In this work, a new formulation is presented for the model predictive control (MPC) of a process system that is represented by a finite set of models, each one corresponding to a different operating point. The general case is considered of systems with stable and integrating outputs in closed-loop with output feedback. For this purpose, the controller is based on a non-minimal order model where the state is built with the measured outputs and the manipulated inputs of the control system. Therefore, the state can be considered as perfectly known and, consequently, there is no need to include a state observer in the control loop. This property of the proposed modeling approach is convenient to extend previous stability results of the closed loop system with robust MPC controllers based on state feedback. The controller proposed here is based on the solution of two optimization problems that are solved sequentially at the same time step. The method is illustrated with a simulated example of the process industry. The rigorous simulation of the control of an adiabatic flash of a multi-component hydrocarbon mixture illustrates the application of the robust controller. The dynamic simulation of this process is performed using EMSO - Environment Model Simulation and Optimization. Finally, a comparison with a linear MPC using a single model is presented.

In this work, the process of capsular polysaccharide production by Neisseria meningitidis serogroup C was studied. Batch experimental runs were conducted in a set of one-liter bioreactors with 0.5 L of Frantz cultivation medium. Cultivation temperature and pH were controlled at optimal pre-established values. The dynamic behavior of the bacteria was analyzed based on biomass growth, glucose uptake, polysaccharide production and dissolved oxygen time profile obtained in a set of experimental runs with initial concentrations of glucose that varied from 5 to 13.5 g/L. Formulated hypotheses were then employed in the construction of a phenomenological model of the bioprocess under study that successfully described the dynamic behavior of the system and can be used in future control and optimization of the industrial process of capsular polysaccharide production.

The classical control theory is based on the design of linear controllers for systems described by linear models. However, there exist some situations where it is not recommended, or even impossible, to use a linear controller. One of those situations arises when the magnitude of the process gain experiences a dramatic variation within the operating range of interest. A classic example of a chemical process where this situation occurs is the pH control around the neutralization point in a continuous stirred tank. In this work, the pH control for a strong acid - strong base system is addressed. To solve this problem, a nonlinear H∞ control law is derived based on a nonlinear model previously developed. The attainment of that control law is done with the help of recent mathematical results from the authors concerning the solution of Hamilton-Jacobi-Isaacs inequalities. The nonlinear controller is implemented on an experimental reactor and its performance is compared with a PID control law tuned according to the classical minimum error integral criteria. The obtained results show that the nonlinear H∞ control theory can be a good alternative to solve this difficult SISO (Single Input - Single Output) control problem.

Only ten years were enough for hybrid neural network-first principle models (HNM) reach a status of a standard industrial tool. This modeling strategy is employed here to represent the production of sorbitol and gluconic acid from glucose and fructose, using permeabilized and immobilized Zymomonas mobilis cells. Mass component balances are derived for the substrate concentrations. A multilayered neural network is used to represent the reaction rate. Experimental results were used to develop and validate the model. The HNM allows the elucidation of the phenomena involved in the process. It is observed from the results that the resistance for mass transfer from the liquid to the particles is increased at higher substrate concentrations and that the reaction rate depends on the concentrations of substrate and product in the particles. Additionally, it may be stated that the flexibility of the HNM allows the development of a model that would otherwise be difficult, if based solely on phenomenological principles.

The optimization problem of a fed-batch alcoholic fermentation by Zymomonas mobilis, solved using Pontryagin's maximum principle and the singular control theory, is used to discuss the main challenges that usually arise during the implementation of optimal strategies. For each problem a practical solution is proposed. In order to solve the optimization problem, a hybrid neural model is developed using experimental data from batch and fed-batch fermentations. The calculated optimal solution is experimentally implemented in open-loop scheme.