Fuzzy logic controller implementation on a microbial electrolysis cell for biohydrogen production and storage

Gabriel Khew Mun Hong,Mohd Azlan Hussain,3,Ahmad Khairi Abdul Wahab*

1 Department of Chemical Engineering,Faculty of Engineering,Universiti Malaya,50603 Kuala Lumpur,Malaysia

2 Department of Biomedical Engineering,Faculty of Engineering,Universiti Malaya,50603 Kuala Lumpur,Malaysia

3 Centre for Separation Science and Technology (CSST),Faculty of Engineering,Universiti Malaya,50603 Kuala Lumpur,Malaysia

Keywords:Fuzzy logic control Process control Nonlinear Microbial electrolysis cell Renewable energy Hydrogen

ABSTRACT This work presents the implementation of fuzzy logic control(FLC)on a microbial electrolysis cell(MEC).Hydrogen has been touted as a potential alternative source of energy to the depleting fossil fuels.MEC is one of the most extensively studied method of hydrogen production.The utilization of biowaste as its substrate by MEC promotes the waste to energy initiative.The hydrogen production within the MEC system,which involves microbial interaction contributes to the system’s nonlinearity.Taking into account of the high complexity of MEC system,a precise process control system is required to ensure a wellcontrolled biohydrogen production flow rate and storage application inside a tank.Proportionalderivative-integral (PID) controller has been one of the pioneer control loop mechanism.However,it lacks the capability to adapt properly in the presence of disturbance.An advanced process control mechanism such as the FLC has proven to be a better solution to be implemented on a nonlinear system due to its similarity in human-natured thinking.The performance of the FLC has been evaluated based on its implementation on the MEC system through various control schemes progressively.Similar evaluations include the performance of Proportional-Integral (PI) and PID controller for comparison purposes.The tracking capability of FLC is also accessed against another advanced controller that is the model predictive controller(MPC).One of the key findings in this work is that the FLC resulted in a desirable hydrogen output via MEC over the PI and PID controller in terms of shorter settling time and lesser overshoot.

1.Introduction

The demand for energy increases as the growth in global population is at a rapid pace consequent to the expansion in industrialization [1].The need for energy has proven to be an essential prerequisite,as it is required to conduct all types of human activities globally.Despite realizing the current energy crisis,mankind is still taking energy usage for granted [2].A study reported that fossil fuel reserves could only support a maximum of 40 years for petroleum,60 years for natural gas and 156 years for coal [3].On another note,the overreliance on fossil fuel as the main source of energy since the First Industrial Revolution has also negatively impacted the environment.The excessive use of fossil fuel has caused global climate change due to the emission of greenhouse pollutants,which leads to formation of compounds such as COx,Nox,SOxand CxHy[4,5].

The search for an alternative source of renewable energy has to be conducted extensively in order to replace the depleting fossil fuels [6].This fact is supported by various reasons such as hydrogen being the most abundant element in the universe,which makes it a sustainable source.The non-toxic nature of hydrogen makes it an environmentally pleasant source of energy as well.The high energy storage capacity of hydrogen,which is 120 MJ(33.33 kW·h),exceeds double for most type of fuels [7,8].Hydrogen could also contribute to major economic growth on a global scale [9].

Microbial electrolysis cell (MEC) is designed to produce gases such as hydrogen or chemicals with added values from biowaste.The MEC operates in a manner such that the exo-electrogenic bacteria oxidizes (degrade) organic matter and transfer the electrons to a solid electrode (anode) when the biowaste is being converted to hydrogen ions.The electrons then travel through an external circuit and combine with protons at the anaerobic cathode,resulting in the generation of hydrogen [10,11].Typically,it would not be possible to drive the hydrogen evolution reaction (HER) at the cathode due to the insufficient reducing power attainable.However,by supplementing the process with a small voltage,the occurrence of cathodic HER in MEC is possible.Due to various microbial interactions within the MEC system,the biohydrogen production process is highly complex and nonlinear in nature [12,13].The microbial interactions resulted from the competition between the anodophilic and methanogenic microorganisms to consume the substrates in the anodic compartment of the MEC system[14,15].Fig.1 illustrates on how the MEC operates [16].

