The worldwide energy demands and resource consumption are rising despite the efforts for
energy saving and emission reduction. This results from the combination of the supply chain …

Process systems are characterized by nonlinearity, uncertainty, large scales, and also the
need of pursuing both safety and economic optimality in operations. As a result they are …

In this work, we develop reduced-order machine learning models using feature selection
methods for distributed model predictive control (DMPC) of nonlinear processes …

The kernel of industrial advanced process control (APC) lies in the formulation and solution
of model predictive control (MPC) problems, which specify the controller moves according to …

This “white paper” is a concise perspective based on a session during FIPSE 3, held in
Rhodes, Greece, June 20–23, 2016. This was the third conference in the series “Future …

This paper provides a perspective on the major challenges and directions in academic
process control research over the next 5–10 years, and its industrial implementation. Large …

An appropriate subsystem configuration is a prerequisite for a successful distributed
control/state estimation design. Existing subsystem decomposition methods are not …

In this work, we consider the distributed economic model predictive control (EMPC) of a
wastewater treatment plant described by Benchmark Simulation Model No. 1 and compare …

First, we point out that available decomposition-based control and optimization algorithms
are essentially based on some I block structure i in the underlying I network i topology of the …

Distributed optimization using multiple computing agents in a localized and coordinated
manner is a promising approach for solving large-scale optimization problems, eg, those …