Robust optimization is an approach for modeling optimization problems under uncertainty where the modeler aims at finding decisions that are optimal for the worst‐case realization of the uncertainties within a given set of values. Typically, the original uncertain optimization problem is converted into an equivalent deterministic form, called the robust counterpart, using strong duality arguments and then solved by standard optimization algorithms. A methodology is proposed for the treatment of optimal control problems applying the multiobjective optimization differential evolution algorithm associated with the concept of mean effective for the insertion of robustness. The results obtained with applications in chemical systems demonstrate that the method conveyed is configured as an interesting approach for the solution of robust optimization problems.
This paper presents the use of Support Vector Machines (SVM) methodology for fault detection and diagnosis. Two approaches are addressed: the SVM for classification (Support Vector Classification-SVC) and SVM for regression (Support Vector Regression-SVR). A comparison was made between the two techniques through the study of a reactor of cyclopentenol production. In the case studied, different fault scenarios were introduced and it was evaluated which technique was able to detect and diagnose them. Finally, a comparison was made between the fault detection methodologies based on SVM and Dynamic Principal Component Analysis (DPCA) based detection techniques for a jacketed CSTR.
In fertilizers industries the granulation is an essential operation to form pellets with good quality. The granular product has improved handling, hardness, solubility, resistance to segregation and meets requirements such as the size, shape and particle size distribution through appropriate manipulation of the process variables. There are several types of granulators, however, this work is intended to study a granulator known as rotating disk, which promotes agitation of the particles by rotating around its axis. Although these devices are used industrially, cannot be found in the literature many details about the fluid dynamics in these operations. To study the fluid dynamics behavior of these particles on a rotation disk was analyzed the variables: rotation axis and filling degree. It was verified the existence of flow regimes which depends on these variables: rolling, cascading and centrifugation. Also, it was evaluated the dynamic angle of repose, that characterizes the rolling regime. This work aimed to obtain results of fluid dynamics that describe the behavior of solids flowing in a rotating disk. Thus, to meet the objectives of this work, simulations was carried out through the techniques of Computational Fluid Dynamics (CFD) and Discrete Element (DEM) to evaluate different parameter values: restitution coefficient (η), friction coefficient (μ) and the coefficient of elasticity (k) of the linear model "spring-dashpot" to find a good set of parameters that characterizes this system.
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