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Modelling and mathematical optimisation of wastewater treatment in food industries

The current paper describes the work carried out in the Horizon 2020 AFTERLIFE project –  "Advanced Filtration TEchnologies for the Recovery and Later conversIon of relevant Fractions from wastEwater" – (Grant Agreement no. 745737) which focuses on bioprocess modelling and optimisation usi...

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Autores principales: Rodriguez-Perez, Santiago, Cabeza Sanchez, Alvaro, Lopez-Abelairas, María
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446076/
https://www.ncbi.nlm.nih.gov/pubmed/37645304
http://dx.doi.org/10.12688/openreseurope.14737.1
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author Rodriguez-Perez, Santiago
Cabeza Sanchez, Alvaro
Lopez-Abelairas, María
author_facet Rodriguez-Perez, Santiago
Cabeza Sanchez, Alvaro
Lopez-Abelairas, María
author_sort Rodriguez-Perez, Santiago
collection PubMed
description The current paper describes the work carried out in the Horizon 2020 AFTERLIFE project –  "Advanced Filtration TEchnologies for the Recovery and Later conversIon of relevant Fractions from wastEwater" – (Grant Agreement no. 745737) which focuses on bioprocess modelling and optimisation using computational tools. The project addresses the development of a flexible, cost- and resource-efficient process framed in the zero-waste and circular economy approach for the recovery and valorisation of the relevant fractions from wastewater. The first step of such a process is an initial step consisting of a cascade of membrane filtration units to separate the total solids in sewage. Then, the concentrates recovered in each unit will be treated to obtain high-pure extracts and metabolites or to be converted into value-added biopolymers (polyhydroxyalkanoates). Moreover, the outflow of the process is an ultra-pure water stream that can be directly reused. Following a holistic approach, the design and optimisation of the AFTERLIFE process will improve performance and reduce the costs associated with wastewater treatment by maximising the value recovery. The paper focuses on the work done developing and implementing computational tools to model and optimise the design of the process. A framework for modelling-based optimisation has been developed. The applied optimisation approach is not computationally demanding and can be systematically applied to different processes. Finally, a use case establishing a scenario for testing the developed framework is described. The defined process model and optimisation methodology were applied to simulate the treatment of wastewater from the fish processing industry. The performance of the optimisation tool is analysed considering the simulation results.
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spelling pubmed-104460762023-08-29 Modelling and mathematical optimisation of wastewater treatment in food industries Rodriguez-Perez, Santiago Cabeza Sanchez, Alvaro Lopez-Abelairas, María Open Res Eur Software Tool Article The current paper describes the work carried out in the Horizon 2020 AFTERLIFE project –  "Advanced Filtration TEchnologies for the Recovery and Later conversIon of relevant Fractions from wastEwater" – (Grant Agreement no. 745737) which focuses on bioprocess modelling and optimisation using computational tools. The project addresses the development of a flexible, cost- and resource-efficient process framed in the zero-waste and circular economy approach for the recovery and valorisation of the relevant fractions from wastewater. The first step of such a process is an initial step consisting of a cascade of membrane filtration units to separate the total solids in sewage. Then, the concentrates recovered in each unit will be treated to obtain high-pure extracts and metabolites or to be converted into value-added biopolymers (polyhydroxyalkanoates). Moreover, the outflow of the process is an ultra-pure water stream that can be directly reused. Following a holistic approach, the design and optimisation of the AFTERLIFE process will improve performance and reduce the costs associated with wastewater treatment by maximising the value recovery. The paper focuses on the work done developing and implementing computational tools to model and optimise the design of the process. A framework for modelling-based optimisation has been developed. The applied optimisation approach is not computationally demanding and can be systematically applied to different processes. Finally, a use case establishing a scenario for testing the developed framework is described. The defined process model and optimisation methodology were applied to simulate the treatment of wastewater from the fish processing industry. The performance of the optimisation tool is analysed considering the simulation results. F1000 Research Limited 2022-05-19 /pmc/articles/PMC10446076/ /pubmed/37645304 http://dx.doi.org/10.12688/openreseurope.14737.1 Text en Copyright: © 2022 Rodriguez-Perez S et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software Tool Article
Rodriguez-Perez, Santiago
Cabeza Sanchez, Alvaro
Lopez-Abelairas, María
Modelling and mathematical optimisation of wastewater treatment in food industries
title Modelling and mathematical optimisation of wastewater treatment in food industries
title_full Modelling and mathematical optimisation of wastewater treatment in food industries
title_fullStr Modelling and mathematical optimisation of wastewater treatment in food industries
title_full_unstemmed Modelling and mathematical optimisation of wastewater treatment in food industries
title_short Modelling and mathematical optimisation of wastewater treatment in food industries
title_sort modelling and mathematical optimisation of wastewater treatment in food industries
topic Software Tool Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446076/
https://www.ncbi.nlm.nih.gov/pubmed/37645304
http://dx.doi.org/10.12688/openreseurope.14737.1
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