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Application of Artificial Intelligence techniques to optimize the management of seawater reverse osmosis desalination plants with a particular interest in marine vessels

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Author
Camacho Espino, Jorge
Date
2024
URI
http://riull.ull.es/xmlui/handle/915/38837
Abstract
Water scarcity is a pressing global problem. Global population growth and climate change are two main reasons for increasing water stress in many countries. Different solutions are being developed to solve this problem, such as rainwater harvesting, seawater desalination, wastewater treatment and reuse, and sustainable water management. Seawater desalination using reverse osmosis (SWRO) is a widespread membrane technology worldwide that obtains fresh ocean water. This doctoral thesis is based on improving and optimizing the management and energy efficiency of seawater desalination plants and its application in small marine vessels. Different Machine Learning (ML) and Artificial Intelligence (AI) techniques have been applied to create prediction tools to improve plant control. Through these AI techniques, it has been possible to predict the optimal values of the equipment actuators to fulfill different requirements proposed by the author. Some of the variables considered in the thesis and used in the control systems have been conductivity, temperature, and flow of seawater (SW), the pressure of the high-pressure pump (HPP), the flow and conductivity of permeate water and the consumption of energy, or specific energy consumption (SEC) of the plant. Data from two different SWRO desalination plants has been collected. As a result of different studies carried out to solve the problems presented, three scientific articles have been published. These papers make up the compendium publications of this thesis. In each of the three articles, satisfactory results have been obtained, which have allowed the control of the desalination plants studied to be improved. These results have been possible due to the study and optimization of different operational parameters of interest in the desalination process. These predictive tools could be extrapolated to other seawater desalination plants.
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Universidad de La Laguna

Universidad de La Laguna

Pabellón de Gobierno, C/ Padre Herrera s/n. | 38200 | Apartado Postal: 456 | San Cristóbal de La Laguna, Santa Cruz de Tenerife - España | Teléfono: (+34) 922 31 90 00