As the days of hydrogen utilization being a commercial source of energy draw closer,its optimal storage criterions remain broadly studied for application purposes.Current hydrogen storage materials and research methods require further development from the perspective of cost and safety [17,18].The heat flux that remains present in the hydrogen storage tank leading to selfpressurization requires a high degree or safety and control[19,20].This prompted the need to develop a reasonable and viable control strategy on hydrogen storage tank [21].The adoption of process control shall not limit to other aspects such as process optimization,ensuring environmental regulations,reliability and customer specifications [22].

The proportional-integral-derivative (PID) controller has been regarded as one of the most commonly used feedback controllers.Its application varies in many engineering sectors such as industrial process and process instrumentation [23].PID controllers are customarily favoured due to their robustness and simplicity upon implementation [24].However,conventional PID controller has its shortcomings such as its difficult tuning procedures.It requires repeated trials of tuning in order to avoid potential instability during the tuning and modelling experimental procedures[25].The other notable disadvantage of conventional PID controller is its slow adaptability when disturbances affect the system it is being implemented on [26].In addition,the PID controller is not capable to control effectively when the chemical process dynamics move towards a range of nonlinearity [25,27].Such technological challenges always persist and has to be overcame before MEC could be deployed for commercialization [28].

Fig.1. Operating principle of MEC with Proton Exchange Membrane (PEM) [16].

Table 1 Parameters used in the operation of MEC

An advanced process controller called the model predictive control(MPC)is to enable a system to predict future control action and control signals with current input and output variables.It is occasionally adopted as a preferred control methodology due to its predictive-based algorithm [29].MPC uses model explicitly to compute predictive output of a system within a future time horizon [30].MPC has been widely selected as the option to deal with biochemical process that generally exhibit nonlinearity [31,32].Fanet al.[32] developed a control mechanism to ensure a stable voltage output from a microbial fuel cell (MFC)viaMPC.The outcome of the study concluded that with the adoption of the appropriate MPC,it has resulted in fast response characteristics coupled with good steady-state behaviour and robustness.The challenges that lie within the implementation of MPC in chemical processes is the need of prior experience from control engineer and availability of precise models to make acceptable technical decisions [33].

On the other hand,a recent work conducted by Yahyaet al.[12],implemented an artificial neural network (ANN) based control strategies within the MEC system by manipulating the electrode potential.The judgment of the controller selection for the study is to adopt its capability to control a highly complex MEC system.The ANN based controller has resulted in a preferable control response over the PID controller.This is attributed to the ANN’s faster response time and minimal overshoots coupled with lesser offset error values [34].However,the need for ANN controller to have a large amount of reliable data for training purposes proves to be its main drawback.Insufficient training data would lead to poor performance of the ANN controller [35].

For that matter,the fuzzy logic controller (FLC) offers many attainable advantages in comparison to these other controllers.It emphasizes on approximate reasoning in contrast to fixed and exact reasoning.The implementation of FLC can express the similarity in human-natured thinking,which provides great robustness and universal approximation theorem [36,37].The mechanism of fuzzy theory is represented by linguistic constructs such as‘‘many”,‘‘low”,‘‘medium”,‘‘often”and‘‘few”,unlike the Boolean Logic[38].This then enables the construction of a fuzzy logic algorithm to be user-friendly without complicated mathematical modelling [27].Furthermore,the configuration of a fuzzy-based controller does not require large amount of data,which ease the calibration procedure [39].FLC is evidently more capable to be implemented on a nonlinear process than the conventional PID controller[40].

