Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study

Accurate prediction of microalgae growth is crucial for understanding the impacts of light dynamics and optimizing production. Although various mathematical models have been proposed, only a few of them have been validated in outdoor cultivation. This study aims to investigate the use of machine learning algorithms in microalgae growth modeling. Outdoor cultivation data of Phaeodactylum tricornutum in flat-panel airlift photobioreactors for 50 days were used to compare the performance of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) with traditional models, namely Monod and Haldane. The results indicate that the machine learning models outperform the traditional models due to their ability to utilize light history as input. Moreover, the LSTM model shows an excellent ability to describe the light acclimation effect. Last, two potential applications of these models are demonstrated: 1) use as a biomass soft sensor and 2) development of an optimal harvest strategy for outdoor cultivation.

Automated summary

This study investigates the use of machine learning algorithms in modeling microalgae growth and compares their performance with traditional models. The results show that machine learning models outperform traditional models by utilizing light history as input and have excellent ability in describing the light acclimation effect. Two potential applications of these models, including their use as a biomass soft sensor and development of optimal harvest strategy for outdoor cultivation, are also demonstrated.