Performance evaluation of microbial fuel cell by artificial intelligence methods

Highlights

• Three artificial intelligence (AI) methods for modeling output voltage of microbial fuel cell (MFC) system is discussed.

• AI models efficiently establish the relationship between output voltage and input factors of MFC.

• Out of three methods, MGGP evolves a model with better generalization ability.

• MGGP shows excellent potential to predict performance of MFC and can be used to gain better insights into MFC system.

Abstract

In the present study, performance of microbial fuel cell (MFC) has been modeled using three potential artificial intelligence (AI) methods such as multi-gene genetic programming (MGGP), artificial neural network and support vector regression. The effect of two input factors namely, temperature and ferrous sulfate concentrations on the output voltage were studied independently during two operating conditions (before and after start-up) using the three AI models. The data is randomly divided into training and testing samples containing 80% and 20% sets respectively and then trained and tested by three AI models. Based on the input factor, the proposed AI models predict output voltage of MFC at two operating conditions. Out of three methods, the MGGP method not only evolve model with better generalization ability but also represents an explicit relationship between the output voltage and input factors of MFC. The models generated by MGGP approach have shown an excellent potential to predict the performance of MFC and can be used to gain better insights into the performance of MFC.

Introduction

Microbial fuel cell (MFC) is an electrochemical device that converts the chemical energy in organic wastes into electricity by means of catalytic activities (reactions) of living microorganisms (Wei, Yuan, Cui, Han, & Shen, 2012). The phenomenon of generation of electricity from the contaminants in wastewater could provide environmental and economic benefits. Researchers have conducted several studies on power generation from the organic wastes in water. MFC technology has promising benefits but its practical implementation is limited due to low power generation, low energy efficiency as well as high material costs involved. According to recent developments in microorganisms, electrodes, operating conditions, matrix, ionic strength, different substrates and electrochemical characteristics, it was found that both microorganism as well as a biological factors has a significant impact on the power generation and thus responsible for the successful implementation of MFC (Logan et al., 2006).

Researchers pointed out that number of factors influence the power generation in MFC. One such factor is temperature which directly influences growth and reproduction of microorganism thus affecting intracellular and extracellular chemical process. Experimental studies are often conducted to determine the effect of various factors that influence the performance of MFC. The effect of temperature and anode media on the performance of MFC was studied by Min, Roman, and Angelidaki (2008). The maximum power density estimated at temperatures 30 °C and 22 °C was found to be 70 mW/m² and 43 mW/m² respectively. It was also noticed that at 15 °C, there was no power generation. The reason for this behavior of MFC can be that the catalytic activity of microorganism could have become inactive at 15 °C. Similar behavior of MFC was observed by Zhang and Shen (2006). It can be observed from this study that the microbial activity became inactive at 50 °C. Enhanced performance of the MFC is observed in the temperature range of 25–45 °C. Wei et al. (2012) studied the effect of temperature and hydraulic retention on the performance of a two-chambered MFC. Their studies showed that the MFC exhibited a rapid start-up for high substrate concentrations and was able to generate maximum power for long period of time.

It is also important to note that though experiments can satisfactorily determine the influence of various factors on the performance characteristics of fuel cell, limitations exists on the factors of cost and time required to perform the experiments. To overcome this limitation, several studies focused on developing mathematical models for estimating the performance of MFC (Choi et al., 2003, Picioreanu et al., 2010, Pinto et al., 2010, Pinto et al., 2012, Sousa and Gonzalez, 2005, Yao et al., 2004). These mathematical models (Vijayaraghavan and Wong, 2013a, Wong and Vijayaraghavan, 2012b) prove to be an effective tool for investigating the influence of various factors on MFC performance in addition to being cost effective and less time consuming (Oliveira, Simões, Melo, & Pinto, 2013). The mathematical models are generally formulated using differential and algebraic equations (Vijayaraghavan and Wong, 2013b, Wong and Vijayaraghavan, 2012a) and their derivation is based on the different phenomenon taking place inside the MFC. Although, these models provide accurate prediction, the formulation of these models requires a thorough knowledge on the functionality and the configuration of the MFC system.

Application of soft computing methods such as genetic programming (GP), artificial intelligence (AI), fuzzy logic and neural networks can be used as an alternative method for modeling complex physical non-linear systems such as a fuel cell system. These methods require input training data which can be obtained from the experimental data that is based on a specific design and operating condition of a fuel cell. Based on the input, the soft computing method can then be able to generate meaningful solutions for complicated problems (Castelli et al., 2013, Garg et al., 2013a, Garg et al., 2013b, Nazari, 2012, Peteiro-Barral et al., 2013).

Additionally, among the various soft computing methods described above, GP offers the advantage of a fast and cost-effective explicit formulation of a mathematical model based on multiple variables without any pre-definition of non-linear structure of the model (Al-Sahaf et al., 2012, Giot and Rosenberger, 2012, Kala, 2012, Kovacic and Brezocnik, 2003, Mabu et al., 2013, Mabu et al., 2012, Milfelner et al., 2005, Tsakonas, 2013, Tsakonas and Gabrys, 2012). The analytical model hence obtained can then be used by fuel cell developers to optimize the performance of their fuel cell based on specific operating conditions. It is to the best of authors’ knowledge that limited or no work exists on the application of soft computing methods on the performance prediction of an MFC system. Hence, in the present study, the performance characteristics of a MFC is modeled using three soft computing techniques viz., multi-gene genetic programming (MGGP), support vector regression (SVR) and artificial neural network (ANN). These methods are applied to model the operating voltage of MFC based on two input parameters (temperature and ferrous sulfate concentrations) at two operating conditions such as before start-up (BS) and after start-up (AS). An explicit formulation is derived for the output voltage as a function of time and each input parameter (temperature and ferrous sulfate concentrations) for BS and AS operation of MFC. Additionally, the performance of MGGP method to those of SVR and ANN has been compared. The remainder of this paper is organized as follows. In Section 2, the experimental details of the MFC are discussed in brief. In Section 3, three AI methods namely the MGGP, SVR and ANN are discussed and applied to data collected in Section 2. In Section 4, the performance of three AI methods is compared. Finally, Section 5 concludes with the recommendations for future work.