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Dr Karthik Rajendran, Assistant Professor from the Department of Environmental Science, has added another paper to his list of publications. His paper titled Towards green whiskey production: anaerobic digestion of distillery by-products and the effects of pretreatment has been published in the Journal of Cleaner Production (Q1 category) with an impact factor of 9.2.

Abstract of the research

Using renewable biogas from anaerobic digestion of distillery by-products as a low carbon heat source can decarbonise the distillery process and support the distillery industry for a transition to a more sustainable production process. The study investigated the anaerobic digestion performance of different types of whiskey by-products and the effects of acid pre-treatment on the digestion of solid by-products. Results of biomethane potential assays showed that the methane yield from the unprocessed by-products was 330 mL/g volatile solids (VS) from draff, 495 mL/g VS from thin stillage, and 503 mL/g VS from thick stillage. For the processed by-products, the specific methane yield was 370 mL/g VS from cake maize, 382 mL/g VS from wet distillers’ grains with solubles (WDGS), and 545 mL/g VS from syrup. Acid pre-treatment (1% H2SO4 at 135 ◦C for 15 min) did not significantly improve the methane yield from solid by-products (such as draff and WDGS) but reduced the digestion time by 54.5% for cake maize. The microbial community analysis revealed that methane production from the untreated and acid-pre-treated solid by-products (draff and WDGS) was mainly through the hydrogenotrophic methanogenesis pathway. The gross thermal energy in the form of methane produced from 100 tonnes of mixed unprocessed by-products (draff, thin stillage, and thick stillage) was calculated as 24.4 MWthh equivalents to 60.6% of the thermal energy consumed in whiskey production, which affected the same percentage of CO2 emissions reduction.

Explanation of the research

Many industries meet their energy demand based on the fossil fuels such as coal, oil, and natural gas, which increases carbon dioxide emissions. Alcohol production is one of the heavy fossil fuel using industries, especially in distillation. The waste after alcohol production can be used to produce methane, which can be used as energy in distillation, reducing the need for energy consumption. By consuming the waste and producing energy, up to 60% of thermal energy could be reduced. This also reduces the CO2 emission by 60%. Alcohol industries can use their waste to decarbonise the energy demand, thus meeting the net-zero. India is expected to reach net-zero by 2070, which will be a bigger addition as a part of it.

In this research, Dr Karthik Rajendran has collaborated with Professor Jerry Murphy, UCC, Ireland, and Dr Richen Lin, UCC, Ireland. Applying the similar concept in the Indian context is his future plan for this research.

 

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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.

 

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