A systematic PAT Soft Sensor screening and development methodology applied to the prediction of free fatty acids in industrial biodiesel production. (15th December 2020) - Rato, Tiago J.; Neves, Diogo M.G.; Antunes, Anabela; Reis, Marco S.
Abstract: A systematic approach for advanced soft sensor development was applied to predict free fatty acid (FFA) content from NIR spectra under real plant conditions, namely in process streams from a biofuel producing unit integrating raw materials with time-varying complex matrices (e.g., wasted cooking oils, WCOs). The proposed methodology systematically screened through 52 combinations of preprocessing and inferential modeling methods, including current state of the art predictive methodologies, and the recently proposed multiresolution soft sensors. The model's prediction capabilities were compared through a rigorous framework based on Monte Carlo double cross-validation and statistical hypothesis testing. This study used 119 samples with FFA content in the range of 0.1030% to 5.6740%. The best model is based on the novel multiresolution soft sensor framework. This model had a coefficient of determination ( R 2 ) of 0.9792 and a prediction root mean squared error of 0.1187 (13.6% lower than the best model based on standard modelling methodologies). The proposed approach can be easily replicated to other scenarios where soft sensors based on Process Analytical Technology (PAT) are to be developed.