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A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teaching-learning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem.

The ever-increasing population of India accompanied by the recent concerns regarding fossil fuel depletion and environmental pollution has made it indispensable to develop alternate mode of transportation. Electric vehicle (EV) market in India is expanding. For acceptance of EVs among the masses, development of charging infrastructure is of paramount importance. This paper formulates and solves the charging infrastructure-planning problem for Guwahati, India, that will develop as a smart city soon. The allocation of charging station problem was framed in a multi-objective framework considering the economic factors, power grid characteristics, such as voltage stability, reliability, power loss, as well as EV user's convenience, and random road traffic. The placement problem was solved by using a Pareto dominance-based hybrid algorithm amalgamating chicken swarm optimization (CSO) and the teaching learning-based optimization (TLBO) algorithm. Finally, the Pareto optimal solutions were compared by fuzzy decision-making.

Lignocellulosic biomass has great potential for biogas production, but there are various factors which affect the performance of lignocellulosic biomass. Among the various factors, temperature is one of the important factors which play a significant role in biogas production from lignocellulosic biomass. Biogas production was studied for bamboo dust, sawdust, sugarcane bagasse and rice straw, all separately mixed with cattle dung. The effect of temperature on biogas production from various lignocellulosic biomasses was studied for temperature range from 35°C to 55°C at steps of 5°C. The objective of this work is to develop a mathematical model for evaluating the effect of temperature on the rate of biogas production from various lignocellulosic biomasses. The new mathematical model is derived by modification of the modified Gompertz model. The new model is found to be suitable for lignocellulosic biomass mixed with cattle dung in the temperature range 35°C to 55°C. The resulting estimated biogas production is found to be highly correlated to the experimental data of present study.

Abstract  Lipase was immobilized in silk fibers through glutaraldehyde cross-linking to a maximum loading of 59 U/g silk-fiber and the immobilized lipase was utilized for the hydrolysis of sunflower oil (Helianthus annuus). The hydrolytic activity of the lipase, which was poor in biphasic oil in water system, was increased significantly when the sunflower oil was emulsified in aqueous medium. The hydrolytic activities of the immobilized lipase were 48.73 ± 1.26 U, 36.11 ± 0.96 U, and nil when the substrate sunflower oil was used as emulsion created by a rhamnolipid biosurfactant, Triton X100, and ultrasonication, respectively. Although the efficiency of the immobilized lipase was less than 12% than the corresponding free lipase, the immobilized lipase could be reused for the biosurfactant-mediated hydrolysis of sunflower oil up to third cycle of the reaction. The yield of the fatty acids in the second, third, and fourth cycles were 49.45%, 22.91%, and 5.09%, respectively, of the yield obtained in the first cycle. [ABSTRACT FROM AUTHOR]