In this work, a robust model-based cascade control scheme for composition regulation in chemical reactors is proposed. The controller is based on a two loop control in a composition-temperature configuration. State-observers are introduced to provide robustness to the controller via the estimation of lumped model uncertainties. The second loop is introduced with a favorable choice of the Arrhenius equation as a virtual control input. The control design is illustrated based on two cases studies, a tubular reactor exhibiting two temperature dynamics (quasi-linear and hot-spot) and a CSTR presenting multiple steady-states. Numerical results indicate that proposed controller is robust in the face of external disturbances, operation changes, and model uncertainties.
The monitoring and diagnosis of crystallization processes are difficult due to the interaction of nucleation and crystal growth phenomena. In recent years, image and time series analysis using fractal methodologies showed potential as an alternative for monitoring crystal growth, although the available results are scarce. In this work, the multifractal detrended fluctuation analysis (MF‐DFA) was applied to temperature time series obtained from a laboratory‐scale cane sugar crystallization operated at different operating conditions. MF‐DFA reflects that the crystallization process exhibits multifractal properties associated with the dynamic behavior of the underlying phenomena. Thus, multifractal analysis can identify how operational changes influence the crystal growth and formed crystal mass.
The sweetening units are the most important in natural gas processing. Packed bed absorption columns are widely used in the sweetening process; however, their operation and control are not simple due to their highly non-linear behavior derived from their distributed nature and interaction between multiple physical phenomena. In this work, two robust model-based control schemes are implemented to regulate the CO2 concentration at the outlet of a packed bed absorption column in the gas sweetening process. The model of an industrial-scale absorption column and the structure of the controllers, i) control based on modeling error compensation (MEC) ideas, and ii) nonlinear model predictive control (NMPC) are described. Numerical results show that the proposed robust model-based controllers can regulate the controlled variable to the desired reference despite external disturbances, set-point changes, and uncertainties in the absorption column model.
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