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Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design
Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic...
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| Main Authors: | , , , , , |
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| Format: | Default Article |
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2018
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| Online Access: | https://hdl.handle.net/2134/36305 |
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| _version_ | 1818167936539426816 |
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| author | Ehecatl Antonio del Rio-Chanona Jonathan Wagner Haider Ali Fabio Fiorelli Dongda Zhang Klaus Hellgardt |
| author_facet | Ehecatl Antonio del Rio-Chanona Jonathan Wagner Haider Ali Fabio Fiorelli Dongda Zhang Klaus Hellgardt |
| author_sort | Ehecatl Antonio del Rio-Chanona (3927527) |
| collection | Figshare |
| description | Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modelling. However, this approach presents computational intractability and numerical instabilities when simulating large-scale systems, causing time-intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data-driven surrogate modelling framework which considerably reduces computing time from months to days by exploiting state-of-the-art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi-objective optimization was incorporated to generate a Pareto-frontier for decision-making, advancing its applications in complex biosystems modelling and optimization. |
| format | Default Article |
| id | rr-article-9241235 |
| institution | Loughborough University |
| publishDate | 2018 |
| record_format | Figshare |
| spelling | rr-article-92412352018-11-15T00:00:00Z Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design Ehecatl Antonio del Rio-Chanona (3927527) Jonathan Wagner (5214482) Haider Ali (5574935) Fabio Fiorelli (7128827) Dongda Zhang (3927530) Klaus Hellgardt (1310934) Chemical engineering not elsewhere classified Surrogate modelling Convolutional neural network Hybrid stochastic optimization Excreted biofuel Photobioreactor design Chemical Engineering not elsewhere classified Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modelling. However, this approach presents computational intractability and numerical instabilities when simulating large-scale systems, causing time-intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data-driven surrogate modelling framework which considerably reduces computing time from months to days by exploiting state-of-the-art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi-objective optimization was incorporated to generate a Pareto-frontier for decision-making, advancing its applications in complex biosystems modelling and optimization. 2018-11-15T00:00:00Z Text Journal contribution 2134/36305 https://figshare.com/articles/journal_contribution/Deep_learning_based_surrogate_modeling_and_optimization_for_Microalgal_biofuel_production_and_photobioreactor_design/9241235 CC BY-NC-ND 4.0 |
| spellingShingle | Chemical engineering not elsewhere classified Surrogate modelling Convolutional neural network Hybrid stochastic optimization Excreted biofuel Photobioreactor design Chemical Engineering not elsewhere classified Ehecatl Antonio del Rio-Chanona Jonathan Wagner Haider Ali Fabio Fiorelli Dongda Zhang Klaus Hellgardt Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design |
| title | Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design |
| title_full | Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design |
| title_fullStr | Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design |
| title_full_unstemmed | Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design |
| title_short | Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design |
| title_sort | deep learning based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design |
| topic | Chemical engineering not elsewhere classified Surrogate modelling Convolutional neural network Hybrid stochastic optimization Excreted biofuel Photobioreactor design Chemical Engineering not elsewhere classified |
| url | https://hdl.handle.net/2134/36305 |