Yan and Fan[41]conducted a study on the integration of fuzzy control on a PID controller for a MFC system.Such configuration allows great precision of PID control with the agility and adaptability of a fuzzy controller.The research has resulted in a significant reduction in time taken for the MFC to reach its setpoint upon the implementation of fuzzy logic traits on the PID controller.The integration also sees a reduction in the percentage of overshoot in the MFC system.

The application of fuzzy-based controller extends to other highly complex biochemical process as well.To address the issue of nonlinearity of biological approach of wastewater treatment,Bououdenet al.[42] implemented a controller based on the Takagi-Sugeno fuzzy models.The aim of this study is to ensure a fixed level of pollution at the outlet of the treatment system.The Takagi-Sugeno based controller identifies the nonlinearities within the biological treatment process,which are attributed to the substrate consummation rate.The implementation resulted in the output of effluent being insensitive to the variation of the influent and presence of noise due to the adaptive mechanism of the fuzzybased controller.

Another common biochemical process known as fermentation involves complicated kinetics and various time-varying parameters contributing to the nonlinear dynamics of the process.The concentration of dissolved oxygen inside a fermenter is a crucial parameter that has to be well-controlled in the fermentation of baker’s yeast.Vasicˇkaninováet al.[43]designed a fuzzy-based controller to ensure a desired profile of oxygen concentration by regulating the gas phase dilution rate periodically.Comparison of control performances on the fermenter between conventional proportional-integral(PI)controller and the fuzzy-adaptive PI controller was conducted.The fuzzy-based PI controller has then produced better outcome with a more precise setpoints tracking and better disturbance rejection throughout the fermentation process.

In spite of the performance enhancement on MFC from various literature studies [41-45],there has been no prior research to leverage the benefit of a fuzzy based controller on the MEC system as of today.Hence,the main novelty of this work lies with the adoption of FLC to control the MEC system to enhance controlled hydrogen gas production.The ease in configuring the FLC without sophisticated mathematical modelling of the MEC are provided in this study.Evaluations are based on the observation of settling time,overshooting and integral absolute error.Similar evaluations would be conducted by the implementation of the PI,PID and MPC controllers onto the MEC system as well for performance comparison purposes with our proposed FLC-based strategy.

2.Microbial Electrolysis Cell (MEC)

The mathematical model utilised in this study is based on the multi-population MEC model that has been developed by Pintoet al.[14].Yahyaet al.[12] has suggested a few modifications in their work,which are utilized in this case study:

(1) The model is modified into a fed-batch reactor whereas Pinto model uses a continuous system,instead.

(2) The biofilm formation and retention consist of the twophase model biofilm growth,which are the anodic biofilms(Layer 1) and a cathode biofilm population (Layer 2).Whereas the Pinto model uses three phases,an outer biofilm layer (Layer 1),an inner biofilm (Layer 2) and cathode biofilms (Layer 3).The justification of utilizing the two-phase model is that it is more practical and easier to apply in a real plant.

(3) The proposed model only takes into consideration the metabolic activities of the methanogenic acetoclastic and methanogenic hydrogenophilic microorganisms whereas the Pinto model has an additional fermentation process,which impose unnecessary difficulties to the modelling of the system.

Fig.2. Model validation of MEC between (a) Azwar-modified Pinto MEC model [34] and (b) this work.

Fig.3. Closed-loop block diagram of MEC system with FLC.

Fig.4. Basic configuration of a FLC [54].

Fig.5. Membership function (a) error,(b) rate of change of errors and (c) change in applied voltage.

2.1.Mass balances for the MEC system

The dynamic mass balance equations for componentsS(concentration of substrate),xa(concentration of anodophilic microorganism),xm(concentration of acetoclastic microorganism),xh(concentration of hydrogenotrophic microorganism) andMox(oxidized mediator fraction per electricigenic microorganism) in the designed MEC system are represented by the equations below:

Table 2 Rule base for the FLC implemented on MEC system

whereQH2is hydrogen production rate (ml·d-1).

