A seasonally validated coupled thermal-biological model for predicting and optimizing microalgae productivity in outdoor raceway ponds.

Authors

Min Yang, Wenbo Cui, Taotao Zeng, Yu Tao, Ying Chen, Ai-Jie Wang

Published in

Bioresource technology. Pages 136048. Oct 11, 2026. Epub Oct 11, 2026.

Abstract

Open raceway ponds (ORPs) are widely used for the outdoor large-scale cultivation of microalgae, but their productivity is strongly affected by the coupling of multiple environmental and operational factors. To improve prediction under real outdoor conditions, this study developed a mechanistic model that couples heat transfer with multi-limiting growth kinetics. The model integrates temperature dynamics with light availability, salinity, dissolved oxygen inhibition, and nitrogen limitation, while also accounting for gas-liquid mass transfer and nutrient consumption. It was calibrated and validated using outdoor data of Scenedesmus obliquus UTEX393 collected over four seasons: spring, summer, autumn, and winter. The thermal model showed good predictive performance, with an error of - 0.36 ± 1.55 °C. The growth model captured the overall seasonal dynamics of ash-free dry weight (AFDW), ammonium nitrogen (NH₄⁺-N), and dissolved oxygen (DO), although DO predictions showed greater uncertainty than AFDW and NH₄⁺-N, particularly during autumn and winter. Based on the validated coupled model, culture depth and harvest concentration were further optimized to clarify their combined effects on productivity under different seasonal conditions. The results showed that a greater culture depth and a lower harvest concentration were preferable in summer to alleviate heat stress and self-shading, with the optimal combination being 25 cm and 300 mg/L. In winter, shallow cultivation with a higher harvest concentration was more suitable, with the optimal combination being 7.5 cm and 700 mg/L. This study provides a mechanistic framework for dynamic prediction and operational optimization of outdoor microalgae cultivation in ORPs.

PMID:
42860227
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.

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