, etc., to accurately monitor small-scale gas flow.

[0012] The data acquisition unit includes, but is not limited to, sensors for collecting parameters such as pH, temperature, pressure, gas composition, oxidation-reduction potential (ORP), alkalinity, etc., and can realize online real-time data acquisition of one or more fermentation processes in at least one fermentation unit and / or data acquisition unit. And the data acquisition unit is equipped with hardware and software interfaces compatible with multiple open platforms to support the flexible integration and remote expansion of the system.

[0013] Furthermore, the data acquisition unit internally sets an automatic real-time temperature and pressure standardization compensation algorithm, a water vapor removal compensation algorithm, and a purge gas overestimation removal compensation algorithm, and configures the compensation algorithm according to actual needs to output experimental data that meet different data.

[0014] Furthermore, the data acquisition unit has capabilities such as a database, file storage, and a user interface.

[0015] Furthermore, the data acquisition unit can transmit data online in real time.

[0016] Furthermore, the online real-time data includes, but is not limited to, organic loading rate, hydraulic retention time, gas composition, gas production rate.

[0017] Furthermore, the system settings also include at least one sensor for measuring pH, temperature, pressure, gas composition, ORP, alkalinity, VFA, biodegradable organic matter, or fermentation metabolites.

[0018] Furthermore, the organic load (OL) is calculated each time the load is added to the system and its average value is taken for generating a report. According to whether the feed is solid or liquid, the online calculation method of the organic load is as follows:

[0019] Wherein, OL is the organic load, which is the amount of organic matter added once or cumulatively; F is the feed amount, which is the total amount added once or cumulatively; Conc is the material concentration, which is the organic concentration of the added material.

[0020] Furthermore, the organic loading rate (OLR) is used to quantify the daily feed volume of the fermenter (solid raw materials are represented by VS and liquid raw materials are represented by COD) per unit volume, and the unit is g VS L -1 d -1 or g COD L -1 d -1 OLR is calculated from the fermenter volume, feed concentration and total feed volume, and is therefore the best parameter for adjusting feed and evaluating reactor performance.

[0021]

[0022] Among them, OLR is the organic loading rate, OL is the organic load, which means the amount of organic matter added once or cumulatively, V is the volume of the fermentation tank, t is the feeding time, It indicates the feeding interval time, which is not disturbed by the user at different feeding times and can accurately obtain data.

[0023] Furthermore, the hydraulic retention time (HRT) that can be calculated online is calculated online by the ratio of the fermentation tank volume to the feed amount:

[0024] Where V is the volume of the fermenter, t is the feeding time, Indicates the feeding interval time, F is the feeding amount, which is the total amount added at one time or cumulatively; Furthermore, in this system, the organic loading rate (OLR) and hydraulic retention time (HRT) can be calculated in real time and displayed with the normalized gas flow rate. To reduce data processing and transmission, the user can select different time intervals to study the data in detail, or select a longer time interval to observe the process in general. Other key process parameters such as specific gas production (SGP) and gas yield of total feed and organic feed can also be calculated and stored.

[0025] Furthermore, choosing a reasonable hydraulic retention time can achieve high methane production. In an actual CSTR reactor, a short HRT may flush the microorganisms out of the reactor, resulting in fermentation failure. In a CSTR, the recommended HRT is 20 days, which can reduce the risk of microorganisms being washed out of the fermenter. A long HRT can increase the time for microorganisms to degrade materials, which can increase biogas production, but it also reduces biogas production efficiency. Therefore, it is important to balance gas production and HRT.

[0026] Preferably, when OLR > 3, the set time of HRT is 20 - 30 days; when OLR ≤ 3, the set time of HRT is 15 - 20 days.

[0027] Furthermore, the system analyzes the gas components generated by the bioreactor through the gas component monitoring unit, the gas flow metering unit measures the volume of the generated gas, the data acquisition unit collects the above information, calculates the organic loading rate (OLR) and hydraulic retention time (HRT), and optimizes parameter regulation through piecewise linear interpolation based on historical data; The data acquisition unit and the feeding and discharging control unit realize the combined dynamic adjustment of multiple parameters such as gas production volume, methane concentration, pH, or alkalinity; The control model for the operation of the data acquisition unit and the feeding and discharging control unit includes a superior controller, an inferior controller, an anaerobic process dynamic module, and an extreme value search model; among them, the feeding control unit adopts a two - level control architecture, and the superior controller and the inferior controller jointly cooperate to achieve fine regulation of the reaction process.

[0028] The feeding regulation unit monitors the changes in key parameters (temperature, pH value, stirring speed, feeding rate) during the fermentation process in real time, and the system judges whether the fermentation state is normal in real time. When an abnormal situation is detected, the system can automatically perform feedback adjustment. According to the calculated organic loading rate value, it adjusts the stirring speed, feeding rate, etc., and evenly distributes the required organic matter content to a longer feeding cycle to ensure a stable fermentation environment.

[0029] Furthermore, the system receives the difference e between the set gas production rate value and the actual gas production rate value through the superior controller 2 , and the controller outputs the pH set value according to the difference; performs a difference operation on the pH set value and the actual pH measurement value collected from the fermentation unit to obtain the pH deviation value e 1 , and inputs it to the inferior controller; the inferior controller outputs the feeding rate according to the pH deviation value e 1 to adjust the anaerobic process; the fermentation unit operates based on the feeding rate and outputs parameters such as the actual gas production rate and the gas production rate change rate; at the same time, it feeds back the actual gas production rate and the gas production rate change rate and other parameters to the expert decision - making model, and the model dynamically adjusts the gas production rate set value according to the feedback parameters and the optimization strategy; forming a closed - loop control system to achieve the dynamic optimization and regulation of the anaerobic fermentation process.

