Neural Networks for Chemical Engineers: (v. 6 Computer Aided Chemical Engineering)

Although neural and connectionist models have been known for decades, their first appearance in chemical engineering was as late as 1988. This book is an attempt to expedite a cautious intake of neural networks into chemical engineering. Besides core chemical engineering, it includes applications in process engineering, biochemical engineering, and metallurgical engineering. Of the 27 chapters, six cover theoretical issues and the remaining 21 cover applications.

Table of Contents:
Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham; unsupervised neural learning, E. Oja; back-propagation and its variations, D. Tsaptsinos; the general approximation problem for feed-forward neural networks, A.B. Bulsari; connectionism vs symbolism - an overview, A.B. Bujosa et al; introduction to connectionist computer vision systems, D.W. Moolman et al. Part 2 Applications: lime kiln simulation and control by neural networks, B. Ribeiro and A. Dourado Correia; concentration estimation using neural networks and partial conventional models, B. Schenker and M. Agarwal; data rectification for dynamic processes using artificial neural networks, T.W. Karjala and D.M. Himmelblau; applications of neural networks in process dynamics, A.B. Bulsari; process modelling for fault detection using neural networks, T. Fujiwara; local modelling as a tool for semi-empirical or semi-mechanistic process modelling, B.A. Foss and T.A. Johansen; estimation of measurement error variances and process data reconciliation, C. Aldrich and J.S.J. van Deventer; process monitoring and visualization using self-organizing maps, O. Simula and J. Kangas; an overview of dynamic system control using neural networks, P. Zufiria; nonlinear system identification using neural networks - dynamics and instabilities, R. Rico-Martinez et al; pattern-based interpretation of on-line process data, J.F. Davis and C.-M. Wang; modelling ill-defined behaviour of reacting systems using neural networks, C. Aldrich and J.S.J. van Deventer; global vs local networks in identification and control -a case study of neutralization, M.N. Karim and B. Eikens; modelling chemical processes using multiresolution representation neural networks, K. Yoda and T. Furuya; the videographic characterisation of flotation froths using neural networks, D.W. Moolman et al; fuzzy modelling using two connectionist architectures, J. Zhang and A.J. Morris; system identification using elman and Jordan networks, D.T. Pham et al; time-series prediction with on-line correction of kalman gain - a connectionist approach, A. Dobnikar et al; neural networks based control strategies for a continuous polymerisation reactor, N. Watanabe; statistical and neural methods in classification and modelling, E.B. Martin et al; clustering and statistical techniques in neural networks, V. Venkatasubramanian and R. Rengaswamy.