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The 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering, contains the papers presented at the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering joint event. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students, and consultants for chemical industries. - Presents findings and discussions from the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering joint event
32nd European Symposium on Computer Aided Process Engineering: ESCAPE-32 contains the papers presented at the 32nd European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Toulouse, France. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students and consultants for chemical industries who work in process development and design. - Presents findings and discussions from the 32nd European Symposium of Computer Aided Process Engineering (ESCAPE) event
This title introduces the underlying theory and demonstrates practical applications in different process industries using hybrid modeling. It reviews hybrid modeling approach applicability in wide range of process industries, recommends how to increase hybrid model performance and throw Insights into cost efficient practices in modeling techniques Discusses advance process operation maximizing the benefits of available process knowledge and Includes real-life and practical case studies
Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Applications and Case Studies includes information about the key applications of these new methods, as well as practitioners' accounts of real-life applications and case studies. Written by thought leaders in the field from around the world, Smart Manufacturing: Applications and Case Studies is essential reading for graduate students, researchers, process engineers and managers. It is complemented by a companion book titled Smart Manufacturing: Concepts and Methods, which describes smart manufacturing methods in detail. - Includes examples of applications of smart manufacturing in process industries - Provides a thorough overview of the subject and practical examples of applications through well researched case studies - Offers insights and accounts of first-hand experiences to motivate further implementations of the key concepts of smart manufacturing
Advances in Bioprocess Engineering, the latest release in the Current Developments in Biotechnology and Bioengineering series, provides a comprehensive overview of bioprocess systems, kinetics, bioreactor design, batch and continuous reactors and introduces key principles that enable bioprocess engineers to engage in analysis, optimization and design with consistent control over biological and chemical transformations. The bioprocessing sector is also updating its technologies with state-of-the art techniques to keep up with the rising demand of the industry and R&D. This book covers these aspects, taking readers through a step-by-step journey of bioprocessing while also guiding them towards a new era and future. - Covers state-of-the-art, technological advancements in the field of bioprocessing - Includes design and scale-up of bioreactors, monitoring and control systems, advances in upstream and downstream processing - Includes design and development of fermentation processes such as the suitability of experimental design, full factorial, central composite design, Box-Behnken, Plackett-Burman, and more
Industry 4.0 is revolutionizing chemical manufacturing. Today's chemical companies are swiftly embracing the digital era, recognizing the significant benefits of interconnected products, production equipment, and personnel. As technology advances and production volumes grow, there is an increasing need for new computational tools and innovative solutions to address everyday challenges. AI in Chemical Engineering: Unlocking the Power Within Data introduces readers to the essential concepts of machine learning and their application in the chemical and process industries, aiming to enhance efficiency, adaptability, and profitability. This work delves into the transformation of traditional plant operations into integrated and intelligent systems, providing readers with a foundation for developing and understanding the tools necessary for data collection and analysis, thereby gaining valuable insights and practical applications. Introduces the principles and applications of unsupervised learning and discusses the role of machine learning in extracting information from plant data and transforming it into knowledge Conveys the concepts, principles, and applications of supervised learning, setting the stage for developing advanced monitoring systems, complex predictive models, and advanced computer vision applications Explores implementation of reinforced learning ideas for chemical process control and optimization, investigating various model structures and discussing their practical implementation in both simulation and experimental units Incorporates sample code examples in Python to illustrate key concepts Includes real-life case studies in the context of chemical engineering and covers a wide variety of chemical engineering applications from oil and gas to bioengineering and electrochemistry Clearly defines types of problems in chemical engineering subject to AI solutions and relates them to subfields of AI This practical text, designed for advanced chemical engineering students and industry practitioners, introduces concepts and theories in a logical and sequential manner. It serves as an essential resource, helping readers understand both current and emerging developments in this important and evolving field.
Models and simulations are widely being used for design, optimization, fault detection and diagnosis, and various other decision-making purposes. Increasingly, models are developed at different scales and levels, all the way from molecular level to the large-scale process systems scale. Modelling of Chemical Process Systems gives readers a feel for the multiscale modelling. As models have been developed for various applications, a general systematic method for building model has emerged. This book starts with the history of modelling and its usefulness, describing modelling steps in detail. Examples have been chosen carefully from both conventional chemical process systems to contemporary systems, including fuel cell and bioprocesses. Modelling theories are complemented with case studies that explain step-by-step modelling methodologies. This book also introduces the application of machine learning techniques to model chemical process systems. This makes the book an indispensable reference for academics and professionals working in modelling and simulation. - Includes case studies that explain step-by-step modelling methodologies - Covers detailed multiscale modelling of chemical processes, providing examples from traditional and novel areas - Provides modelling insight at micro and macro-scale levels, including machine learning techniques
Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important
Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides the opportunity for hands-on experience. An important goal of the book is to help the student or practitioner learn and implement neural networks quickly and inexpensively using commercially available, PC-based software tools. Detailed network specifications and training procedures are included for all neural network examples discussed in the book.