This paper proposes a framework for the analysis of complex optimization problems under uncertainty. The usual multiparametric programing approach is skipped through the use of metamodels to mimic a set of already solved ...

This paper presents a framework for nonlinear constrained optimization of complex systems, in which the objective function and the constraints are represented by black box functions. The proposed approach replaces the ...

In the chemical process industry, the decision-making hierarchy is inherently model-based. The scale and complexity of the considered models (e.g., enterprise, plant or unit model) depend on the decision-making level (e.g., ...

This work proposes a methodology for multivariate dynamic modeling and multistep-ahead prediction of nonlinear systems using surrogate models for the application to nonlinear chemical processes. The methodology provides a ...

This paper presents a hybrid approach to enhance the performance of the data-based Pattern Classification Techniques (PCTs) used for Fault Detection and Diagnosis (FDD) of nonlinear dynamic noisy processes. The method ...

This paper investigates data based modelling of complex nonlinear processes, for which a first principle model useful for process monitoring and control is not available. These empirical models may be used as soft sensors ...

Fault diagnosis (FD) using data-driven methods is essential for monitoring complex process systems, but its performance is severely affected by the quality of the used information. Additionally, processing huge amounts of ...

This paper presents a sequential dynamic optimization methodology applicable to solve the optimal control problem of complex highly nonlinear processes. The methodology is based on the use of kriging metamodels to obtain ...

El análisis de la sostenibilidad (económica, social y/o ambiental) asociada a la toma de decisiones de gestión requiere considerar globalmente toda la cadena de suministro implicada en la producción de un determinado bien ...