2.2.Electrochemical process

In order to determine the corresponding MEC voltage,the theoretical values of the electrode potentials have to be subtracted by the ohmic,activation and concentration losses.In the operation of MEC,resistance of the flow of ions in the electrolyte and electrode could result in ohmic losses.The partial resistances consists of the counter-electromotive force(ECEF),activation loss(ηact),concentration loss (ηconc) and ohmic loss (ηohm).Each of the partial resistances shall be determined individually.The electrochemical balance can then be written as below:

Ohm’s Law is applied to determine the ohmic losses(ηohm=IMECRint) to determine the concentration losses,it has to be divided between the anode (ηconc,A) and the cathode (ηconc,C)reactant mass transfer in the MEC.On account of the small size of H2molecules,the concentration loss at cathode can be neglected,as it results in a large diffusion coefficient of H2in a gas diffusion electrode used as a cathode.Thus,the concentration loss could be computed using the Nernst Equation as follows[46]:

In determining the activation loss values,the anode (ηact,A) and cathode (ηact,C) value can be separated due to slow reaction kinet

ics.The fact that MEC is operating at high overpotential at the cathode side [10],ηact,Ais assumed to be significantly smaller than the ηact,Cand is neglected.With the assumption that oxidation and reduction coefficients,which represent the activation barrier symmetry,are identical,the Butler-Volmer equation could be simplified to as follows [47]:

wherei0is exchange current density in reference condition(A·cm-1);Asur,Ais anodesurfacearea (m2);β is reduction or oxidation transfer coefficient.

Based on the previously defined Ohm’s Law and combining Eqs.(7)-(9),IMECcan be evaluated as below:

As there is a presence of activation losses at the cathode,the calculation ofIMECrequires a numerical solution due to the nonlinear nature of Eq.(10) as ηact,C=f(IMEC).However,there is a possibility that the equation could result in the negative value ofIMECin the events where the summation of ηact,C,ηconc,AandECEFis to be greater than theEapplied.As a countermeasure to such problem,only non-negative values ofIMECare taken into consideration.

To ensure the model accuracy during the start-up period,Pintoet al.[46] has proposed improvement to be implemented,whichRintshall be linked to the concentration of electricigenic microorganisms (xe):

where,Rminis lowest observed internal resistance(Ω);Rmaxis highest observed internal resistance (at startup) (Ω);KRis constant to determine the curve steepness (L·(mg x)-1).

2.3.Model validation

In order to validate the accuracy of the MEC mathematical model elaborated earlier,it is compared with experimental work,where an open-loop experimental study of a fed-batch MEC reactor by Azwar[34]is selected as the reference for such purposes.Fig.2 presents the resulting hydrogen production rate at the applied constant voltage of 1.8 V.The hydrogen obtained from this work,which is depicted by Fig.2(b) is shown to achieve stable states with values closely similar to that of the experimental studies as shown in Fig.2(a).This then validates the accuracy of the mathematical modelling adopted for this work,which is to be utilized in the control studies later.

3.Design of Control System

In order to determine the setpoint for the flow rate of hydrogen produced from the MEC system to be stored into the storage tank,known values from published literature are taken as the case study.Johnsonet al.[48]conducted a test to determine the optimal condition for hydrogen gas to be charged into a storage system made up of nine units of 37.4 L standard cylindrical tanks,where the refuelling of hydrogen gas operates at a flow rate of 0.43 kg·h-1with a pressure of 40 MPa.

Fig.6. Control surface of the FLC implemented on MEC system.

An assumption that the refuelling process for a period of 12 h operates at nominal room temperature between 20 °C to 25 °C is made.The flow rate of the hydrogen gas into the storage system is then at 192.5 L·d-1by having the density of hydrogen to be the conversion media.Further assumptions are subsequently made to cater a relatively smaller production capacity of hydrogen MEC system in this work.Therefore,only one-cylinder tank is taken into consideration and it is scaled down by a factor of 10.Hence,the required flow rate of hydrogen gas from the MEC system to achieve appropriate storage is determined at 2.14 L·d-1,which is deemed as the setpoint for this study.

