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Metaheuristic-Optimized Artificial Neural Networks for Scaling Prediction and Chemical Dosage Optimization in Thermal Desalination.

Sofia Arkhstan
Volume 5

Abstract

Thermal desalination plants are widely used for large-scale freshwater production, particularly in regions with high seawater salinity and limited natural water resources. However, scaling formation remains one of the most critical operational challenges affecting the efficiency, reliability, and lifespan of thermal desalination systems. Scale deposits caused by calcium carbonate, calcium sulfate, magnesium hydroxide, and other sparingly soluble salts can reduce heat transfer efficiency, increase thermal energy consumption, restrict brine flow, accelerate corrosion, and require frequent shutdowns for cleaning and maintenance. Effective prediction of scaling tendency and accurate chemical dosage optimization are therefore essential for maintaining stable plant operation, minimizing operational cost, and improving desalination performance. To address these challenges, this paper proposes a metaheuristic-optimized Artificial Neural Network framework for scaling prediction and chemical dosage optimization in thermal desalination systems.

The proposed framework utilizes historical and real-time plant data, including seawater temperature, feed salinity, pH, total dissolved solids, calcium concentration, magnesium concentration, sulfate concentration, bicarbonate alkalinity, brine concentration factor, top brine temperature, stage pressure, recovery ratio, flow rate, heat-transfer performance, antiscalant dosage, and cleaning history. A comprehensive preprocessing pipeline is applied to remove abnormal readings, handle missing sensor values, normalize process variables, and construct scaling-related indicators such as saturation index, concentration polarization tendency, brine supersaturation level, and heat-exchanger fouling rate. The Artificial Neural Network is designed to model the nonlinear relationships between feedwater chemistry, thermal operating conditions, chemical dosing, and scaling risk.

To improve prediction accuracy and reduce dependence on manual tuning, metaheuristic optimization algorithms are incorporated to optimize key ANN parameters, including the number of hidden neurons, learning rate, activation functions, initial weights, biases, regularization coefficients, and training configuration. Algorithms such as Particle Swarm Optimization, Grey Wolf Optimizer, Genetic Algorithm, Whale Optimization Algorithm, and Differential Evolution can be employed to identify near-optimal network structures and dosing strategies. The optimized ANN predicts scaling probability, severity level, and expected deposition risk under different operating conditions, while the dosage optimization module recommends appropriate antiscalant dosing levels that minimize chemical consumption without compromising scale control.

The proposed model can be evaluated using operational records from thermal desalination plants, pilot-scale experiments, and process simulation data. Performance is assessed using classification and regression metrics, including accuracy, sensitivity, specificity, F1-score, Mean Absolute Error, Root Mean Square Error, coefficient of determination, false alarm rate, and early warning lead time. Chemical optimization performance is evaluated through reduction in antiscalant consumption, improvement in heat-transfer efficiency, decrease in cleaning frequency, reduction in specific energy consumption, and maintenance of safe scaling indices. Comparative analysis is conducted against conventional saturation-index methods, rule-based dosing strategies, standard artificial neural networks, support vector regression, random forest, and non-optimized machine learning models.

Experimental evaluation is expected to demonstrate that the metaheuristic-optimized ANN provides more accurate scaling prediction and more efficient chemical dosage recommendations than conventional approaches. By capturing complex nonlinear interactions among seawater chemistry, temperature, brine concentration, and plant operating conditions, the proposed framework can support proactive scale management and prevent excessive or insufficient chemical dosing. Its integration into thermal desalination monitoring and control systems can improve operational stability, reduce chemical cost, enhance heat-transfer performance, extend equipment lifetime, and contribute to more energy-efficient and sustainable freshwater production.

 

Keywords

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