Explainable Machine Learning Models for Identifying Key Operational Parameters Affecting Desalination Plant Performance.

Sofia Arkhstan

Explainable Machine Learning Models for Identifying Key Operational Parameters Affecting Desalination Plant Performance.

Sofia Arkhstan
Volume 5

Abstract

Desalination plant performance is influenced by a complex interaction of operational, environmental, and water quality parameters that affect freshwater production, energy consumption, membrane integrity, thermal efficiency, fouling tendency, scaling risk, and overall process reliability. In both membrane-based and thermal desalination systems, variables such as feedwater salinity, temperature, turbidity, pH, operating pressure, flow rate, recovery ratio, pretreatment efficiency, chemical dosage, membrane age, differential pressure, top brine temperature, steam consumption, and cleaning frequency can significantly alter plant efficiency. Conventional performance assessment methods often rely on empirical correlations, fixed operating thresholds, or operator experience, which may be insufficient to capture nonlinear relationships among process variables and may provide limited insight into the root causes of performance decline. To address these limitations, this paper proposes an explainable machine learning framework for identifying key operational parameters affecting desalination plant performance.

The proposed framework utilizes historical and real-time data collected from desalination plant monitoring systems, including process sensors, water quality analyzers, energy meters, maintenance logs, chemical dosing records, and supervisory control systems. A comprehensive preprocessing pipeline is applied to handle missing values, remove abnormal readings, normalize numerical variables, align multi-rate measurements, and construct performance indicators such as specific energy consumption, permeate flux, salt rejection rate, recovery ratio, gain output ratio, production efficiency, fouling index, scaling tendency, and plant availability. Multiple machine learning models, including Random Forest, XGBoost, LightGBM, CatBoost, support vector regression, and artificial neural networks, are developed to predict desalination performance outcomes under varying operating conditions.

To enhance transparency and practical usability, explainable artificial intelligence techniques are incorporated into the modeling framework. Global interpretability methods, including feature importance analysis, SHAP values, permutation importance, and partial dependence analysis, are used to identify the most influential variables affecting plant-level performance. Local explanation methods are employed to explain individual performance deviations, enabling operators to understand why specific operating periods are associated with reduced production, increased energy consumption, lower salt rejection, or elevated fouling risk. These explanations can reveal critical operational drivers such as high feed salinity, increased differential pressure, reduced pretreatment efficiency, elevated turbidity, excessive recovery ratio, membrane aging, steam flow variation, or suboptimal chemical dosing.

The proposed framework can be evaluated using operational datasets from Reverse Osmosis, Multi-Stage Flash, Multi-Effect Distillation, or hybrid desalination plants. Model performance is assessed using regression and classification metrics, including Mean Absolute Error, Root Mean Square Error, coefficient of determination, accuracy, sensitivity, specificity, F1-score, and prediction stability. The explanatory outputs are validated through expert review, operational event analysis, and comparison with established desalination process knowledge. Experimental evaluation is expected to demonstrate that explainable machine learning models can accurately predict plant performance while providing actionable insights into the operational parameters responsible for efficiency losses and reliability issues.

By combining predictive modeling with transparent interpretation, the proposed framework provides a practical decision-support tool for desalination plant optimization. It can assist operators in prioritizing corrective actions, optimizing pressure and recovery settings, improving pretreatment control, adjusting chemical dosage, planning membrane cleaning, reducing energy consumption, and preventing performance degradation. The proposed explainable machine learning approach supports more reliable, efficient, and sustainable desalination operation by transforming complex plant data into interpretable knowledge that can guide real-time monitoring, predictive maintenance, and process optimization.

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References

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