[0030] Furthermore, the superior fuzzy controller receives the measured value of the gas production rate of the reactor, compares it with the set target gas production rate to generate an error e 2 ; outputs a new pH set value (or the correction amount to the original set value) according to the fuzzy rule and transmits it to the inferior controller. The inferior PID controller compares this pH set value with the actual pH measurement value of the reactor to obtain a deviation e1 and output a control signal to adjust the rotation speed (feed flow rate) of the feed pump. The reactor operates at the new feed rate, and its state such as gas production rate and pH is fed back through sensors. Among them, the pH sensor has a fast response and can reflect the acid-base change of the fermentation broth in real time; the gas production flow sensor accumulates the gas production volume to calculate the gas production rate, which belongs to slow feedback. The upper-level controller also has safety thresholds (pH lower limit 6.8, upper and lower limits of gas production rate, etc.) set manually to trigger the protection logic under abnormal conditions. When an abnormality is detected (such as pH being lower than the threshold or the gas production deviation being too large), the fuzzy control module will activate corresponding control rules, such as measures to force the reduction of the feed. The entire framework forms a closed-loop system, in which the outer loop (gas production control) and the inner loop (pH control) are nested and work together to achieve multi-objective control of the CSTR fermentation process (maintaining a stable pH and optimizing the gas production volume).

[0031] The adaptive collaborative control method of this modular CSTR fermentation simulation equipment sets that the feed control unit can also adjust the feed rate based on pH and gas production rate, which is divided into a two-level cascade control system, including: a lower-level controller and an upper-level controller: (1) The lower-level controller is used to maintain the pH value within the set range; Control output: Adjust the feed speed; Proportional control: u 1 (t) = u 10 + K 1 × e 1 (t); Among them, u 1 (t): Feed speed adjustment value, u 10 : Initial offset value of the feed speed, K 1 : Lower-level proportional gain, e 1 (t): Error between the actual pH value and the set value (e 1 (t) = pH - pH a ); (2) The upper-level controller is used to optimize the biogas production of the system; Control output: Dynamically adjust the pH set value of the lower-level controller; Proportional-integral control: u 2 (t) = u 20 + K 2 × e 2 (t) + K 2i ×∫e 2 (t)dt; Among them, u 2 (t): Adjustment amount of the set value of the lower-level pH, u 20 : Initial pH set value, K 2: Upper proportional gain, K 2i : Upper integral gain, e 2 (t): Error between the target value and the actual value of gas production (e 2 (t)=Q - Q a , where Q is the target gas production rate and Q a is the actual gas production rate).

[0032] The lower - level controller adopts a variable - proportional - gain PID control algorithm to adjust the feed flow rate in real - time to maintain the pH stable within the set range. The proportional gain KP of the PID controller is dynamically adjusted according to the change of ΔpH; The upper - level controller dynamically adjusts according to the deviation e a between the gas production rate Q 2 and the target gas production rate Q, and adjusts the target pH and the feed control strategy; Furthermore, the adaptive cooperative control method of the modular CSTR fermentation simulation equipment includes: An expert decision - feedback module periodically corrects the gas production rate Q based on the system operation trend, load change, and disturbance response. The correction is achieved by applying a small positive disturbance without significantly disturbing the system stability, actively detecting the reactor load limit, and realizing the improvement of processing capacity and extremum - seeking regulation.

[0033] The expert decision - feedback module realizes dynamic optimization in combination with a rule - driven extremum - seeking model, including, 1. Extremum - search strategy: When the pH set value reaches the lower limit (such as 6.95), the system automatically reduces the feed rate to prevent the continuous decrease of pH, and at the same time dynamically balances the gas production efficiency and system stability by adjusting the thresholds of the upper and lower limits of the gas production rate (e 2 _max and e 2 _min); In the startup stage (OLR < 1.5g VS L -1 d -1 ), a higher e 2 _max (such as 0.10 L gas · L reactor -1 ·d -1 ) is set to accelerate the enrichment of the microbial community and improve the initial gas production rate; When approaching the maximum system load (OLR > 3.0g VS L -1 d -1 ), reduce e 2 _max to 0.04 L gas ·L reactor -1 ·d -1And reduce e 2 _min range (such as ±0.05) to reduce the feed "pushing authority" and avoid VFA accumulation.

[0034] 2. Dynamic threshold adjustment mechanism: The dynamic threshold adjustment mechanism introduced in the model is an adaptive control strategy based on the real-time system status. 2 _max and e 2 _min), balance gas production efficiency and system stability. According to the coordinated response of real-time gas production rate and pH. Its core logic is as follows:

[0035] Where, β=0.002(mg / L) -1 , which means that for every 1 mg / L increase in VFA concentration, e 2 _min increased by 0.002, pushing the system to increase feed to alleviate VFA accumulation; γ=0.15, which means that when pH deviates from the set value by 1 unit, e 2 _min was increased by 0.15, and the acid-base imbalance was corrected by adjusting the feed rate.

[0036] 3. Transient balance control strategy: The transient equilibrium control strategy introduced in the model is a dynamic control method for the startup, load mutation, and external disturbance recovery stages of the anaerobic fermentation system, aiming to quickly stabilize key parameters (such as pH, VFA concentration, and gas production rate) to prevent instability or efficiency loss.

[0037] When the system is in a high load transient state (such as OLR>2.5g VS L -1 d -1 ), prioritize maintaining the lower limit of pH set point (6.8-7.0), and balance gas production and stability through the following strategies:

[0038] Among them, λ = 0.3 is the attenuation coefficient, By real-time monitoring of pH, VFA, and gas production rate, when VFA>2000 mg / L, the feed rate was forced to be reduced to 50% of the baseline value.

[0039] The control method includes the following strategies: Collect system pH value every 8 hours and calculate pH deviation 1 , according to e 1 Amplitude Automatic Update K in PID Controller p value; The gas production rate Q is collected every 24 hours aAnd calculate the deviation e from the target gas production rate Q 2 , for judging the current processing load and reaction efficiency; Based on the deviation e 2 In the interval where it is located, the expert module outputs a correction suggestion to dynamically adjust the target gas production rate Q and the target pH, realizing the adaptive load regulation of the system and maximizing the gas production performance.

