Neural Networks In Bioprocessing And Chemical Engineering

Author: D. R. Baughman
Editor: Academic Press
ISBN: 1483295656
Size: 18,29 MB
Format: PDF, ePub, Docs
Read: 586

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. Each chapter contains an introduction, chapter summary, references to further reading, practice problems, and a section on nomenclature Includes a PC-compatible disk containing input data files for examples, case studies, and practice problems Presents 10 detailed case studies Contains an extensive glossary, explaining terminology used in neural network applications in science and engineering Provides examples, problems, and ten detailed case studies of neural computing applications, including: Process fault-diagnosis of a chemical reactor Leonard Kramer fault-classification problem Process fault-diagnosis for an unsteady-state continuous stirred-tank reactor system Classification of protein secondary-structure categories Quantitative prediction and regression analysis of complex chemical kinetics Software-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessing Quality control and optimization of an autoclave curing process for manufacturing composite materials Predictive modeling of an experimental batch fermentation process Supervisory control of the Tennessee Eastman plantwide control problem Predictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems

Encyclopaedia Of Neural Networks In Bioprocessing And Chemical Engineering 4 Volumes

ISBN: 9781781545393
Size: 16,63 MB
Format: PDF, Mobi
Read: 304

Refinery Engineering

Author: Ai-Fu Chang
Editor: John Wiley & Sons
ISBN: 3527666850
Size: 11,52 MB
Format: PDF, Docs
Read: 716

A pioneering and comprehensive introduction to the complex subject of integrated refinery process simulation, using many of the tools and techniques currently employed in modern refineries. Adopting a systematic and practical approach, the authors include the theory, case studies and hands-on workshops, explaining how to work with real data. As a result, senior-level undergraduate and graduate students, as well as industrial engineers learn how to develop and use the latest computer models for the predictive modeling and optimization of integrated refinery processes. Additional material is available online providing relevant spreadsheets and simulation files for all the models and examples presented in the book.

Step Growth Polymerization Process Modeling And Product Design

Author: Kevin Seavey
Editor: John Wiley & Sons
ISBN: 9780470292495
Size: 18,40 MB
Format: PDF, Kindle
Read: 674

Understand quantitative model step-growth polymerization plans and how to predict properties of the product polymer with the essential information in Step-Growth Polymerization Process Modeling and Product Design. If you want to learn how to simulate step-growth polymerization processes using commercial software and seek an in-depth, quantitative understanding of how to develop, use, and deploy these simulations, consult this must-have guide. The book focuses on quantitative relationships between key process input variables (KPIVs) and key process output variables (KPOVs), and the integrated modeling of an entire polymer manufacturing train.

Artificial Intelligence In Chemical Engineering

Author: Thomas E. Quantrille
Editor: Elsevier
ISBN: 0080571212
Size: 13,42 MB
Format: PDF, Docs
Read: 677

Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering. Allows the reader to learn AI quickly using inexpensive personal computers Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions Includes a computer diskette for an illustrated case study Demonstrates an expert system for separation synthesis (EXSEP) Presents a detailed review of published literature on expert systems and neural networks in chemical engineering

Application Of Neural Networks And Other Learning Technologies In Process Engineering

Author: Mohamed Azlan Hussain
Editor: World Scientific
ISBN: 1848161468
Size: 17,70 MB
Format: PDF, ePub, Docs
Read: 390

This book is a follow-up to the IChemE symposium on OC Neural Networks and Other Learning TechnologiesOCO, held at Imperial College, UK, in May 1999. The interest shown by the participants, especially those from the industry, has been instrumental in producing the book. The papers have been written by contributors of the symposium and experts in this field from around the world. They present all the important aspects of neural network utilisation as well as show the versatility of neural networks in various aspects of process engineering problems OCo modelling, estimation, control, optimisation and industrial applications. Contents: Modelling and Identification; Hybrid Schemes; Estimations and Control; New Learning Technologies; Experimental and Industrial Applications. Readership: Academic and industrial researchers, chemical engineers and control engineers."

22nd European Symposium On Computer Aided Process Engineering

Editor: Elsevier
ISBN: 0444594566
Size: 12,49 MB
Format: PDF, Kindle
Read: 191

Computer aided process engineering (CAPE) plays a key design and operations role in the process industries. This conference features presentations by CAPE specialists and addresses strategic planning, supply chain issues and the increasingly important area of sustainability audits. Experts collectively highlight the need for CAPE practitioners to embrace the three components of sustainable development: environmental, social and economic progress and the role of systematic and sophisticated CAPE tools in delivering these goals. Contributions from the international community of researchers and engineers using computing-based methods in process engineering Review of the latest developments in process systems engineering Emphasis on a systems approach in tackling industrial and societal grand challenges

Process Modelling And Simulation In Chemical Biochemical And Environmental Engineering

Author: Ashok Kumar Verma
Editor: CRC Press
ISBN: 1482205939
Size: 15,76 MB
Format: PDF, Docs
Read: 177

The use of simulation plays a vital part in developing an integrated approach to process design. By helping save time and money before the actual trial of a concept, this practice can assist with troubleshooting, design, control, revamping, and more. Process Modelling and Simulation in Chemical, Biochemical and Environmental Engineering explores effective modeling and simulation approaches for solving equations. Using a systematic treatment of model development and simulation studies for chemical, biochemical, and environmental processes, this book explains the simplification of a complicated process at various levels with the help of a "model sketch." It introduces several types of models, examines how they are developed, and provides examples from a wide range of applications. This includes the simple models based on simple laws such as Fick’s law, models that consist of generalized equations such as equations of motion, discrete-event models and stochastic models (which consider at least one variable as a discrete variable), and models based on population balance. Divided into 11 chapters, this book: Presents a systematic approach of model development in view of the simulation need Includes modeling techniques to model hydrodynamics, mass and heat transfer, and reactors for single as well as multi-phase systems Provides stochastic and population balance models Covers the application and development of artificial neural network models and hybrid ANN models Highlights gradients based techniques as well as statistical techniques for model validation and sensitivity analysis Contains examples on development of analytical, stochastic, numerical, and ANN-based models and simulation studies using them Illustrates modeling concepts with a wide spectrum of classical as well as recent research papers Process Modelling and Simulation in Chemical, Biochemical and Environmental Engineering includes recent trends in modeling and simulation, e.g. artificial neural network (ANN)-based models, and hybrid models. It contains a chapter on flowsheeting and batch processes using commercial/open source software for simulation.

Artificial Intelligence In Process Engineering

Author: Michael Mavrovouniotis
Editor: Elsevier
ISBN: 0323153143
Size: 12,83 MB
Format: PDF, Kindle
Read: 393

Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers.