Dynamic optimisation of batch distillation with a middle vessel using neural network techniques

A rigorous model (validated against experimental pilot plant data) of a Middle Vessel Batch Distillation Column (MVC) is used to generate a set of data, which is then used to develop a neural network (NN) based model of the MVC column. A very good match between the "plant" data and the dat...

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Main Authors: Greaves, M.A., Mujtaba, I.M., Barolo, M., Trotta, A., Hussain, Mohd Azlan
Format: Article
Published: Elsevier 2002
Subjects:
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author Greaves, M.A.
Mujtaba, I.M.
Barolo, M.
Trotta, A.
Hussain, Mohd Azlan
author_facet Greaves, M.A.
Mujtaba, I.M.
Barolo, M.
Trotta, A.
Hussain, Mohd Azlan
author_sort Greaves, M.A.
collection UM
description A rigorous model (validated against experimental pilot plant data) of a Middle Vessel Batch Distillation Column (MVC) is used to generate a set of data, which is then used to develop a neural network (NN) based model of the MVC column. A very good match between the "plant" data and the data generated by the NN based model is eventually achieved. A dynamic optimisation problem incorporating the NN based model is then formulated to maximise the total amount of specified products while optimising the reflux and reboil ratios. The problem is solved using an efficient algorithm at the expense of few CPU seconds.
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spelling um.eprints-70742021-02-10T03:40:44Z http://eprints.um.edu.my/7074/ Dynamic optimisation of batch distillation with a middle vessel using neural network techniques Greaves, M.A. Mujtaba, I.M. Barolo, M. Trotta, A. Hussain, Mohd Azlan TA Engineering (General). Civil engineering (General) TP Chemical technology A rigorous model (validated against experimental pilot plant data) of a Middle Vessel Batch Distillation Column (MVC) is used to generate a set of data, which is then used to develop a neural network (NN) based model of the MVC column. A very good match between the "plant" data and the data generated by the NN based model is eventually achieved. A dynamic optimisation problem incorporating the NN based model is then formulated to maximise the total amount of specified products while optimising the reflux and reboil ratios. The problem is solved using an efficient algorithm at the expense of few CPU seconds. Elsevier 2002 Article PeerReviewed Greaves, M.A. and Mujtaba, I.M. and Barolo, M. and Trotta, A. and Hussain, Mohd Azlan (2002) Dynamic optimisation of batch distillation with a middle vessel using neural network techniques. Computer Aided Chemical Engineering, 10. pp. 505-510. ISSN 1570-7946, DOI https://doi.org/10.1016/S1570-7946(02)80112-2 <https://doi.org/10.1016/S1570-7946(02)80112-2>. https://doi.org/10.1016/S1570-7946(02)80112-2 doi:10.1016/S1570-7946(02)80112-2
spellingShingle TA Engineering (General). Civil engineering (General)
TP Chemical technology
Greaves, M.A.
Mujtaba, I.M.
Barolo, M.
Trotta, A.
Hussain, Mohd Azlan
Dynamic optimisation of batch distillation with a middle vessel using neural network techniques
title Dynamic optimisation of batch distillation with a middle vessel using neural network techniques
title_full Dynamic optimisation of batch distillation with a middle vessel using neural network techniques
title_fullStr Dynamic optimisation of batch distillation with a middle vessel using neural network techniques
title_full_unstemmed Dynamic optimisation of batch distillation with a middle vessel using neural network techniques
title_short Dynamic optimisation of batch distillation with a middle vessel using neural network techniques
title_sort dynamic optimisation of batch distillation with a middle vessel using neural network techniques
topic TA Engineering (General). Civil engineering (General)
TP Chemical technology
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AT trottaa dynamicoptimisationofbatchdistillationwithamiddlevesselusingneuralnetworktechniques
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