(740e) Model Predictive Control with Real-Time Sustainability Monitoring: Application to an Advanced Energy System | AIChE

(740e) Model Predictive Control with Real-Time Sustainability Monitoring: Application to an Advanced Energy System


Lima, F. V. - Presenter, West Virginia University
Mirlekar, G., West Virginia University
Li, S., Pacific Northwest National Laboratory
Modern hydrogen production units are tasked with producing the most hydrogen possible while dealing with flow variations caused by changing power demands. Classical methods for hydrogen production employing the water-gas shift reaction are governed by equilibrium limitations that take effect at high temperatures and high concentrations of H2. The implementation of a membrane reactor with temperature control enables the hydrogen concentration and temperature to reach an equilibrium at a higher concentration of H2 [1]. Another challenge that is prevalent in this process is the changing input flowrate of the syngas produced from upstream reforming process conditions. These changes in syngas production can cause drastic temperature changes within the reactor. The aforementioned challenges can be addressed by the implementation of advanced controllers that can cope with dynamic changes associated with different conditions, such as temperature oscillations and mitigation of hot spots.

In this presentation, linear and nonlinear model predictive control (MPC) methods are implemented on a designed water-gas shift membrane reactor model developed in Aspen Custom Modeler. The implementation aim is to increase the production of hydrogen by considering the temperature control performed by manipulating the flow rates of the coolant entering the cooling jacket at different reactor zones as well as the reactor sweep flowrate. The control strategies considered for this application are: Quadratic Dynamic Matrix Control (QDMC), Nonlinear MPC (NMPC), and a Biomimetic-based controller cast as MPC (BIO-CS as MPC) [2]. The coolant usage is constrained by the use of quadratic programing (QP), sequential quadratic programing (SQP), or dynamic operations toolbox (DYNOP) solvers, depending on the employed MPC type, to match industrial standards. To mimic industrial conditions, the flowrate of hydrogen in the sweep stream is changed by +15% from its operating steady state.

The developed MPCs are able to successfully take the system to the desired setpoint considering the presence of disturbances in the feeding syngas stream. To assess the dynamic performance of the implemented controllers, a novel sustainability monitoring system is designed for the transient phase. Specifically, several sustainability indicators for efficiency, economic, material and environmental aspects are used to monitor the process performance in real time in terms of sustainability. To explicitly visualize the multidimensional sustainability indicators during transient, a multivariate plotting method is developed using dynamic radar diagrams.


  1. Georgis D, Lima FV, Almansoori A, Daoutidis P (2014). Thermal management of a water–gas-shift membrane reactor for high-purity hydrogen production and carbon capture. Industrial & Engineering Chemistry Research, 53(18):7461-9.
  2. Mirlekar G, Li S, Lima FV (2017). Design and Implementation of a Biologically Inspired Optimal Control Strategy for Chemical Process Control. Industrial & Engineering Chemistry Research, 56(22):6468-79.