Manjunath Patel G. C.

This book explores the urgent quest for sustainable energy solutions by examining potential renewable energy sources that meet global demands. As fossil fuels deplete at an alarming rate, this book addresses the critical challenges in selecting sustainable feedstocks and optimizing processes for industrial-scale biodiesel production. With a focus on Garcinia-gummi-gutta seeds as a promising feedstock, the book provides a detailed analysis of oil extraction, biofuel conversion, and the practical application of biodiesel in diesel engines. Key concepts explored include selecting and optimizing transesterification variables, engine performance, and emission characteristics. The authors employ cutting-edge tools such as statistical design of experiments and artificial intelligence to offer insights into biodiesel production's physics, kinetics, and mechanics. Readers will discover experimental results, intelligent modeling techniques, and optimization strategies that enhance biodiesel yield and engine efficiency while minimizing emissions. This resource is designed for engineers and researchers in renewable energy and biofuel production. It offers a systematic framework from feedstock selection to engine optimization, making it invaluable for those seeking to advance their knowledge in sustainable energy solutions. Whether you're a novice or a seasoned professional, this book provides the tools and insights needed to drive innovation in biodiesel production at an industrial scale.

This book presents the potential applications of hard materials as well as the latest trends and challenges in machining hard materials. Models for online monitoring to adjust parameters to obtain desired machining characteristics (i.e. reverse modelling) are discussed in this book. The conflicting requirements (i.e. maximize: material removal rate, roundness and minimize: surface roughness, dimensional ovality, co axiality, tool wear) in machining for industry personal is solved using advanced optimization tools. In addition, the framework for experimental modelling, predictive physic-based forward and reverse process models and optimization for better machining characteristics applicable to industry are proposed.