Birla Institute of Technology and Science

About: Birla Institute of Technology and Science is a education organization based out in Pilāni, Rajasthan, India. It is known for research contribution in the topics: Computer science & Population. The organization has 8897 authors who have published 13947 publications receiving 170008 citations.

A bibliometric analysis of green manufacturing and similar frameworks

Abstract: Purpose – The purpose of this paper is to review the green manufacturing and similar frameworks in order to trace the origin, definitions, scope, similarities, differences, and publications of these manufacturing frameworks. Design/methodology/approach – A review of 113 research articles is conducted for various terms, namely, green manufacturing (GM); environmentally conscious manufacturing; environmentally responsible manufacturing; environmentally benign manufacturing; sustainable manufacturing; clean manufacturing; cleaner production; sustainable production with reference to triple bottom line, product life cycle engineering, systems approach, resource and energy efficiency, supply chain, pollution prevention and closed loop system/6R. Findings – It can be said with reasonable confidence that all these eight frameworks have been used interchangeably by researchers but it requires some standardization. It has been observed during literature review that to standardize the terminology researchers have to...

Study of electronic properties, stabilities and magnetic quenching of molybdenum-doped germanium clusters: a density functional investigation

Abstract: Evolution of electronic structures, properties and stabilities of neutral and cationic molybdenum encapsulated germanium clusters (Mo@Gen, n = 1 to 20) has been investigated using the linear combination of atomic orbital density functional theory method with effective core potential. From the variation of different thermodynamic and chemical parameters of the ground state clusters during the growth process, the stability and electronic structures of the clusters is explained. From the study of the distance-dependent nucleus-independent chemical shifts (NICS), we found that Mo@Ge12 with hexagonal prism-like structure is the most stable isomer and possesses strong aromatic character. Density of states (DOS) plots of different clusters is then discussed to explain the role of d-orbitals of the Mo atom in hybridization. Quenching of the magnetic moment of the Mo atom with increase in the size of the cluster is also discussed. Finally, the validity of the 18-electron counting rule is applied to further explain the stability of the metallo-inorganic magic cluster Mo@Ge12 and the possibility of Mo-based cluster-assembled materials is discussed.

Development and comparison of neural network based soft sensors for online estimation of cement clinker quality

Abstract: The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models.