[0040] Furthermore, for the adaptive cooperative control method of the modular CSTR fermentation simulation equipment, the lower proportional gain K in the lower-level controller 1 Adapts according to the difference D(t) between the current gas production rate and the gas production rate at the previous moment , and can regulate the feeding rate F(t).

[0041]

[0042] The specific strategy of the expert library decision model is as follows: (1) When , Then the output value , and at this time, the feeding can be increased; (2) When , F(t) K 2 × , and at this time, it is preferred to feed according to the original feeding amount; (3) When , and D in the previous monitoring period , F(t) K 3 × , and at this time, the feeding should be stopped and the gas production change situation should be observed for 1 day.

[0043] Among them, Is the empirical value after system debugging.

[0044] The adaptive cooperative control method of the modular CSTR fermentation simulation equipment in this embodiment sets that the feeding control unit can also adjust the feeding rate based on the alkalinity and gas production rate, which is divided into a two-layer cascade control system. Among them, Lower-level controller: Function: Maintain the total alkalinity (TA) in the reactor within the dynamically set interval to prevent system instability caused by acid accumulation.

[0045] Control output: Adjust the feeding rate of the alkaline additive (such as sodium bicarbonate) or the dilution liquid reflux ratio.

[0046] Adaptive proportional-integral control:

[0047] Among them, u 1 (t): Alkaline additive dosing rate adjustment value, e 1 (t) = TA actual -TA set : In the deviation between the real-time alkalinity value and the set value, K 1 and K 1i : The dynamically adjusted proportional and integral gains are adaptively updated according to the alkalinity change rate (ΔTA / Δt).

[0048] Superior controller: Function: By dynamically optimizing the alkalinity set value (TA set ), maximize the biogas production rate (Q gas ) Control output: Real-time adjustment of the alkalinity set value of the subordinate controller Control algorithm (model predictive optimization):

[0049] Among them, TA opt : Optimal alkalinity empirical value based on historical data, λ: Stability weight factor to prevent drastic fluctuations in the set value.

[0050] This model searches within the range of TA to obtain the optimal TA value when the objective function is maximized.

[0051] The expert decision feedback module (alkalinity-load collaborative optimization) includes: 1. Dynamic load increase strategy: Apply a stepped organic load perturbation (amplitude ≤ 5%) every 12 or 24 hours, and simultaneously monitor the alkalinity buffering capacity (β = ΔVFA / ΔTA) and the sensitivity of the gas production rate response (S = ΔQ gas / ΔOLR).

[0052] According to the judgment of whether the condition "if , then roll back to the previous stable load" is satisfied, the load increase is allowed to continue.

[0053] 2. Extreme value locking mechanism: When it is detected that the change rate of the gas production rate approaches zero, i.e., D(t) ≈ 0, start the strategy: fine-tune TA set in the range of ±200 mg / L with a step size of 50 mg / L, and simultaneously determine the optimal alkalinity operating point through the gas production activation energy model (E α = f(TA, VFA)).

[0054] Advantages of the present invention: The important innovation of the present invention lies in integrating a dynamic regulation system based on a control model, which can automatically calculate and visualize key process parameters in real time and achieve real-time dynamic adjustment of the feeding rate. The system is compatible with manual and automatic feeding modes, has the ability to monitor key indicators such as pH value, methane component of gas, and gas production volume in real time, and can automatically calculate the organic loading rate (OLR) and hydraulic retention time (HRT) of the CSTR reactor.

[0055] Based on the double-layer control loop with pH or alkalinity and gas production rate as the core, the system introduces an extremum search model to construct a feedback regulation mechanism, which can dynamically identify the optimal feeding strategy, extract key characteristic parameters, enhance the prediction accuracy, and model the daily gas production volume and the change in daily gas production volume to enhance the adaptability of the system under variable working conditions. This mechanism can effectively capture the complex dynamic characteristics in the anaerobic fermentation system and support refined feeding adjustment and operation optimization. Through this control model, the system can optimize the feeding rate according to the real-time gas production situation, dynamically adapt to the changes in the fermentation state, and provide an intuitive regulation basis for operators through the dynamic visualization window output by the system.

[0056] On this basis, the present invention particularly emphasizes the pursuit of "high load but stable operation state": in actual operation, the goal of maximizing OLR should seek the Pareto optimal solution among processing capacity, gas production benefit, and system stability. Therefore, this system combines multi-parameter online monitoring means (such as VFA real-time sensors, CH 4 content analyzers) with control model algorithms (such as ADM1 module or extremum search model) to ensure that the system can maintain stable and efficient operation under non-linear complex dynamics and avoid the risk of instability caused by blindly pursuing the theoretical limit load.

[0057] The present invention realizes intelligent and automated operation, significantly reduces manual intervention, reduces operation and management costs, and improves experimental efficiency. Compared with the traditional experience-dependent control method, this system realizes the precise regulation of the anaerobic fermentation process and the optimization of process parameters through the coupling of a double-layer control architecture and an intelligent model, significantly improving the overall performance and operation reliability of the system. Brief Description of the Drawings

[0058] Figure 1 is the system block diagram of the present invention; Figure 2 is the organic loading rate regulation strategy of Embodiment 2 of the present invention; Figure 3 is the daily gas production volume and the change diagram of daily gas production volume of Embodiment 2 of the present invention; Figure 4 is the schematic diagram of the control model of Embodiment 2 of the present invention; Figure 5 is the daily gas production volume and gas production fluctuation diagram of Control Group 1 of the present invention; Figure 6 This is the daily gas production and gas production fluctuation graph of the control group 2 of the present invention. Detailed implementation manners

[0059] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0060] As Figure 1 shown, a modular CSTR fermentation simulation equipment and an adaptive collaborative control method include a plurality of functional modules that can be independently disassembled and combined. The functional modules at least include a fermentation unit, a gas component monitoring unit, a gas flow metering unit, a data acquisition unit, and a feeding and discharging control unit; The fermentation unit includes a bioreactor, a stirring device, and a temperature control system, and each fermentation unit has a modular design, which is convenient for flexible expansion and combination.

