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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Furlong, Vitor B. [1] ; Correa, Luciano J. [2] ; Lima, Fernando V. [3] ; Giordano, Roberto C. [1, 4] ; Ribeiro, Marcelo P. A. [1, 4]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Fed Sao Carlos, Grad Program Chem Engn, POB 676, BR-13565905 Sao Carlos, SP - Brazil
[2] Univ Fed Lavras, Dept Engn, POB 3037, BR-37200000 Lavras, MG - Brazil
[3] West Virginia Univ, Dept Chem & Biomed Engn, Morgantown, WV 26506 - USA
[4] Univ Fed Sao Carlos, Chem Engn Dept, POB 676, BR-13565905 Sao Carlos, SP - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: PROCESSES; v. 8, n. 4 APR 2020.
Citações Web of Science: 0
Resumo

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%. (AU)

Processo FAPESP: 16/10636-8 - Da fábrica celular à biorrefinaria integrada Biodiesel-Bioetanol: uma abordagem sistêmica aplicada a problemas complexos em micro e macroescalas
Beneficiário:Roberto de Campos Giordano
Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Temático