Publication
Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach
| dc.contributor.author | Fernandez de Canete, J. | |
| dc.contributor.author | del Saz-Orozco, P. | |
| dc.contributor.author | Gómez-de-Gabriel, J. | |
| dc.contributor.author | Baratti, R. | |
| dc.contributor.author | Ruano, Antonio | |
| dc.contributor.author | Rivas-Blanco, I. | |
| dc.date.accessioned | 2021-01-15T17:25:42Z | |
| dc.date.available | 2021-01-15T17:25:42Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.compchemeng.2020.107146 | pt_PT |
| dc.identifier.issn | 0098-1354 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/14968 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Neural networks | pt_PT |
| dc.subject | Activated sludge process | pt_PT |
| dc.subject | Genetic algorithms | pt_PT |
| dc.subject | Soft-sensing | pt_PT |
| dc.subject | Optimized control | pt_PT |
| dc.title | Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.startPage | 107146 | pt_PT |
| oaire.citation.title | Computers & Chemical Engineering | pt_PT |
| oaire.citation.volume | 144 | pt_PT |
| person.familyName | Ruano | |
| person.givenName | Antonio | |
| person.identifier.orcid | 0000-0002-6308-8666 | |
| person.identifier.rid | B-4135-2008 | |
| person.identifier.scopus-author-id | 7004284159 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 13813664-b68b-40aa-97a9-91481a31ebf2 | |
| relation.isAuthorOfPublication.latestForDiscovery | 13813664-b68b-40aa-97a9-91481a31ebf2 |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Computers and Chemical Engineering.pdf
- Size:
- 4.14 MB
- Format:
- Adobe Portable Document Format