[0061] Figure 4 The control system described in (1) The lower-level controller is used to maintain the pH value within a set range; Control output: Adjust the feeding speed; Proportional control: u 1 (t) = u 10 + K 1 × e 1 (t); where, u 1 (t): Feed speed adjustment value, u 10 : Initial offset value of the feed speed, K 1 : Lower-level proportional gain, e 1 (t): Error between the actual pH value and the set value (e 1 (t) = pH - pH a ); (2) The upper-level controller is used to optimize the biogas production of the system; Control output: Dynamically adjust the pH set value of the lower-level controller; Proportional-integral control: u 2 (t) = u 20 + K 2 × e 2 (t) + K 2i ×∫e2 \(\int_{}^{}(t)dt\); where \(u\) 2 (t): the adjustment amount of the set value of the lower - level pH, \(u\) 2 : the initial pH set value, \(K\) 2 : the upper - level proportional gain, \(K\) 2i : the upper - level integral gain, \(e\) 2 (t): the error between the gas production target value and the actual value (\(e\) 2 (t)=Q - \(\hat{Q}\) a , where \(Q\) is the target gas production rate, \(\hat{Q}\) a is the actual gas production rate).

[0062] The lower - level controller adopts a variable - proportional - gain PID control algorithm to adjust the feed flow rate in real - time to maintain the pH stable within the set range. The proportional gain \(K_P\) of the PID controller is dynamically adjusted according to the change of \(\Delta pH\); The upper - level controller dynamically adjusts according to the deviation \(e\) a between the gas production rate \(\hat{Q}\) 2 and the set target gas production rate \(Q\), and adjusts the target pH and the feed control strategy; The adaptive collaborative control method of the modular CSTR fermentation simulation equipment, the method includes: An expert decision - feedback module, based on the system operation trend, load change and disturbance response, periodically corrects \(Q\). The correction is achieved by applying a small positive disturbance on the premise of not significantly disturbing the system stability, actively detecting the reactor load limit, and realizing the improvement of processing capacity and extremum - seeking regulation.

[0063] Collect the system pH value every 8 hours and calculate \(\Delta pH\), and automatically update the \(K_P\) value in the PID controller according to the amplitude of \(\Delta pH\); Collect the gas production rate \(\hat{Q}\) every 24 hours a and calculate the deviation \(e\) 2 between it and the target gas production rate \(Q\), \(\hat{Q}\) and pH, to realize the system adaptive load regulation and the maximization of gas production performance.

[0064] Each outer - loop control period (taking one day as an example): (a) Read the gas production rate \(Q(t - 1)\) of the previous day and the actual gas production rate \(Q(t)\) of the current day, and the change amount of the calculated gas production rate \(D(t)\) changes adaptively, and can adjust the feed rate \(F(t)\).

[0065] \(D(t)=\)

[0066] (b) Input the difference \(D(t)\) into the expert decision - making model, and infer the new adjustment amount \(\Delta pH\) of the pH according to the expert decision - making model. For example, if \(e\) 2is positive and large, output a positive ΔpH s (increase the set pH); if e 2 is negative and has a large absolute value, output a negative ΔpH s (decrease the set pH); when the error is small, output close to zero.

[0067] The specific strategy of the expert database decision model is as follows: (1) When then the output value and at this time, the feed can be increased; (2) When then and at this time, it is preferred to feed at the original feed rate; F(t) K 2 × (3) When and in the previous monitoring period D then and at this time, the feeding should be stopped and the gas production change should be observed for 1 day. F(t) K 3 × where

[0068] is the empirical value after system debugging. (c) Update the pH of the lower-level controller: pH(t) = pH(t - 1)+ ΔpH (and limit the pH setting not to be lower than the safety lower limit of 6.8).

[0069] (d) Run the lower-level control in each inner-loop control period (for example, every half day): Read the current actual pH value, compare it with the actual pH value to obtain the deviation e

[0070] . 1 .

[0071] (e) Calculate the lower-level control output, that is, the feed rate adjustment amount u 1 (t). The lower-level PID controller calculates the control action according to the deviation e 1 , and usually proportional control is used here: u 1 (t) = u 10 + K 1 × e 1 (t).

[0072] (f) Send u 1 (t) to the actuator to adjust the feed rate. If u 1 (t) is positive and large, it means that the feed needs to be increased; if it is negative, the feed is reduced. The feed adjustment process can be achieved by adjusting the pump speed or opening the bypass, etc.

[0073] (g) The reactor operates for a period of time under the new feed conditions, and the sensor continuously monitors parameters such as pH and gas production. Before entering the next control cycle, the system determines whether there is an abnormal disturbance: checks whether the pH has been lower than 6.8 during this cycle, whether the gas production rate has fluctuated violently exceeding the set threshold, or whether other monitored parameters (such as alkalinity) exceed the standard. If an abnormality occurs, enter the disturbance handling branch: execute predetermined control actions such as temporarily shutting down the feed, extending the control interval of this cycle, etc., and then jump to (i); if there is no abnormality, continue directly.

[0074] (h) Disturbance handling: Execute corresponding control strategies (feed reduction, feed stop or other operations) according to the disturbance type, and continuously monitor until the indicators return to normal or reach the predetermined time, and then resume normal control.

[0075] (i) Update historical data: Record the gas production volume, pH, feed volume, etc. of this cycle for future analysis and parameter tuning. Subsequently, wait for the start of the next control cycle, return to step (a), and repeat the cycle.

[0076] The above process realizes a complete closed-loop control cycle. During continuous operation, the upper-level fuzzy control adjusts the gas production target every cycle, making the system gradually approach the optimal load; the lower-level control stabilizes the pH in real time to ensure that the system does not exceed the limit; at the same time, the monitoring module guards at all times. Once an abnormal change in the feed or load is detected, measures are immediately taken to pull the system back to the safe area; the above process demonstrates how this optimized control strategy switches between normal regulation and abnormal correction, so as to ensure the continuous and efficient operation of the CSTR reactor.

