Complete and practical guidance on using biodegradable feedstocks for biodiesel production Feedstocks for Sustainable Biodiesel Production: Characterization, Selection, and Optimization helps readers understand the advantages, challenges, and potential of different biodegradable feedstock options that can be used in biodiesel production, covering methods of feedstock sourcing extraction, environmental concerns, cost-benefit aspects, practical applications, and more. Specific biodegradable feedstocks covered in this text include chrysobalamus icaco, cussonia bateri, elaeis guineensis, waste cooking oils, moringa oleifera, jatropha curcas, chlorophyceae (unicellular green algae), fucus vesiculosus (micro algae), afzelia africana, cucurbita pepo, hura crepitans, cuyperus esculentus, colocynthus vulgaris, and others. This book explores topics such as: Key characteristics of biodiesel, using biodiesel as an alternative to petroleum diesel, and a review of the latest industry standards, practices, and trends Basis of the selection of specific (including nonedible) feedstocks for different applications and the addition of new, innovative feedstocks in recent years Specific sustainability benefits of nonedible feedstocks, which can be grown on abandoned land where they do not compete with food crops Government policies aimed at finding fossil fuel alternatives which will increase biodegradable feedstock adoption Experimental and predictive modeling of biodiesel produced from novel feedstocks using computational intelligence techniques Providing both core foundational knowledge on the subject as well as insight on how to practically transition away from fossil fuels, this book is an essential reference for engineering professionals with a specific interest in biodiesel production, sustainability, renewable energy, and environmental conservation.
This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
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