Comparative Analysis of a Family of Sliding Mode Observers under Real-Time Conditions for the Monitoring in the Bioethanol Production

Abstract

Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sensors, or simply because some variables cannot be directly measured. From the above, a model-based approach such as state observers may be a viable alternative to solve the estimation problem. This work shows a comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production. These observers were selected for their robust performance under model uncertainties and finite-time estimation convergence. The selected sliding-mode observers were the first-order sliding mode observer, the proportional sliding mode observer, and the high-order sliding mode observer. For estimation purposes, a power law kinetic model for ethanol production by Saccharomyces cerevisiae was performed. A hybrid methodology allows the kinetic parameters to be adjusted, and an approach based on inference diagrams allows the observability of the model to be determined. The experimental results reported here show that the observers under analysis were robust to modeling errors and measurement noise. Moreover, the proportional sliding-mode observer was the algorithm that exhibited the best performance.

Monitoring Ethanol Fermentation in Real Time by a Robust State Observer for Uncertainties

“…These types of models, however, together with the so‐called macroscopic methods, from the widely accepted extensive property equilibrium method of chemical engineering, provide the user with a convenient starting point for the analysis of microbial systems and for general purposes of design [38]. These types of models, although very simple, have been successfully applied in many studies, as well as in industry, for observation, control, and optimization purposes [1, 38]. In applying such a simplified model, one must of course be cautious when using it [39, 41].…”

“…Aguilar et al [60] presented a structure with stabilizing effects on the observer performance should to the proportional gain and provides asymptotic convergence. Alvarado et al [38] implemented it in a batch fermenter under real-time conditions and demonstrated good performance:…”

“…The coefficient of performance has a good degree of fit with respect to the sample values when E ∈ (0,1], if E > 0.9 represents a good coupling between the model and the experimental data [36,37]. RSD is a statistical measure that describes the spread of the data with respect to the mean and the result expressed as a percentage [38]. Tab.…”

“…Petre et al [37] coupled an asymptotic observer in intervals to a robust adaptive control law for controlling a recycled wastewater treatment process with a time delay. Alvarado et al [38] implemented in real time a family of sliding mode observers, which were applied to alcoholic fermentation with Saccharomyces cerevisiae. The instrumentation and execution of the observation strategies were performed in a cRIO-9030-NI [38].…”

“…Alvarado et al. [38] implemented in real time a family of sliding mode observers, which were applied to alcoholic fermentation with Saccharomyces cerevisiae . The instrumentation and execution of the observation strategies were performed in a cRIO‐9030‐NI [38].…”