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Article

Machine Learning Models Applied to Manage the Operation of a Simple SWRO Desalination Plant and Its Application in Marine Vessels

1
Higher Polytechnic School of Engineering (EPSI), University of La Laguna (ULL), Avda. Francisco Larroche s/n, 38071 Santa Cruz de Tenerife, Spain
2
Water Department, Canary Islands Institute of Technology (ITC), Playa de Pozo Izquierdo s/n, 35119 Santa Lucía, Spain
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Author to whom correspondence should be addressed.
Academic Editors: Giuseppe Pezzinga, José Luis Sánchez-Lizaso and Licínio M. Gando-Ferreira
Water 2021, 13(18), 2547; https://doi.org/10.3390/w13182547
Received: 28 June 2021 / Revised: 25 August 2021 / Accepted: 13 September 2021 / Published: 16 September 2021
In this work, two machine learning techniques, specifically decision trees (DTs) and support vector machines (SVMs), were applied to optimize the performance of a seawater reverse osmosis (SWRO) desalination plant with a capacity of 100 m3 per day. The input variables to the system were seawater pH, seawater conductivity, and three requirements: permeate flow rate, permeate conductivity, and total energy consumed by the desalination plant. These requirements were decided based on a cost function that prioritizes the water needs in a vessel and the maximum possible energy savings. The intelligent system modifies the actuators of the plant: feed flow rate control and high-pressure pump (HPP) operating pressure. This tool is proposed for the optimal use of desalination plants in marine vessels. Although both machine learning techniques output satisfactory results, it was concluded that the DTs technique (HPP pressure: root mean square error (RMSE) = 0.0104; feed flow rate: RMSE = 0.0196) is more accurate than SVMs (HPP pressure: RMSE = 0.0918; feed flow rate: RMSE = 0.0198) based on the metrics used. The final objective of the paper is to extrapolate the implementation of this smart system to other shipboard desalination plants and optimize their performance. View Full-Text
Keywords: desalination; seawater reverse osmosis (SWRO); artificial intelligence; machine learning desalination; seawater reverse osmosis (SWRO); artificial intelligence; machine learning
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MDPI and ACS Style

Marichal Plasencia, G.N.; Camacho-Espino, J.; Ávila Prats, D.; Peñate Suárez, B. Machine Learning Models Applied to Manage the Operation of a Simple SWRO Desalination Plant and Its Application in Marine Vessels. Water 2021, 13, 2547. https://doi.org/10.3390/w13182547

AMA Style

Marichal Plasencia GN, Camacho-Espino J, Ávila Prats D, Peñate Suárez B. Machine Learning Models Applied to Manage the Operation of a Simple SWRO Desalination Plant and Its Application in Marine Vessels. Water. 2021; 13(18):2547. https://doi.org/10.3390/w13182547

Chicago/Turabian Style

Marichal Plasencia, Graciliano N., Jorge Camacho-Espino, Deivis Ávila Prats, and Baltasar Peñate Suárez. 2021. "Machine Learning Models Applied to Manage the Operation of a Simple SWRO Desalination Plant and Its Application in Marine Vessels" Water 13, no. 18: 2547. https://doi.org/10.3390/w13182547

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