To ensure a steady desired output of hydrogen flow rate into the storage system,a robust control system has to be implemented.PID controller has been the norm to be used on various applications and processes.However,their evaluation of time domain response in terms of steady-state error,effect of parameter variation and time delay has proven to be unsuitable for a nonlinear process such as MEC.Advanced control system such as the FLC is suitable to be applied on the MEC due to its proven robust ability to control processes with nonlinearity [49-51].The MEC system is simulated with the Simulink MATLAB Toolbox.The parameters involved and their corresponding values are shown in Table 1.

Fig.7. Results of closed-loop MEC response with constant QH2 setpoint at 2.14 L·d-1 by using fuzzy logic,PI and PID controllers.

3.1.Fuzzy Logic Controller (FLC)

The design of the fuzzy logic controller implemented onto the MEC system is elaborated in this section.This includes the membership functions and rules within the FLC.Fig.3 represents the scheme for implementation of the FLC in a closed-loop system with two inputs.The first input is the error(e)with timet,which can be written as follows:

The second input is the rate of change of error(de/dt)with timet.The FLC then computes the change of applied voltage (ΔEapp)with timet,which is then fed into the MEC to control the hydrogen production.

Fig.8. Results of closed-loop MEC response with multiple QH2 setpoints by using fuzzy logic,PI and PID controllers.

The detailed operation of the FLC can be referred to Fig.4.Upon receiving the inputs,which in this study are theeand de/dt,the fuzzification blocks firstly convert them into suitable defined fuzzy sets.The inference mechanism[53]then evaluate and combine the membership functions with defined fuzzy rule-base to determine the fuzzy output,which in this study is the ΔEapp.Finally,the output is translated into real values by the defuzzification process,to be fed into the MEC system [52,54].

Prior closed-loop data with implementation of Proportional-Integral (PI) and PID controllers onto the MEC system has been gathered to provide the guidelines to determine the values of the fuzzy sets in accordance to their respective membership functions.In this study,triangular type membership functions with Mamdani inference is adopted.The fuzzy domain foreis [-1.5,1.5] with fuzzy sets of {N2,N1,Z,P1,P2},which can be referred to Fig.5(a).In Fig.5(b),the normalized values ofde/dthas a fuzzy domain of [-280,280] with fuzzy sets of {NF,NS,Z,PS,PF}.The output of the fuzzy-based controller,ΔEappis meant to manipulate the voltage being applied onto the MEC system to control theQH2.The output also adopted the triangular type membership functions with fuzzy domain of [-1,1] and corresponding fuzzy sets of {HD,MD,LD,Z,LI,MI,HI},which can be seen in Fig.5(c).

Table 2 represents the rule base of the FLC for ΔEappoutput with correspondingeand de/dtinputs.A control surface of the same fuzzy rule base controller that is implemented on the MEC system is illustrated in Fig.6.

3.2.Proportional-Integral (PI) and Proportional-Integral-Derivative(PID) controllers

In order to rate the performance of the FLC,PI and PID controller are implemented onto the respective MEC system with the same control objective for comparison purposes.The PI and PID controllers are tuned optimally based on the PID Tuner application available in the Simulink environment.

Fig.9. Results of closed-loop MEC response with alternating counter-electromotive force (ECEF) by using fuzzy logic and PID controllers.

3.3.Model Predictive Controller (MPC)

To further evaluate the performance of FLC’s implementation onto MEC system,a comparison against another advanced process control methodology which is MPC has been carried out.The MPC is developed based on the adoption of the MEC models as elaborated in the earlier portion of this work [12].

In the setting up of MPC,the controller receives designated setpoint along with the latest value of hydrogen flow rate.The corresponding manipulated variable,which is the applied voltage is computed within the MPC algorithm to be fed into MEC system.The constructed MPC implemented on the MEC system has prediction horizon of 5 with sampling time of 0.1 d.

