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Applications Of Markov Chains In Chemical Engineering

Author: A. Tamir
Publisher: Elsevier
ISBN: 9780080527390
Size: 56.32 MB
Format: PDF
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Markov chains make it possible to predict the future state of a system from its present state ignoring its past history. Surprisingly, despite the widespread use of Markov chains in many areas of science and technology, their applications in chemical engineering have been relatively meager. A possible reason for this phenomenon might be that books containing material on this subject have been written in such a way that the simplicity of Markov chains has been shadowed by the tedious mathematical derivations. Thus, the major objective of writing this book has been to try to change this situation. There are many advantages, detailed in Chapter 1, of using the discrete Markov-chain model in chemical engineering. Probably, the most important advantage is that physical models can be presented in a unified description via state vector and a one-step transition probability matrix. Consequently, a process is demonstrated solely by the probability of a system to occupy or not occupy a state. The book has been written in an easy and understandable form, where complex mathematical derivations are abandoned. The fundamentals of Markov chains are presented in Chapter 2 with examples from the bible, art and real life problems. An extremely wide collection is given of examples viz., reactions, reactors, reactions and reactors as well as combined processes, including their solution and a graphical presentation of it, all of which demonstrates the usefulness of applying Markov chains in chemical engineering.

Chemical Engineering

Author: Tanase Gh. Dobre
Publisher: John Wiley & Sons
ISBN: 3527306072
Size: 25.92 MB
Format: PDF, Kindle
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A description of the use of computer aided modeling and simulation in the development, integration and optimization of industrial processes. The two authors elucidate the entire procedure step-by-step, from basic mathematical modeling to result interpretation and full-scale process performance analysis. They further demonstrate similitude comparisons of experimental results from different systems as a tool for broadening the applicability of the calculation methods. Throughout, the book adopts a very practical approach, addressing actual problems and projects likely to be encountered by the reader, as well as fundamentals and solution strategies for complex problems. It is thus equally useful for student and professional engineers and chemists involved in industrial process and production plant design, construction or upgrading.

Polystochastic Models In Chemical Engineering

Author: Octavian Iordache
Publisher: VSP
ISBN: 9789067640633
Size: 20.93 MB
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Polystochastic models describe systems whose mode of evolution varies randomly according to the rule given by a hierarchy of conditioning processes. The author discusses the models used for random flow, the dynamics of dispersed systems, real flow and turbulent mixing. Attention is focussed on the fundamental role played by polystochastic models in chemical engineering. The author also draws attention to other areas of application such as the optimization of stochastic systems, dynamic programming and decision sciences, adaptive systems, communications and biophysics.

Advances In Chemical Engineering

Author:
Publisher: Academic Press
ISBN: 9780080885216
Size: 69.38 MB
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Multi-scale analysis and simulation of chemical processing and reactions have received unprecedented attention in recent years; however, measurement technology focused on multi-scale structures, particularly on meso-scale phenomena, has not been sufficiently addressed. This volume of Advances in Chemical Engineering focuses on the "Characterization of Flow, Particles and Interfaces" to alert the chemical engineering community to this challenging issue presenting six meso-scale measurement technologies.

Modern Aspects Of Electrochemistry 39

Author: Constantinos G. Vayenas
Publisher: Springer Science & Business Media
ISBN: 0387317015
Size: 17.40 MB
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This volume of Modern Aspects covers a wide spread of topics presented in an authoritative, informative and instructive manner by some internationally renowned specialists. Professors Politzer and Dr. Murray provide a comprehensive description of the various theoretical treatments of solute-solvent interactions, including ion-solvent interactions. Both continuum and discrete molecular models for the solvent molecules are discussed, including Monte Carlo and molecular dynamics simulations. The advantages and drawbacks of the resulting models and computational approaches are discussed and the impressive progress made in predicting the properties of molecular and ionic solutions is surveyed. The fundamental and applied electrochemistry of the silicon/electrolyte interface is presented in an authoritative review by Dr. Gregory Zhang, with emphasis in the preparation of porous silicon, a material of significant technological interest, via anodic dissolution of monocrystalline Si. The chapter shows eloquently how fundamental electrokinetic principles can be utilized to obtain the desired product morphology. Markov chains theory provides a powerful tool for modeling several important processes in electrochemistry and electrochemical engineering, including electrode kinetics, anodic deposit formation and deposit dissolution processes, electrolyzer and electrochemical reactors performance and even reliability of warning devices and repair of failed cells. The way this can be done using the elegant Markov chains theory is described in lucid manner by Professor Thomas Fahidy in a concise chapter which gives to the reader only the absolutely necessary mathematics and is rich in practical examples.

Models Of Random Processes

Author: Igor N. Kovalenko
Publisher: CRC Press
ISBN: 9780849328701
Size: 35.54 MB
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Devising and investigating random processes that describe mathematical models of phenomena is a major aspect of probability theory applications. Stochastic methods have penetrated into an unimaginably wide scope of problems encountered by researchers who need stochastic methods to solve problems and further their studies. This handbook supplies the knowledge you need on the modern theory of random processes. Packed with methods, Models of Random Processes: A Handbook for Mathematicians and Engineers presents definitions and properties on such widespread processes as Poisson, Markov, semi-Markov, Gaussian, and branching processes, and on special processes such as cluster, self-exiting, double stochastic Poisson, Gauss-Poisson, and extremal processes occurring in a variety of different practical problems. The handbook is based on an axiomatic definition of probability space, with strict definitions and constructions of random processes. Emphasis is placed on the constructive definition of each class of random processes, so that a process is explicitly defined by a sequence of independent random variables and can easily be implemented into the modelling. Models of Random Processes: A Handbook for Mathematicians and Engineers will be useful to researchers, engineers, postgraduate students and teachers in the fields of mathematics, physics, engineering, operations research, system analysis, econometrics, and many others.

Bayesian Process Monitoring Control And Optimization

Author: Bianca M. Colosimo
Publisher: CRC Press
ISBN: 1420010700
Size: 59.61 MB
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Although there are many Bayesian statistical books that focus on biostatistics and economics, there are few that address the problems faced by engineers. Bayesian Process Monitoring, Control and Optimization resolves this need, showing you how to oversee, adjust, and optimize industrial processes. Bridging the gap between application and development, this reference adopts Bayesian approaches for actual industrial practices. Divided into four parts, it begins with an introduction that discusses inferential problems and presents modern methods in Bayesian computation. The next part explains statistical process control (SPC) and examines both univariate and multivariate process monitoring techniques. Subsequent chapters present Bayesian approaches that can be used for time series data analysis and process control. The contributors include material on the Kalman filter, radar detection, and discrete part manufacturing. The last part focuses on process optimization and illustrates the application of Bayesian regression to sequential optimization, the use of Bayesian techniques for the analysis of saturated designs, and the function of predictive distributions for optimization. Written by international contributors from academia and industry, Bayesian Process Monitoring, Control and Optimization provides up-to-date applications of Bayesian processes for industrial, mechanical, electrical, and quality engineers as well as applied statisticians.