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Volume 8
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10.3390/pr8040407
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Article

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

by
Vitor B. Furlong
1,
Luciano J. Corrêa
2,
Fernando V. Lima
3,
Roberto C. Giordano
1,4 and
Marcelo P. A. Ribeiro
1,4,*
1
Graduate Program of Chemical Engineering, Federal University of São Carlos, P.O. Box 676, São Carlos 13565-905, SP, Brazil
2
Department of Engineering, Federal University of Lavras, P. O. Box 3037, Lavras 37200-000, MG, Brazil
3
Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
4
Chemical Engineering Department, Federal University of São Carlos, P.O. Box 676, São Carlos 13565-905, SP, Brazil
*
Author to whom correspondence should be addressed.
Processes 2020, 8(4), 407; https://doi.org/10.3390/pr8040407
Submission received: 29 January 2020 / Revised: 23 March 2020 / Accepted: 27 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue Bioenergy Systems, Material Management, and Sustainability)
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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%.
Keywords: artificial neural network; biomass enzymatic hydrolysis; fuzzy logic; local linear model tree; moving horizon estimation; process monitoring; soft sensing artificial neural network; biomass enzymatic hydrolysis; fuzzy logic; local linear model tree; moving horizon estimation; process monitoring; soft sensing

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MDPI and ACS Style

Furlong, V.B.; Corrêa, L.J.; Lima, F.V.; Giordano, R.C.; Ribeiro, M.P.A. Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights. Processes 2020, 8, 407. https://doi.org/10.3390/pr8040407

AMA Style

Furlong VB, Corrêa LJ, Lima FV, Giordano RC, Ribeiro MPA. Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights. Processes. 2020; 8(4):407. https://doi.org/10.3390/pr8040407

Chicago/Turabian Style

Furlong, Vitor B., Luciano J. Corrêa, Fernando V. Lima, Roberto C. Giordano, and Marcelo P. A. Ribeiro. 2020. "Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights" Processes 8, no. 4: 407. https://doi.org/10.3390/pr8040407

APA Style

Furlong, V. B., Corrêa, L. J., Lima, F. V., Giordano, R. C., & Ribeiro, M. P. A. (2020). Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights. Processes, 8(4), 407. https://doi.org/10.3390/pr8040407

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