Microbial fuel cell wastewater treatment system based on Ai

Abstract

The invention relates to the field of fuel cell treatment, and discloses an Ai-based microbial fuel cell wastewater treatment system, which comprises a system overall framework and an innovation concept module, the output end of the system overall framework and the output end of the innovation concept module are in signal connection with an AI optimization MFC operation module, and the AI optimization MFC operation module is in signal connection with the AI optimization MFC operation module. The output end of the AI optimization MFC operation module is in signal connection with a modular extensible design module, and the output end of the modular extensible design module is in signal connection with an energy self-supply and energy storage management module. According to the Ai-based microbial fuel cell wastewater treatment system, in the working mode that the AI algorithm optimization unit dynamically regulates and controls electrodes, equipment loss or microbial activity decline is warned in advance in combination with the predictive maintenance unit, energy output is maximized, pollutant degradation is accelerated, meanwhile, the problem that a traditional MFC is low in power generation efficiency and unstable is effectively solved, and the wastewater treatment effect is improved. Efficient and stable operation of the system is guaranteed, electrode loss or microbial activity decline can be warned in advance by utilizing an AI predictive maintenance function, and downtime is shortened.

Description

Technical Field

[0001] The present invention relates to the field of fuel cell treatment, and in particular to an Ai-based microbial fuel cell wastewater treatment system. Background Art

[0002] Microbial fuel cell wastewater treatment is a technology that uses microbial metabolic activity to convert organic pollutants in wastewater into electrical energy. In microbial fuel cells, microorganisms decompose organic matter in the anode chamber and release electrons. These electrons are transferred to the cathode chamber through an external circuit and combine with electron acceptors such as oxygen to generate water or other products, thereby achieving the dual purposes of wastewater purification and electrical energy recovery. This technology can not only effectively treat wastewater and reduce environmental pollution, but also convert chemical energy in wastewater into usable electrical energy, with significant environmental benefits and energy recovery potential.

[0003] In addition, in the field of wastewater treatment, traditional microbial fuel cell (MFC) technology has the problems of low and unstable power generation efficiency. It is difficult to dynamically adjust the operating parameters according to changes in wastewater quality, and there is a lack of effective equipment maintenance warning mechanism, resulting in long downtime, affecting the overall performance of the system. At the same time, existing MFC systems often lack modular and scalable design, and it is difficult to flexibly adapt to the needs of wastewater treatment of different scales and types. The equipment deployment cost and maintenance difficulty are high. In addition, traditional MFC systems have limitations in energy utilization, and cannot achieve self-sufficiency and intelligent management of energy. It is difficult to simultaneously solve the problems of wastewater treatment and clean energy production, which limits its application in multiple scenarios. Therefore, there is an urgent need for a microbial fuel cell wastewater treatment system based on AI technology to improve system efficiency, stability and adaptability, reduce operating costs, and achieve the dual goals of wastewater treatment and energy recovery. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an Ai-based microbial fuel cell wastewater treatment system, which has the advantages of high power generation efficiency and stable system operation, and solves the problems of low power generation efficiency and unstable system operation.

[0006] (2) Technical solution

[0007] In order to achieve the above-mentioned high power generation efficiency and stable system operation purposes, the present invention provides the following technical solutions: an Ai-based microbial fuel cell wastewater treatment system, comprising an overall system architecture and an innovative concept module, wherein the output end signal of the overall system architecture and innovative concept module is connected to an AI-optimized MFC operation module, the output end signal of the AI-optimized MFC operation module is connected to a modular and scalable design module, the output end signal of the modular and scalable design module is connected to an energy self-supply and energy storage management module, and the output end signal of the energy self-supply and energy storage management module is connected to a system implementation and application expansion module;

[0008] The AI ​​optimized MFC operation module includes a water quality parameter monitoring unit, the output end signal of the water quality parameter monitoring unit is connected to the AI ​​algorithm optimization unit, the output end signal of the AI ​​algorithm optimization unit is connected to the predictive maintenance unit, and the output end signal of the predictive maintenance unit is connected to the energy efficiency improvement strategy unit.

