Indian Institute of Technology Guwahati

About: Indian Institute of Technology Guwahati is a education organization based out in Guwahati, Assam, India. It is known for research contribution in the topics: Adsorption & Catalysis. The organization has 6933 authors who have published 17102 publications receiving 257351 citations.

A review on the upgradation techniques of pyrolysis oil

Abstract: This paper provides insights on the properties of bio-oil obtained from various feedstock and several available mechanisms for bio-oil upgrading. A comprehensive detail on the catalytic cracking, hydrodeoxygenation, esterification, supercritical extraction and steam reforming processes are reviewed. Each upgrading technique has both advantages and disadvantages. The hydrodeoxygenation which has the major breakthrough in the bio-oil upgrading, even though there is no specific inference on the process chemistry in addition to various unfavorable conditions, this process holds good in reducing the oxygen contents of raw bio-oil. Various types of catalysts, their limitations along with the advantages, the use of novel catalysts and catalyst deactivation due to char formation at specified conditions among different upgrading techniques are presented. From this review, it was found that there is no specific catalyst for the specific compound upgrading. There is no specific reaction pathways defined for the processing of bio-oil. All the reviews and researches so far are constrained to the individual compounds of bio oil rather than as a whole. Some key points which are to be addressed for established process of bio-oil upgrading may include finding multifunctional catalysts which can improve the bio-oil properties suitable to blend with existing transportation fossil fuels or to directly use as transportation fuels, studies at pilot plant level, establishment of design concepts as in existing petroleum refineries, and numerical approaches to find various possible reaction pathways leading to upgraded bio-oil.

A 'green' chitosan silver nanoparticle composite as a heterogeneous as well as micro-heterogeneous catalyst

Abstract: In this paper, we report on the catalytic activity of a new metal nanoparticle–polymer composite consisting of Ag nanoparticles (NPs) and environmentally friendly ('green') chitosan. The polymer (chitosan) not only acted as the reducing agent for the metal ions, but also stabilized the product NPs by anchoring them. The majority of the particles produced in this way had sizes less than 5 nm. The catalytic activity of the composite was investigated photometrically by monitoring the reduction of 4-nitrophenol (4NP) in the presence of excess NaBH4 in water, under both heterogeneous and micro-heterogeneous conditions. The reaction was first order with respect to the concentration of 4NP. We also observed that the apparent rate constant, kapp, for the reaction was linearly dependent on the amount of Ag NPs present in the composite. Moreover, the turn-over frequency (TOF) of the catalyst was found to be (1.5 ± 0.3) × 10−3 s−1, when the reaction was carried out under heterogeneous conditions. The Ag NPs in the composite retained their catalytic activities even after using them for ten cycles. Our observations also suggest that the catalytic efficiency under micro-heterogeneous conditions is much higher than under heterogeneous conditions. Thus the composite we have represents an ideal case of an environmentally friendly and stable catalyst, which works under heterogeneous as well as micro-heterogeneous conditions with the advantage of nanoscopic particles as the catalyst.

Pre-treatment and anaerobic digestion of food waste for high rate methane production – A review

Abstract: Food waste with high decomposition potential can be successfully digested anaerobically for the production of biogas. As the fossil-fuel reserves decline anaerobic digestion can be a better alternative as a renewable energy source. The byproducts such as biogas with 50–60% methane content can be efficiently used for electricity production and the final digested sludge as a fertilizer. Even though anaerobic digestion is a proven technology, still there exist some technical difficulties (organic loading rate, solid retention time, biogas composition, specific gas production) and scientific understandings (carbon to nitrogen ratio, volatile fatty acids production, pH variation, nutrient concentration) in operating reactors for solid organic wastes. First the paper gives an overview of certain fundamental aspects of anaerobic digestion considered important for the digestion of food waste and its biochemical reactions. Then it describes food waste as the substrate for anaerobic digestion and its optimal conditions for the increased activity of biogas production. Finally it has been reviewed about the performance of the different pre-treatment methods and anaerobic reactor configurations in the digestion of food waste for increasing methane content in the biogas.

A novel approach for ANFIS modelling based on full factorial design

Abstract: Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input-output data pairs of the system under consideration. The size of the input-output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper, we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data and a data set collected from a thermal power plant of the North Eastern Electric Power Corporation (NEEPCO) Limited. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced and thereby computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.