(209c) A Multi-Layer Process Control Framework for Sustainability: Application to Biomass/Coal Co-Gasification System

Authors: 
Li, S., West Virginia University
Ruiz-Mercado, G. J., U.S. Environmental Protection Agency
Lima, F. V., West Virginia University
Industry, government, and society have begun to shift from economic stand-alone focus to the inclusion of sustainability in the decision-making process. This shift is due to the adverse environmental impact and unsustainable development caused by human activities, including chemical industry releases. As a result, process systems engineering (PSE) approaches have been developed, predominantly for incorporating sustainability into chemical process design and optimization. However, the development of sustainability-oriented control schemes is still scarce, especially when compared to the number of available efforts on steady-state sustainability assessment and optimization. Previous work has shown that process control plays a critical role in the transient sustainability and emission performance, especially for systems with different operating conditions (e.g., steady-state multiplicity [1]). The main challenges that have been mentioned in previous literature are: 1) existence of multiple objectives for optimization involving social, economic and environmental issues (e.g., pollution prevention and abatement); and 2) difficulty of integrating the sustainability indicators into a process control framework [2].

To address these challenges, a novel multi-layer process control framework is proposed to drive the system to a sustainable operating point that is defined using a multi-objective optimization algorithm. In particular, a sustainable Model Predictive Control (MPC) scheme is formulated based on dimensionless sustainability performance indicators from the U.S. EPA’s GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a Multi-Objective Process Evaluator) tool [3]. Such indicators are associated with the process state variables and can capture the sustainability information of the current process condition, including economic, environmental and social aspects. The selected sustainability indicators are employed as constraints in the designed controller to maintain the process operation within a pre-defined sustainable zone, where the performance indicator values are higher than the desired thresholds. To characterize this sustainable control zone, dynamic radar plots are developed to visualize the multidimensional sustainability indicators during a transient period.

The developed method is illustrated via a biomass/coal co-gasification process for syngas production with the end goal of methanol manufacturing. For this application, the whole process model is developed in Aspen Hysys based on existing literature information [4-5]. With the established models in Hysys and a link for data communication between Hysys and MATLAB, a multi-objective optimization problem is solved to maximize profit and optimize the process sustainability performance (e.g., environmental release and resource use minimization), by employing a genetic algorithm-based approach developed in MATLAB. A reduced-order model for the process is also derived in MATLAB for control implementation purposes. In this presentation, the details on the application results of this multi-layer control framework for improving sustainability are discussed, focusing on the process dynamic sustainability performance. These results demonstrate the capability of the proposed sustainable control scheme for maintaining the process within sustainable zones during transient periods.

References:

  1. Li S, Mirlekar G, Ruiz-Mercado GJ, Lima FV. Development of chemical process design and control for sustainability. Processes. 2016;4(3):23.
  2. Daoutidis P, Zachar M, Jogwar SS. Sustainability and process control: A survey and perspective. J. Process Control. 2016;44:184–206.
  3. Ruiz-Mercado GJ, Gonzalez MA, Smith RL. Sustainability indicators for chemical processes: III. biodiesel case study. Ind. Eng. Chem. Res. 2013;52(20):6747–60.
  4. Li S, Feliachi Y, Agbleze S, Ruiz-Mercado GJ, Smith RL, Meyer DE, Gonzalez MA, Lima FV. A process systems framework for rapid generation of life cycle inventories for pollution control and sustainability evaluation. Clean Technol. Environ. Policy. 2018;20: 1543-1561.
  5. Robinson PJ, Luyben WL. Simple dynamic gasifier model that runs in Aspen Dynamics. Ind. Eng. Chem. Res. 2008;7784–92.