[0009] Preferably, the water quality parameter monitoring unit can utilize a sensor network to monitor key water quality parameters such as pH, temperature, and organic matter concentration in wastewater in real time. The AI ​​algorithm optimization unit dynamically adjusts the operating mode of the electrode material, such as voltage and current, based on the monitoring data through the AI ​​algorithm to maximize energy output and accelerate pollutant degradation. In addition, the monitoring data is cleaned and calibrated using advanced signal processing technology to ensure data accuracy and reliability. In addition, the unit also has remote monitoring and data transmission capabilities, which can upload monitoring data to the cloud or central control system in real time, providing timely and comprehensive data support for subsequent AI algorithm optimization.

[0010] Preferably, the modular expandable design module includes a modular unit structure design unit, the output end signal of the modular unit structure design unit is connected to a unit for adapting to diversified wastewater types, the output end signal of the unit for adapting to diversified wastewater types is connected to a modular connection technology unit, and the output end signal of the modular connection technology unit is connected to a cost-benefit analysis unit.

[0011] Preferably, the modular unit structure design unit can describe in detail the structural design of the modular units, including the independent operation capabilities and stacking configurations of each unit. The diversified wastewater adaptation unit can design adaptable modular combination solutions based on the needs of different wastewater types, such as domestic sewage and agricultural wastewater. By optimizing module configurations and adjusting treatment processes, it can effectively treat different types of wastewater. Furthermore, this unit also has the ability to respond quickly and flexibly, and can promptly adjust treatment strategies according to changes in wastewater properties, ensuring that the system is always in the optimal treatment state.

[0012] Preferably, the energy self-supply and energy storage management module includes a closed-loop energy management system design unit, the output end signal of the closed-loop energy management system design unit is connected to a supercapacitor energy storage unit, the output end signal of the supercapacitor energy storage unit is connected to an AI energy storage mode switching logic unit, and the output end signal of the AI ​​energy storage mode switching logic unit is connected to an energy efficiency monitoring and optimization unit.

[0013] Preferably, the closed-loop energy management system design unit designs a closed-loop energy management system that directly powers the internal components of the system by designing a power generation part. The AI ​​energy storage mode switching logic unit can design a logic algorithm for automatically switching the energy storage mode based on the AI's energy supply and demand forecast. The energy efficiency monitoring and optimization unit is used to monitor the energy efficiency performance of the system in real time and continuously optimize the energy management strategy through the AI ​​algorithm.

[0014] Preferably, the system implementation and application expansion module includes a prototype verification and data collection unit, the output end signal of the prototype verification and data collection unit is connected to the AI ​​model training and optimization unit, the output end signal of the AI ​​model training and optimization unit is connected to the industrial cooperation and promotion unit, and the output end signal of the industrial cooperation and promotion unit is connected to the market potential analysis and strategic planning unit.

[0015] Preferably, the prototype verification and data collection unit builds a small laboratory-level device to test power generation efficiency and treatment effects under different wastewater conditions and collect operational data. The AI ​​model training and optimization unit uses the collected operational data to train the AI ​​model, optimize the control strategy, and improve system performance. The AI ​​model training and optimization unit also has the ability to continuously learn and self-optimize, and can continuously adjust and improve the model based on newly collected data to ensure that the system is always in optimal operating condition. In addition, this unit also provides detailed model evaluation methods and performance monitoring indicators, providing strong guarantees for the long-term stable operation of the system.

[0016] (3) Beneficial effects

[0017] Compared with the existing technology, the present invention provides an Ai-based microbial fuel cell wastewater treatment system with the following beneficial effects:

[0018] 1. This AI-based microbial fuel cell wastewater treatment system uses AI to optimize the water quality parameter monitoring unit in the MFC operation module to collect key wastewater parameters in real time. Under the dynamic regulation of the electrode working mode by the AI ​​algorithm optimization unit, combined with the predictive maintenance unit, it provides early warning of equipment loss or decreased microbial activity. While maximizing energy output and accelerating pollutant degradation, it effectively solves the low and unstable power generation efficiency of traditional MFCs, ensuring efficient and stable operation of the system. The AI ​​predictive maintenance function can provide early warning of electrode loss or decreased microbial activity, reducing downtime.

