Estimation of kinetic rates in a baker's yeast fed-batch bioprocess by using non-linear observers

Abstract

The growth rates of the microorganisms in bioreactors are described by complex kinetic expressions, and the modelling and estimation of such kinetics are essential for the design of control strategies. This study deals with the online estimation of kinetic rates of a baker's yeast process, taking place inside a fed-batch bioreactor. This bioprocess is widely used in bioindustry; its model is highly non-linear and, furthermore, the available online measurements are missing and the reaction kinetics is not perfectly known. The unknown kinetics is estimated by using non-linear observers, based on high-gain approach. Two high-gain observers are designed and implemented: one for the specific growth rates and the other for the reaction rates of the baker's yeast bioprocess. The multiple estimation schemes do not require any model for the kinetic rates. The tuning of the proposed observers is reduced to the calibration of a single parameter. An observer-based estimator is also implemented in order to use it for comparisons. Numerical simulations are included to test the behaviour and the performance of the proposed observers.

Adaptive Monitoring of Biotechnological Processes Kinetics

“…The system stability is investigated based on the analysis of system (12), where matrix A and vectors x and u are presented as:…”

“…Such an operational model was proposed by Bastin and Dochain [4] as the general dynamical model (GDM) of bioprocesses in stirred tank reactors (STR). Approaches based on GDM software sensors are widely applied simultaneously with other approaches for nonlinear systems, such as extended Kalman and Luenberger filters [5][6][7][8], moving horizon [9,10] neural-network based observers [11], high-gain approach [12], multirate observers [13], sliding mode-observers [14,15], interval SS [16], cascade SS [17][18][19], and joint estimation of state variables and parameters [1,20,21], among others.…”

“…Usually, the software sensors are designed using operational models with constant yield coefficients [4,12,15,20,21]. For many industrial biotechnological processes, like wastewater treatment and processes in inhomogeneous mediums, the reproducibility is poor [18,22].…”

Robust Estimation-Based Control Strategies for Induction Motors

“…In what concern the estimation of unknown process parameters (e.g., the stator resistance) and of the load torque, acting as an external disturbance on the rotor, a parameter estimator and a disturbance observer were developed. e parameter estimator is derived from a typical one used in biotechnology applications [14,15]. e disturbances observer provides an estimation result which can be used within a robust observer-based control method [16,17].…”

Software sensor design considering oscillating conditions as present in industrial scale fed‐batch cultivations

“…The basis of the adaptive observers/estimators design is a general dynamic model as proposed by Bastin and Dochain (1990). These software sensors are considered as classical ones and have been widely applied in the past simultaneously with other approaches for nonlinear systems, such as extended Kalman and Luenberger filters (Soons et al, 2006; Veloso et al, 2009), neural‐network based observers (Acuña et al, 1998; Georgieva and Feyo de Azevedo, 2009), the high‐gain approach (Selişteanu et al, 2012), multirate observers (Lyubenova et al, 2011), and others.…”