Encyclopaedia Of Advanced Data Analysis And Modelling In Chemical Engineering

Encyclopaedia of Advanced Data Analysis and Modelling in Chemical Engineering

Applying Multiple-Reaction Stoichiometry to Chemical Reactor Modelling

This book delves into the realm of Chemical Reaction Engineering (CRE) by showcasing the practical application of multiple-reaction stoichiometry. The authors critically assess various approaches commonly taught in undergraduate CRE courses to establish the relationships between changes in chemical species. In doing so, they propose an innovative conceptual alternative that is specifically tailored for undergraduate lectures. The book carefully selects composition measures that effectively harness the power of stoichiometric relationships in elementary reacting systems and models, which are typically covered in these courses. Going beyond the basics, it also offers a profound discussion on the value of chemical stoichiometry for tackling more intricate reaction systems and detailed models. Moreover, the book presents a simplified procedure that minimizes the reliance on complex linear algebra techniques, making the book accessible to a wider range of readers.

Encyclopedia of Data Science and Machine Learning

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.