1. MATHEMATICAL MODELING, CONTROL AND DIAGNOSIS OF MICROBIAL FUEL CELLS
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Research network GreTInMat
Four-day working meeting
Starosel, Bulgaria, June 18 – 21, 2018
MATHEMATICAL MODELING, CONTROL AND
DIAGNOSIS OF MICROBIAL FUEL CELLS
Kosta Boshnakov
University of Chemical Technology and Metallurgy,
Department of Industrial Automation
8, St. Kliment Ohridski, Blvd., 1756 Sofia, Bulgaria

2. In the presentation are included scientific investigations connected with mathematical modeling, control and diagnosis of Microbial Fuel Cells (MFCs) for wastewater treatment and simultaneously electricity generation Research is joint between Prof. Kosta Boshnakov from University of Chemical Technology and Metallurgy (UCTM) and Prof. Fan Liping and Dr. Li Chong from Shenyang University of Chemical Technology, China. Dr. Li Chong was PhD student at UCTM and defended PhD Thesis in July 2017 under supervision of Prof. Kosta Boshnakov and Prof. Fan Liping

3. Content Why are we interested in Microbial Fuel Cells (MFCs)?
Microbial Fuel Cells - nature of the ongoing processes
Laboratory Microbial Fuel Cell (MFC) with auxiliary systems
Mathematical modeling of two-chamber MFC
Simulation model of two chamber Microbial Fuel Cell
Fuzzy control of microbial fuel cells
Model-based Fault Detection and Diagnosis
Conclusions

4. Why are we interested in Microbial Fuel Cells (MFCs)? (1/2)
Wastewater treatment plants working on contemporary level, consumes a significant amount of energy and cause environmental problems
From energy recovery point of view it is estimated that municipal wastewater contains approximately 9.3 times more energy than currently needed for its treatment in a modern municipal wastewater treatment plant (WWTP) [2]
As an emerging technology, MFCs are, due to their many unique advances, a promising candidate for realizing the sustainability in wastewater treatment [1]

5. Why are we interested in Microbial Fuel Cells (MFCs)? (2/2)
Microbial Fuel Cells (MFCs) can generate electricity from nearly all sources of biodegradable organic matter in wastewaters, including simple molecules such as acetate, ethanol, and glucose, and polymers such as polysaccharides, proteins, and cellulose
On laboratory- and pilot- scale level for wastewater treatment and energy recovery very active investigations are conducted with Microbial Fuel Cells (MFCs)

6. Microbial Fuel Cells - nature of the ongoing processes

7. Microbial Fuel Cells (MFCs) (1/3)
Schematic diagram of the working principle of MFCs for electricity production and pollutant degradation [1].
A typical MFC system consists of an anode compartment and a cathode compartment with or without a Proton Exchange Membrane (PEM)

8. Microbial Fuel Cells (MFCs) (2/3)
In the anode, organic substrates (electron donors) are oxidized by exoelectrogens, generating electrons and protons. As example the anode reaction for acetate is:
The electrons are transferred to the anode material and then pass through an external electric circuit to the cathode

9. Microbial Fuel Cells (MFCs) (3/3)
At the cathode, a terminal electron acceptor, such as oxygen, nitrate, or sulfate, accepts the electrons and combines with protons to produce new reduced products. The reaction on the cathode is:
Protons diffuse from the anode to the cathode through the electrolyte and Proton Exchange Membrane (PEM) in order to achieve electroneutrality

10. Laboratory Microbial Fuel Cell (MFC) with auxiliary systems

11. Experimental system for microbial fuel cells investigations (1/2)
Double-chamber MFC:
Anode chamber (right)
Cathode chamber (left)

12. Experimental system for microbial fuel cells investigations [10] (2/2)
Anode chamber (right)
Electric stirrer
pH meter
Inject anolyte with pH buffer
Cathode chamber (left)
DO meter
Inject catholyte with air

13. Mathematical modeling of two-chamber MFC
The research related to the development of a control system, fault detection and diagnostic system at this stage are simulation, which requires the creation of a simulation model

14. Mathematical modeling of two-chamber Microbial Fuel Cell [8,9] (1/8)
The model integrated the bio-electrochemical kinetics and mass and charge balances within MFC. The parameters in the model are estimated on the base of minimization of the absolute difference between experimental data and simulated of the model.
The model is tested for the substrates: acetate and glucose and glutamic acid

15. Mathematical modeling of two-chamber MFC (2/8)
The anode reaction is in anaerobic or anoxic conditions:

16. Mathematical modeling of two-chamber MFC (3/8)
The reduction the dissolved oxygen in the cathode is suggested as:

17. Mathematical modeling of two-chamber MFC (4/8)
It is assumed that both the anode and cathode compartments can be treated as continuously stirred tank reactors.
The four components in the anode compartment are acetate, dissolved CO2, hydrogen ion (H+) and biomass (X)
The three components of the cathode compartment are dissolved oxygen (DO), hydroxyl ion (OH-) and cations (M+)

