Microbial fuel cells (MFCs) are biochemical systems producing green energy through the microbial degradation of organic contaminants in wastewater. The Oxygen Reduction Reaction (ORR) that takes place at MFC cathode decides the overall output of energy generation. Hence, the selection of ORR catalyst becomes pivotal in MFC fabrication for its efficiency and cost effectiveness. Gopa Nandikes P, PhD Scholar, Department of Environmental Science, proposes perovskite-based nanocatalyst as an excellent replacement to Platinum in his paper “Perovskite-Based Nanocomposite Electrocatalysts: An alternative to Platinum ORR Catalyst in Microbial Fuel Cell Cathodes”. The paper is published in ‘Energies Journal’ having an Impact Factor of 3.04.

The paper comprehensively summarises all the studies conducted with perovskite-based ORR catalyst in MFC, its unique reaction mechanism and the synergistic effect with carbon. The paper also throws light into various challenges and prospects to further improve the ORR activity of perovskite-based catalysts.

Abstract of the Research

Microbial fuel cells (MFCs) are biochemical systems having the benefit of producing green energy through the microbial degradation of organic contaminants in wastewater. The efficiency of MFCs largely depends on the cathode oxygen reduction reaction (ORR). A preferable ORR catalyst must have good oxygen reduction kinetics, high conductivity, and durability, together with cost-effectiveness. Platinum-based electrodes are considered a state-of-the-art ORR catalyst. However, the scarcity and higher cost of Pt are the main challenges for the commercialization of MFCs; therefore, in search of an alternative, cost- effective catalysts, those such as doped carbons and transition-metal based electrocatalysts have been researched for more than a decade. Recently, perovskite-oxide based nanocomposites have emerged as a potential ORR catalyst due to their versatile elemental composition, molecular mechanism, and the scope of nanoengineering for further developments. In this article, we discuss various studies conducted and opportunities associated with perovskite-based catalysts for ORR in MFCs. Special focus is given to a basic understanding of the ORR reaction mechanism through oxygen vacancy, modification of its microstructure by introducing alkaline earth metals, electron transfer pathways and the synergistic effect of perovskite and carbon. At the end, we also propose various challenges and prospects to further improve the ORR activity of perovskite-based catalysts.

Publishing a paper in the second-best journal in the discipline of Environmental Engineering and having an impact factor of 9.7 is obviously a significant achievement. The Department of Environmental Science is elated to inform you that the paper, “Dynamic Simulation and Optimization of Anaerobic Digestion Processes using MATLAB” has been published by Dr Karthik Rajendran, Assistant Professor of Environmental Science, and his PhD student, Mr Prabhakaran G in ‘Bioresource Technology’ journal.

Abstract of the research

Time series-based modelling provides a fundamental understanding of process fluctuations in an anaerobic digestion process. However, such models are scarce in literature. In this work, a dynamic model was developed based on modified Hill’s model using MATLAB, which can predict biomethane production with time series. This model can predict the biomethane production for both batch and continuous processes, across substrates and at diverse conditions such as total solids, loading rate, and days of operation. The deviation between the literature and the developed model was less than ±7.6%, which shows the accuracy and robustness of this model. Moreover, statistical analysis showed there was no significant difference between literature and simulation, verifying the null hypothesis. Finding a steady and optimized loading rate was necessary from an industrial perspective, which usually requires extensive experimental data. With the developed model, a stable and optimal methane yield generating loading rate could be identified at minimal input.

About the research

Anaerobic Digestion (AD) is a natural process that converts organic waste into biogas, in the absence of oxygen, which can be used as cooking fuel or for electricity generation. Biogas generation depends on various operational parameters of the AD processes like temperature, organic loading rate, and pH. For example, the speed of a car depends on various parameters like mileage per litre, type of fuel (petrol or diesel), engine power, type of gear, and road type. The optimum speed of a car can be defined by the manufacturer. Likewise, the optimum biogas/ biomethane can be calculated by computer simulations. If the loading rate is increased, the biogas yield increases up to a particular time and then decreases due to overloading like human bodies (eating a large amount of food may strain or cause failure of the digestive system), then the biogas plant will be a failure.

Optimising the loading rate through experiment was not easy, as multiple trials were necessary and it will take a longer time and high cost. In this work, the researchers did the optimisation based on the loading rate over the time period. The loading rate was optimised to maximum methane production, which also showed the region of stability from an operational perspective.

Practical implementations of the research

The practical implications of this work are, to use it in real-time operations of an AD plant and in research laboratories to estimate the best region of operation in terms of loading rate and yield. This work shows that longer days of operation could optimise better loading rates or could help in reaching a steady-state condition in real-time biogas plants.

Future research plans

Real-time biogas plants are deficient in the availability of data to do the computer simulation by using the mathematical model. To overcome this problem, researchers are planning to do Artificial Intelligence (Machine learning)- based biogas prediction by data-driven techniques. It will reduce the complexity with higher accuracy. In future, the machine learning model will integrate with real-time bioreactor for self-diagnosis and better decision making.