Machine Learning in Aluminium Reduction

Kwaku Boadu

ISBN 10: 6200300186 / ISBN 13: 9786200300188

Published by LAP LAMBERT Academic Publishing Apr 2022, 2022

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 228 pp. Englisch.

Seller Inventory # 9786200300188

Bibliographic Details

• Title: Machine Learning in Aluminium Reduction
• Publisher: LAP LAMBERT Academic Publishing Apr 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 6200300186
• ISBN 13: 9786200300188
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 14D millimeters 220 millimeters width by 150 millimeters height by 14 millimeters depth
• Weight: 358 grams

About this title

Synopsis

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.