Prediction of intensified ethanol fermentation of sugarcane using a deep learning soft sensor and process analytical technology
Elmer Ccopa Rivera, Celina K. Yamakawa, Carlos EV Rossell, Jonas Nolasco, Hyun J. Kwon
Abstract
Abstract BACKGROUND Intensified ethanol fermentation produces higher ethanol concentrations while reducing water and energy requirements. Nevertheless, the inhibitory and detrimental effect of the cellular stress barriers in this process further complicates the nonlinear dynamic relationship between the variables that directly reflect the fermentation quality. These key variables are hard to measure in real time and therefore cannot be directly controlled. RESULTS This work presents the development of a soft sensor that predicts in real time the ethanol and substrate concentrations of an intensified fermentation. The soft sensor uses feedforward neural networks (FNNs) with easily measurable process analytical technology (PAT) tools. The application of advanced PAT tools such as redox potential and capacitance, in addition to temperature and pH are explored as input variables. The complex kinetic relationship between the studied variables was captured with FNN architectures with a single hidden layer and between 95 and 175 hidden neurons for the different cases studied. Acceptable predictions are achieved for the concentration of ethanol (RMSE = 9.5 and R 2 = 0.97) and substrate (RMSE = 17.02 and R 2 = 0.92). CONCLUSIONS The results confirm that the proposed soft sensor can accurately predict the ethanol and substrate concentrations. Collectively, capacitance, redox potential, temperature and pH provide a powerful platform of PAT tools that can directly infer key variables showing the fermentation quality in real time. This study provides a significant step towards the systematic development of a reliable soft sensor with integration of advanced PAT tools. © 2023 Society of Chemical Industry.