18. Mathematical modeling of two-chamber MFC (5/8)
For anode compartment

19. Mathematical modeling of two-chamber MFC (6/8)
For cathode compartment

20. Mathematical modeling of two-chamber MFC (7/8)
Cell current density
The flux of the M+ ions via the membrane

21. Mathematical modeling of two-chamber MFC (8/8)
The charge balances at the anode and cathode
Ca and Cc are the capacitances of the anode and cathode respectively
ηa-anodic over potential;
ηc-over potential at the cathode;
Cell voltage

22. Simulation model of two chamber Microbial Fuel Cell

24. Simulation results of MFC

25. Fuzzy logic voltage control of microbial fuel cells

26. Fuzzy control of microbial fuel cells (1/5)
Closed-loop adaptive fuzzy based constant voltage control for a microbial fuel cell [11]:
The controller is designed to adjust the input molar flow of fuel feed to anode
The control input of the plant is adjusted by using a main fuzzy controller
An auxiliary fuzzy controller is applied to adjust the factor Ku of the main fuzzy controller to weaken the steady-state error caused by the fuzzy control method

27. Fuzzy control of microbial fuel cells (2/5)
Control rules and membership functions of the main fuzzy controller: Ec NB NM NS ZE PS PM PB E

28. Fuzzy control of microbial fuel cells (3/5)
Control rules and membership functions of the auxiliary adaptive fuzzy controller:
ec* NB NM NS ZE PS PM PB e*

29. Fuzzy control of microbial fuel cells (4/5)
Simulation results of adaptive fuzzy based constant voltage control for a microbial fuel cell:
3-Dimensional Representation of the two Fuzzy Controller
The adaptive process of quantifying factor Ku in the main fuzzy control

30. Fuzzy control of microbial fuel cells (5/5)
Simulation results of adaptive fuzzy based constant voltage control for a microbial fuel cell:
Simulation results compared fuzzy control with uncontrolled
Simulation results compared adaptive fuzzy control with fuzzy control

31. Fault Detection and Diagnosis (FDD) for Microbial Fuel Cell

32. Fault Detection and Diagnosis (FDD) for Microbial Fuel Cell
The main goal of this investigation is fault detection and isolation for Microbial Fuel Cells including anomalous phenomena
What are our conception for MFC anomalous phenomena:
Reduction in efficiency or failures of elements in the design of the fuel cell and the auxiliary subsystems serving the cell
Deviations from the nominal mode of operation of the cell

33. Characteristics of Microbial Fuel Cells from diagnostic point of view
The microbial fuel cell is dynamic system
Dynamic and characteristics are described by ordinary nonlinear differential equations.
The symptoms are with different dynamics
The dynamics of the cell can be classified as slow, which is characteristic of microbiological processes. The time constants for the different channels range from minutes to several hours

34. Analysis of possible faults
The possible anomalies, that are investigated in this presentation in the microbial fuel cell together with the auxiliary subsystems are the following:
Problems connected with system for pHain (f1)
Problems with system for pHcin (f2)
Problems with membrane efficiency Am (f3)
Problems with microorganism’s concentration X (f4)

35. Model-based Fault Detection and Diagnosis

36. Fault Detection and Diagnosis (FDD) based on sensitivity analysis
The approach that is used in this presentation for FDD of MFC in on-line mode is based on modification of the sensitivity analysis, described in [3]
In the procedure is used the actual and theoretical sensitivity of the residue in relation of failure

37. System for Fault Detection and Isolation (FDI) for MFC (1/3)

38. System for Fault Detection and Isolation (FDI) for MFC (2/3)
The system is for on-line use
MFC means Microbial Fuel Cell
Qa and Qc are flows to anode and cathode compartments
Mathematical model of MFC is for realizing the Model-Based Diagnosis (MBD)
U, pHa and pHc are measurable symptoms for FDI
r1, r2 and r3 are residuals between symptoms, measured in MFC and values of symptoms predicted from Model of MFC

39. System for Fault Detection and Isolation (FDI) for MFC (3/3)
Calculation of the residuals
Fault detection (FD) is block for FD purpose
Start is block for starting the Fault Isolation (FI) procedure
Signaling for fault and information for the fault are interfaces

40. MIMO dynamic simulation model of MFC (1/3)
The model is with Wiener structure (in series connected linear dynamic part and non-linear static part)

41. MIMO dynamic simulation model of MFC (2/3)
Qa and Qc are flows to anode and cathode compartments and are manipulated variables for MFC
Parameters for diagnosis are pHain , pHcin , Am and X
Predicted by model values of symptoms are
The common type of the transfer functions is:
intermediate variables

42. MIMO dynamic simulation model of MFC (3/3)
Input variables for the model
Nonlinear static part
Output variables – designation on the chart

43. Fault Detection (FD) for MFC
Two kind of possibilities for Fault Detection:
On the base of thresholds
On the base of Principal Component Analysis (PCA) using criteria: (1) Hoteling (T2) and (2) Squared Prediction Error (SPE)

44. Faults Isolation The possible faults are marked as
The faults aren’t measurable
The theoretical relative sensitivity is calculated on the base of simulation experiments
The actual relative sensitivity is calculated on-line on the base of measurements

45. Theoretical relative sensitivity
Table 1 Theoretical relative sensitivity regarding the fault fi
The requirement for faults isolation is in every one row to don’t have equal values.
The table shows that there are no equal values in the rows for different faults, which is the condition that individual failures can be isolated!

