Control of α-amylase production by Bacillus subtilis

Abstract

This study proposes two adaptive control algorithms for the fed-batch production of α-amylase. The first one uses online information from hardware measuring glucose. Online information of both biomass and glucose concentrations measured with different frequency is used in the second algorithm. Hardware measuring variables are inputs for software sensors of glucose concentration and (specific) glucose consumption rate. Either of the algorithms do not require any kinetic coefficients. This is a benefit, because the kinetic coefficients can vary during cultivation and between cultivations, leading to low process reproducibility and the non-stationary state of the bioprocess. The results of simulation investigations show good performance of the proposed control schemes.

Adaptive Monitoring of Biotechnological Processes Kinetics

“…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.…”

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.…”