[0019] 2. The Ai-based microbial fuel cell wastewater treatment system, through the modular unit structure design unit of the modular scalable design module and the unit adaptable to various wastewater types, realizes flexible stacking and combination of equipment and efficient treatment of different types of wastewater such as domestic sewage and agricultural wastewater. The energy self-supply and energy storage management module builds an internal power supply closed loop through the closed-loop energy management system design unit. The power generation part directly powers the system components, and the excess electricity is stored by the supercapacitor energy storage unit. Combined with the AI ​​energy storage mode switching logic unit, off-grid operation and energy efficiency intelligent allocation are realized, reducing deployment costs and maintenance difficulties, and simultaneously solving wastewater treatment and clean energy production problems. It is suitable for multi-scenario applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a system flow chart of the present invention;

[0021] Figure 2 This is the flow chart of the AI-optimized MFC operation module of the present invention;

[0022] Figure 3 A flow chart of modular and extensible design modules for the present invention;

[0023] Figure 4 This is a flow chart of the energy self-supply and energy storage management module of the present invention;

[0024] Figure 5 This is a flow chart of the system implementation and application expansion module of the present invention.

[0025] In the figure: 1. System overall architecture and innovative concept module; 2. AI optimized MFC operation module; 21. Water quality parameter monitoring unit; 22. AI algorithm optimization unit; 23. Predictive maintenance unit; 24. Energy efficiency improvement strategy unit; 3. Modular and scalable design module; 31. Modular unit structure design unit; 32. Adaptation to diverse wastewater types unit; 33. Modular connection technology unit; 34. Cost-benefit analysis unit; 4. Energy self-supply and energy storage management module; 41. Closed-loop energy management system design unit; 42. Supercapacitor energy storage unit; 43. AI energy storage mode switching logic unit; 44. Energy efficiency monitoring and optimization unit; 5. System implementation and application expansion module; 51. Prototype verification and data collection unit; 52. AI model training and optimization unit; 53. Industrial cooperation and promotion unit; 54. Market potential analysis and strategic planning unit. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] Example 1

[0028] Combine Figures 2 to 5 , an Ai-based microbial fuel cell wastewater treatment system, including a system overall architecture and innovative concept module 1, the system overall architecture and innovative concept module 1 output end signal is connected to an AI optimized MFC operation module 2, the AI ​​optimized MFC operation module 2 output end signal is connected to a modular scalable design module 3, the modular scalable design module 3 output end signal is connected to an energy self-supply and energy storage management module 4, the energy self-supply and energy storage management module 4 output end signal is connected to a system implementation and application expansion module 5;

[0029] The AI ​​optimized MFC operation module 2 includes a water quality parameter monitoring unit 21, the output end signal of the water quality parameter monitoring unit 21 is connected to the AI ​​algorithm optimization unit 22, the output end signal of the AI ​​algorithm optimization unit 22 is connected to the predictive maintenance unit 23, and the output end signal of the predictive maintenance unit 23 is connected to the energy efficiency improvement strategy unit 24.

[0030] Furthermore, the water quality parameter monitoring unit 21 can use the sensor network to monitor key water quality parameters such as pH value, temperature, and organic matter concentration in the wastewater in real time. The AI ​​algorithm optimization unit 22 dynamically adjusts the working mode of the electrode material, such as voltage and current, through the AI ​​algorithm based on the monitoring data to maximize energy output and accelerate the degradation of pollutants.

[0031] Example 2

[0032] See Figure 1-5A microbial fuel cell wastewater treatment system based on Ai, the modular and scalable design module 3 includes a modular unit structure design unit 31, the output signal of the modular unit structure design unit 31 is connected to the diversified wastewater type adaptation unit 32, the output signal of the diversified wastewater type adaptation unit 32 is connected to the modular connection technology unit 33, the output signal of the modular connection technology unit 33 is connected to the cost-benefit analysis unit 34, the energy self-supply and energy storage management module 4 includes a closed-loop energy management system design unit 41, the output signal of the closed-loop energy management system design unit 41 is connected to the supercapacitor energy storage Unit 42, the output end signal of the supercapacitor energy storage unit 42 is connected to the AI ​​energy storage mode switching logic unit 43, the output end signal of the AI ​​energy storage mode switching logic unit 43 is connected to the energy efficiency monitoring and optimization unit 44, the system implementation and application expansion module 5 includes a prototype verification and data collection unit 51, the output end signal of the prototype verification and data collection unit 51 is connected to the AI ​​model training and optimization unit 52, the output end signal of the AI ​​model training and optimization unit 52 is connected to the industrial cooperation and promotion unit 53, and the output end signal of the industrial cooperation and promotion unit 53 is connected to the market potential analysis and strategic planning unit 54.

