Model-Based Monitoring of Biotechnological Processes—A Review

Abstract

The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.

Advancing Wine Fermentation: Extended Kalman Filter for Early Fault Detection

“…Among model-driven methods, nonlinear observers and filters, such as the Extended Kalman Filter (EKF), are among the most popular (Lyubenova et al, 2021). In Bárzaga-Martell et al (2021), the authors combine an EKF with an asymptotic observer for the estimation of biomass and ethanol from high-density yeast cultures using measurements of dissolved oxygen, CO 2 and glucose.…”

State Estimation in a Biodigester via Nonlinear Logistic Observer: Theoretical and Simulation Approach

“…Exponential observers: These are based on the process model and use some measurements from a hardware sensor; the main disadvantage of these observers is that they depend heavily on the process model quality [15]. The extended Luenberger observer [16], the receding horizon observer, and the high-gain observers [17] are exponential observers.…”

Simultaneous State and Kinetic Observation of Class-Controllable Bioprocesses

“…In the last decades, SSs have become established as promising tools for monitoring different bioprocesses [4][5][6][7][8][9][10][11][12][13]. In [7,11] model-based and data-based SSs are reviewed.…”

“…In the last decades, SSs have become established as promising tools for monitoring different bioprocesses [4][5][6][7][8][9][10][11][12][13]. In [7,11] model-based and data-based SSs are reviewed. It should be noted that the choice of SS method for a specific process and in particular biotechnological processes is not an easy task and requires an analysis of the following information: (i) complexity of the specific process; (ii) full/partial knowledge of the system' dynamics;…”