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and Stefano Curcio
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
An Overview on Artificial Neural Networks: The Characteristics and Applications
Modeling and Optimization of Second-Generation Biofuels Obtainment by Neural and Hybrid Models
Reactor Modeling Based on an Artificial Neural Network Approach: Black Box and Gray Box Modeling
Modeling of Membrane Process Performance by Artificial Neural Network
Neural Networks in Thermoelastic Stress Control
Artificial Neural Network Applications in Reservoir Engineering
Hybrid Neural Network Modelling for Process Monitoring and Control
Hybrid ANN-Mechanistic Models for General Chemical and Biochemical Processes