Feedforward Artificial Neural Networks-Model Prediction Control (FANN-MPC) for Semi-Simultaneous Saccharification and Fermentation (SSSF) Bioethanol Process
Keywords:Process Control, Model Predictive Control, Bioethanol, Nonlinear Model Predictive Control, Semi-simultaneous saccharification and fermentation (SSSF)
The main focus of this work is on the development of nonlinear model-based control for the production of bioethanol from lignocellulosic materials following the semi-simultaneous saccharification and fermentation (SSSF) process. It is observed from the current study that the SSSF process is able to give higher yield and higher ethanol concentration in comparison with the simultaneous saccharification and fermentation (SSF) process, and the separate hydrolysis and fermentation (SHF) process. The SSSF process in this study, which is referred to as the SSSF 24, includes 24 hours of pre-hydrolytic phase, and 48 hours of SSF phase. Mechanistic models for the fed-batch operating mode of SSSF 24 were initially developed to represent the actual dynamics of this fermentation process. Feedforward Artificial Neural Network (FANN) model was developed in nonlinear model predictive control (NMPC) to improve the performance of the controller. Data for the training, testing and validation procedure to construct the FANN were generated through randomization of the input cellulose concentration and dilution rate using the mechanistic models. The total sum of squared errors (SSE) for training and testing, and coefficient of determination, R2, value for fed-batch operating mode are 0.0508 and 0.9998, respectively, for their best FANN architecture of 2 – 15 – 1 with 15 hidden neurons. The FANN-model predictive control (NMPC) strategy developed for the fed-batch operating mode of SSSF 24 has good setpoint tracking ability and is robust
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