[0077]

Example 1

[0078] The adaptive cooperative control method of the modular CSTR fermentation simulation equipment in this embodiment sets that the feed control unit can also adjust the feed rate based on alkalinity and gas production rate, which is divided into a two-layer cascade control system, where, Lower-level controller: Function: Maintain the total alkalinity (TA) in the reactor within a dynamically set range to prevent system instability caused by acid accumulation.

[0079] Control output: Adjust the dosing rate of the alkaline additive (such as sodium bicarbonate) or the dilution liquid reflux ratio.

[0080] Adaptive proportional-integral control:

[0081] where u 1 (t): The adjusted value of the dosing rate of the alkaline additive, e 1 (t) = TA actual - TA set : The deviation between the real-time alkalinity value and the set value, K 1 and K 1i : The dynamically adjusted proportional and integral gains, which are adaptively updated according to the alkalinity change rate (ΔTA / Δt).

[0082] Superior controller: Function: By dynamically optimizing the alkalinity set value (TA set ), maximize the biogas production rate (Q gas ) Control output: Adjust the alkalinity set value of the inferior controller in real time Control algorithm (model predictive optimization):

[0083] where TA opt : The optimal empirical value of alkalinity based on historical data, λ: The stability weight factor to prevent the set value from fluctuating violently.

[0084] This model searches within the range of TA to obtain the optimal TA value when the objective function is maximized.

[0085] The expert decision feedback module (alkalinity-load collaborative optimization) includes: 1. Dynamic load increase strategy: Apply a step-type organic load perturbation (amplitude ≤ 5%) every 12 or 24 hours, and simultaneously monitor the alkalinity buffering capacity (β = ΔVFA / ΔTA) and the sensitivity of the gas production rate response (S = ΔQ gas / ΔOLR).

[0086] According to the judgment of whether the condition "if , then roll back to the previous stable load" is satisfied, the load increase is allowed to continue.

[0087] 2. Extreme value locking mechanism: When it is detected that the change rate of the gas production rate approaches zero, i.e., D(t) ≈ 0, start the strategy: Fine-tune TA set within the range of ±200 mg / L in steps of 50 mg / L, and simultaneously through the gas production activation energy model (Eα = f(TA, VFA)) to determine the optimal alkalinity operating point.

[0088]

Example 2

[0089] In a specific embodiment, a continuous feeding experiment is carried out. The materials in the feeding tank are pretreated. This experimental device includes multiple functional modules that can be independently disassembled and combined. The functional modules at least include a fermentation unit, a gas component monitoring unit, a gas flow metering unit, a data acquisition unit, and a feeding and discharging control unit. The fermentation unit of this embodiment includes a bioreactor, a temperature control system, and a stirring device. The bioreactor uses a CSTR reactor, and the data acquisition unit can collect parameters such as pH, temperature, pressure, gas composition, ORP, alkalinity, and VFA.

[0090] In this embodiment, this method has been proven to significantly improve the hydrolysis efficiency of the materials. During the fermentation process, the materials after hydrothermal pretreatment have a faster gas production rate and a higher feeding response speed. In this embodiment, the pretreated materials are Miscanthus stems (crushed to 20 mesh after air-drying for 30 days) and leaves (cut into 1 - 1.5 cm), and are stored at 4°C for later use.

[0091] The modular CSTR fermentation simulation equipment is set with multiple channels, so usually more than one fermentation or gas component and gas flow metering unit are required.

[0092] It includes a fermentation unit (CSTR reactor), a gas component monitoring unit, a gas flowmeter unit, and a data acquisition and control unit. The core components in the fermentation unit are a bioreactor, a condenser tube, and a stirring paddle. Sensors for measuring pH, temperature, pressure, gas composition, ORP (oxidation-reduction potential), alkalinity, VFA (volatile fatty acids), biodegradable organic matter, and fermentation metabolites are integrated in the data acquisition and control unit.

[0093] Organic loading rate in the startup stage: At an organic loading rate of 0.5 g VS L -1 d -1 The materials are put into the CSTR reactor from the feeding device at this organic loading rate, and the stirring device is started for mixing. After the materials are in full contact with the microorganisms in the inoculated sludge, gas production begins.

[0094] Increase of organic loading rate: As the fermentation process progresses, the organic loading rate is gradually increased to enrich the microbial community suitable for the fermented materials until the organic loading rate is stable.

[0095] Solid retention time: Set to 20 days.

[0096] During the reaction, the unknown quantities are calculated: Organic Loading Rate (OLR): The amount of organic matter input, which is known by the set feed rate and raw material characteristics. Standardized gas volume: The system calculates the gas volume under standard conditions through a standardized algorithm for temperature, pressure and humidity. Concentration of key metabolites: The generation rate of metabolites is inferred based on the changes in gas composition and metabolic models.

[0097] This interactive calculation of known and unknown quantities enables the system to achieve precise control and optimization, and automatically adjust parameters to ensure the stability and efficiency of the fermentation process.

[0098] OL

[0099] Wherein, OL is the organic load, which is the amount of organic matter added once or cumulatively; F is the feed amount, which is the total amount added once or cumulatively; Conc is the material concentration, which is the organic concentration of the added material.

[0100]

[0101] Among them, OLR is the organic loading rate, OL is the organic load, which means the amount of organic matter added once or cumulatively, V is the volume of the fermentation tank, t is the feeding time, Indicates the feeding interval time.

[0102]

[0103] Where V is the volume of the fermenter, t is the feeding time, Indicates the feeding interval time, F is the feeding amount, which is the total amount added at one time or cumulatively; When fermentation is abnormal, the system automatically calculates and re-formulates the feeding strategy by adjusting the material residence time and the organic load rate. First, historical data were collected to obtain data points of system performance indicators (e.g., gas production rate, methane content, and volatile fatty acid accumulation) under different OLRs.

