Machine Learning in Aluminium Reduction: Adaptive Control of Alumina Concentration in the Hall-Héroult Cell Using Neural Network - Softcover

Boadu, Kwaku

 
9786200300188: Machine Learning in Aluminium Reduction: Adaptive Control of Alumina Concentration in the Hall-Héroult Cell Using Neural Network
Softcover
ISBN 10: 6200300186 ISBN 13: 9786200300188
Verlag: LAP LAMBERT Academic Publishing, 2022

Inhaltsangabe

Aluminium smelting the world over has had two major constraints: environmental protection and energy costs. Since the method and efficiency of alumina feed in the smelting process impacts environmental pollution and production efficiency greatly, much of the industry’s investment money has been spent researching into better feed control systems – feed delivery and feed strategies. The subject matter of this thesis dwells on the latter, continuing the search for an efficient adaptive alumina feed strategy in the Hall-Héroult aluminium reduction cell. Neurocomputing is applied to the problem of on-line estimation of alumina mass balance in the electrolytic cell. Simulated and real electrolytic resistance/alumina concentration data was used as input vectors to train a single-layer feed forward loop-back NEURAL NETWORK constructed with six constraint equations and six degrees of freedom in search for a prediction algorithm. A contribution is proposed to alumina feed control strategies by developing a neural network-based adaptive feed control algorithm, robust against cell resistance variations, and implementable on retrofit state-of-the-art aluminium reduction cell microcomputers.

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Über die Autorin bzw. den Autor

Now a lawyer and lecturer in University of Ghana School of Law, his first degree was in Computer Science. Interested in process control when working for VALCO, Ghana [Kaiser Aluminum, USA] he conducted this M.Ph. Electrical Engineering research. He retired early from IT/engineering, and specialized in IT Law in University of Edinburgh, Scotland.

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Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2022
Sprache
Englisch
ISBN 10 
6200300186
ISBN 13 
9786200300188
Einband
Taschenbuch
Anzahl der Seiten
228
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
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