Stochastic Modelling in Process Technology

Produk Detail:
  • Author : Herold G. Dehling
  • Publisher : Elsevier
  • Pages : 290 pages
  • ISBN : 9780080548975
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Stochastic Modelling in Process Technology

Download or Read online Stochastic Modelling in Process Technology full in PDF, ePub and kindle. this book written by Herold G. Dehling and published by Elsevier which was released on 03 July 2007 with total page 290 pages. We cannot guarantee that Stochastic Modelling in Process Technology book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent systems, and high-intensity processes displaying a highly complex behaviour are becoming omnipresent in the processing industry. This book discusses an elegant, but little-known technique for formulating process models in process technology: stochastic process modelling. The technique is based on computing the probability distribution for a single particle's position in the process vessel, and/or the particle's properties, as a function of time, rather than - as is traditionally done - basing the model on the formulation and solution of differential conservation equations. Using this technique can greatly simplify the formulation of a model, and even make modelling possible for processes so complex that the traditional method is impracticable. Stochastic modelling has sporadically been used in various branches of process technology under various names and guises. This book gives, as the first, an overview of this work, and shows how these techniques are similar in nature, and make use of the same basic mathematical tools and techniques. The book also demonstrates how stochastic modelling may be implemented by describing example cases, and shows how a stochastic model may be formulated for a case, which cannot be described by formulating and solving differential balance equations. Introduction to stochastic process modelling as an alternative modelling technique Shows how stochastic modelling may be succesful where the traditional technique fails Overview of stochastic modelling in process technology in the research literature Illustration of the principle by a wide range of practical examples In-depth and self-contained discussions Points the way to both mathematical and technological research in a new, rewarding field

Stochastic Modelling in Process Technology

Stochastic Modelling in Process Technology
  • Author : Herold G. Dehling,Timo Gottschalk,Alex C. Hoffmann
  • Publisher : Elsevier
  • Release : 03 July 2007
GET THIS BOOK Stochastic Modelling in Process Technology

There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent systems, and high-intensity processes displaying a highly complex behaviour are becoming omnipresent in the processing industry. This book discusses an elegant, but little-known technique for formulating process models in process technology: stochastic process modelling. The technique

Stochastic Models Statistics and Their Applications

Stochastic Models  Statistics and Their Applications
  • Author : Ansgar Steland,Ewaryst Rafajłowicz,Krzysztof Szajowski
  • Publisher : Springer
  • Release : 04 February 2015
GET THIS BOOK Stochastic Models Statistics and Their Applications

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of

Markov Processes for Stochastic Modeling

Markov Processes for Stochastic Modeling
  • Author : Oliver Ibe
  • Publisher : Newnes
  • Release : 22 May 2013
GET THIS BOOK Markov Processes for Stochastic Modeling

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas

Stochastic Models in Reliability and Maintenance

Stochastic Models in Reliability and Maintenance
  • Author : Shunji Osaki
  • Publisher : Springer Science & Business Media
  • Release : 02 November 2012
GET THIS BOOK Stochastic Models in Reliability and Maintenance

Our daily lives can be maintained by the high-technology systems. Computer systems are typical examples of such systems. We can enjoy our modern lives by using many computer systems. Much more importantly, we have to maintain such systems without failure, but cannot predict when such systems will fail and how to fix such systems without delay. A stochastic process is a set of outcomes of a random experiment indexed by time, and is one of the key tools needed to

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
  • Author : Mark Pinsky,Samuel Karlin
  • Publisher : Academic Press
  • Release : 30 July 2021
GET THIS BOOK An Introduction to Stochastic Modeling

Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Fourth Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of

Stochastic Modelling for Systems Biology Third Edition

Stochastic Modelling for Systems Biology  Third Edition
  • Author : Darren J. Wilkinson
  • Publisher : CRC Press
  • Release : 07 December 2018
GET THIS BOOK Stochastic Modelling for Systems Biology Third Edition

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been

GPS Stochastic Modelling

GPS Stochastic Modelling
  • Author : Xiaoguang Luo
  • Publisher : Springer Science & Business Media
  • Release : 06 July 2013
GET THIS BOOK GPS Stochastic Modelling

Global Navigation Satellite Systems (GNSS), such as GPS, have become an efficient, reliable and standard tool for a wide range of applications. However, when processing GNSS data, the stochastic model characterising the precision of observations and the correlations between them is usually simplified and incomplete, leading to overly optimistic accuracy estimates. This work extends the stochastic model using signal-to-noise ratio (SNR) measurements and time series analysis of observation residuals. The proposed SNR-based observation weighting model significantly improves the results of

An Introduction to Continuous Time Stochastic Processes

An Introduction to Continuous Time Stochastic Processes
  • Author : Vincenzo Capasso,David Bakstein
  • Publisher : Birkhäuser
  • Release : 29 May 2015
GET THIS BOOK An Introduction to Continuous Time Stochastic Processes

This textbook, now in its third edition, offers a rigorous and self-contained introduction to the theory of continuous-time stochastic processes, stochastic integrals, and stochastic differential equations. Expertly balancing theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required. Key topics include: Markov processes Stochastic differential equations Arbitrage-free markets and financial derivatives Insurance risk Population dynamics, and epidemics Agent-based models

Generalized Stochastic Processes

Generalized Stochastic Processes
  • Author : Stefan Schäffler
  • Publisher : Birkhäuser
  • Release : 03 July 2018
GET THIS BOOK Generalized Stochastic Processes

This textbook shall serve a double purpose: first of all, it is a book about generalized stochastic processes, a very important but highly neglected part of probability theory which plays an outstanding role in noise modelling. Secondly, this textbook is a guide to noise modelling for mathematicians and engineers to foster the interdisciplinary discussion between mathematicians (to provide effective noise models) and engineers (to be familiar with the mathematical backround of noise modelling in order to handle noise models in

Stochastic Methods in Fluid Mechanics

Stochastic Methods in Fluid Mechanics
  • Author : Sergio Chibbaro,Jean-Pierre Minier
  • Publisher : Springer Science & Business Media
  • Release : 05 September 2013
GET THIS BOOK Stochastic Methods in Fluid Mechanics

Since their first introduction in natural sciences through the work of Einstein on Brownian motion in 1905 and further works, in particular by Langevin, Smoluchowski and others, stochastic processes have been used in several areas of science and technology. For example, they have been applied in chemical studies, or in fluid turbulence and for combustion and reactive flows. The articles in this book provide a general and unified framework in which stochastic processes are presented as modeling tools for various issues

Stochastic Models In Engineering Technology And Management Proceedings Of The Australia japan Workshop

Stochastic Models In Engineering  Technology And Management   Proceedings Of The Australia japan Workshop
  • Author : Osaki Shunji,Murthy D N Pra
  • Publisher : World Scientific
  • Release : 27 April 1993
GET THIS BOOK Stochastic Models In Engineering Technology And Management Proceedings Of The Australia japan Workshop

Chinese Remainder Theorem, CRT, is one of the jewels of mathematics. It is a perfect combination of beauty and utility or, in the words of Horace, omne tulit punctum qui miscuit utile dulci. Known already for ages, CRT continues to present itself in new contexts and open vistas for new types of applications. So far, its usefulness has been obvious within the realm of “three C's”. Computing was its original field of application, and continues to be important as regards