State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
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...
| Authors: | , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2021 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/152828 |
| Online Access: | http://hdl.handle.net/11336/152828 |
| Access Level: | Open access |
| Keyword: | ON-LINE MONITORING PROFILES TRACKING CONTROL FED-BATCH BIOPROCESS NON-LINEAR AND MULTIVARIABLE SYSTEM STATE ESTIMATION GAUSSIAN PROCESS https://purl.org/becyt/ford/2.4 https://purl.org/becyt/ford/2 |
| Summary: | 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. |
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