Forecasting algae growth in photo-bioreactors using attention LSTMs

Sustainability is the current global challenge. This is reflected in the demand for healthy food and CO
neutrality. These challenges can be met with the industrial cultivation of algae: Algae can be used as food supplements, nutraceuticals, pharmaceuticals, fuel, CO
sinks, and obtain high relative yield density per area. Current limitations in their large-scale use exists, as scaling up from laboratory environments to pilot applications typically requires more than 5 years, because of highly complex interactions in the growth behavior: They are influenced by current and past environmental conditions. These interactions make current pilot applications inefficient due to insufficient control and monitoring techniques. This limitation can be countered: By using modern communication and evaluation technologies, a “smart” bioreactor can be developed, which evaluates algae growth in real-time, performs process adaptations and thus significantly accelerates algae growth and scale-up. Therefore, an algae bioreactor was established at the University of Technology Sydney. The subject of this paper is the study of algae growth using Long Short-Term Memory Neural Networks (LSTMs). In order to learn the behavior of algae in the shortest possible series of experiments, repetitive change intervals were run by systematically varying the environmental parameters. LSTMs were trained to model algae growth. Attention mechanism is used on variable and temporal direction for importance. The LSTM is compared to a Transformer and an ARIMA. Based on the trained models, the behavior of algae growth is interpreted.