4.Results and Discussion

4.1.Robustness tests against PI and PID controllers

The FLC is tested progressively through five robustness tests to evaluate its performance upon being implemented onto the MEC.The tests are:

(1) Constant setpoint

(2) Multiple setpoints tracking

(3) Internal disturbance rejection

(4) External disturbance rejection

(5) Noise disturbance rejection

Subsequently,the performance of FLC is compared with the corresponding outcomes for both the PI and PID controllers implemented onto the same MEC system.

The integral absolute error (IAE)is selected as the performance indicator of the control system,is expressed by the following equation:

Fig.10. Result of closed-loop MEC response with alternating constants to determine internal resistance (Rint) curve steepness by using fuzzy logic,PI and PID controllers.

4.1.1.Constant setpoint tracking

The MEC system is operated based on the predetermined setpoint of 2.14 L·d-1and it is maintained throughout the whole operation.The system is then observed on its rise time and overshoot profiles.

Referring to Fig.7,MEC with FLC managed to reach the setpoint of theQH2,setpointwithout significant overshoot being identified.Such key finding is important as attaining non-overshooting is an essential requirement in many nonlinear plants [55].Upon reaching the setpoint,the fuzzy-based controlled MEC shows no signs of deviating from the desired value.PI and PID however exhibit overshoot in the output before settling down to the setpoint approximately after Day 4.

4.1.2.Multiple setpoint tracking

Multiple setpoints are specified in this control scheme test.Response from the output of the system is observed to evaluate the tracking capability of the control systems with dynamic setpoint changes.An alternating setpoints of 1.93 L·d-1and 2.35 L·d-1are defined in the operation.The setpoints are varied every 2 days within the operation.

Fig.11. Results of closed-loop MEC response with alternating temperatures (T) by using fuzzy logic,PI and PID controllers.

Referring to Fig.8,FLC exhibits excellent tracking capability in the events of sudden change in setpoints.TheQH2of the fuzzy logic controlled MEC is able to reach its initial setpoint of 1.93 L·d-1without any significant overshoot.Upon setpoint shift in Day 2 within the operation,the MEC system detects the change and theEappincreases to elevateQH2to the latest setpoint of 2.35 L·d-1.Subsequent shifts in setpoint produce similar nature of output with very minimal overshoot and deviation from the setpoint.The desirable transient response demonstrated by FLC is an important requirement for controlling nonlinear systems [55].On the contrary,the output ofQH2by the MEC system with the implementation of PI and PID controllers has noticeable overshoots upon reaching its respective setpoints.The longer settling time of both PI and PID controller could not ensure theQH2to be maintained constant for a significant time period at the different setpoint changes.

4.1.3.Internal disturbance rejection

In the presence of a load or internal disturbance,the control system plays a vital role to ensure that the output of the system remains aligned with the setpoint.In this work,the counterelectromotive force (ECEF) within the MEC system is varied in the operation by alternating its values between -0.385 V and-0.315 V at an interval of 2 days with nominal setpoint of 2.14 L·d-1.

With reference to Fig.9,the MEC system with the implemented FLC demonstrates its capability to ensure theQH2is maintained at its setpoint in the presence of alternatingECEF.On the other hand,the undesirable overshoot and longer settling time of the MEC system response by PI and PID controllers are very much evident.

The internal resistance (Rint) of the MEC is selected as the next internal disturbance to evaluate the FLC’s disturbance rejection capability.The constant to determine the curve steepness,(KR)value in Eq.(11) is alternated to vary theRintwithin the MEC system.The internal disturbance is introduced by changing theKRvalue between 0.021 and 0.027 for an interval of 2 days with a constant setpoint of 2.14 L·d-1.

Fig.12. Result of closed-loop MEC response with alternating pressures(P)by using fuzzy logic,PI and PID controllers.

