Download or read book Stochastic Modelling in Process Technology written by Herold G. Dehling and published by Elsevier. This book was released on 2007-07-03 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book An Introduction to Stochastic Modeling written by Howard M. Taylor and published by Academic Press. This book was released on 2014-05-10 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Download or read book Stochastic Models in Reliability and Maintenance written by Shunji Osaki and published by Springer Science & Business Media. This book was released on 2012-11-02 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 analyze the future behavior quantitatively. Reliability and maintainability technologies are of great interest and importance to the maintenance of such systems. Many mathematical models have been and will be proposed to describe reliability and maintainability systems by using the stochastic processes. The theme of this book is "Stochastic Models in Reliability and Main tainability. " This book consists of 12 chapters on the theme above from the different viewpoints of stochastic modeling. Chapter 1 is devoted to "Renewal Processes," under which classical renewal theory is surveyed and computa tional methods are described. Chapter 2 discusses "Stochastic Orders," and in it some definitions and concepts on stochastic orders are described and ag ing properties can be characterized by stochastic orders. Chapter 3 is devoted to "Classical Maintenance Models," under which the so-called age, block and other replacement models are surveyed. Chapter 4 discusses "Modeling Plant Maintenance," describing how maintenance practice can be carried out for plant maintenance.

Download or read book Stochastic Modelling of Social Processes written by Andreas Diekmann and published by Academic Press. This book was released on 2014-05-10 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Modelling of Social Processes provides information pertinent to the development in the field of stochastic modeling and its applications in the social sciences. This book demonstrates that stochastic models can fulfill the goals of explanation and prediction. Organized into nine chapters, this book begins with an overview of stochastic models that fulfill normative, predictive, and structural–analytic roles with the aid of the theory of probability. This text then examines the study of labor market structures using analysis of job and career mobility, which is one of the approaches taken by sociologists in research on the labor market. Other chapters consider the characteristic trends and patterns from data on divorces. This book discusses as well the two approaches of stochastic modeling of social processes, namely competing risk models and semi-Markov processes. The final chapter deals with the practical application of regression models of survival data. This book is a valuable resource for social scientists and statisticians.

Download or read book Stochastic Modeling written by Hossein Bonakdari and published by Elsevier. This book was released on 2022-04-13 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Modeling: A Thorough Guide to Evaluate, Pre-Process, Model and Compare Time Series with MATLAB Software allows for new avenues in time series analysis and predictive modeling which summarize more than ten years of experience in the application of stochastic models in environmental problems. The book introduces a variety of different topics in time series in the modeling and prediction of complex environmental systems. Most importantly, all codes are user-friendly and readers will be able to use them for their cases. Users who may not be familiar with MATLAB software can also refer to the appendix. This book also guides the reader step-by-step to learn developed codes for time series modeling, provides required toolboxes, explains concepts, and applies different tools for different types of environmental time series problems. Provides video tutorials on the use of codes Includes a companion site with 3,000 lines of programming, 70 principal codes and 100 pseudo codes Highlights multiple methods to Illustrate each problem

Download or read book Markov Processes for Stochastic Modeling written by Oliver Ibe and published by Newnes. This book was released on 2013-05-22 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. Presents both the theory and applications of the different aspects of Markov processes Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.

Download or read book Basics of Applied Stochastic Processes written by Richard Serfozo and published by Springer Science & Business Media. This book was released on 2009-01-24 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics. Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerative processes, Poisson processes, and Brownian motion. This volume gives an in-depth description of the structure and basic properties of these stochastic processes. A main focus is on equilibrium distributions, strong laws of large numbers, and ordinary and functional central limit theorems for cost and performance parameters. Although these results differ for various processes, they have a common trait of being limit theorems for processes with regenerative increments. Extensive examples and exercises show how to formulate stochastic models of systems as functions of a system’s data and dynamics, and how to represent and analyze cost and performance measures. Topics include stochastic networks, spatial and space-time Poisson processes, queueing, reversible processes, simulation, Brownian approximations, and varied Markovian models. The technical level of the volume is between that of introductory texts that focus on highlights of applied stochastic processes, and advanced texts that focus on theoretical aspects of processes.

Download or read book Stochastic Processes and Models in Operations Research written by Neelamegam Anbazhagan and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores the mathematical tools and equations relevant for solving complex problems within business and industrial settings. This research-based publication provides comprehensive exposure to the concepts, trends, and technologies relevant to stochastic process modelling to solve operations research problems.

