Estimation of biomass enzymatic hydrolysis state in stirred tank reactor through moving horizon algorithms with fixed and dynamic fuzzy weights

Abstract

Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kineticmodeling and reactionmonitoring are hindered due to the conditions of themedium,while increasing themixing power. An algorithmthat addresses these challengesmight improve the reactor performance. In thiswork, a soft sensor that is based on agitation powermeasurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in aMoving Horizon Estimator (MHE) algorithmto improve theprediction of state variablesduring biomass hydrolysis. The algorithmisdeveloped and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filterweights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%.