Výsledky vyhledávání - "Ehecatl A. Del Rio‐Chanona"

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification

Autor: Max R. Mowbray, Chufan Wu, Alexander W. Rogers, Ehecatl A. Del Rio‐Chanona, Dongda Zhang

Publikováno v: Mowbray, M R, Wu, C, Rogers, A W, Rio-Chanona, E A D & Zhang, D 2023, ' A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification ', Biotechnology and Bioengineering, vol. 120, no. 1, pp. 154-168 . https://doi.org/10.1002/bit.28262

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a long

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81c14da3a470052a8567632cc06f5f37
https://uom.elsevierpure.com/en/publications/137e6d55-792a-447b-94b9-c09484add6fa

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A transfer learning approach for predictive modeling of bioprocesses using small data

Autor: Keju Jing, Alexander W. Rogers, Jiangtao Yan, Ehecatl Antonio del Rio-Chanona, Fernando Vega-Ramon, Dongda Zhang

Publikováno v: Rogers, A W, Vega-Ramon, F, Yan, J, Río-Chanona, E A, Jing, K & Zhang, D 2022, ' A transfer learning approach for predictive modeling of bioprocesses using small data ', Biotechnology and Bioengineering, vol. 119, no. 2, pp. 411-422 . https://doi.org/10.1002/bit.27980

Predictive modeling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, a subdomain of machine learning that serves to transfer knowledge from a generalized model to a more domain-specific model, prov

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d57818606ad957fb7cc075abafdeb04
https://doi.org/10.1002/bit.27980

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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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Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning

Autor: Cesare Caputo, Michel-Alexandre Cardin, Pudong Ge, Fei Teng, Anna Korre, Ehecatl Antonio del Rio Chanona

Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbd8ae7f3079682c6c54c1c433265572
http://arxiv.org/abs/2212.04136

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Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station

Autor: Akhil Ahmed, Marta Zagorowska, Ehecatl Antonio Del Rio-Chanona, Mehmet Mercangoz

Publikováno v: 2022 European Control Conference (ECC)

Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it is necessary to provide accurate models of the characteristics that c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cff706b85e37e44b0b8652037dea764b
https://doi.org/10.23919/ecc55457.2022.9838042

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Model-free safe reinforcement learning for chemical processes using Gaussian processes

Autor: Dongda Zhang, Thomas R. Savage, Max Mowbray, Ehecatl Antonio del Rio Chanona

Publikováno v: IFAC-PapersOnLine. 54:504-509

Model-free reinforcement learning has been recently investigated for use in chemical process control. Through the iterative creation of an approximate process model, control actions are able to be explored and optimal policies generated. Typically, t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3766ff637c417be7b4cd330297d30aeb
https://doi.org/10.1016/j.ifacol.2021.08.292

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