The inevitable presence of uncertain parameters in critical applications of process
optimization can lead to undesirable or infeasible solutions. For this reason, optimization …

An analytical review of the state of multiproduct chemical plants has been proposed. A
comprehensive analysis is made of works of Russian and international scientists on the …

Deep learning models are a class of approximate models that are proven to have strong
predictive capabilities for representing complex phenomena. The introduction of deep …

This paper presents a hybrid Model Predictive Control (hMPC) approach to improve Electric
Vehicle (EV) stability when cornering or in high-risk driving conditions. By using …

Optimisation under uncertainty has always been a focal point within the Process Systems
Engineering (PSE) research agenda. In particular, the efficient manipulation of large amount …

In this article, motivated by the need for efficient closed-loop implementation of the control
objectives set within the integrated planning, scheduling and control (iPSC) problem we …

In this paper, we present a strategy for the exact solution of multiparametric quadratically
constrained quadratic programs (mpQCQPs). Specifically, we focus on multiparametric …

Scheduling of multipurpose batch chemical plant is always affected by uncertain factors,
including processing time of tasks. When the processing time deviates from its nominal …

Systematic scenario generation (SG) methods have emerged as an invaluable tool to
handle uncertainty towards the efficient solution of stochastic programming (SP) problems …

Upgrading the capacity of existing transmission lines is essential for meeting the growing
energy demands, facilitating the integration of renewable energy, and ensuring the security …