Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process

In this paper, we propose a bi-objective dynamic optimization model involving a nonlinear time-delay system to optimize the 1,3-propanediol (1,3-PD) production in a microbial batch process, where the productivity of 1,3-PD and the consumption rate of glycerol are taken as the two objectives. The ini...

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Main Authors: Liu, C., Gong, Z., Teo, Kok Lay, Loxton, Ryan, Feng, E.
Format: Journal Article
Published: Springer Verlag 2016
Online Access:http://purl.org/au-research/grants/arc/DP140100289
http://hdl.handle.net/20.500.11937/52606
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author Liu, C.
Gong, Z.
Teo, Kok Lay
Loxton, Ryan
Feng, E.
author_facet Liu, C.
Gong, Z.
Teo, Kok Lay
Loxton, Ryan
Feng, E.
author_sort Liu, C.
building Curtin Institutional Repository
collection Online Access
description In this paper, we propose a bi-objective dynamic optimization model involving a nonlinear time-delay system to optimize the 1,3-propanediol (1,3-PD) production in a microbial batch process, where the productivity of 1,3-PD and the consumption rate of glycerol are taken as the two objectives. The initial concentrations of biomass and glycerol, and the terminal time of the process are the decision variables. By a time-scaling transformation, we first transform the problem to the one with fixed terminal time but involving a new system with variable time-delay. The normalized normal constraint method is then used to convert the resulting problem into a sequence of single-objective dynamic optimization problems. A gradient-based optimization method incorporating the constraint transcription technique is developed to solve each of these single-objective dynamic optimization problems. Finally, numerical results are provided to demonstrate the effectiveness of the proposed solution method.
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institution Curtin University Malaysia
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last_indexed 2025-11-14T09:52:25Z
publishDate 2016
publisher Springer Verlag
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spelling curtin-20.500.11937-526062022-10-12T02:45:17Z Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process Liu, C. Gong, Z. Teo, Kok Lay Loxton, Ryan Feng, E. In this paper, we propose a bi-objective dynamic optimization model involving a nonlinear time-delay system to optimize the 1,3-propanediol (1,3-PD) production in a microbial batch process, where the productivity of 1,3-PD and the consumption rate of glycerol are taken as the two objectives. The initial concentrations of biomass and glycerol, and the terminal time of the process are the decision variables. By a time-scaling transformation, we first transform the problem to the one with fixed terminal time but involving a new system with variable time-delay. The normalized normal constraint method is then used to convert the resulting problem into a sequence of single-objective dynamic optimization problems. A gradient-based optimization method incorporating the constraint transcription technique is developed to solve each of these single-objective dynamic optimization problems. Finally, numerical results are provided to demonstrate the effectiveness of the proposed solution method. 2016 Journal Article http://hdl.handle.net/20.500.11937/52606 10.1007/s11590-016-1105-6 http://purl.org/au-research/grants/arc/DP140100289 Springer Verlag fulltext
spellingShingle Liu, C.
Gong, Z.
Teo, Kok Lay
Loxton, Ryan
Feng, E.
Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process
title Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process
title_full Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process
title_fullStr Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process
title_full_unstemmed Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process
title_short Bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process
title_sort bi-objective dynamic optimization of a nonlinear time-delay system in microbial batch process
url http://purl.org/au-research/grants/arc/DP140100289
http://hdl.handle.net/20.500.11937/52606