Dynamic Real-Time Optimization of Industrial Polymerization Processes with Fast Dynamics

Abstract

This paper addresses real-time optimization strategies which can be readily implemented in industrial polymerization processes, even in case they show very fast dynamics. At the upper layer dynamic and steady-state real-time optimizations (D-RTO and RTO) are suggested and compared. A novel multistage formulation for the real-time dynamic optimization problem is introduced. It relies on a purely economic objective without additional stabilizing terms and facilitates an integrated treatment of a sequence of alternating dynamic and stationary operational stages. A case study shows that closed-loop D-RTO allows reducing off-spec material as well as exploiting or rejecting disturbances to maximize overall profit. The computational delay not only determines closed-loop performance but also significantly impacts the profit margin. The results indicate that even the simplest variant of the investigated strategies can significantly improve economic performance, since the transitions can be completed much faster than in current industrial practice.

Multi Set-Point Explicit Model Predictive Control for Nonlinear Process Systems

“…The interaction between real-time optimisation and model predictive control can be categorised broadly into three classes: (i) dynamic RTO (d-RTO), (ii) static RTO (s-RTO) and (iii) economic model predictive control (e-MPC). Both s-RTO and d-RTO are twolayer schemes where reference trajectories are passed to the layer of APC in the form of set-points [6]. While under the static real-time optimisation paradigm, the optimisation problem is solved at specific instances whenever new data become available or when steady state is achieved, in the d-RTO paradigm, the system's transient behaviour is explicitly considered, thus resulting in dynamic optimisation problems.…”

Integrated scheduling and control in discrete-time with dynamic parameters and constraints

“…With ever-increasing computational power, the segregation of optimization is being reanalyzed through efforts such as model predictive control for supply-chain management [5], combined nonlinear estimation and control [6,7], dynamic real-time optimization (DRTO) [8,9,10], and economic model predictive control (EMPC) [11,12]. These past efforts have proven valuable in practice [1].…”

“…DRTO has an economic objective function similar to that of a scheduler. DRTO is solved more frequently than scheduling problems and leverages the predictive power inherent in a dynamic firstprinciples model to calculate intermediate set points used by MPC for optimal product transitions [8,11].…”

GEKKO Optimization Suite

“…Another valuable area that is useful in a variety of applications is dynamic optimization. Applications include chemical production planning [1], energy storage systems [2,3], polymer grade transitions [4], integrated scheduling and control for chemical manufacturing [5,6], cryogenic air separation [7], and dynamic process model parameter estimation in the chemical industry [8]. With a broad and expanding pool of applications using dynamic optimization, the need for a simple and flexible interface to pose problems is increasingly valuable.…”