[0104] The adaptive collaborative control method of the modular CSTR fermentation simulation equipment of this embodiment is configured such that the feed control unit can also adjust the feed rate based on pH and gas production rate and is divided into a two-layer cascade control system, including: a lower controller and an upper controller: (1) The lower controller is used to maintain the pH value within a set range; Control output: adjust feed speed; Proportional control: u 1 (t) = u 10 + K 1 × e 1 (t); Among them, u1 (t): Feed rate adjustment value, u 10 : Initial offset value of the feed rate, K 1 : Lower proportional gain, e 1 (t): Error between the actual pH value and the set value (e 1 (t) = pH - pH a ); (2) The upper - level controller is used to optimize the biogas production of the system; Control output: Dynamically adjust the pH set value of the lower - level controller; Proportional - integral control: u 2 (t) = u 20 + K 2 × e 2 (t) + K 2i ×∫e 2 (t)dt; Among them, u 2 (t): Adjustment amount of the set value of the lower - level pH, u 20 : Initial pH set value, K 2 : Upper - level proportional gain, K 2i : Upper - level integral gain, e 2 (t): Error between the gas production target value and the actual value (e 2 (t) = Q - Q a , where Q is the target gas production, Q a is the actual gas production).

[0105] The lower - level controller adopts a variable - proportional - gain PID control algorithm to adjust the feed flow rate in real - time to maintain the pH stable within the set range. Among them, the proportional gain KP of the PID controller is dynamically adjusted according to the change of ΔpH; The upper - level controller makes dynamic adjustments according to the deviation e 2 between the gas production rate Q and the target gas production rate Q, and adjusts the target pH and the feed control strategy; Furthermore, the adaptive cooperative control method of the modular CSTR fermentation simulation equipment, this method includes: An expert decision - making feedback module, based on the system operation trend, load change and disturbance response, makes periodic corrections to Q. The correction is achieved by applying a small positive disturbance on the premise of not significantly disturbing the system stability, actively detecting the reactor load limit, and realizing the improvement of the processing capacity and the extremum seeking control.

[0106] The control method includes the following steps: The control mechanism adopts a double - loop structure: The lower - level controller (proportional control): Based on the pH error, adjust the feed rate in real - time, The formula is u 1 (t)=u 10 +K 1 xe 1 (t); Upper controller (proportional-integral control): Adjusts the pH set value according to the biogas production error, The formula is

[0107] where K 1 Is adaptively adjusted according to the difference in gas production rate D(t), and the threshold interval is D_max = 0.3, D_min = -0.45.

[0108] The expert decision feedback module realizes dynamic optimization by combining a rule-driven extreme value search model, including, 1. Extreme value search strategy: When the pH set value reaches the lower limit (such as 6.95), the system automatically reduces the feeding rate to prevent the continuous decrease of pH, and at the same time adjusts e 2 _max and e 2 _min thresholds to dynamically balance gas production efficiency and system stability; In the startup phase (OLR < 1.5g VS L -1 d -1 ), set a higher e 2 _max (such as 0.10 L gas · L reactor -1 ·d -1 ) to accelerate the enrichment of the microbial community and improve the initial gas production rate; When approaching the maximum system load (OLR > 3.0g VS L -1 d -1 ), reduce e 2 _max to 0.04 L gas ·L reactor -1 ·d -1 And narrow the range of e 2 _min (such as ±0.05), reduce the "pushing permission" of the feed, and avoid VFA accumulation.

[0109] 2. Dynamic threshold adjustment mechanism: The dynamic threshold adjustment mechanism introduced by the model is an adaptive control strategy based on the real-time system state. By dynamically updating the upper and lower limits of the difference in gas production rate (e 2 _max and e 2 _min), it balances gas production efficiency and system stability. According to the coordinated response of the real-time gas production rate and pH. Its core logic is as follows:

[0110] where β = 0.002 (mg / L) -1 , indicating that for every 1 mg / L increase in VFA concentration, e 2 _min increases by 0.002, driving the system to increase the feed to relieve VFA accumulation; γ = 0.15, indicating that for every 1 unit deviation of pH from the set value, e 2 _min increases by 0.15, correcting the acid-base imbalance by adjusting the feed rate.

[0111] 3. Transient equilibrium control strategy: The transient equilibrium control strategy introduced by the model is a dynamic control method for the start-up, load mutation, and external disturbance recovery stages of the anaerobic fermentation system, aiming to quickly stabilize key parameters (such as pH, VFA concentration, gas production rate) and prevent instability or efficiency loss.

[0112] When the system is in a high-load transient state (e.g., OLR > 2.5 g VS L -1 d -1 ), the lower limit of the pH set value (6.8 - 7.0) is preferentially maintained, and the gas production and stability are balanced through the following strategy:

[0113] where λ = 0.3 is the attenuation coefficient, By real-time monitoring of pH, VFA, and gas production rate, when VFA > 2000 mg / L, the feed rate is forced to be reduced to 50% of the reference value.

[0114] The specific regulation process is as follows: If the VFA concentration suddenly rises to 2600 mg / L and the pH drops to 6.75 due to excessive feeding, the rule-driven model is triggered, the feed rate is reduced by 50%, and at the same time, a buffer solution (such as sodium bicarbonate) is quickly added to increase the alkalinity.

[0115] The model sets the lower limit of the minimum gas production rate according to e 2 _min(t) = 0.002×2600 + 0.15×(6.75 - 7.0) = 5.1625; and gradually restores the gas production rate by optimizing the pH set value.

[0116] When VFA < 800 mg / L and the pH is stable above 6.95, e 2 _max is gradually relaxed and normal feeding is restored.

[0117] The expert decision feedback module also combines the real-time monitoring data of the data acquisition and control unit, such as ORP, volatile fatty acid content, and pH value, to dynamically adjust the feed parameters.

[0118] In this embodiment, to further verify the advantages of the present invention, a comparative experiment was set up. In addition to using the continuous anaerobic fermentation system and control method of the present invention, two control experiments were set up simultaneously: One group adopted the traditional method of adjusting the feed rate only based on experience (Control Group 1); The other group adopted the method of predicting gas production based on a general linear regression model and adjusting the feed (Control Group 2).

