Analyzing Multi-stage Reverse Osmosis Desalination Using Artificial Intelligence

Population growth has resulted in a decrease in readily available sources of potable water. Desalination is one of many approaches that has been studied and proposed as a way out of this predicament. In this study, multistage Reverse Osmosis desalination process is used in the model, since it has th...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2022, Vol.13 (10)
Main Authors: Tekle, Batiseba, Alazzam, Azmi, Ibrahim, Abdulwehab, Malkawi, Ghassan, NajiMoqbel, Abdulaziz Fares, Qureshi, Nissar, Hamadat, Ahmed, Jr, Filomento O. Corona
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Desalination
Drinking water
Genetic algorithms
Neural networks
Optimization
Optimization techniques
Performance prediction
Population growth
Regression models
Reverse osmosis
Support vector machines
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Summary:Population growth has resulted in a decrease in readily available sources of potable water. Desalination is one of many approaches that has been studied and proposed as a way out of this predicament. In this study, multistage Reverse Osmosis desalination process is used in the model, since it has the potential to achieve a higher purity percentage than the single-stage RO desalination process. Some researchers have studied the distinctive tools of AI, specifically Artificial Neural Network as regression model and the genetic Algorithms as an optimization technique in the process of desalination and water treatments. This paper aims to examine multistage RO desalination by employing various artificial intelligence (AI) techniques, including Artificial Neural Network (ANN) and Support Vector Machine (SVM). Both training methods used for this research come under the category of regression algorithms, which are used to establish a predictive link between variables and labels. The main finding of this study was the noticeable decrease of Mean Square Error (MSE) in second stage when data was trained using the ANN. While on the other hand the MSE increased in second stage when the data was trained using the SVM. It can be concluded that the results of this research indicate that applying ANN and SVM to RO desalination process modelling would yield substantial improvements. Future work will be focusing on predicting and improving the performance of ANN and SVM prediction with other function variables.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0131077