46. Euclidean distance calculation
For every one possible fault , the Euclidean distance is calculated on the base of the following expression
The vector with Euclidean distances corresponding to different faults is obtained:
The minimal value of Euclidean distance corresponds to the fault

47. Model-based System for Fault Detection and Diagnosis

50. Simulation results

51. Simulation a fault for pHain
On the Figure are shown the changes of the Euclidean distances in time
The fault is simulated by a step function.
It can be seen from the graphs that even in the first minutes of occurrence of the fault in (pHain) it is indicated by the fact that the Euclidean distance has a minimum value with respect to other distances

52. Simulation a problems for X
On the figure is shown that the minimum value have
This mean that the problem is in X

53. Conclusions (1/2)
Laboratory scale Microbial Fuel Cell (MFC) for wastewater treatment and electricity generation is developed and investigated.
Simulation model of two-chamber Microbial Fuel Cells is created and investigated.
Adaptive fuzzy controller for MFC with ability for self-adapting of fuzzy parameter is creadted.
The controller can maintain the constant voltage output of MFC system even on the condition of fast load changes and have very good steady-state behavior.
Simulation experiments have been carried out to demonstrate the system's performance

54. Conclusions (2/2)
System for online model based diagnosis of microbial fuel cell is developed.
The faults isolation is made on the base of residuals between measured values of symptoms and its predicted values and subsequent sensitivity analysis are developed.
From simulation investigations, is looking that model-based diagnosis is more precise if the model is with the needed accuracy
The presented systems are investigated for step and linear function for simulating the faults
The model-based FDI system is very sensitive to small values of faults

55. References (1/3)
1.Liu,W., S.Cheng, Microbial fuel cells for energy production from wastewaters: the way toward practical application, Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2014, 15(11) pp Li,W., H.Yu, Z.He, Towards sustainable wastewater treatment by using microbial fuel cells-centered technologies, Energy & Environmental Science, 2014, 7, pp Escobet,T., D.Feroldi, S. de Lira, V.Puig, J.Quevedo, J.Riera, M.Serra, Model-based fault diagnosis in PEM fuel cell system, Journal of Power Sources, Vol.192 (2009), pp

56. References (2/3)
4.Li, Chong, Diagnosis of Microbial Fuel Cells on the Base of ANFIS, Сборник на Националната научно-техническа конференция с международно участие “Автоматизация в минната индустрия и металургията”, БУЛКАМК’2016, ноември 2016, София, стр…
5.Boshnakov, K., Chong Li, L. Fan, Model-based diagnosis of faults in microbial fuel cells, Proceedings of the International Conference Automatics and Informatics’2016, October 4-5, 2016, Sofia, Bulgaria, pp (in Bulgarian)
6.Li, Chong, K.Boshnakov, L.Fan, Sustainable Voltage Reutilization of a Microbial Fuel Cell of a Low Organic Load Using pH Buffer Solution Injection, Journal of Chemical Technology and Metallurgy, 52, 1, 2017, 66 – 74

57. References (3/3)
7.Du,Z., H.Li, T.Gu, A state of art review on microbial fuel cells: A promising technology for wastewater treatment and bioenergy, Biotechnology Advances, Vol.25, 2007, pp
8.Fan,L., J.Zhang, X.Shi, Performance improvement of a microbial fuel cell based on model predictive control, Int. J. Electrocem. Sci., 10 (2015), pp
9.Zeng,Y.,Y.Choo, B.Kim,P.Wu, Modelling and simulation of two-chamber microbial fuel cell, Journal of Power Sources, Vol.195, 2010, pp.79-89
10.Li, Chong, K.Boshnakov, L.Fan, Voltage Control of Microbial Fuel Cells for Wastewater Treatment, Science, Engineering & Education, 1, (1), 2016, 43-52
11.Fan,L., Chong Li, K.Boshnakov, Performance Improvement of a Microbial Fuel Cell Based on Adaptive Fuzzy Control, Pak. J. Pharm. Sci., Vol.27, No.3, May 2014, pp

58. Thank you for your attention!
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Thank you for your attention!
Acknowledgment:
„The research networkhas been financially supported by the Operational Programme "Science and education for smart growth" of the European Union cofounded by the European Social Fund through the project BG05M2ОP “Support for the development of capacity of doctoral students and young researchers in the field of engineering, natural and mathematical sciences”