Fig.10 denotes FLC resulted with the good response in the presence of internal disturbance with minimized overshoot.This is evident when theQH2does not shows large deviation away from the setpoint.On the other hand,the PI and PID controllers demonstrate undesirable overshooting ofQH2upon reaching the designated setpoint.In addition,the longer settling time of both PI and PID controller in comparison to the FLC can be observed.

4.1.4.External disturbance rejection

Temperature is selected as one of the external disturbances in the operation of the MEC.The temperature is alternately varied between 303.15 K and 293.15 K every 2 d with the setpoint preserved at a constant value of 2.14 L·d-1.

Fig.11 depicts the severe fluctuation ofQH2away from its setpoint at the shift in operating temperature.FLC validates its superiority with quick response to minimize error as a whole,upon the introduction of the disturbances.PI and PID controllers also possess some capabilities to reject external disturbances.However,due to their distinct overshoot and longer settling time,the outputs of the MEC system could never be maintained at their setpoints accordingly.

Next,pressure is manipulated in this study as the second external disturbance within the MEC system.The external disturbance is generated by altering the pressure applied on the anodic compartment every 2 d between the values of 91.2 kPa and 111.5 kPa.A setpoint forQH2of 2.14 L·d-1is maintained throughout the whole control application as well.

The deviation ofQH2away from its setpoint upon the change in the operating pressure can be seen in Fig.12.Despite this,FLC resulted in a preferable output from MEC due to its relatively smaller overshoot in the presence of external disturbance.Nonetheless,the PI and PID controller exhibit significant overshoot upon reaching the setpoint,coupled by longer settling time.

Fig.13. Results of closed-loop MEC response with introduction of noise by using fuzzy logic,PI and PID controllers.

Table 3 Integral Absolute Error (IAE) for various controller schemes

4.1.5.Noise disturbance in output values

The presence of noise,which originated from the chemical process has to reduced down to a minimal.The elimination of noise is vital as it interferes with the measurement of sensors or instruments within the system [56].Noise is introduced into the MEC system of this work by having a band-limited white noise with noise power of 0.0001 to mimic the practical and real time control applications.There is similar constant setpoint of 2.14 L·d-1throughout the whole operation.

In reference to Fig.13,there seems to be a dynamic behaviour ofQH2andEappupon the introduction of noise into the operation.It is observed that FLC attempts to ensure the output does not deviate drastically away from the designated setpoint in spite of the presence of noise.On the other hand,the responses of both PI and PID controllers show observable poorer noise rejection capability as compared to the FLC.

A study by Yahyaet al.[28] highlighted the close relationship between theIMECandQH2.Such linkage is affirmed in this work asIMECis dependent on theEapp.It is vital for theEappto provide an instantaneous response in the event whereQH2deviates away from theQH2,Setpoint.The observed results of MEC’s responses shows that the FLC is capable to maintain the close relationship betweenIMECandQH2.

Fig.14. Results of closed-loop MEC response with introduction of noise by using fuzzy logic controller and model predictive controller.

Fig.15. Results of closed-loop MEC response by using fuzzy logic controller and model predictive controller with variation of (a) xa0 and (b)μmax,h.

A summary of integral absolute error (IAE) for each control strategies are tabulated in Table 3 for each robustness tests,respectively.As the IAE evaluates the accumulation of errors throughout the operation of MEC system,the lesser value of IAE signifies better control responses.For every test that have been conducted throughout this work,FLC obtained lowest values of IAE,which is attributed to its precise control methodology.The performance of PID controller is shown to be overall better than the PI controller with lower error values.

4.2.Performance evaluation of FLC against MPC

The analysis of FLC evaluation against MPC is done based on single and multi-setpoint tracking.With reference to Fig.14(a),it can be seen that the MPC enables the MEC to reach its operating setpoint slightly faster than FLC.MPC also shows very similar settling time as its FLC counterpart in terms of setpoint tracking.However,FLC has small overshoots as compared to the MPC.Such trait is vital to comply with the precise readiness in filling up hydrogen storage tankviaMEC.

