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Artificial neural networks in chemical engineering / Angelo Basile, Marjan Alavi, Ph. D. and Stefano Curcio (Institute on Membrane Technology of the Italian National Research Council, ITM-CNR, University of Calabria, and others).

Published
Hauppauge, New York : Nova Science Publishers, Inc., [2018]
Physical Description
1 online resource
Additional Creators
Basile, Angelo (Angelo Bruno)
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Contents
Preface; An Overview on Artificial Neural Networks: The Characteristics and Applications; Abstract; 1. Introduction; 1.1. A Brief History of ANN; 2. Computational Models of Neurons; 3. Creation of an ANN Model; 3.1. Data Collection; 3.2. Creating the Network; 3.3. Training the Network; 3.4. Validating the Network; 3.4.1. Early Stopping; 3.4.2. Regularization; 4. Application of ANN in Chemical Engineering; 4.1. Oil and Gas Industry; 4.2. Chemical Reaction Engineering; 4.3. Wastewater Treatment; 5. Conclusion; Nomenclature; Acronym; References., Modeling and Optimization of Second-Generation Biofuels Obtainment by Neural and Hybrid ModelsAbstract; Nomenclature; List of Acronyms; List of Symbols; Greek Letters; Introduction; Case Studies Description; Materials and Methods; Model Development; Results and Discussion; Case Study 1; Case Study 2; Case Study 3; Conclusion; References; Reactor Modeling Based on an Artificial Neural Network Approach: Black Box and Gray Box Modeling; Abstract; Nomenclature; Greek Letters; Subscripts; Superscripts; Acronym; 1. Introduction; 2. Case Study; 2.1. Reaction and Kinetics; 2.2. Reaction Kinetics., 2.3. Gray-Box Modeling of the DME Reactor2.3.1. Evaluating Ri Using a Diffusional Model; 2.3.2. Evaluating Ri* Using a Hybrid Neural Network Model; 2.4. Simulation; 2.5. Network Data Generation, Training, Validation and Testing; Conclusion; References; Modeling of Membrane Process Performance by Artificial Neural Network; Abstract; Acronyms List; Nomenclature List; Greek Letter List; 1. Introduction; 2. Membrane Processes; 3. Modeling of Membrane Processes; 3.1. Black Box Models; 3.2. Models Based on Transport Phenomena; 3.3. CFD Models; 3.4. Artificial Neural Network (ANN) Models., 3.4.1. Algorithms for Training Neural Networks3.4.1.1. Back Propagation (BP) Algorithm; 3.4.1.2. Quasi-Newton algorithm; 3.4.1.3. Levenberg-Marquardt Algorithm; 3.4.2. Literature of ANN Models for Membrane Processes; 3.4.2.1. ANN Modeling of GS Processes; 3.4.2.2. ANN Modeling of Polymer Electrolyte Membrane Fuel Cells (PEMFCs); 3.4.2.3. ANN Modeling of Membrane Distillation (MD) Processes; 3.4.2.4. ANN Modeling of Membrane Bioreactor (MBR) Processes; 3.4.2.5. ANN Modeling of Membrane Reactors (MRs); Conclusion; References; Neural Networks in Thermoelastic Stress Control; Abstract., and AbbreviationsIntroduction; Rotor Strength Modelling with Neural Network; FEM Based Neural Network Training; Neural Network Testing Using Experimental Data; Prediction of Temperature and Stress at the Rotor Critical Location; Conclusion; References; Artificial Neural Network Applications in Reservoir Engineering; Abstract; Nomenclature; Greek; Introduction; Generalized Workflow and Theoretical Discussion for ANN Applications in Reservoir Engineering; Artificial Neural Network Methodology; ANN Optimization Workflow; ANN Applications in Reservoir Engineering Problems.
Subject(s)
ISBN
9781536118681
1536118680
9781536118445 (hardcover)
1536118443
Bibliography Note
Includes bibliographical references and index.
View MARC record | catkey: 43230638