WO2023200331A1

WO2023200331A1 - A system and method for simulating and controlling a microbial fuel cell

Info

Publication number: WO2023200331A1
Application number: PCT/MY2023/050024
Authority: WO
Inventors: Muhammad Nihal NASEER; Syed Asad A. ZAIDI; Yasmin ABDUL WAHAB; Juhana Jaafar
Original assignee: Universiti Malaya
Priority date: 2022-04-13
Filing date: 2023-04-07
Publication date: 2023-10-19

Abstract

The present invention relates to a system (100) and method for simulating and controlling at least one microbial fuel cell or MFC (30). The system (100) comprises a simulation module (10), a control module (20), and at least one MFC (30). The system (100) is configured to simulate a bio-electrochemical process of the MFC (30) and predict responses of the MFC (30) in generating electrical energy. The system (100) is also configured to utilise the predicted responses to control the operation of the MFC (30) and monitor an electrical energy requirement supplied by the MFC (30) to an integrated equipment. An example of the integrated equipment is a desalination unit used for a water treatment process.

Description

A SYSTEM AND METHOD FOR SIMULATING AND CONTROLLING A MICROBIAL FUEL CELL

FIELD OF INVENTION

The present invention relates to a system and method for simulating and controlling at least one microbial fuel cell. More particularly, the present invention relates to a system and method for simulating and controlling at least one microbial fuel cell for bio-electricity production.

BACKGROUND OF THE INVENTION

Microbial fuel cells are devices that utilise biochemical reactions from microorganisms to produce electrical energy. Microbial fuel cells operate on a principle of redox reactions, whereby microorganisms oxidise organic fuel to generate products such as electrons that are used to generate electrical energy. Among bio-electricity production technologies, microbial fuel cells have the highest energy conversion efficiency and standard free energy. Microbial fuel cells are also capable of reducing sludge production, reducing the need for aeration, managing odour control, etc. Due to their advantages and potential, microbial fuel cells have been gaining interest as a source for renewable and clean energy production. Therefore, various devices have been developed to assist and optimise the production of electrical energy using microbial fuel cells.

One example of such devices is disclosed in United States Patent Publication No. US 2021/0280888 A1 which provides a microbial fuel cell that includes a plurality of electrode assemblies. Each of the electrode assemblies is composed of a negative electrode, a positive electrode, and an ion transfer layer immersed in an electrolysis solution. The negative electrode is composed of a carbon material formed by laminating a plurality of graphene on one another to increase an electric conductivity between the carbon material and an external circuit during a cell reaction.

Although a lot of devices have been developed to optimise the production of electrical energy using microbial fuel cells, most of the devices heavily rely on hardware design aspects of the microbial fuel cells. As such, these devices present certain limitations, especially during the process of scaling up the microbial fuel cells for practical applications. In addition to that, some design aspects require complex mathematical modelling that is difficult to comprehend and requires a relatively long computational time. To optimise the production of electrical energy, it is desirable to determine the behaviours of the microbial fuel cells before any practical applications are carried out. Therefore, there is a need to address the abovementioned drawbacks.

SUMMARY OF INVENTION

According to one aspect of the present invention, a system (100) for simulating and controlling at least one microbial fuel cell or MFC (30) is provided. The system (100) comprises the at least one MFC (30) configured to perform a bio-electrochemical process to generate electrical energy. The system (100) is characterised in that the system (100) further comprises a simulation module (10) configured to execute a simulation model to predict responses of the at least one MFC (30); and a control module (20) configured to monitor and control the operation of the at least one MFC (30) and energy utilisation supplied by the at least one MFC (30) to an integrated equipment, wherein the control module (20) is connected to the at least one MFC (30) and the simulation module (10).

Preferably, the simulation module (10) further includes an input sub-module (11) configured to receive a set of parameters inputted by a user into the simulation model; a steady-state sub-module (12) configured to execute a steady-state simulation of the at least one MFC (30); a dynamic-state sub-module (13) configured to execute a dynamic-state simulation of the at least one MFC (30); and a response sub-module (14) configured to compute predicted responses of the at least one MFC (30) based on the results of the steady-state simulation and the dynamic state-simulation.

