National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

Photodegradation of methyl violet 6B and methylene blue using tin-oxide nanoparticles (synthesized via a green route)

Abstract: Green synthesis of tin dioxide nanoparticles were developed by microwave heating method using 1:1, 1:2 and 1:3 volumetric ratio of water and glycerol, wherein glycerol acts as a good complexing as well as capping agent. This method resulted in the formation of spherical SnO 2 nanoparticles with an average diameter ∼8–30 nm. The synthesized SnO 2 NPs were characterized by transmission electron microscopy (TEM), selected area electron diffraction (SAED) and Fourier transformed infrared spectroscopy (FT-IR). The optical properties were investigated using UV–vis spectroscopy. The photocatalytic activity of synthesized SnO 2 NPs was evaluated for the degradation of two different toxic dyes namely, Methyl Violet 6B and Methylene blue dye under direct sunlight.

A critical review of biogas production and usage with legislations framework across the globe

Abstract: This review showcases a comprehensive analysis of studies that highlight the different conversion procedures attempted across the globe. The resources of biogas production along with treatment methods are presented. The effect of different governing parameters like feedstock types, pretreatment approaches, process development, and yield to enhance the biogas productivity is highlighted. Biogas applications, for example, in heating, electricity production, and transportation with their global share based on national and international statistics are emphasized. Reviewing the world research progress in the past 10 years shows an increase of ~ 90% in biogas industry (120 GW in 2019 compared to 65 GW in 2010). Europe (e.g., in 2017) contributed to over 70% of the world biogas generation representing 64 TWh. Finally, different regulations that manage the biogas market are presented. Management of biogas market includes the processes of exploration, production, treatment, and environmental impact assessment, till the marketing and safe disposal of wastes associated with biogas handling. A brief overview of some safety rules and proposed policy based on the world regulations is provided. The effect of these regulations and policies on marketing and promoting biogas is highlighted for different countries. The results from such studies show that Europe has the highest promotion rate, while nowadays in China and India the consumption rate is maximum as a result of applying up-to-date policies and procedures.

A memory based differential evolution algorithm for unconstrained optimization

Abstract: This is a Flowchart of MBDE algorithm. A novel "Memory Based DE" algorithm proposed for unconstrained optimization.The algorithm relies on "swarm mutation" and "swarm crossover".Its robustness increased vastly with the help of the "Use of memory" mechanism.It obtains competitive performance with state-of-the-art methods.It has better convergence rate and better efficiency. In optimization, the performance of differential evolution (DE) and their hybrid versions exist in the literature is highly affected by the inappropriate choice of its operators like mutation and crossover. In general practice, during simulation DE does not employ any strategy of memorizing the so-far-best results obtained in the initial part of the previous generation. In this paper, a new "Memory based DE (MBDE)" presented where two "swarm operators" have been introduced. These operators based on the pBEST and gBEST mechanism of particle swarm optimization. The proposed MBDE is employed to solve 12 basic, 25 CEC 2005, and 30 CEC 2014 unconstrained benchmark functions. In order to further test its efficacy, five different test system of model order reduction (MOR) problem for single-input and single-output system are solved by MBDE. The results of MBDE are compared with state-of-the-art algorithms that also solved those problems. Numerical, statistical, and graphical analysis reveals the competency of the proposed MBDE.

Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach

Abstract: This article presents the feasibility of using support vector regression (SVR) technique to determine the fresh and hardened properties of self-compacting concrete. Two different kernel functions, namely exponential radial basis function (ERBF) and radial basis function (RBF), were used to develop the SVR model. An experimental database of 115 data samples was collected from different literatures to develop the SVR model. The data used in SVR model have been organized in the form of six input parameters that covers dosage of binder content, fly ash, water–powder ratio, fine aggregate, coarse aggregate and superplasticiser. The above-mentioned ingredients have been taken as input variables, whereas slump flow value, L-box ratio, V-funnel time and compressive strength have been considered as output variables. The obtained results indicate that the SVR–ERBF model outperforms SVR–RBF model for learning and predicting the experimental data with the highest value of the coefficient of correlation (R) equal to 0.965, 0.954, 0.979 and 0.9773 for slump flow, L-box ratio, V-funnel and compressive strength, respectively, with small values of statistical errors. Also, the efficiency of SVR model is compared to artificial neural network (ANN) and multivariable regression analysis (MVR). In addition, a sensitivity analysis was also carried out to determine the effects of various input parameters on output. This study indicates that SVR–ERBF model can be used as an alternative approach in predicting the properties of self-compacting concrete.