Optimal adaptive control of (bio)chemical reactors: past, present and future

Abstract

In this paper an overview of optimal adaptive control of (bio)chemical reactors is presented. Following the paradigm of the Minimum Principle of Pontryagin the derivation of optimal control sequences for fed-batch production processes is briefly revisited. Next, it is illustrated how the obtained optimal profiles can be exploited in the characterization of nearly optimal control sequences in terms of the qualitative behavior of the specific growth and production rates as function of the limiting substrates. Implementing this knowledge leads in a natural way to the design of (nearly) optimal adaptive feedback controllers. Special emphasis will lie on the potential of on-line biomass measurements (obtained with the Biomass Monitor) in the estimation algorithm of the growth kinetics being the adaptive component of the controller. Extensions towards fermentation processes with (i) multiple substrates and (ii) non-monotonic kinetics are also included. Finally, perspectives towards optimal adaptive control of not perfectly mixed (bio)chemical reactor systems, such as chemical tubular reactors, are outlined.

Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives

“…Extracellular heteropolysaccharides, for instance, are growth-associated substances produced by many species of lactic acid bacteria (Degeest et al 2001) as are xanthan gum produced by Xanthomonas campestris (Faria et al 2010), or polyhydroxybutyric acid, by Alcaligenes eutrophus (Smets et al 2004). The ability to maintain cells in a particular metabolic or physiological state related to μ, promoting a particular metabolism for the production of secondary metabolites, such as prodigiosins (Chang et al 2011), is of particular relevance to biotechnology and pharmaceutical industries.…”

Allosteric control model of bone remodelling containing periodical modes

“…All these forms of control have the potential to represent allosteric regulation and this could be very interesting when studying the RANKL/OPG balance regulation, for example [9]. Classical Michaelis-Menten kinetics [10] is one the most working approximation of many models in different fields of biochemistry, microbiology and biotechnology, for example, in pharmacological models [11], chemostat models [12], or batchkinetics models [13][14][15]. A number of research publications discuss the Michaelis-Menten control approach applied to the enzyme network [16][17][18][19][20][21].…”

Modeling and optimization of activated sludge bioreactors for wastewater treatment taking into account spatial inhomogeneities

“…The objective is usually to control the inlet flow rate of the bioreactor for attaining a prescribed target (e.g., a small prescribed amount of pollutant at the bioreactor outlet) in a finite given time. Particularly, the maximization of bacteria production in a well mixed fedbatch bioreactor has been studied using different optimization techniques, as Pontryagin Maximum Principle (see [6]), Genetic Algorithms (see [7,8,9,10]) or Hybrid Stochastic-Deterministic Methods (see [6], [11]). The effects of varying the inlet flow velocity and the substrate concentration input in continuous bioreactors have been studied as well (see for instance [12], [13], [14] and [15]).…”