Preferably, the control module (20) further includes an external power source configured to provide an external power supply to operate the integrated equipment; and an alarm or notification unit used to provide an alert in audio or visual form for the operation of the at least one MFC (30).

Preferably, the integrated equipment is a desalination unit, and wherein the at least one MFC (30) provides the electrical energy to power the desalination unit for water treatment. According to another aspect of the present invention, a method for simulating and controlling at least one microbial fuel cell or MFC (30) is provided. The method is characterised by the steps of inputting a set of parameters into a simulation module (10); executing a steady-state simulation by a steady-state sub-module (12); executing a dynamic-state simulation by a dynamic-state sub-module (13); computing predicted responses of the at least one MFC (30) by a response sub-module (14), wherein the predicted responses include voltage and power density of the at least one MFC (30); and analysing the predicted responses to control the operation of the at least one MFC (30) by a control module (20).

Preferably, the method further comprises the step of monitoring and controlling energy utilisation supplied by the at least one MFC (30) to a desalination unit by the control module (20).

Preferably, the step of monitoring and controlling energy utilisation supplied by the at least one MFC (30) to the desalination unit by the control module (20) includes computing the amount of additional voltage or power density required to power and operate the desalination unit by subtracting the value of voltage or power density obtained from the simulation module (10) from the amount of actual voltage or power density needed by the desalination unit; and triggering an external power source to supply the additional voltage or power density to the desalination unit.

Preferably, the step of executing the steady-state simulation by the steadystate sub-module (12) includes computing rate of reactions in an anode compartment and a cathode compartment of the at least one MFC (30); and computing concentrations of components ofthe at least one MFC (30), wherein the concentrations of components include hydrogen ion concentration, carbon dioxide concentration, and acetate concentration in the anode compartment of the at least one MFC (30) and oxygen concentration in the cathode compartment of the at least one MFC (30).

Preferably, the step of executing the dynamic-state simulation by the dynamicstate sub-module (13) includes computing losses ofthe at least one MFC (30), wherein the losses include ohmic losses, concentration losses, and activation losses. Preferably, the step of analysing the predicted responses to control the operation of the at least one MFC (30) by the control module (20) includes monitoring the amount of fuel substrate that has been consumed during an oxidation process by analysing the value of power density received from the simulation module (10); determining whether the value of power density is below a pre-determined threshold; generating an alert of low fuel feed flow rate if the value of power density is below the pre-determined threshold; and initiating a flow of additional fuel substrate into an anode compartment of the at least one MFC (30) if the value of power density is below the pre-determined threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

FIG. 1 illustrates a block diagram of a system (100) for simulating and controlling at least one microbial fuel cell or MFC (30) according to an embodiment of the present invention.

FIG. 2 illustrates a diagram of a simulation model for predicting responses of the MFC (30).

FIG. 3 illustrates a flowchart of a method for simulating and controlling at least one microbial fuel cell or MFC (30) according to an embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

A preferred embodiment of the present invention will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

An initial reference is made to FIG. 1 which illustrates a block diagram of a system (100) for simulating and controlling at least one microbial fuel cell or MFC (30) according to an embodiment of the present invention. The system (100) comprises a simulation module (10), a control module (20), and at least one MFC (30). The system (100) is configured to simulate a bio-electrochemical process of the MFC (30) and predict responses of the MFC (30) in generating electrical energy. The system (100) is also configured to utilise the predicted responses to control the operation of the MFC (30) and monitor an electrical energy requirement supplied by the MFC (30) to an integrated equipment. Examples of the integrated equipment is a desalination unit used for a water treatment process, an anaerobic digester used for monitoring an anaerobic digestion of organic waste by microorganisms, etc.

