Doctoral Degrees (Electrical and Electronic Engineering)

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Browsing Doctoral Degrees (Electrical and Electronic Engineering) by Author "Aldrich, Christiaan"

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    The simulation and optimization of steady state process circuits by means of artificial neural networks
    (Stellenbosch : Stellenbosch University, 1993) Aldrich, Christiaan; van Deventer, J. S. J.; Stellenbosch University. Faculty of Engineering. Department of Electrical and Electronic Engineering.
    ENGLISH ABASTRACT: Since the advent of modern process industries engineers engaged in the modelling and simulation of chemical and metallurgical processes have had to contend with two important dilemmas. The first concerns the ill-defined nature of the processes they have to describe, while the second relates to the limitations of prevailing computational resources. Current process simulation procedures are based on explicit process models in one form or another. Many chemical and metallurgical processes are not amenable to this kind of modelling however, and can not be incorporated effectively into current commercial process simulators. As a result many process operations do not benefit from the use of predictive models and simulation routines and plants are often poorly designed and run, ultimately leading to considerable losses in revenue. In addition to this dilemma, process simulation is in a very real way constrained by available computing resources. The construction of adequate process models is essentially meaningless if these models can not be solved efficiently - a situation occurring all too often. In the light of these problems, it is thus not surprising that connectionist systems or neural network methods are singularly attractive to process engineers, since they provide a powerful means of addressing both these dilemmas. These nets can form implicit process models through learning by example, and also serve as a vehicle for parallel supercomputing devices. In this dissertation the use of artificial neural networks for the steady state modelling and optimization of chemical and metallurgical process circuits is consequently investigated. The first chapter is devoted to a brief overview of the simulation of chemical and metallurgical plants by conventional methods, as well as the evolution and impact of computer technology and artificial intelligence on the process industries. Knowledge of the variance covariance matrices of process data is of paramount importance to data reconciliation and gross error detection problems, and although various methods can be employed to estimate these often unknown variances, it is shown in the second chapter that the use of feedforward neural nets can be more efficient than conventional strategies. In the following chapter the important problem of gross error detection in process data is addressed. Existing procedures are statistical and work well for systems subject to linear constraints. Non-linear constraints are not handled well by these methods and it is shown that back propagation neural nets can be trained to detect errors in process systems, regardless of the nature of the constraints. In the fourth chapter the exploitation of the massively parallel information processing structures of feedback neural nets in the optimization of process data reconciliation problems is investigated. Although effective and sophisticated algorithms are available for these procedures, there is an ever present demand for computational devices or routines that can accommodate progressively larger or more complex problems. Simulations indicate that neural nets can be efficient instruments for the implementation of parallel strategies for the optimization of such problems. In the penultimate chapter a gold reduction plant and a leach plant are modelled with neural nets and the models shown to be considerably better than the linear regression models used in practice. The same technique is also demonstrated with the modelling of an apatite flotation plant. Neural nets can also be used in conjunction with other methods and in the same chapter the steady state simulation and optimization of a gravity separation circuit with the use of two linear programming models and a neural net are described.