Data Analytics For Process Engineers

This book provides an industry-oriented data analytics approach for process engineers, including data acquisition methods and sources, exploratory data analysis and sensitivity analysis, data-based modelling for prediction, data-based modelling for monitoring and control, and data-based optimization of processes. While many of the current data analytics books target business-related problems, the rationale for this book is a specific need to understand and select applicable data analytics approaches pragmatically to analyze process engineering–related problems; this tailored solution for engineers gets amalgamated with governing equations, and in several cases, with the physical understanding of the phenomenon being analyzed. We also consider this book strategically conceived to help map Education 4.0 with Industry 4.0 since it can support undergraduate and graduate students to gain valuable and applicable data analytics stills that can be further used in their workplace. Moreover, it can be used as a reference book for professionals, a quick reference to data analytics tools that can facilitate and/or optimize their process engineering tasks.

This is a convenient, one-volume reference that provides process engineers with quick information on the major equipment, processes and materials used in chemical, food, water/wastewater, fuel and other types of process engineering. The data is presented in short articles, supplemented and illustrated by tables, diagrams, charts and formulas. The data is organized in twenty short chapters with a detailed index for easy reference. Much of the data is economically presented in tables.

"Reading the book, you can feel the long practical experience of the author. The text is easy to read, even where concepts can be complex. The strong theoretical background of the author is well known from other publications. In this book, however, the topics are presented on a level that every engineer and scientist in the chemical industry and process industry should know and can understand... This book would have been very helpful at the beginning of my career to close the addressed gap. Therefore, I can strongly recommend it not only to all students close to their degree, but also to engineers and scientists just starting their industrial career in the related industrial sectors that are subsumed under the term process industry (chemical or petrochemical industry, pharmaceutical industry, food industry, biochemical industry, environmental technology, etc.). The book is like an investment. Doing a better job and getting a better job evaluation might pay for the book ..." Prof. Dr.-Ing. Claus Fleischer, Frankfurt University of Applied Sciences Process Engineering is based on almost 30 years of practical experience of the author in process simulation, design and development. The book is a missing link between students and practitioners. The author has coached many graduates in their first months and knows what the typical questions are. Coming from the university, graduates often do not know which relevance their knowledge has and how to apply it in real life, whereas established practitioners often stick to the narrow way of their experience, forgetting that science continuously makes progress. There is a gap to be bridged. From his own professional experience, the author covers many topics of the process engineering business, but three guest contributions are a valuable supplement to the content of the third edition. Already in the 2nd edition, Verena Haas from BASF SE wrote an excellent chapter on dynamic process simulation. For the new 3rd edition, Gökce Adali and Michael Benje added two chapters on digitalization and patents, respectively. Preparing the reader for the everyday business!

Advanced Data Analysis and Modeling in Chemical Engineering provides the mathematical foundations of different areas of chemical engineering and describes typical applications. The book presents the key areas of chemical engineering, their mathematical foundations, and corresponding modeling techniques. Modern industrial production is based on solid scientific methods, many of which are part of chemical engineering. To produce new substances or materials, engineers must devise special reactors and procedures, while also observing stringent safety requirements and striving to optimize the efficiency jointly in economic and ecological terms. In chemical engineering, mathematical methods are considered to be driving forces of many innovations in material design and process development. Presents the main mathematical problems and models of chemical engineering and provides the reader with contemporary methods and tools to solve them Summarizes in a clear and straightforward way, the contemporary trends in the interaction between mathematics and chemical engineering vital to chemical engineers in their daily work Includes classical analytical methods, computational methods, and methods of symbolic computation Covers the latest cutting edge computational methods, like symbolic computational methods

Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies. Features: Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution

In this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time. Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results. From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing. Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in your company’s data. The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries.

Imagine if your process manufacturing plants were running so well that your production, safety, environmental, and profitability targets were being met so that your subject matter experts could focus on data-driven business improvements. Through proper use and analysis of your existing operations data, your company can become an industry leader and reward your stakeholders. Written in an engaging and easily understandable manner, this book demonstrates a step-by-step process of how an organization can effectively utilize technology and make the necessary culture changes to achieve operational excellence. You will see how several industry-leading companies have used an effective real-time data infrastructure for mission-critical business use cases. The book also addresses challenges involved, such as effectively integrating operational (OT) data with business (IT) systems to enable a more proactive, predictive management model for a fleet of process plants. Some of the things you will take away: Learn how a real-time data infrastructure enables transformation of raw sensor data into contextualized information for operational insights and business process improvement. Understand how reusing the same operational data for multiple use cases significantly impacts fleet management, profitability, and asset stewardship. See how a simple digital unit template representing production flows can be repeatedly used to identify critical inefficiencies in plant operations. Discover best practices of deploying real-time situational awareness alerts and predictive analytics. Realize how to transform your organization into a data-driven culture for continuous sustainable improvement. Find out how leading companies integrate operations data with business intelligence and predictive analytics tools in a corporate on-premises or cloud-enabled environment. Learn how industry-leading companies have imaginatively used a real-time data infrastructure to improve yields, reduce cycle times, and slash operating costs. This book is targeted for process industries production and operations leadership, senior engineers, IT management, CIOs, and service providers to those industries. Academics will benefit from latest data analysis strategies. This book guides readers to use the best, results-proven approaches to ensure operational excellence.

The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field.