State estimation and nonlinear tracking control simulation approach. Application to a bioethanol production system
| Autor: | Leandro Rodriguez, M. Cecilia Fernández, M. Nadia Pantano, Gustavo Scaglia |
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| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
FED-BATCH BIOPROCESS Computer science NON-LINEAR AND MULTIVARIABLE SYSTEM Bioengineering 01 natural sciences symbols.namesake Control theory 010608 biotechnology Gaussian process GAUSSIAN PROCESS Parametric statistics Artificial neural network 010405 organic chemistry STATE ESTIMATION Estimator General Medicine 0104 chemical sciences purl.org/becyt/ford/2.4 [https] Nonlinear system purl.org/becyt/ford/2 [https] ON-LINE MONITORING Linear algebra symbols Performance improvement PROFILES TRACKING CONTROL Biotechnology |
| Zdroj: | CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET |
| ISSN: | 1615-7605 1615-7591 |
| DOI: | 10.1007/s00449-021-02558-y |
| Popis: | Tracking control of specifc variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is refected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators. Fil: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina Fil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina Fil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina |
| Databáze: | OpenAIRE |
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