The simulation module (10) is configured to execute a simulation model to predict the responses of the MFC (30). The simulation module (10) executes the simulation model based on a set of parameters inputted by a user such as membrane thickness, volume of anode compartment, volume of cathode compartment, etc. The simulation model may be developed from mathematical models used to compute various responses necessary for the operation of the MFC (30). For example, the predicted responses of the MFC (30) may include but is not limited to voltage and power density of the MFC (30). The simulation module (10) may include any simulation platform used to develop and execute the simulation model on any computing hardware such as a Simulink® platform. The simulation module (10) further comprising an input sub-module (11), a steady-state sub-module (12), a dynamic-state submodule (13), and a response sub-module (14).

The input sub-module (11) is configured to receive the set of parameters inputted by the user into the simulation model such as membrane thickness, volume of anode compartment, volume of cathode compartment, etc.

The steady-state sub-module (12) is connected to the input sub-module (11). The steady-state sub-module (12) is configured to execute a steady-state simulation ofthe MFC (30). The steady-state simulation computes various parameters of the MFC (30) such as rate of reactions in the anode compartment and the cathode compartment of the MFC (30), and concentrations of components in the anode and cathode compartments of the MFC (30) such as hydrogen ion concentration, carbon dioxide concentration, acetate concentration, and oxygen concentration.

The dynamic-state sub-module (13) is connected to the input sub-module (11) and the steady-state sub-module (12). The dynamic-state sub-module (13) is configured to execute a dynamic-state simulation of the MFC (30). The dynamic-state simulation computes various losses of the MFC (30) such as ohmic losses, concentration losses, activation losses, etc.

The response sub-module (14) is connected to the input sub-module (11) and the dynamic-state sub-module (13). The response sub-module (14) is configured to compute the predicted responses of the MFC (30) based on the results of the steadystate simulation and the dynamic state-simulation. The response sub-module (14) outputs a characteristic curve and the predicted responses of the MFC (30) for display and analysis.

FIG. 2 illustrates a diagram of the simulation model for predicting the responses of the MFC (30). To predict the responses of the MFC (30), the steady-state submodule (12) and the dynamic-state sub-module (13) executes the steady-state simulation and the dynamic-state simulation according to the set of parameters inputted by the user through the input sub-module (11). Based on the results of the steady-state simulation and the dynamic-state simulation, the response sub-module (14) outputs a characteristic curve and predicted responses of the MFC (30) for display and analysis.

The control module (20) is connected to the simulation module (10). The control module (20) is configured to monitor and control the operation of the MFC (30). The control module (20) analyses the predicted responses of the MFC (30) received from the simulation module (10) and generates output signals to control the operation of the MFC (30). For example, the control module (20) may generate output signals to control the flow rate of fuel feed into the MFC (30) based on the predicted power density of the MFC (30). The control module (20) is also configured to monitor and control the energy utilisation supplied by the MFC (30) to the integrated equipment. For example, the MFC (30) may be integrated with the desalination unit (not shown) used for the water treatment process. To determine the energy utilisation of the desalination unit, the control module (20) may compute the amount of power required to operate the desalination unit based on the predicted power density of the MFC (30). The control module (20) may include additional components used to facilitate the operation of the MFC (30). For example, the control module (20) may include an external power source (not shown) such as a solar panel or a battery, wherein the external power source is configured to provide an external power supply to operate the desalination unit. The control module (20) may also include an alarm or notification unit (not shown) configured to provide an alert in audio or visual form forthe operation of the MFC (30). For example, the alarm or notification unit may be a buzzer or a light-emitting diode or LED that emits an alert when the control module (20) detects that the fuel of the MFC (30) is near to fully consumed.

