Special issue on nonlinear modeling, estimation and control of biological systems

Abstract

Dynamic modeling, state estimation and control are nowadays central techniques in the design, analysis, and optimization of bioprocesses in the industry, particularly with the increased importance of Process Analytical Technologies (PAT). The aim of this special issue is to account for some recent developments in the field by presenting a collection of ten articles written by distinguished colleagues who enthusiastically responded to our idea of dedicating a special issue to this subject. The first paper by David et al. develops a nitrogenbackboned model of wine-making in standard and nitrogen-added fermentations. Nitrogen has a strong impact on the key bio-mechanisms involved during the grape-must fermentation, but also on the synthesis of flavour markers determining the aromatic profile of the wine. This paper presents a dynamical mass balance model describing the main physiological phenomena involved in standard batch fermentations, i.e., consumption of sugar and nitrogen and synthesis of ethanol. It also includes nitrogen compounds such as hexose transporters. Moreover, a common practice in wine-making is the addition of nitrogen during the fermentation to boost and shorten the process duration. A tractable representation of this boost effect is, therefore, developed as an extension of the first model. It is apparent that yeast makes a different use of nitrogen depending on the fermentation stage at which the addition is effected, balancing the regrowth of biomass and the synthesis of supplementary hexose transporters. These models are validated in line with experimental evidence deduced from extensive experimental studies. Microalgae are often seen as a potential biofuel producer. To predict achievable productivities in the so-called raceway culturing system, the dynamics of photosynthesis has to be taken into account. In particular, the dynamical effect of inhibition by an excess of light (photoinhibition) must be represented. In the second paper by Hartmann et al., a model considering both photosynthesis and growth dynamics is proposed. This model involves three different time scales. The response of this model to fluctuating light with different frequencies is studied by slow–fast approximations. Therefore, three different regimes are identified, for which a simplified expression of the model can be derived. These expressions give a hint on productivity improvement, which can be expected by stimulating photosynthesis with a faster hydrodynamics. The lack of sensors for some relevant state variables in fermentation processes can be coped by developing appropriate software sensors. In the third paper by Acuna et al., various Artificial Neural Network (ANN) and Support Vector Machine (SVM) models are compared when acting as software sensors of biomass concentration for a Solid-Substrate Cultivation (SSC) process. In particular, Nonlinear AutoRegressive models with eXogenous variables (NARX) and Nonlinear AutoRegressive Moving Average models with eXogenous variables (NARMAX) are considered. Results show that NARMAX-SVM outperforms the other models with a symmetric mean absolute percentage error, also called SMAPE index, under 9 for a 20 % amplitude noise. In addition, NARMAX models perform better than NARX models under the same noise conditions because of their better predictive capabilities as J. A. Moreno Instituto de Ingenieria, Universidad Nacional Autonoma de Mexico, Cd. Universitaria, CP 04510 Mexico, D.F., Mexico e-mail: JMorenoP@ii.unam.mx