Keywords: Scheduling and Optimization

Keywords: Model-based Control
Abstract: Flexible manufacturing has been the trend in the area of the modern chemical process nowadays. One of the essential characteristics of flexible manufacturing is to track time-varying target trajectories (e.g. diversity and quantity of products). A possible tool to achieve time-varying targets is contraction theory. However, the contraction theory was developed for continuous-time systems and there lacks analysis and synthesis tools for discrete-time systems. This article develops a systematic approach to discrete-time contraction analysis and control synthesis using Discrete-time Control Contraction Metrics (DCCM) which can be implemented using Sum of Square (SOS) programming. The proposed approach is demonstrated by an illustrative example.

Keywords: Modeling and Identification
Abstract: Mathematical modelling supports the understanding of basic biological mechanisms and is the basis for bioprocess simulation, prediction, control and optimization. Dynamic macroscopic models can be derived from the concept of elementary flux modes (EFMs), which provide a comprehensive representation of all possible pathways through a metabolic network. As the number of EFMs drastically increases with the size of the metabolic network, a procedure to reduce the number of EFMs and select the most informative ones is required. For this purpose, this study proposes a methodology to select a minimal suboptimal set of elementary flux modes allowing the development of reduced macroscopic models. The algorithm is divided into two steps. First, the concept behind the cosine-similarity algorithm is extended for a large number of EFMs to cut the initial set by removing all the collinear modes. Next, the algorithm is used to extract only the most informative modes from the reduced set by means of a series of optimization problems. The algorithm performance is illustrated with datasets from hybridoma cultures in batch and perfusion modes, and two metabolic networks with different levels of description.

Keywords: Model-based Control, Industrial Applications
Abstract: This paper is concerned with the dynamics and the productivity of biomass in a continuous microalgae culture contaminated by predators. We propose a general model describing a cultivation system where photolimited microalgae are grazed by zooplankton. We state necessary and sufficient conditions for the existence of a coexistence equilibrium. If this equilibrium exists, then microalgae and predators converge either to it or to a globally asymptotically stable periodic solution. In absence of the coexistence equilibrium, any solution approaches either an equilibrium characterized by the presence of algae and the absence of predators, or the washout equilibrium. This description of the dynamics allows to define the microalgae productivity in the long-term (i.e. when an attracting set is reached). With the help of numerical simulations, we show that operating the system at optimal (constant) dilution rate triggers the extinction of predators.

Keywords: Model-based Control
Abstract: In this paper we investigate a novel tuning methodology for a patient-individualized selection of the parameters of a proportional-integral-derivative (PID) controller that regulates the maintenance phase of general anesthesia. In particular, the knowledge of demographic data is exploited to determine the values of the parameters for each specific patient. The proposed approach is focused on the closed-loop administration of propofol by using the Bispectral Index Scale as controlled variable. Simulation results suggest that, with respect to a previously devised population-based PID tuning approach, the new methodology is more robust with respect to both intra-patient and inter-patient variability, at the cost of a slight decrement in the controller bandwidth.