What is it about?
In this study, we present a comparison of two prediction models, namely Multiple linear regression (MLR ) and artificial neural network ( ANN) for a Boujdour reverse osmosis seawater desalination plant. With a high number of inputs, ANN was more efficient than MLR in predicting conductivity permeate.
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Why is it important?
Determination models by different methods of prediction is essential to evaluate performance systems. the aim of this research is to find the model of prediction permeate conductivity of reverse osmosis station in order to have a clear vision of the prediction permeate conductivity parameter in the future.
Perspectives
I think this article will be useful and exciting and may help to make predictions by statistical modeling, whether in the field of desalination or elsewhere. Neural networks are powerful and capable of determining prediction models with several inputs.
siham kherraf
Faculty of science of Rabat Morocco
Read the Original
This page is a summary of: Forecasting of Permeate Conductivity using MLR and ANN Methods of
Boujdour Seawater Reverse Osmosis Desalination Plant, Current Analytical Chemistry, July 2023, Bentham Science Publishers,
DOI: 10.2174/1573411019666230221143245.
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