Faculty Publications

Now showing 1 - 4 of 4

Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor
(Elsevier Ltd, 2016) Nair, V.V.; Dhar, H.; Kumar, S.; Thalla, A.K.; Mukherjee, S.; Wong, J.W.C.
The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65 L CH4/kg VS added. A target of 60–70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment. © 2016 Elsevier Ltd
Life cycle assessment of municipal solid waste management options for India
(Elsevier Ltd, 2019) Khandelwal, H.; Thalla, A.K.; Kumar, S.; Kumar, R.
Life Cycle Assessment (LCA) tool can be used for environmental assessment of Municipal Solid Waste Management (MSWM) system. The present study aims to evaluate the impact of MSWM system in Nagpur city, India under four different scenarios. i.e., composting combined with landfilling (S1), material recovery facility (MRF) & composting combined with landfilling (S2), MRF & anaerobic digestion (AD) combined with landfilling (S3) and MRF, AD & composting combined with landfilling (S4) using LCA tool. The sensitivity analysis was also performed for evaluating the influence of recycling rate of valuable resources in all the considered scenarios. The scenarios were compared using Gabi 8.5.0.79 model and CML-1A impact characterization method. S2 was found to have the least environmental impacts on global warming, human toxicity, eutrophication, and photochemical ozone creation potential categories. The sensitivity analysis indicated an inversely proportional relation between change in recycling rate and total environmental burdens. © 2019 Elsevier Ltd
Evaluation, ranking, and selection of pretreatment methods for the conversion of biomass to biogas using multi-criteria decision-making approach
(Springer, 2020) Vannarath, A.; Thalla, A.K.
Lignocellulosic biomass resources include agri-waste and agri-biomass which are utilized as a suitable feedstock for bioenergy production. The recalcitrant nature of these biomass can be reduced by the application of various pretreatment methods to access the cellulosic content. This study depicts the evaluation and ranking of different pretreatment methods, and selecting the rank 1 as the best pretreatment method using multiple attribute decision-making approach to facilitate the increased biogas yield. The evaluation was done using technique for order preference by similarity to ideal solution (TOPSIS) and integrated design of experiments (DoE)–TOPSIS. Seven alternatives with five relevant attributes were adopted for this study. Based on the above decision-making framework, alkaline pretreatment (Ca(OH)2 (8%)) option was ranked first for both the techniques. The second and third options were NaOH and NH3.H2O (10%) pretreatment, respectively. The integrated DoE–TOPSIS method has reduced the uncertainty in results by considering different weight sets and replications. The model results and experimental results were in good agreement and portray the best pretreatment method to be employed in the anaerobic digestion, thus, minimizing the series of digestion test during the downstream process of pretreatment aided anaerobic digestion. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Effects of chemical pretreatments on material solubilization of Areca catechu L. husk: Digestion, biodegradability, and kinetic studies for biogas yield
(Academic Press, 2022) Vannarath, A.; Thalla, A.K.
This study aimed to understand the pretreatment-aided anaerobic digestion of lignocellulosic residues and to assess the substrate solubilization capacity of pretreatment processes. We evaluated the feasibility of biogas production using chemically pretreated Areca catechu L. (Arecanut husk, AH). AH was pretreated for 24h at two different temperatures—25 °C and 90 °C with four different chemicals viz. H2SO4 (acidic), NaOH (alkaline), H2O2 (oxidative), and ethanol in 1% H2SO4 (organosolv) under each temperature. AH solubilization assessment included analyses of parameters such as volatile solids to total solids (VS:TS) ratio, soluble chemical oxygen demand, total phenolic content, and biomass composition. Alkaline pretreatment of AH at 90 °C resulted in the maximum biogas yield of 683.89mL/gVS, which was 2.3 times more than that obtained using raw AH without pretreatment. Methane content of biogas produced using AH pretreated with 2–10% of NaOH was found to be between 71.53% and 75.06%; methane content of biogas using raw AH was 62.31%. In order to describe the AH degradation patterns, biogas production potential from pretreated AH was evaluated using bacterial kinetic growth models (First-order exponential, logistic, transference, and modified Gompertz models). The modified Gompertz and logistic models (correlation coefficient >0.99) were found to have the best fit of all kinetic models for the cumulative experimental biogas curve. We formulated a multiple linear regression equation depicting the biodegradability index (BI) as a technical tool to determine biomethane production; BI is represented as a function of biomass composition (cellulose, hemicellulose, and lignin), with a high correlation (>0.95). Based on our analyses of AH pretreatment and substrate utilization for biogas production, we propose that the biochemical composition of lignocellulosic residues should be carefully considered to ensure their biodegradability when subjected to anaerobic digestion. © 2022 Elsevier Ltd

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