Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning.

Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to complex operational and environmental interactions. Neural Ordinary Differential Equations, Elastic Net, Stacking, and Categorical Boosting were applied as artificial intelligence methods to predict chemical oxygen demand (COD) removal efficiency, biomass productivity, biomass yield, and energy yield. Among these, the Stacking model demonstrated superior predictive performance across all targets. Interpretable machine learning methods were employed to identify key features and establish their workable ranges, which included dissolved oxygen (0.3-2.8 mg L⁻), illuminance (2995.3-6000.0 lux), and light energy (20.0-40.0 kWh) for COD removal efficiency; organic loading rate (OLR, 5.7-7.5 g COD L⁻ d⁻), hydraulic retention time (HRT, 0.2-3.2 d), and COD concentration (5.3-10.1 g L⁻) for biomass productivity; COD/N ratio (609.0-800.0), OLR (0.1-2.4 g COD L⁻ d⁻), and illuminance (2661-6000 lux) for biomass yield; and pH (6.5-7.9) and HRT (1.2-2.6 d) for energy yield. The two-dimensional partial dependence plots revealed that optimal interactions between two key input features resulted in COD removal efficiency >72%, biomass productivity >28 g L⁻ d⁻, biomass yield> 0.96 g COD g COD⁻, energy yield> 0.49 g kWh⁻. This work advances the understanding of PSB optimization in wastewater treatment through a combination of advanced machine learning and interpretability analysis, offering potential for more efficient resource recovery and process optimization.