Sliding mode observer-based control for a class of bioreactors

Estimation and control of non-linear variables in a continuous fermentation process using sliding mode techniques

“…This parameter was first introduced by Gonzalez and used in Gonzalez et al (2001). Gonzalez et al estimated this variable directly using a sliding mode approach, where one of the states of the corresponding sliding mode observer is the substrate consumption rate, and then employed a linearizing controller.…”

“…Online measurement may not be practical because of the lack of economically priced, reliable and sterilizable transducers (Bastin and Van Impe, 1995;Dochain, 2003;Farza et al, 1999;Gonzalez et al, 2001;Veloso et al, 2009). Previously, indirect techniques such as filtering and calorimetric balances have been introduced to obtain the non-measurable variables manually but it is reported that the techniques are time consuming, require the direct involvement of personnel, and may prove unstable and expensive (Gonzalez et al, 2001). An alternative strategy is to consider using available online data to estimate nonmeasurable variables using an observer or soft sensor (Aguilar-Lo´pez and Maya-Yescas, 2005;Bastin and Van Impe, 1995;Cheruy, 1997;de Assis and Filho, 2000;Veloso et al, 2009).…”

Sliding Mode Observer based Control for a Continuous Fermentation Process

“…The following basic assumptions are imposed on the fermentation process: A variable, is introduced to compress the yield coefficient, specific reaction rate, biomass concentration and substrate concentration into one parameter known as the substrate consumption rate: = (5) which has been used in (Gonzalez et al, 2001). Gonzalez estimated this variable directly using a sliding mode approach, where one of the states of the corresponding sliding mode observer is the substrate consumption rate, and then employed a linearizing controller.…”

“…biomass concentration (da Costa Albuquerque et al, 2008, Jenzsch et al, 2006, Veloso et al, 2009. Online measurement may not be practical due to the lack of economically priced, reliable and sterilisable transducers (Veloso et al, 2009, Farza et al, 1999, Gonzalez et al, 2001, Dochain, 2003, Bastin and Van Impe, 1995. Previously, indirect techniques such as filtering and calorimetric balances have been introduced to manually obtain non-measurable variables but it is reported that the techniques are time consuming, require the involvement of personnel and may prove unstable and expensive (Gonzalez et al, 2001).…”

“…Online measurement may not be practical due to the lack of economically priced, reliable and sterilisable transducers (Veloso et al, 2009, Farza et al, 1999, Gonzalez et al, 2001, Dochain, 2003, Bastin and Van Impe, 1995. Previously, indirect techniques such as filtering and calorimetric balances have been introduced to manually obtain non-measurable variables but it is reported that the techniques are time consuming, require the involvement of personnel and may prove unstable and expensive (Gonzalez et al, 2001). An alternative strategy is to consider using available online data to estimate non-measurable variables using an observer or soft sensor (Veloso et al, 2009, Bastin and Van Impe, 1995, Cheruy, 1997, Aguilar-López and Maya-Yescas, 2005, de Assis and Filho, 2000.…”