[0119] During the experiment, other conditions such as the fermentation raw material, reactor type, environmental temperature, etc. were kept consistent. For the key indicator of gas production, during the process of increasing the organic loading rate, in the system of the present invention, as the organic loading rate gradually increased from 1.0 g VS L - 1 d -1 to 3.5 g VS L -1 d -1 , the total gas production showed a stable growth trend. When the organic loading rate was 2 g VS L -1 d -1 , the total gas production reached twice that of the initial stage; at the high loading stage of 3.5 g VS L -1 d -1 , the gas production was close to the theoretical maximum. For Control Group 1, after the organic loading rate was increased, the gas production fluctuated violently. When it was 2 g VS L -1 d -1 , it only increased by 1.2 times, and during the subsequent process of increasing the load, there was a situation of gas production stagnation or even decline (as shown in Figure 5 ); although there was a certain increase in Control Group 2, the growth rate was significantly lower than that of the system of the present invention. When it was 3.5 g VS L -1 d -1 , it only reached 70% of the gas production of the system of the present invention (as shown in Figure 6 ). In terms of gas production stability, through the synergistic effect of the two-stage controller model, in the early regulation stage (organic loading rate of 1.0 - 1.5 g VS L -1 d -1 ), the fluctuation range of the gas production rate was less than ±5%. When the organic loading rate was increased to 2 g VS L -1 d -1 , although the fluctuation increased to ±10%, it could be quickly adjusted and reached a stable level of ±3% in the 5th week. The gas production rate of Control Group 1 fluctuated greatly all the time. When the organic loading rate was 2 g VS L -1 d -1The hourly fluctuation amplitude reaches more than ±15% and is difficult to stabilize; the fluctuation amplitude of the control group 2 stabilizes at about ±8% after the organic loading rate is increased, and the adjustment effect is inferior to that of the system of the present invention. Through these comparative experiments, it is fully proved that the present invention has a significant improvement effect on the gas production volume, gas production stability, etc. during the anaerobic fermentation process, and the advantages of the coordinated regulation of each key parameter (such as organic loading rate, hydraulic retention time, etc.) in the present invention.

[0120] Based on the calculated K value range, it is divided proportionally: Calculate the boundary values of each interval Table 1 Strategy table for different intervals K value range pH change situation Feeding strategy K>0.3 pH too low Increase feeding rate −0.45≤K1≤0.3 pH moderate Maintain normal feeding K<−0.45 pH too high Reduce feeding rate If the pH exceeds 0.3, the feed rate is increased by 0.5 step size; If the pH drops by less than 0.3 and the increase does not exceed 0.2, the feed rate remains unchanged; If the pH increases by more than 0.2, the feed rate is decreased by 0.1 step size.

[0121] The obtained stage changes and regulations are as Figure 3 shown. In the first to third weeks: the organic loading rate is 1.0 - 1.5 gVS L -1 d -1 , the gas production change is small, the microorganisms completely consume the organic matter, and the system operates stably. Starting from the fourth week: after the organic loading rate is increased to 2 g VS L -1 d -1 , the system fluctuations appear. At this time, by extending the material residence time or slowing down the feed rate, the system gradually returns to stability.

[0122] Combined with Figure 2 and Figure 3 shown, the experimental device described in the present invention realizes the following regulation effects through the monitoring and adjustment functions of the data acquisition and control unit: 1. Gas production rate stability: (1) In the early regulation stage (the organic loading rate is 1.0 - 1.5 g VS L -1 d -1 ), the fluctuation amplitude of the gas production rate is within ±5%, showing the high-efficiency consumption ability of the initial microorganisms for organic matter and good system stability; (2) When the organic loading rate is increased to 2 g VS L -1 d -1 , the gas production rate of the system once fluctuates and increases to ±10%. By adjusting the feed rate and material residence time, the change of the gas production rate gradually tends to be stable and is controlled within the range of ±3% in the 5th week.

[0123] 2. Response effects of dynamic adjustment: (1) The system automatically triggers the feed rate adjustment mechanism by monitoring the changing trend of the gas production rate in real time to achieve dynamic management of the organic load; (2) When the detected gas production rate rises, the control unit increases the feed rate to improve the reaction efficiency; when the gas production rate drops, the control unit dynamically reduces the feed rate and decreases the organic load rate to prevent system overload; (3) During the entire experimental period, the dynamic regulation of the feed rate enables the system to quickly adapt to the load changes in different stages.

[0124] 3. Data output and analysis: (1) During the process of gradually increasing the organic load rate, the total gas production of the system shows a stable growth trend; when the organic load rate is 2 g VS L -1 d -1 , the total gas production reaches twice that of the initial stage; when reaching the high load stage of 3.5 gVS L -1 d -1 , the gas production is close to the theoretical maximum gas production value, verifying the high fermentation efficiency of the device; (2) From the gas production rate change curve, it can be seen that the regulation strategy can effectively cope with the gas production rate fluctuations and keep the system within the high-efficiency operation range; during the high load stage, although the gas production rate changes greatly, the overall gas production shows an upward trend, indicating that the device has good absorption and adjustment ability for disturbances; (3) During the system operation, when the organic load rate is adjusted to 2, 3, 4 g VS L -1 d -1 , the feed rate shows fluctuating changes, and the corresponding gas production volatility also gradually decreases, and the feed rate tends to be stable, effectively maintaining within the high-efficiency operation range.

[0125] In this embodiment, through the strategy provided by the adjustment mechanism of the platform, from material pretreatment to real-time regulation, the optimization of the high-efficiency gas production process is realized. Combining the high-precision monitoring and dynamic regulation strategy of the experimental device can cope with the gas production fluctuations in different stages and ensure the operation efficiency and stability of the system.

[0126] Among them, gas flow: the gas production rate measured in real time. Temperature: the temperature inside the reactor, providing a basis for calculating the standardized gas flow and organic load rate. Pressure: the pressure of the gas, affecting the volume measurement result.

[0127] During the fermentation process, special situations such as no feeding or inconsistent feeding intervals may have the following effects on the equipment: No feeding will lead to a decrease in the organic load rate (OLR) of the system, thereby affecting the activity of microorganisms and reducing the biogas production. Long-term no feeding may lead to a decrease in the activity of microorganisms, thereby affecting the overall fermentation efficiency of the system. In the case of no feeding, the system adaptively adjusts the operating conditions (such as reducing the stirring speed, etc.) to maintain the stability of the reaction environment.

