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Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant.

Rajesh Mahadeva¹, Mahendra Kumar², Vinay Gupta¹

¹Department of Physics, Khalifa University of Science and Technology, 127788, Abu Dhabi, United Arab Emirates.

Scientific Reports

|February 22, 2023

Summary

This summary is machine-generated.

The Modified Whale Optimization Algorithm (MWOA) hybridized with Artificial Neural Networks (ANN) accurately predicts Reverse Osmosis desalination plant performance. This novel approach offers superior modeling for optimizing industrial plant design with minimal errors.

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Area of Science:

Optimization techniques
Artificial Intelligence
Chemical Engineering

Background:

Nature-inspired optimization methods are crucial for industrial process parameter optimization.
These methods offer simplicity, speed, and efficiency, saving time, money, and energy.
Reverse Osmosis (RO) desalination is a key technology for water production.

Purpose of the Study:

To develop accurate predictive models for RO desalination plant performance.
To utilize a hybrid Modified Whale Optimization Algorithm (MWOA) and Artificial Neural Networks (ANN) approach.
To improve upon existing models like Response Surface Methodology (RSM) and standard ANN.

Main Methods:

Hybridization of MWOA with ANN to create ten predictive models (MWOA-ANN Models 1-10).
Utilizing plant datasets with parameters: feed flow rate, evaporator inlet temperature, feed salt concentration, and condenser inlet temperature.
Evaluating model performance based on permeate flux prediction accuracy and error minimization.

Main Results:

MWOA-ANN models demonstrated superior permeate flux prediction compared to existing methods.
MWOA effectively optimized ANN weights and biases, preventing overfitting.
Model-6 (1 hidden layer, 11 nodes, 13 search agents) achieved R²=99.1% and MSE=0.005, with residual errors within limits.

Conclusions:

The developed MWOA-ANN models are highly effective for predicting RO desalination plant performance.
These models offer a promising tool for identifying optimal process parameters for industrial plant designers.
The study highlights the potential of hybrid optimization and AI techniques in process engineering.