Journal of Water Process Engineering· 2026Q1
A comparative study of mechanistic and hybrid model structures for microalgae-based tertiary wastewater treatment systems
- 0citations
- Q1SCImago
- 2026year
Short summary
Residual-corrected hybrid models improved microalgae biomass (X alg ) prediction accuracy by 0.15–0.42 in R 2 and nutrient (S PO 4 , S NO 3 ) RMSE by 4–62% compared to mechanistic models in pilot-scale wastewater treatment.
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Key points
- Mechanistic models incorporating internal nutrient storage and biomass decay showed superior predictive accuracy over simpler models.
- Residual-corrected hybrid models improved R 2 for microalgae biomass by 0.15–0.42.
- Residual-corrected hybrid models reduced RMSE for phosphate and nitrate by 4–62% compared to base mechanistic models.
- Factor-prediction hybrid models matched mechanistic model performance for light limitations with fewer parameters.
AI-generated from the title and abstract; the full text is not read.
Abstract
Microalgae-based wastewater treatment (WWT) systems are a sustainable approach to nutrient removal with biomass valorisation. However, accurately modelling these systems remains complex due to the intricate relations between environmental input variability and biological processes. This study conducts a systematic comparison of mechanistic and hybrid modelling methodologies for predicting biomass growth and nutrient dynamics (ammonia, nitrate, phosphate) in pilot-scale systems operated under natural conditions with real municipal effluent after secondary treatment. Mechanistic models included Monod-, Droop-, and Caperon-Meyer-type nutrient kinetics, with variations that incorporated light, temperature, and biomass decay factors. Additionally, two hybrid modelling approaches were tested, residual-corrected hybrids, where machine learning (ML) algorithms adjust the residuals of mechanistic model predictions, and factor-prediction hybrids, where ML techniques estimate values for environmental limitation factors. Models were calibrated and validated with experimental data for microalgae biomass (X alg ), ammonia (S NH4 ), nitrate (S NO3 ), and phosphate (S PO4 ), and assessed with R 2 and root mean squared error (RMSE) metrics. Findings indicate that mechanistic models incorporating internal nutrient storage and biomass decay provide superior predictive accuracy when compared to those based solely on external nutrient concentrations. Residual-corrected hybrid models enhanced prediction accuracies for X alg by 0.15–0.42 in R 2 and for S PO 4 and S NO 3 by 4–62% in RMSE, depending on the base model. Factor-prediction hybrids performed similarly to mechanistic models, considering light limitations, although with a lower number of calibrated parameters. This study discusses trade-offs between model complexity, interpretability, and predictive capability, offering a framework for integrating mechanistic and data-driven methodologies in microalgal WWT systems.
The authors' abstract, as published at the source. Journal of Water Process Engineering, 2026 · DOI ↗
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