[0128] Inconsistent feeding intervals can lead to fluctuations in the organic loading rate, directly affecting the biogas production and resulting in periodic fluctuations in gas production, which is not conducive to the standardization and measurement of gas flow. The intermittent switching between high and low loads can affect the temperature, pH value, and microbial activity in the reactor, making the fermentation environment unstable.

[0129] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0130] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A modular CSTR fermentation simulation equipment, characterized in that: include: A plurality of independently disassembled and assembled functional modules, wherein the functional modules at least include a fermentation unit, a gas component monitoring unit, a gas flow metering unit, a data acquisition unit, and an inlet and outlet material control unit; The fermentation unit includes a bioreactor, a stirring device and a temperature control system, and each fermentation unit has a modular design, which is convenient for flexible expansion and combination; The fermentation unit is connected to the data acquisition unit, and is used to transmit at least one item of online real-time monitoring data during the fermentation process to the inlet and outlet control unit, so as to realize remote visual viewing and automatic regulation; The gas component monitoring unit is arranged between the fermentation unit and the gas flow metering unit, and is used to perform component analysis on the fermentation gas and transmit the analysis data to the data acquisition unit, and at the same time, transport the gas to the gas flow metering unit; The gas flow metering unit is used to measure the gas production flow and send the measurement data to the data acquisition unit to achieve full process data monitoring and closed-loop control support.

2. The modular CSTR fermentation simulation equipment according to claim 1, characterized in that: The bioreactor is equipped with a feeding unit and a discharging unit for realizing the quantitative addition of reactants and the automatic discharge of fermentation products; the equipment supports the parallel operation of no less than 6 fermentation units and can be used for simultaneous comparative tests of multiple reactors.

3. The modular CSTR fermentation simulation equipment according to claim 1, characterized in that: The data acquisition unit includes but is not limited to sensors for collecting pH, temperature, pressure, gas composition, oxidation-reduction potential (ORP), and alkalinity parameters, and can realize online real-time data acquisition of one or more fermentation processes in at least one fermentation unit and / or data acquisition unit, and the data acquisition unit is equipped with hardware and software interfaces compatible with multiple open platforms to support flexible integration and remote expansion of the system.

4. The modular CSTR fermentation simulation equipment according to claim 1, characterized in that: The data acquisition unit has built-in automatic real-time temperature and pressure standardization compensation algorithm, water vapor removal compensation algorithm and flushing gas overestimation removal compensation algorithm, and configures the compensation algorithm according to actual needs to output experimental data that meet different data.

5. An adaptive collaborative control method for the modular CSTR fermentation simulation equipment according to any one of claims 1 to 4, characterized in that: The steps include: (1) The gas production volume is measured in real time by the gas flow metering unit, the pH value or total alkalinity of the reaction system is monitored by a pH meter or an online alkalinity meter, and the component characteristics of the produced gas are analyzed by the gas component monitoring unit; all monitoring data are collected and integrated by the data acquisition unit; (2) Based on the data collection results, dynamically adjust the key process parameters of the system organic load (OLR) through the inlet and outlet control unit to meet the set operation goals; (3) The control system is constructed based on a multi-level control model, which includes an upper-level controller, a lower-level controller, an anaerobic process dynamic module and an extreme value search model. Among them, the feed control unit adopts a two-level control architecture, in which the upper-level controller and the lower-level controller work together to achieve fine control of the reaction process.

6. The adaptive cooperative control method according to claim 5, characterized in that: The method includes: A two-layer cascade control system includes a lower-level controller and an upper-level controller; The lower controller is used to maintain the pH value within a set range; Control output: adjust feed speed; Proportional control: u1(t) = u 10 + K1× e1(t); Among them, u1(t): feed speed adjustment value, u 10 : initial bias value of feed rate, K1: lower stage proportional gain, e1(t): error between actual pH value and set value (e1(t) = pH - pH a ); The superior controller is used to optimize the biogas production of the system; Control output: Dynamically adjust the pH set point of the lower controller; Proportional-integral control: u2(t) = u 20 + K2 × e2(t) + K 2i × ∫e2(t)dt; Where, u2(t): the set value adjustment of the lower pH, u 20 : Initial pH setting value, K2: Upper proportional gain, K 2i : Upper integral gain, e2(t): The error between the target value and the actual value of gas production (e2(t) = Q - Q a , where Q is the target gas production, Q a is the actual gas production), The lower controller uses a variable proportional gain PID control algorithm to adjust the feed flow rate in real time to maintain stable operation of pH within the set range, where the proportional gain KP of the PID controller is dynamically adjusted according to the change of ΔpH; The upper controller, based on the gas production rate Q a The deviation e2(t) from the target gas production rate Q is dynamically adjusted to adjust the target pH and feed control strategy.

7. The adaptive cooperative control method according to claim 5, characterized in that: The method includes: An expert decision feedback module periodically corrects Q based on system operation trends, load changes and disturbance responses. The correction actively detects the reactor load limit by applying a small positive disturbance without significantly disturbing the system stability, thereby achieving processing capacity improvement and extreme value optimization control. The control method includes the following strategies: Collect system pH value and calculate ΔpH every 8 hours, and automatically update KP value in PID controller according to ΔpH amplitude; The gas production rate Q is collected every 24 hours a And calculate the deviation e2 from the target gas production rate Q to determine the current processing load and reaction efficiency; Based on the interval where the deviation e2 is located, the expert module outputs correction suggestions and dynamically adjusts the target gas production rate Q and target pH to achieve system adaptive load regulation and maximize gas production performance.

8. The adaptive cooperative control method according to claim 6, characterized in that: The lower-level proportional gain K1 in the lower-level controller changes adaptively according to the difference D(t) between the current gas production rate and the gas production rate at the previous moment, so that the feed rate F(t) can be regulated.

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