In single setpoint test,IAE obtained shows that MPC has accumulated higher deviation from its setpoint of 0.0924 in comparison to FLC of 0.0005.It is alluded that FLC has superior multi-setpoint tracking as seen in Fig.14(b),which IAE of 0.1733 over MPC with 0.3246.

4.3.Analysis of nonlinear parameters on the FLC performance

The FLC implemented on MEC is tested on its capability to produce hydrogen gas with stable flowrateviamanipulation of internal parameters in the mathematical model.As the MEC is known to exhibit nonlinear traits due to these parameters,the robustness and versatility of the FLC has to be validated under these conditions.

In the first analysis,the initial concentration of anodophilic microorganisms (xa0) is between 275 mg·L-1and 1000 mg·L-1.Referring to Fig.15(a),the FLC implemented on the MEC shows stable output in the hydrogen gas output with these changes.

Next,the maximum growth rate of the hydrogenotrophic microorganism,(μmax,h) within the MEC model is altered between 0.5 d-1and 1.25 d-1.The outcome in Fig.15(b) further justify the robust control methodology of FLC with minimal deviation and stable actions in terms of the hydrogen gas produced as the output.

5.Conclusions

This work presents the novel implementation of FLC on a MEC system to produce biohydrogen.The production of hydrogen gasviaMEC exhibits nonlinear behaviour and the presence of various microbial interactions within the system further contributes to this high complexity.In efforts to ensure a well-controlled condition of hydrogen production at the desired output,a precise control system has to be implemented.The PID controller has been one of the pioneer and preferred selection of control system because of its simplicity of tuning.However,the nonlinear behaviour of MEC system poses a challenge to the PID controller as unforeseen disturbances affecting the system require retuning of controller.This then requires an advanced controller such as the FLC to control the hydrogen production of MEC.

In this study,FLC has been implemented onto the MEC system as the proposed advanced controller and its performance is evaluated against PI and PID controller.The FLC system is then tested progressively based on various control schemes.Evaluation is conducted through the comparison of their respective IAE,the overshoot and settling time of the process responses.The MEC system with FLC has generally produced outputs that are much more desirable over the conventional PI and PID controllers.This is as results of FLC having lower value of cumulative errors in the IAE evaluation.The FLC also demonstrated quicker response in the presence of disturbances.Such insensitivity to disturbance is attributed with shorter settling time ofQH2and the MEC system does not display considerable overshoot inQH2upon reaching its setpoint.The minimal overshoot and shorter settling time in the events of setpoint changes proves that the FLC has excellent capability in tracking assigned setpoint.

The performance of FLC is then assessed against another advanced control methodology,which is MPC.FLC is shown to have slight superiority over MPC in ensuring a well-controlled hydrogen production for the MEC.Analysis of the nonlinear effects were also conducted for the FLC upon implementation on the MEC to evaluate its versatility,where internal parameters of MEC mathematical model are varied to study how well can the FLC adapt to such changes.The outcome of the assessment depicts FLC giving acceptable and stable hydrogen production with minimal deviation from the designated setpoint.

One of the main advantages of FLC is its capability to express similar human-natured thinking in comparison to the Boolean logic.The configuration of a fuzzy logic algorithm is relatively simple without involving sophisticated mathematical modelling.The good control demonstrated by FLC upon implementation on MEC system could ensure a controlled flow rate of biohydrogen to be stored safely inside a storage tank.In this study,the prospect of FLC casts a new light to be implemented on other highly complex and nonlinear chemical processes for maintaining stability.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the UMRG RP006H-13ICT Project,University of Malaya,Malaysia.The computational facilities and technical guidance from the respective academic staffs from the Department of Chemical Engineering and Biomedical Engineering,Faculty of Engineering,University of Malaya are highly appreciated.