Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

Vitor B Furlong,Luciano J Corrêa,Marcelo P A Ribeiro,Fernando V Lima,Roberto C Giordano

doi:10.3390/pr8040407

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, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements 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 a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed 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 filter weights 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%.

Highlights

The optimization of reactors to operate at high solids concentrations is an important step to enable the enzymatic hydrolysis of lignocellulosic biomass as a feasible technology
The use of Moving Horizon Estimator (MHE) together with the soft sensing was successful in improving improving the prediction of desired state variables, regardless of the weights used within the implementation
The novel fuzzy dynamic weights for MHE greatly improved the prediction in both training data sets (16.2% Root Mean Squared Error (RMSE) reduction when compared to the model prediction alone) and test data sets (36.8% RMSE reduction when compared to the model prediction alone)

Summary

Introduction

The optimization of reactors to operate at high solids concentrations is an important step to enable the enzymatic hydrolysis of lignocellulosic biomass as a feasible technology. Operating under these conditions can cause some issues in the saccharification kinetics, from substrate and product inhibition to heterogeneous agitation, due to several challenges. Some of these challenges can be attributed to reactor homogenization, which is a difficult task at high apparent viscosity. Great attention should be paid to this production state if a financially sound second generation production technology is one’s end goal

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Conclusion