Zobrazeno 1 - 9 of 9 pro vyhledávání: '"Bovinille Anye Cho"'
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Akademický článek
An MIQP framework for metabolic pathways optimisation and dynamic flux analysis
Autor: Lucas Gerken-Starepravo, Xianfeng Zhu, Bovinille Anye Cho, Fernando Vega-Ramon, Oliver Pennington, Ehecatl Antonio del Río-Chanona, Keju Jing, Dongda Zhang
Publikováno v: Digital Chemical Engineering, Vol 2, Iss , Pp 100011- (2022)
Dynamic flux analysis methods have been widely used for deciphering complex metabolic fluxes transients. However, many of them require frequent experimental measurements and are ineffective in dealing with under-determined metabolic reaction networks
Externí odkaz: https://doaj.org/article/d1d75d62aa5945a39a9cae5094b794eb
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2
Dynamic modelling of Rhodopseudomonas palustris biohydrogen production: Perturbation analysis and photobioreactor upscaling
Autor: Robert W.M. Pott, Brandon Sean Ross, Bovinille Anye Cho, Dongda Zhang, Jan-Pierre du Toit, Ehecatl Antonio del Rio Chanona
Publikováno v: International Journal of Hydrogen Energy. 46:36696-36708
Developing kinetic models to simulate Rhodopseudomonas palustris biohydrogen production within different configurations of photobioreactors (PBRs) poses a significant challenge. In this study, two types of PBRs: schott bottle-based and vertical tubul
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::557e6a08be9be4375c277751eca08b98
https://doi.org/10.1016/j.ijhydene.2021.08.162
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6
Machine learning for biochemical engineering: A review
Autor: Ziqi Song, Max Mowbray, Chufan Wu, Ehecatl Antonio del Rio-Chanona, Thomas R. Savage, Bovinille Anye Cho, Dongda Zhang
Publikováno v: Mowbray, M, Savage, T R, Wu, C, Song, Z, Anye Cho, B, del Rio-Chanona, E A & Zhang, D 2021, ' Machine learning for biochemical engineering: A review ', Biochemical Engineering Journal . https://doi.org/10.1016/j.bej.2021.108054
Biochemical Engineering Journal
The field of machine learning is comprised of techniques, which have proven powerful approaches to knowledge discovery and construction of ‘digital twins’ in the highly dimensional, nonlinear and stochastic domains common to biochemical engineeri
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09c02dc10d87ab1424336d8dd411eea6
https://doi.org/10.1016/j.bej.2021.108054
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7
The development of a thermosiphon photobioreactor and analysis using Computational Fluid Dynamics (CFD)
Autor: Robert W.M. Pott, Bovinille Anye Cho
Publikováno v: Chemical Engineering Journal. 363:141-154
The energy input into closed photobioreactors (PBRs) for aeration and/or agitation by pneumatic and mechanical devices impacts their material, operational and production costs, accounting for up to circa 80% of the total costs. This work describes th
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bd4d63667ee7a2189f4f9552f5a9d41a
https://doi.org/10.1016/j.cej.2019.01.104
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8
Synergising biomass growth kinetics and transport mechanisms to simulate light/dark cycle effects on photo‐production systems
Autor: Robin Smith, Miguel Ángel de Carvalho Servia, Dongda Zhang, Bovinille Anye Cho, Ehecatl Antonio del Rio Chanona
Publikováno v: Cho, B A, Servia, M Á D C, Chanona, E A D R, Smith, R & Zhang, D 2021, ' Synergising biomass growth kinetics and transport mechanisms to simulate light/dark cycle effects on photo-production systems ', Biotechnology and Bioengineering . https://doi.org/10.1002/bit.27707
Light attenuation is a primary challenge limiting the upscaling of photobioreactors for sustainable bio-production. One key to this challenge, is to model and optimise the light/dark cycles so that cells within the dark region can be frequently trans
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0232ff628360a00a5bbdd6cd6a740dd5
https://www.research.manchester.ac.uk/portal/en/publications/synergising-biomass-growth-kinetics-and-transport-mechanisms-to-simulate-lightdark-cycle-effects-on-photoproduction-systems(25ba424d-36b5-49c5-8714-475682fdfe6d).html
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9
Combining model structure identification and hybrid modelling for photo‐production process predictive simulation and optimisation
Autor: Dongda Zhang, Thomas R. Savage, Bovinille Anye Cho
Publikováno v: Zhang, D, Savage, T R & Cho, B A 2020, ' Combining model structure identification and hybrid modelling for photo-production process predictive simulation and optimisation ', Biotechnology and Bioengineering, vol. 117, no. 11, pp. 3356-3367 . https://doi.org/10.1002/bit.27512
Zhang, D, Savage, T R & Cho, B A 2020, ' Combining model structure identification and hybrid modelling for photo-production process predictive simulation and optimisation ', Biotechnology and Bioengineering . https://doi.org/10.1002/bit.27512
Integrating physical knowledge and machine learning is a critical aspect of developing industrially focused digital twins for monitoring, optimisation, and design of microalgal and cyanobacterial photo-production processes. However, identifying the c
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd2f3e567d38f7cbfc7ff1c78268354c
https://doi.org/10.1002/bit.27512
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