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دانلود ایبوک Artificial Neural Networks in Chemical Engineering

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Description

This book introduces readers to the Artificial Neural Network (ANN) and Hybrid Neural (HN) models: two effective tools, which can be exploited to design and control industrial processes. Different topics including modeling, simulation and process design are covered. More efficient analyses and descriptions of real case studies, ranging from membrane technology to the obtaining of second-generation biofuels are also provided. One of the major advantages of the described techniques is represented by the possibility of obtaining accurate predictions of complex systems, whose behaviors might be difficult to describe by conventional first-principle models. One of the major impacts of the present book is to show the true interactions and interconnectivities among different topics belonging to chemical, bio-chemical engineering, energy, bio-processes and bio-technique research fields. Some of the main goals are here are to provide a deep and detailed knowledge about the main features of both ANN and HN models, and to iterate possible topologies to integrate in these ANN and mechanistic models; to cover a wide spectrum of different problems as well as innovative and unconventional modeling techniques; to show how various kinds of advanced models can be exploited either to predict the behavior or to optimize the performance of real processes.

Table of Contents

Front Matter

pp Intro-x; 12 pages

An Overview on Artificial Neural Networks: The Characteristics and Applications

pp 1-20; 20 pages

Modeling and Optimization of Second-Generation Biofuels Obtainment by Neural and Hybrid Models

pp 21-38; 18 pages

Reactor Modeling Based on an Artificial Neural Network Approach: Black Box and Gray Box Modeling

pp 39-56; 18 pages

Modeling of Membrane Process Performance by Artificial Neural Network

pp 57-96; 40 pages

Neural Networks in Thermoelastic Stress Control

pp 97-122; 26 pages

Artificial Neural Network Applications in Reservoir Engineering

pp 123-204; 82 pages

Hybrid Neural Network Modelling for Process Monitoring and Control

pp 205-228; 24 pages

Hybrid ANN-Mechanistic Models for General Chemical and Biochemical Processes

pp 229-256; 28 pages

Index

pp 257-257; 1 page

Blank Page

pp Intro-265; 276 pages

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