The MFC (30) is connected to the control module (20). The MFC (30) is configured to perform the bio-electrochemical process to generate the electrical energy, wherein the MFC (30) utilises microorganisms to perform a redox reaction of a fuel substrate to generate the electrical energy. The MFC (30) may be made of any configuration with any suitable materials. For example, the MFC (30) may be a two- compartment MFC (30) comprising of the anode compartment and the cathode compartment with graphite electrodes, wherein the anode compartment and the cathode compartment are separated by a cation-exchange membrane. The MFC (30) may utilise any suitable type of microorganism, substrate, and oxidant to drive the redox reaction of the MFC (30). For example, the MFC (30) may utilise Lysinibacillus species as the chosen microorganism, an acetate compound as the substrate, and oxygen gas as the oxidant. The MFC (30) may be configured as a single cell MFC or a stack MFC. When configured as the stack MFC, the MFC (30) may be integrated with equipment such as the desalination unit to provide additional electrical energy to power the desalination unit for water treatment.

Reference is now made to FIG. 3 which illustrates a flowchart of a method for simulating and controlling at least one MFC (30) according to an embodiment of the present invention. Initially, the set of parameters is inputted into the simulation module (10) by the user through the input sub-module (11) as in step 201. The set of parameters may include but is not limited to membrane thickness, volume of anode compartment, volume of cathode compartment, etc.

Next, the steady-state sub-module (12) executes the steady-state simulation as in step 202. The steady-state simulation includes computing the rate of reactions in the anode compartment and the cathode compartment of the MFC (30). Preferably, the rate of reactions is computed based on the formulas cited by Zeng et. al. (2010) as shown below: rl = 450icell/F (1) r2 = —900icell/F (2) whereby rl refers to the rate of acetate oxidation in the anode compartment, r2 refers to the rate of oxygen reduction in the cathode compartment, icell refers to the cell current density, and F refers to the Faraday’s constant.

The steady-state simulation also includes computing concentrations of components of the MFC (30). Preferably, the concentrations of components include hydrogen ion concentration, carbon dioxide concentration, and acetate concentration in the anode compartment of the MFC (30) and oxygen concentration in the cathode compartment of the MFC (30). Preferably, the concentrations of components are computed based on the formulas cited by Zeng et. al. (2010) as shown below:

Va— = Qa C - C_H) + 8A_mrl (3)

whereby Va refers to the volume of the anode compartment, Vc refers to the volume of the cathode compartment, Qa refers to the flow rate of fuel feed to the anode compartment, Qc refers to the flow rate feeding to the cathode compartment, A_m refers to the cross-section area of the membrane, C™ refers to the concentration of hydrogen ion in the influent of the anode compartment, C_H refers to the concentration of hydrogen ion, C Q2 refers to the concentration of carbon dioxide in the influent of the anode compartment, C_C02 refers to the concentration of dissolved carbon dioxide, C_A refers to the concentration of acetate in the influent of the anode compartment, C_AC refers to the concentration of acetate, CQ” refers to the concentration of dissolved oxygen in the influent of the cathode compartment, C₀₂ refers to the concentration of oxygen, rl refers to the rate of acetate oxidation in the anode compartment, and r2 refers to the rate of oxygen reduction in the cathode compartment.

Thereon, the dynamic-state sub-module (13) executes the dynamic state simulation as in step 203. The dynamic state simulation includes computing losses of the MFC (30), wherein the losses may include but are not limited to ohmic losses, concentration losses, activation losses, etc. Preferably, the losses of the MFC (30) are computed based on the formulas cited by Zeng et. al. (2010) as shown below:

