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This work presents the use of advanced control methodologies for a Polymer Electrolyte Membrane (PEM) fuel cell system. Two Model Predictive Control (MPC) strategies, a multi-parametric and a nonlinear MPC, are combined into a framework (explicit Nonlinear MPC - exNMPC) in order to systematically exploit their synergistic benefits and enhance the behaviour of the system. The new controller solves online a dynamic simultaneous optimization problem using a warm-start procedure that considers a piecewise affine (PWA) approximation defined offline over the partition of the system's feasible space. The proposed control scheme is deployed on an industrial Supervisory Control and Data Acquisition (SCADA) system and its response is explored. Experimental studies demonstrate that the multivariable exNMPC controller exhibits a very good performance in terms of computational requirements while achieving an excellent convergence rate even at varying operating conditions.

Research on integrated supply chain network (SCN) design models that capture financial matters is still in its infancy. However, as SCN managers require holistic decision support models that quantify the financial impact of their production and distribution decisions, this research stream is likely to become a mainstream. This paper aims to enrich the relevant literature by providing a mixed integer non linear programming (MINLP) SCN design model that incorporates the sale and leaseback (SLB), a method that releases the value of real estate, improves balance sheet, and realises tax benefits. The model will assist SCN managers in strategic decision making during a capital budgeting process.

This work presents a methodology for the scheduling of networks that consist of various energy cogeneration units (i.e., generate both electrical energy and heat) under high energy demand uncertainties of bounded form. First, a mathematical framework for the problem in question is developed, and then we present a reactive scheduling approach that relays on multiparametric programming and a receding horizon. In brief, the original mathematical scheduling model is reformulated into a multiparametric programming problem, wherein we consider as uncertain parameters: the energy demands as well as the initial and the final state of the overall system. The resulting parametric optimization problem is solved once and off-line, and afterwards a receding horizon-like approach is applied for the reactive scheduling –that acts on the fly in the energy demand realizations– by performing just function evaluations and avoiding the on-line optimization of such systems. A couple of problem instances are solved to shed light on the potential benefits of the suggested method. The proposed concept has highly practical interest and is very promising since could find application in several other optimization problems that can involve: large-scale optimization problems and process scheduling problems under uncertainty.

A Mixed-Integer Dynamic Optimization (MIDO) model and solution strategy to study the interactions between process design and process control/operability of an ethyl acetate reactive distillation system is presented. It is shown that the resulting design and control scheme can guarantee feasible operation tmder bounded uncertainty at a minimum total average cost, representing 17 to 20% savings over the original design.

This work presents recent developments for the incorporation of process flexibility and robustness (in terms of product quality) in process optimization under uncertainty. The Taguchi robust design methodology is far extended to take into account of process constraints and probabilistic uncertainty. Trade-offs between stochastic flexibility and a robust criterion are then explored based on which a framework for studying the interactions and synergistic benefits between the two operability objectives is established with the objective to maximize an expected profit criterion while explicitly accounting for product quality losses. Extensions for design optimization are also presented to derive the optimal design featuring an optimal degree of flexibility and robustness. A simplified process flowsheet problem is presented to illustrate the basic concepts and the applicability of the proposed integrated framework.