Abstract
Effective prediction of nutrient demands is crucial for optimising microalgae growth, maximising productivity and minimising the waste of resources. With the increasing amount of data related to microalgae cultivation systems, data mining and machine learning models to extract additional knowledge have gained popularity. In the development of such models, a data preprocessing stage is necessary due to the poor data quality. At this stage, cleaning and outlier removal techniques are employed to eliminate missing data and outliers, respectively. Afterwards, data splitting and cross-validation strategies are employed to ensure that the models are trained and evaluated with representative subsets of the data. Principal component analysis is also applied to simplify complex environmental datasets by reducing the number of features while retaining as much information as possible. To further improve prediction capabilities, ensemble methods are incorporated, leveraging multiple models to achieve a higher overall performance. Stacking, a popular ensemble technique, is used to combine the outputs of individual models into a single meta-model. The application of these machine learning methods has been demonstrated using a dataset acquired from the cultivation of the microalgae Dunaliella in a flat-panel photobioreactor. The results have shown that the data mining workflow, in combination with different machine learning models, was able to describe the nutrients’ requirements enforcing good performance of microalgae Dunaliella ?-carotene production in the carotenogenic phase.