[0033] Furthermore, the modular unit structure design unit 31 can describe in detail the structural design of the modular unit, including the independent operation capability and stacking combination method of each unit. The unit 32 for adapting to diverse wastewater types can design adaptable module combination solutions according to the needs of different wastewater types such as domestic sewage and agricultural wastewater. The closed-loop energy management system design unit 41 designs a closed-loop energy management system that directly powers the internal components of the system by designing the power generation part. The AI ​​energy storage mode switching logic unit 43 can design a logic algorithm for automatically switching the energy storage mode based on the AI's energy supply and demand forecast. The energy efficiency monitoring and optimization unit 44 is used to monitor the energy efficiency performance of the system in real time and continuously optimize the energy management strategy through AI algorithms. The prototype verification and data collection unit 51 builds a laboratory-level small device to test the power generation efficiency and treatment effect under different wastewater conditions, and collects operation data. The AI ​​model training and optimization unit 52 uses the collected operation data to train the AI ​​model, optimize the control strategy, and improve system performance.

[0034] In the actual operation process, with the system overall architecture and innovative concept module 1 as the core, the AI ​​optimized MFC operation module 2, the modular scalable design module 3, the energy self-supply and energy storage management module 4 and the system implementation and application expansion module 5 are connected in series in sequence. The AI ​​optimized MFC operation module 2 collects key parameters such as pH value and temperature in wastewater in real time through the sensor network of the water quality parameter monitoring unit 21. The AI ​​algorithm optimization unit 22 dynamically adjusts the electrode working mode based on this, and combines the predictive maintenance unit 23 with the energy efficiency improvement strategy unit 24 to ensure the efficient operation of the system; the modular scalable design module 3 uses the modular unit structure design unit 31 to realize the independent operation and flexible stacking of the unit, and the unit 32 is adapted to adapt to various wastewater types. To meet different wastewater treatment needs, the modular connection technology unit 33 and the cost-benefit analysis unit 34 are used to improve practicality. The energy self-supply and energy storage management module 4 relies on the closed-loop energy management system design unit 41 to build an internal power supply closed loop, and the supercapacitor energy storage unit 42, the AI ​​energy storage mode switching logic unit 43 and the energy efficiency monitoring and optimization unit 44 realize intelligent energy allocation. The system implementation and application expansion module 5 accumulates operating data through the prototype verification and data collection unit 51, iterates the algorithm through the AI ​​model training and optimization unit 52, and finally completes the technology implementation with the help of the industrial cooperation and promotion unit 53 and the market potential analysis and strategic planning unit 54, realizing the intelligent operation of the entire chain of wastewater treatment, energy recovery and industrial promotion.

[0035] In addition, microbial fuel cell technology is embedded in the wastewater treatment system, utilizing organic matter in wastewater to generate electricity. AI algorithms are used to optimize microbial activity and power generation efficiency in real time. Water quality parameters such as pH, temperature, and organic matter concentration are monitored via a sensor network. AI dynamically adjusts the operating modes of electrode materials, such as voltage and current, to maximize energy output and accelerate pollutant degradation. This addresses the low and unstable power generation efficiency of traditional MFCs. AI predictive maintenance capabilities provide early warning of electrode loss or decreased microbial activity, reducing downtime.

[0036] In addition, closed-loop energy management is achieved. The power generation part directly powers the water pumps and sensors in the system, and the excess electricity is stored in the integrated supercapacitor, enabling off-grid operation, reducing deployment costs, facilitating maintenance and upgrades, and being able to adapt to various wastewater types such as domestic sewage and agricultural wastewater. AI can automatically switch energy storage modes based on energy supply and demand forecasts, such as peak electricity consumption periods, to improve overall energy efficiency, simultaneously solve wastewater treatment and clean energy production problems, reduce carbon emissions, and have lower long-term operating costs than traditional treatment plants. It is suitable for sewage treatment in remote areas, circular economy systems in industrial parks, and even emergency water and power supply in disaster areas.

