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Data-driven soft-sensors for online monitoring of batch processes with different initial conditions
dc.contributor.author | Shokry Abdelaleem Taha Zied, Ahmed |
dc.contributor.author | Vicente Núñez, Patricia |
dc.contributor.author | Escudero Bakx, Gerard |
dc.contributor.author | Pérez Moya, Montserrat |
dc.contributor.author | Graells Sobré, Moisès |
dc.contributor.author | Espuña Camarasa, Antonio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Química |
dc.date.accessioned | 2019-04-25T08:47:46Z |
dc.date.available | 2020-10-04T00:25:57Z |
dc.date.issued | 2018-10-04 |
dc.identifier.citation | Shokry , A. [et al.]. Data-driven soft-sensors for online monitoring of batch processes with different initial conditions. "Computers & chemical engineering", 4 Octubre 2018, vol. 118, p. 159-179. |
dc.identifier.issn | 0098-1354 |
dc.identifier.uri | http://hdl.handle.net/2117/131976 |
dc.description.abstract | A soft-sensing methodology applicable to batch processes operated under changeable initial conditions is presented. These cases appear when the raw materials specifications differ from batch to batch, different production scenarios should be managed, etc. The proposal exploits the capabilities of the machine learning techniques to provide practical soft-sensing approach with minimum tuning effort in spite of the fact that the inherent dynamic behavior of batch systems are tracked through other online indirect measurements. Current data modeling techniques have been also tested within the proposed methodology to demonstrate its advantages. Two simulation case-studies and a pilot-plant case-study involving a complex batch process for wastewater treatment are used to illustrate the problem, to assess the modeling approach and to compare the modeling techniques. The results reflect a promising accuracy even when the training information is scarce, allowing significant reductions in the cost associated to batch processes monitoring and control. |
dc.format.extent | 21 p. |
dc.language.iso | eng |
dc.publisher | Pergamon Press |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica |
dc.subject | Àrees temàtiques de la UPC::Enginyeria química |
dc.subject.lcsh | Chemical process control |
dc.subject.other | Soft-sensors |
dc.subject.other | Batch processes |
dc.subject.other | Ordinary Kriging |
dc.subject.other | Support vector machines |
dc.subject.other | Artificial neural networks |
dc.subject.other | Photo-Fenton |
dc.title | Data-driven soft-sensors for online monitoring of batch processes with different initial conditions |
dc.type | Article |
dc.subject.lemac | Control de processos químics |
dc.contributor.group | Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural |
dc.contributor.group | Universitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering |
dc.identifier.doi | 10.1016/j.compchemeng.2018.07.014 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0098135418307117 |
dc.rights.access | Open Access |
local.identifier.drac | 23331931 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Shokry , A.; Vicente, P.; Escudero, G.; Pérez-Moya, M.; Graells, M.; Espuña, A. |
local.citation.publicationName | Computers & chemical engineering |
local.citation.volume | 118 |
local.citation.startingPage | 159 |
local.citation.endingPage | 179 |
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