Integrating mechanistic and deep learning models for accurately predicting the enrichment of polyhydroxyalkanoates accumulating bacteria in mixed microbial cultures.

The enrichment of polyhydroxyalkanoates (PHA) accumulating bacteria (PAB) in mixed microbial cultures (MMC) is extremely difficult to be predicted and optimized. Here we demonstrate that mechanistic and deep learning models can be integrated innovatively to accurately predict the dynamic enrichment of PAB. Well-calibrated activated sludge models (ASM) of the PAB enrichment process provide time-dependent data under different operating conditions. Recurrent neural network (RNN) models are trained and tested based on the time-dependent dataset generated by ASM. The accurate prediction performance is achieved (R2 > 0.991) for three different PAB enrichment datasets by the optimized RNN model. The optimized RNN model can also predict the equilibrium concentration of PAB (R2 = 0.944) and corresponding time, which represents the end of the PAB enrichment process. This study demonstrates the strength of integrating mechanistic and deep learning models to predict long-term variations of specific microbes, helping to optimize their selection process for PHA production.