LSTM based Hybrid Model for Improved E.coli Estimation in Laboratory Bioreactor

A bioreactor is a specialized vessel which has the provision of cell cultivation under a sterile environment and the control of the environmental parameters enhances growth. A variety of products related to the food, beverage, and pharmaceutical industries are produced using bioprocesses. Because of the complex dynamics of bioprocesses, controlling them is a difficult and delicate undertaking. Additionally, because of their high nonlinearity, modelling and parameter estimates are made even more challenging. The difficulty of this endeavour is increased by the dearth of online measurements for the biomass and substrate concentrations. The design of an effective controller for any process requires an efficient model. A combination of more than one type of model in a hybrid form can give a better performance. The first principles model is coupled with the data obtained from the real-time bioreactor setup to yield a hybrid model. The process parameters are estimated utilizing a recurrent neural network approach. The developed hybrid model is tested with the real-time bioreactor response and found to be satisfactory.