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Artificial intelligence for predicting volatile fatty acids rejection in nanofiltration membranes

Year: 2026

Type: article

Abstract: Artificial intelligence (AI) modeling shows strong potential for predicting process performance in environmental technologies, but its application is often constrained by the scarcity of high-quality and comprehensive datasets, which limits model accuracy and generalizability. Synthetic data generation has emerged as a promising strategy to address data scarcity, yet its effectiveness and reliability remain underexplored. In this study, we systematically examined the impact of partially synthetic datasets on the predictive performance of a widely used model – the categorical boosting regressor (CatBoost) – for volatile fatty acids (VFAs) rejection in nanofiltration processes. Ten different synthetic data generation algorithms were tested while varying the ratio of synthetic-to-actual data in the training datasets. Results showed that moderate augmentation (synthetic/actual data ratio ranging from 0.36 to 1.33) improved predictive accuracy (R 2 up to 0.937 vs 0.885 for the baseline with only actual data), whereas excessive reliance on synthetic data led to performance deterioration. A maximum mean discrepancy (MMD)-controlled approach was introduced to constrain synthetic data, further improving accuracy and model explainability. SHapley Additive exPlanations (SHAP) analysis confirmed that the most influential features identified by the models – feed pH, membrane zeta potential, and applied pressure – were consistent with experimental evidence reported in the literature, ensuring domain-consistent and reliable model behavior. Overall, this study provides evidence of the feasibility and benefits of using synthetic data to enhance AI modeling of pressure-driven membrane processes for sustainable resource recovery. The proposed framework offers a pathway to mitigate data scarcity while improving model robustness and interpretability. • AI models accurately predict volatile fatty acid rejection in nanofiltration. • Implementing partially synthetic training datasets affects AI model accuracy. • A synthetic-to-actual data ratio between 0.36 and 1.33 yields improved predictions. • SHAP analysis reveals enhanced model interpretation after data augmentation. • Controlled synthetic data generation ensures reliability and domain consistency.

Source: Journal of Water Process Engineering

DOI: 10.1016/j.jwpe.2025.109412