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00925njm 22002777a 4500 dc Casagli, Francesca author Turolla, Andrea author Batstone, D.J. author Capson Tojo, Gabriel author Ficara, Elena author García Serrano, Joan author González Flo, Eva author Laurent, Julien author Lorenz, Tatjana author Pierrelée, Michaël author Plósz, Benedek Gy. author Ribero da Silva, Gustavo Henrique author Robles, Angel author Rossi, Simone author Rueda Hernández, Estel author Stegemüller, Lars author Steyer, Jean Phillipe author Bernard, Olivier author Valverde Pérez, Borja author 2025-12-01 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: ¿ 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. ¿ 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. ¿ Data limitations: lack of comprehensive, high-quality datasets (e.g., biological, environmental, and operational conditions) constrains model development, particularly for data-driven approaches. ¿ Multi-scale integration: bridging molecular, cellular, and ecosystem-level processes into a unified modelling framework, including physics, remains a significant hurdle. ¿ Parameter and uncertainty management: models often suffer from non-identifiable parameters, sensitivity to approximations, and insufficient validation against long-term experimental data. ¿ 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. ¿ 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. ¿ 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. Concerning the contribution of Universitat Politécnica de Catalunya ⋅ BarcelonaTech, this publication is part of the R&D&I project PID2021- 126564OB-C32 (Cyan2Bio), funded by MICIU/AEI/10.13039/ 501100011033 and by ERDF/EU. JG would like to acknowledge the support provided by the ICREA Academia program. GR would like to acknowledge the Brasilian National Council for Scientific and Technological Development (CNPq process 308663/2021-7). EGF would like to thank the European Union-NextGenerationEU, Spanish Ministry of Science, Innovation and Universities, and Recovery, Transformation and Resilience Plan for her research grant [2021UPF-MS-12]. FC and OB are grateful to the support of the ANR Barrier project (ANR-22-CE34-0021). BVP acknowledges the financial support provided by the Danish Research Council for Independent Research under the Sapere Aude DFF Starting Grant 10.46540/2067–00029B. Peer Reviewed Article signat per xx autors/es: Francesca Casagli a, Andrea Turolla b,*, Damien J. Batstone c, Gabriel Capson-Tojo d, Elena Ficara b, Joan García e, Eva Gonzalez-Flo f, Julien Laurent g, Tatjana Lorenz h, Michaël Pierrelée i, Benedek Gy. Plósz j,n, Gustavo Henrique Ribero Da Silva k, Ángel Robles l, Simone Rossi b,o, Estel Rueda e, Lars Stegemüller m, Jean-Philippe Steyer d, Olivier Bernard a, Borja Valverde-Pérez i a Centre INRIA d’Universit´e Cˆote d’Azur, GreenOwl team, Sophia-Antipolis 06902, France b Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy c Australian Centre for Water and Environmental Biotechnology, The University of Queensland, Brisbane 4072, QLD, Australia d INRAE, Univ Montpellier, LBE, 102 Avenue des ´Etangs, Narbonne 11100, France e GEMMA - Group of Environmental Engineering and Microbiology, Department of Civil and Environmental Engineering, Universitat Polit`ecnica de Catalunya BarcelonaTech, c/ Jordi Girona 1-3, Building D1, E-08034 Barcelona, Spain f GEMMA - Group of Environmental Engineering and Microbiology, Department of Civil and Environmental Engineering, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Polit`ecnica de Catalunya⋅BarcelonaTech, Av. Eduard Maristany 16, Building C5.1, E-08019 Barcelona, Spain g Universit´e de Strasbourg, ENGEES, CNRS, ICube UMR 7357, F-67000 Strasbourg, France h Department of Architecture and Civil Engineering, ZEuUS - Competence Centre for Sustainable Engineering and Environmental Systems, THM - University of Applied Sciences, Wiesenstr. 14, 35390 Giessen, Germany i Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet, Bygning 115, 2800 Kgs, Lyngby, Denmark j Department of Chemical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK k Department of Civil and Environmental Engineering, School of Engineering, S˜ao Paulo State University (UNESP), Bauru, SP, Brazil l Departament d’Enginyeria Química, ETSE-UV, Universitat de Val`encia, Avinguda de la Universitat s/n, Valencia, Burjassot 46100, Spain m Department of Chemical Engineering, Technical University of Denmark, DTU, Søltofts Plads 228A, Lyngby 2800, Denmark n SWING – Department of Built Environment, Oslo Metropolitan University, Postboks 4 St Olavs Plass, 0130 Oslo, Norway o Energy Technologies and Processes Research Group, Generation Technologies and Materials Department, Ricerca sul Sistema Energetico - RSE S.p.A., v. Rubattino 54, 20134 Milano, Italy 6 - Aigua Neta i Sanejament Postprint (published version) Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària Microalgae Cyanobacteria Purple phototrophic bacteria Mechanistic modelling Data-driven modelling Digital twins Wastewater treatment Circular economy Modelling challenges to unlock the power of phototrophic systems for wastewater valorization