Download or read book Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology written by Paola Lecca and published by Elsevier. This book was released on 2013-04-09 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics. Introduces mathematical concepts and formalisms of deterministic and stochastic modelling through clear and simple examples Presents recently developed discrete stochastic formalisms for modelling biological systems and processes Describes and applies stochastic simulation algorithms to implement a stochastic formulation of biochemical and biological kinetics

Download or read book Introduction to Modeling and Analysis of Stochastic Systems written by V. G. Kulkarni and published by Springer. This book was released on 2010-11-03 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.

Download or read book Stochastic Models In Engineering Technology And Management Proceedings Of The Australia japan Workshop written by Shunji Osaki and published by World Scientific. This book was released on 1993-04-27 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings contain a range of papers dealing with different issues relating to the theme of the workshop. The majority of them deal with model formulation and model analysis. A few deal with mathematical techniques that are needed to carry out model analysis. Some of the papers are theoretical whilst others are applied. As a result, the topics covered in these papers highlight the multifaceted nature of stochastic modelling and diversity of the applications of stochastic models.

Download or read book Stochastic Models in Biology written by Narendra S. Goel and published by Elsevier. This book was released on 2013-10-22 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Models in Biology describes the usefulness of the theory of stochastic process in studying biological phenomena. The book describes analysis of biological systems and experiments though probabilistic models rather than deterministic methods. The text reviews the mathematical analyses for modeling different biological systems such as the random processes continuous in time and discrete in state space. The book also discusses population growth and extinction through Malthus' law and the work of MacArthur and Wilson. The text then explains the dynamics of a population of interacting species. The book also addresses population genetics under systematic evolutionary pressures known as deterministic equations and genetic changes in a finite population known as stochastic equations. The text then turns to stochastic modeling of biological systems at the molecular level, particularly the kinetics of biochemical reactions. The book also presents various useful equations such as the differential equation for generating functions for birth and death processes. The text can prove valuable for biochemists, cellular biologists, and researchers in the medical and chemical field who are tasked to perform data analysis.

Download or read book Stochastic Modeling of Microstructures written by Kazimierz Sobczyk and published by Springer Science & Business Media. This book was released on 2001-09-21 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for a general scientific and engineering audience as a guide to current ideas, methods, and models for stochastic modeling of microstructures. It is a reference for professionals in material modeling, mechanical engineering, materials science, chemical, civil, environmental engineering and applied mathematics.

Download or read book Carbon Nanotube Reinforced Polymers written by Roham Rafiee and published by Elsevier. This book was released on 2017-10-06 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: Carbon Nanotube-Reinforced Polymers: From Nanoscale to Macroscale addresses the advances in nanotechnology that have led to the development of a new class of composite materials known as CNT-reinforced polymers. The low density and high aspect ratio, together with their exceptional mechanical, electrical and thermal properties, render carbon nanotubes as a good reinforcing agent for composites. In addition, these simulation and modeling techniques play a significant role in characterizing their properties and understanding their mechanical behavior, and are thus discussed and demonstrated in this comprehensive book that presents the state-of-the-art research in the field of modeling, characterization and processing. The book separates the theoretical studies on the mechanical properties of CNTs and their composites into atomistic modeling and continuum mechanics-based approaches, including both analytical and numerical ones, along with multi-scale modeling techniques. Different efforts have been done in this field to address the mechanical behavior of isolated CNTs and their composites by numerous researchers, signaling that this area of study is ongoing. Explains modeling approaches to carbon nanotubes, together with their application, strengths and limitations Outlines the properties of different carbon nanotube-based composites, exploring how they are used in the mechanical and structural components Analyzes the behavior of carbon nanotube-based composites in different conditions

Download or read book Stochastic Processes and Filtering Theory written by Andrew H. Jazwinski and published by Courier Corporation. This book was released on 2013-04-15 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.

Download or read book Stochastic Models of Manufacturing Systems written by John A. Buzacott and published by Englewood Cliffs, N.J. : Prentice Hall. This book was released on 1993 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: Outlining the major issues that have to be addressed in the design and operation of each type of system, this new text explores the stochastic models of a wide range of manufacturing systems. It covers flow lines, job shops, transfer lines, flexible manufacturing systems, flexible assembly systems, cellular systems, and more. For professionals working in the area of manufacturing system modelling.

Download or read book Stochastic Models of Buying Behavior written by William F. Massy and published by MIT Press (MA). This book was released on 1970 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Approaches to stochastic modeling; Estimating and testing stochastic models; Brand-choice models; Zero-order models; Two state markov models; Linear learning models for brand choice; A probability diffusion model; Application of the probability diffusion model; Purchase incidence models; Models for purchase timing and market penetration; A stochastic model for monitoring new product adoption; Parameter estimations and some emperical results for STEAM; Extension to STEAM.