Machine Learning in Aluminium Reduction: Adaptive Control of Alumina Concentration in the Hall-Héroult Cell Using Neural Network - Brossura
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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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Machine Learning in Aluminium Reduction
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Machine Learning in Aluminium Reduction: Adaptive Control of Alumina Concentration in the Hall-Héroult Cell Using Neural Network
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ISBN 13: 9786200300188
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Machine Learning in Aluminium Reduction
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 228 pp. Englisch. Codice articolo 9786200300188
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 th. Codice articolo 601137754
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Machine Learning in Aluminium Reduction | Adaptive Control of Alumina Concentration in the Hall-Héroult Cell Using Neural Network
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Taschenbuch. Condizione: Neu. Machine Learning in Aluminium Reduction | Adaptive Control of Alumina Concentration in the Hall-Héroult Cell Using Neural Network | Kwaku Boadu | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786200300188 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 121691057
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Machine Learning in Aluminium Reduction
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Taschenbuch. Condizione: Neu. 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. Codice articolo 9786200300188
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Machine Learning in Aluminium Reduction : Adaptive Control of Alumina Concentration in the Hall-Héroult Cell Using Neural Network
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6200300186
ISBN 13: 9786200300188
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9786200300188
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