Modelling challenges to unlock the power of phototrophic systems for wastewater valorization

Abstract

Phototrophic microorganisms are gaining prominence for their dual role in wastewater treatment and resource recovery, converting wastewater into valuable bioproducts. However, their effective deployment needs robust modelling frameworks capable of predicting performance across complex, real-world scenarios. Despite significant advances, key challenges hinder the development and application of such models:

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  Biological complexity: phototrophic systems involve intricate processes (e.g., photosynthesis, nutrient uptake, microbial interactions, and predation) that are difficult to represent accurately due to their dynamic interdependencies.
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  Environmental variability: permanent fluctuations in light, temperature, pH, and toxic compounds in outdoor reactors require high-resolution dynamic data for reliable model calibration and prediction.
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  Data limitations: lack of comprehensive, high-quality datasets (e.g., biological, environmental, and operational conditions) constrains model development, particularly for data-driven approaches.
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  Multi-scale integration: bridging molecular, cellular, and ecosystem-level processes into a unified modelling framework, including physics, remains a significant hurdle.
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  Parameter and uncertainty management: models often suffer from non-identifiable parameters, sensitivity to approximations, and insufficient validation against long-term experimental data.
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  Balancing complexity and applicability: selecting the appropriate level of ecological and mathematical details, tailored to specific applications (e.g., biomass production and nutrient removal) and data availability is critical yet challenging.
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  Computational and interdisciplinary barriers: high computational costs, especially for hybrid and data-driven models, alongside the need for cross-disciplinary collaboration, further complicate model development.
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  To overcome these barriers, this work argues for standardized protocols in model design, calibration and validation, alongside enhanced data collection and reconciliation efforts. Integrating innovative approaches, such as metabolic modelling, machine learning and hybrid modelling into digital twins, will be essential to unlock the full potential of phototrophic systems, bridging the gap between theoretical models and industrial implementation.