Ioana Nascu

Modelling and optimization of an activated sludge wastewater treatment process

Abstract This paper investigates the performance of Model Predictive Control strategies used to improve the energy efficiency and effluent quality of a conventional Wastewater treatment plant. Two different model predictive control techniques are used: the extended predictive self-adaptive control, a model predictive control strategy based on online optimisation, and the multi-parametric model predictive control, a multiparametric control strategy based on offline optimisation. Both control strategies manage to efficiently optimize the aeration energy consumption while maintaining the discharged water quality within the regulations.

A framework for Simultaneous State Estimation and Robust Hybrid Model Predictive Control in Intravenous Anaesthesia

Abstract This work presents a framework for the development of simultaneous hybrid robust multiparametric model predictive control and multi-parametric moving horizon estimation problems for the intravenous anaesthesia process on a set of 12 virtually generated patients. The performance of the hybrid scheme is compared with the nominal hybrid multi-parametric case proposed and is shown to be able to deal with the nonlinearities and the inter- and intra- patient variability.

Computational tools in the assistance of personalized healthcare

Abstract Process Systems Engineering has been many years in the forefront, advancing the standards in healthcare and beyond. Gradually, integrated methods that utilize both experimental and/or clinical data, as well as in silico tools are becoming popular among the medical community. In silico tools have already demonstrated their great potential in various sectors, assisting the industry to produce experiments of significantly reduced cost that allow thorough investigation of the system at hand. Similarly, in biomedical systems, the advancement of the current state of the art through the development of intelligent computational tools can lead to personalized healthcare protocols. The first part of this chapter serves as a brief review of the computational tools commonly used in healthcare, such as big data analytics and dynamic mathematical models. The challenges characterizing biomedical systems, such as data availability and patient variability, are also discussed here. We present the advantages and limitations of the various methods and we suggest a generic framework for the design and testing of advanced in silico platforms. The PARametric Optimization and Control (PAROC) framework presented here is based on the design of high-fidelity, dynamic, mathematical models that are then validated using experimental and/or clinical data. Such models provide the basis for the execution of optimization and control studies for the design of patient-specific treatment protocols. The final part of the chapter is dedicated to the application of PAROC to three different biomedical examples, namely: (i) acute myeloid leukemia, (ii) the anesthesia process, and (iii) diabetes mellitus. The challenges of each case are discussed and the application of the relevant PAROC steps is demonstrated.

Multiparametric model predictive control strategies of the hypnotic component in intravenous anesthesia

This paper presents the development of multiparametric model predictive control strategies for the control of the hypnotic part of the depth of anaesthesia. Based on a detailed compartmental model featuring a pharmacokinetic and a pharmacodynamic part, two different control strategies are employed and tested comparatively with the nominal mp-MPC. The designed strategies: a simultaneous multiparametric moving horizon estimation and model predictive control and a multiparametric model predictive control using a switch for the administration of the drug infusion, are able to tackle some of the most important challenges in control of anaetshesia. The performances of the designed controllers are tested on a set of 12 patients in the induction and maintenance phase and analyzed comparatively. The simulations show good performances and satisfactory behavior.

Development of advanced control strategies for periodic systems: An application to chromatographic separation processes

In this work we investigate the design and application of advanced control strategies to a periodic chromatographic process. For the Multicolumn Countercurrent Solvent Gradient Purification process (MCSGP), a nonlinear process described by a periodic profile, we present a step-by-step procedure that enables the seamless development of explicit advanced MPC controllers for such systems. The designed controllers assure optimal operating conditions, periodic input profiles and continuous process monitoring.