Parameter estimation in kinetic reaction models using nonlinear observers facilitated by model extensions

An essential part of mathematical modelling is the accurate and reliable estimation of model parameters. In biology, the required parameters are particularly difficult to measure due to either shortcomings of the measurement technology or a lack of direct measurements. In both cases, parameters must be estimated from indirect measurements, usually in the form of time-series data. Here, we present a novel approach for parameter estimation that is particularly tailored to biological models consisting of nonlinear ordinary differential equations. By assuming specific types of nonlinearities common in biology, resulting from generalised mass action, Hill kinetics and products thereof, we can take a three step approach: (1) transform the identification into an observer problem using a suitable model extension that decouples the estimation of non-measured states from the parameters; (2) reconstruct all extended states using suitable nonlinear observers; (3) estimate the parameters using the reconstructed states. The actual estimation of the parameters is based on the intrinsic dependencies of the extended states arising from the definitions of the extended variables. An important advantage of the proposed method is that it allows to identify suitable measurements and/or model structures for which the parameters can be estimated. Furthermore, the proposed identification approach is generally applicable to models of metabolic networks, signal transduction and gene regulation

An Economic Model-Based Predictive Control to Manage the Users' Thermal Comfort in a Building

The goal of maintaining users' thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users' productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users' thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.Spanish Ministry of Science and Innovation [DPI2014-56364-C2-1-R]; EU-ERDF funds; Competitiveness and ERDF funds; Fundacion Iberdrola Espana; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

STATISTICAL MEDIA OPTIMIZATION FOR LUTEIN PRODUCTION FROM MICROALGAE Auxenochlorella protothecoides SAG 211-7A

In this study, the heterotrophic production potential of the secondary carotenoid lutein by the green microalgae Auxenochlorella protothecoides SAG 211-7a was investigated. A sequential statistical technique was applied to optimize modified bold’s basal media (MBB) to enhance the lutein production from microalgae Auxenochlorella protothecoides SAG 211-7a. Taguchi orthogonal array method was applied to select the various independent variables which affect the lutein production. It showed that sucrose, yeast extract, MgSO4.7H2O and EDTA were the significant factors affect the lutein production. Further, to increase the lutein yield and to study the interaction between these factors response surface methodology (RSM) was employed. The statistical model was validated with respect to lutein production under the conditions predicted by the model containing sucrose 14.0 g/l, yeast extract 3.0 g/l, MgSO4.7H2O 0.8 g/l and EDTA 0.76 g/l. The production of lutein obtained experimentally using the above medium was 1303 ± 25.32 μg/l, which is in correlation with the predicted value of 1337.21 g/l by the RSM regression study. Thus after sequential statistical media optimization strategy a 5-fold enhancement in lutein production was achieved