Design of multi-parametric NCO tracking controllers for linear dynamic systems

© 2016 The Authors.A methodology for combining multi-parametric programming and NCO tracking is presented in the case of linear dynamic systems. The resulting parametric controllers consist of (potentially nonlinear) feedback laws for tracking optimality conditions by exploiting the underlying optimal control switching structure. Compared to the classical multi-parametric MPC controller, this approach leads to a reduction in the number of critical regions. It calls for the solution of more difficult parametric optimization problems with linear differential equations embedded, whose critical regions are potentially nonconvex. Examples of constrained linear quadratic optimal control problems with parametric uncertainty are presented to illustrate the approach

A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids

This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information

Design and Experimental Validation of an Explicit MPC Controller for Regulating Temperature in PEM Fuel Cell Systems

This paper proposes a temperature controller for PEM fuel cell systems with an air blower as thermal circuit. The objective of this controller is to maintain the stack temperature over a given set-point which is obtained from the results of a real-time optimization algorithm with the goal of minimizing the stack degradation and maximizing the global efficiency. An Explicit MPC is proposed to deal with this control problem which presents delays, the critical sampling time, constraints and disturbances. The simulation results show good performance of the controller which accurately tracks the temperature reference over the overall range of operating conditions. Furthermore, the controller is implemented in real-time on a PEM fuel cell test-bench which is installed in the Fuel Cell Laboratory at the University of Seville

Optimization of a network of compressors in parallel: Operational and maintenance planning – The air separation plant case

A general mathematical framework for the optimization of compressors operations in air separation plants that considers operating constraints for compressors, several types of maintenance policies and managerial aspects is presented. The proposed approach can be used in a rolling horizon scheme. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments as well as the outlet mass flow rates for compressed air and distillation products are optimized under full demand satisfaction. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. Several case studies of an industrial air separation plant are solved. The results demonstrate that the simultaneous optimization of maintenance and operational tasks of the compressors favor the generation of better solutions in terms of total costs

Towards Optimal Energy-Water Supply System Operation for Agricultural and Metropolitan Ecosystems

The energy-water demands of metropolitan regions and agricultural ecosystems are ever-increasing. To tackle this challenge efficiently and sustainably, the interdependence of these interconnected resources has to be considered. In this work, we present a holistic decision-making framework which takes into account simultaneously a water and energy supply system with the capability of satisfying metropolitan and agricultural resource demands. The framework features: (i) a generic large-scale planning and scheduling optimization model to minimize the annualized cost of the design and operation of the energy-water supply system, (ii) a mixed-integer linear optimization formulation, which relies on the development of surrogate models based on feedforward artificial neural networks and first-order Taylor expansions, and (iii) constraints for land and water utilization enabling multi-objective optimization. The framework provides the operational profiles of all energy-water system elements over a given time horizon, which uncover potential synergies between the essential food, energy, and water resource supply systems.Comment: Part of the Foundations of Computer-Aided Process Operations and Chemical Process Control (FOCAPO/CPC) 2023 Proceeding

Key environmental stress biomarker candidates for the optimisation of chemotherapy treatment of leukaemia

The impact of fluctuations of environmental parameters such as oxygen and starvation on the evolution of leukaemia is analysed in the current review. These fluctuations may occur within a specific patient (in different organs) or across patients (individual cases of hypoglycaemia and hyperglycaemia). They can be experienced as stress stimuli by the cancerous population, leading to an alteration of cellular growth kinetics, metabolism and further resistance to chemotherapy. Therefore, it is of high importance to elucidate key mechanisms that affect the evolution of leukaemia under stress. Potential stress response mechanisms are discussed in this review. Moreover, appropriate cell biomarker candidates related to the environmental stress response and/or further resistance to chemotherapy are proposed. Quantification of these biomarkers can enable the combination of macroscopic kinetics with microscopic information, which is specific to individual patients and leads to the construction of detailed mathematical models for the optimisation of chemotherapy. Due to their nature, these models will be more accurate and precise (in comparison to available macroscopic/black box models) in the prediction of responses of individual patients to treatment, as they will incorporate microscopic genetic and/or metabolic information which is patient-specific.peer-reviewe

Advanced computational tools to enhance continuous monoclonal antibody production

Leading pharmaceutical companies invest high percentage of their revenue in the improvement of existing technologies used for the production of monoclonal antibodies (mAbs). Recently, there has been a paradigm shift towards the development of continuous/quasi-continuous purification operations, aiming to reduce capital and operational costs [1]. At the moment, however, there are no standardized methods and/or tools that can be used for global control and monitoring of integrated processes. Mathematical models and advanced computational tools can be the key for the development of robust, integrated processes, as they can provide valuable insight in the process dynamics and ensure optimal operation [2]. However, such processes are usually characterized by complex mathematical models and periodic operation profiles that result into computationally expensive solutions and challenge the development of global control methods and tools. In this work, we are presenting a novel approach for the development of advanced controllers towards the intensification of mAb production, considering the fed-batch culturing of GS-NS0 cells and the semi-continuous Multicolumn Countercurrent Solvent Gradient Purification (MCSGP) process [3]. The controller development is realized via the application of a generic framework for the development of advanced control strategies (PAROC) [4] that involves: (i) development of a high-fidelity process model, (ii) approximation of the complex, process model, (iii) design of the multi-parametric controller, (iv) ‘closed-loop’, in-silico validation of the controller against the process model. The development of the control policies is based on multi-parametric Model Predictive Control (mp-MPC) policies that reduce the online, computational force of the controller by deriving the control inputs as a set of explicit functions of the system states and can be implemented on embedded devices [5]. One of the main advantages of the proposed framework is the ability to test the controllers ‘in-silico’, against the high-fidelity process model and evaluate their performance before operating them online. The results from this study indicate that optimal operation, under maximum purity and productivity yield can be ensured with the development of advanced computational tools. The control policies are applied both in the upstream and the downstream processing; yielding therefore a fertile ground towards the development of a global control strategy that can ensure continuous operation