Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/11097
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dc.contributor.authorVannarath, A.
dc.contributor.authorThalla, A.K.
dc.date.accessioned2020-03-31T08:30:47Z-
dc.date.available2020-03-31T08:30:47Z-
dc.date.issued2019
dc.identifier.citationEnvironment Systems and Decisions, 2019, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11097-
dc.description.abstractLignocellulosic 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.en_US
dc.titleEvaluation, ranking, and selection of pretreatment methods for the conversion of biomass to biogas using multi-criteria decision-making approachen_US
dc.typeArticleen_US
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