[0037] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A microbial fuel cell wastewater treatment system based on Ai, including the overall system architecture and innovative concept module (1), characterized by: The output terminal signal of the system overall architecture and innovative concept module (1) is connected to the AI ​​optimized MFC operation module (2), the output terminal signal of the AI ​​optimized MFC operation module (2) is connected to the modular expandable design module (3), the output terminal signal of the modular expandable design module (3) is connected to the energy self-supply and energy storage management module (4), and the output terminal signal of the energy self-supply and energy storage management module (4) is connected to the system implementation and application expansion module (5); The AI ​​optimized MFC operation module (2) includes a water quality parameter monitoring unit (21), an output terminal signal of the water quality parameter monitoring unit (21) is connected to an AI algorithm optimization unit (22), an output terminal signal of the AI ​​algorithm optimization unit (22) is connected to a predictive maintenance unit (23), and an output terminal signal of the predictive maintenance unit (23) is connected to an energy efficiency improvement strategy unit (24).

2. The Ai-based microbial fuel cell wastewater treatment system according to claim 1, characterized in that: The water quality parameter monitoring unit (21) can use a sensor network to monitor key water quality parameters such as pH value, temperature, organic matter concentration, etc. in wastewater in real time. The AI ​​algorithm optimization unit (22) dynamically adjusts the working mode of the electrode material, such as voltage and current, through the AI ​​algorithm based on the monitoring data to maximize energy output and accelerate pollutant degradation.

3. The Ai-based microbial fuel cell wastewater treatment system according to claim 2, characterized in that: The modular expandable design module (3) comprises a modular unit structure design unit (31), wherein an output terminal signal of the modular unit structure design unit (31) is connected to a unit for adapting to diversified wastewater types (32), an output terminal signal of the unit for adapting to diversified wastewater types (32) is connected to a modular connection technology unit (33), and an output terminal signal of the modular connection technology unit (33) is connected to a cost-benefit analysis unit (34).

4. The Ai-based microbial fuel cell wastewater treatment system according to claim 3, characterized in that: The modular unit structure design unit (31) can describe in detail the structural design of the modular unit, including the independent operation capability and stacking combination mode of each unit. The unit (32) for adapting to diverse wastewater types can design adaptable module combination solutions according to the needs of different wastewater types such as domestic sewage and agricultural wastewater.

5. The Ai-based microbial fuel cell wastewater treatment system according to claim 4, characterized in that: The energy self-supply and energy storage management module (4) includes a closed-loop energy management system design unit (41), the output end signal of the closed-loop energy management system design unit (41) is connected to a supercapacitor energy storage unit (42), the output end signal of the supercapacitor energy storage unit (42) is connected to an AI energy storage mode switching logic unit (43), and the output end signal of the AI ​​energy storage mode switching logic unit (43) is connected to an energy efficiency monitoring and optimization unit (44).

6. The Ai-based microbial fuel cell wastewater treatment system according to claim 5, characterized in that: The closed-loop energy management system design unit (41) is a closed-loop energy management system that directly supplies power to the system's internal components through the design of a power generation part. The AI ​​energy storage mode switching logic unit (43) can design a logic algorithm for automatically switching the energy storage mode based on the AI's energy supply and demand forecast. The energy efficiency monitoring and optimization unit (44) is used to monitor the system's energy efficiency performance in real time and continuously optimize the energy management strategy through the AI ​​algorithm.

7. The Ai-based microbial fuel cell wastewater treatment system according to claim 6, characterized in that: The system implementation and application expansion module (5) includes a prototype verification and data collection unit (51), the output end signal of the prototype verification and data collection unit (51) is connected to the AI ​​model training and optimization unit (52), the output end signal of the AI ​​model training and optimization unit (52) is connected to the industrialization cooperation and promotion unit (53), and the output end signal of the industrialization cooperation and promotion unit (53) is connected to the market potential analysis and strategic planning unit (54).

8. The Ai-based microbial fuel cell wastewater treatment system according to claim 7, characterized in that: The prototype verification and data collection unit (51) builds a laboratory-level small device to test the power generation efficiency and treatment effect under different wastewater conditions and collect operation data. The AI ​​model training and optimization unit (52) uses the collected operation data to train the AI ​​model, optimize the control strategy, and improve system performance.

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