whereby Ha refers to the anodic overpotential, F|_c refers to the cathodic overpotential, R refers to the gas constant, T refers to the operating temperature, F refers to the Faraday’s constant, B refers to the charge transfer coefficient of the cathode, Qa refers to the flow rate of fuel feed to the anode compartment, Va refers to the volume of the anode compartment, K_dec refers to the decay constant for acetate utilisers, fx refers to the reciprocal of wash-out fraction, k° refers to the rate constant of the anode reaction at standard conditions, Y_ac refers to the bacterial yield, A_m refers to the area of the membrane, K_AC refers to the half velocity rate constant for acetate, C" refers to the concentration of acetate in the influent of anode compartment, k2 refers to the forward rate constant of cathode reaction at standard condition, CQ” refers to the concentration of carbon dioxide in the influent of anode compartment, K₀₂ refers to the half velocity rate constant for dissolved oxygen, rl refers to the rate of acetate oxidation in the anode compartment, r2 refers to the rate of oxygen reduction in the cathode compartment, Qc refers to the flow rate feeding to the cathode compartment, L_m refers to the membrane loss, icell refers to the cell current density, dm refers to the membrane thickness, km refers to the electrical conductivity of membrane, dcell refers to the distance between anode and cathode in the cell, and kaq refers to the electrical conductivity of the aqueous solution.

Based on the results of the steady-state simulation and the dynamic-state simulation, the response sub-module (14) then computes the predicted responses of the MFC (30) as in step 204. The predicted responses of the MFC (30) may include but is not limited to voltage and power density. Preferably, the voltage is computed using a Nernst voltage equation cited by Zeng et. al. (2010) as shown below:

Vcell = E - (n_a + He + Lrn) (10) whereby V_cen refers to the voltage, E refers to the open circuit voltage, !]„ refers to the anodic overpotential, He refers to the cathodic overpotential, and L_m refers to the membrane loss.

Preferably, the power density is computed using an equation cited by Zeng et. al. (2010) as shown below:

Pceii = (y_ceii icell) (11) whereby P_ceu refers to the power density, V_ceu refers to the voltage, and icell refers to the cell current density.

The simulation module (10) then transmits the predicted responses to the control module (20). The control module (20) may then analyse the predicted responses and utilise the predicted responses to control the operation of the MFC (30) as in step 205.

A low amount of fuel substrate may result in a low power density of the MFC (30). During the operation of the MFC (30), the control module (20) continuously monitors the amount of fuel substrate that has been consumed during the oxidation process by analysing the power density received from the simulation module (10). The control module (20) then determines whether the power density is below a threshold value. The threshold value may be pre-determined by varying the flow rate of the fuel feed to the anode compartment against the power density in the simulation model. If the power density is below the threshold value, this means that the fuel substrate is near to fully consumed. Once the control module (20) determines that the fuel substrate is near to fully consumed, the control module (20) may generate an alert of a low fuel feed flow rate. The control module (20) also generates the output signal used to initiate automation of the flow of additional fuel substrate into the anode compartment. The additional fuel substrate may be stored in a hopper fuel storage tank positioned at a certain height from the anode compartment. The output signal is then used to activate a door mechanism of the hopper fuel storage tank and allows the additional fuel substrate to flow into the anode compartment of the MFC (30). Alternatively, the fuel substrate may also be added manually into the anode compartment by the user.

When the MFC (30) is integrated with the desalination unit, the control module (20) monitors and controls the energy utilisation of the desalination unit as in step 206. The control module (20) computes the amount of additional voltage or power density required to power and operate the desalination unit by subtracting the voltage or power density obtained from the simulation module (10) from the amount of actual voltage or power density needed by the desalination unit. The control module (20) then triggers the external power source to supply the additional voltage or power density to the desalination unit. For example, suppose the desalination unit requires 10V for operation and the simulation module (10) outputs a value of2V. By subtracting 2V from the 10V, the control module (20) determines that an additional 8V is required to operate the desalination unit. The control module (20) may then trigger the external power source to supply the additional 8V to the desalination unit.

While embodiments of the invention have been illustrated and described, it is not intended that these embodiments illustrate and describe all possible forms of the invention. Rather, the words used in the specifications are words of description rather than limitation and various changes may be made without departing from the scope of the invention.

REFERENCE

[1] Zeng, Y., Choo, Y. F., Kim, B.-H., Wu, P. (2010). Modelling and simulation of two-chamber microbial fuel cell. Journal of Power Sources, 195(1), 79-89. https://doi.Org/10.1016/j.jpowsour.2009.06.101

Claims

1 . A system (100) for simulating and controlling at least one microbial fuel cell or MFC (30) comprising: a) the at least one MFC (30) configured to perform a bio-electrochemical process to generate electrical energy, characterised in that the system (100) further comprising: b) a simulation module (10) configured to execute a simulation model to predict responses of the at least one MFC (30); and c) a control module (20) configured to monitor and control the operation of the at least one MFC (30) and energy utilisation supplied by the at least one MFC (30) to an integrated equipment, wherein the control module (20) is connected to the at least one MFC (30) and the simulation module (10).

2. The system as claimed in claim 1 , wherein the simulation module (10) further includes: a) an input sub-module (11) configured to receive a set of parameters inputted by a user into the simulation model; b) a steady-state sub-module (12) configured to execute a steady-state simulation of the at least one MFC (30); c) a dynamic-state sub-module (13) configured to execute a dynamicstate simulation of the at least one MFC (30); and d) a response sub-module (14) configured to compute predicted responses of the at least one MFC (30) based on the results of the steady-state simulation and the dynamic state-simulation.

3. The system as claimed in claim 1 , wherein the control module (20) further includes: a) an external power source configured to provide an external power supply to operate the integrated equipment; and b) an alarm or notification unit used to provide an alert in audio or visual form for the operation of the at least one MFC (30).

4. The system (100) as claimed in claim 1 , wherein the integrated equipment is a desalination unit, and wherein the at least one MFC (30) provides the electrical energy to power the desalination unit for water treatment.

5. A method for simulating and controlling at least one microbial fuel cell or MFC (30) is characterised by the steps of: a) inputting a set of parameters into a simulation module (10); b) executing a steady-state simulation by a steady-state sub-module (12); c) executing a dynamic-state simulation by a dynamic-state sub-module (13); d) computing predicted responses of the at least one MFC (30) based on the result of the steady-state simulation and the dynamic-state simulation by a response sub-module (14), wherein the predicted responses include voltage and power density of the at least one MFC (30); and e) analysing the predicted responses to control the operation of the at least one MFC (30) by a control module (20).

6. The method as claimed in claim 5, wherein the method further comprises the step of monitoring and controlling energy utilisation supplied by the at least one MFC (30) to a desalination unit by the control module (20).

7. The method as claimed in claim 6, wherein the step of monitoring and controlling energy utilisation supplied by the at least one MFC (30) to the desalination unit by the control module (20) includes: a) computing the amount of additional voltage or power density required to power and operate the desalination unit by subtracting the value of voltage or power density obtained from the simulation module (10) from the amount of actual voltage or power density needed by the desalination unit; and b) triggering an external power source to supply the additional voltage or power density to the desalination unit.

8. The method as claimed in claim 5, wherein the step of executing the steadystate simulation by the steady-state sub-module (12) includes: a) computing rate of reactions in an anode compartment and a cathode compartment of the at least one MFC (30); and b) computing concentrations of components of the at least one MFC (30), wherein the concentrations of components include hydrogen ion concentration, carbon dioxide concentration, and acetate concentration in the anode compartment of the at least one MFC (30) and oxygen concentration in the cathode compartment of the at least one MFC (30). The method as claimed in claim 5, wherein the step of executing the dynamicstate simulation by the dynamic-state sub-module (13) includes computing losses of the at least one MFC (30), wherein the losses include ohmic losses, concentration losses, and activation losses. The method as claimed in claim 5, wherein the step of analysing the predicted responses to control the operation of the at least one MFC (30) by the control module (20) includes: a) monitoring the amount of fuel substrate that has been consumed during an oxidation process by analysing the value of power density received from the simulation module (10); b) determining whether the value of power density is below a predetermined threshold; c) generating an alert of low fuel feed flow rate if the value of power density is below the pre-determined threshold; and d) initiating a flow of additional fuel substrate into an anode compartment of the at least one MFC (30) if the value of power density is below the pre-determined threshold.