(706a) Process Control for Sustainability and Life Cycle Inventory (LC?) Monitoring: 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
In recent years, due to the increasing global energy demand and the fast depletion of fossil fuels, biomass has received considerable attention. Biomass consists of a renewable feedstock alternative to produce energy and chemicals with potential for ameliorating the environmental effects of using fossil fuels. However, the utilization of biomass is facing some challenges, such as limited and intermittent supply, as well as low energy density. Biomass/coal co-gasification can be an economically attractive and technically viable alternative to convert biomass to energy and chemicals while improving over the sustainability performance of a coal gasification process. To investigate the optimal combination of biomass and coal in terms of economics and sustainability, process systems engineering approaches are needed.

A few process systems contributions have been made to optimize the sustainability of chemical processes by incorporating sustainability indicators into process design and optimization. However, an efficient and systematic framework for the integration of advanced process control with sustainability assessment tools is yet to be developed. To fill this gap, a process control strategy for sustainability has been initially proposed considering the evaluation of different sustainability indicators [1, 2]. In such strategy, only performance variables associated with process efficiency were considered in the controller formulation. In this presentation, the novel addition of multiple sustainability objectives to the process controller is explored, focusing on systems characterized by high-dimensional and nonlinear chemical process models. In particular, the developed framework considers the combination of multi-objective optimization and advanced process control to automatically take the chemical process to a sustainable operating point. This framework will be illustrated via a biomass/coal co-gasification process for syngas production with the end goal of acetic acid manufacturing, including the sustainability analysis for different scenarios.

The process control for sustainability framework includes four main parts: (i) steady-state and dynamic modeling; (ii) multi-objective optimization formulation; (iii) implementation of the advanced control strategy; and (iv) Life Cycle Inventory (LCI) monitoring. In this application, the whole process model is developed in Aspen Plus and Aspen Plus Dynamics based on existing literature information [3-4]. With the established models in Aspen and a link for data communication between Aspen and MATLAB, the multi-objective problem is solved to maximize profit and optimize sustainability performance, employing a genetic algorithm-based approach developed in MATLAB. To reach the optimal operating point defined by the optimization algorithm, an advanced biologically-inspired optimal control strategy (BIO-CS) [1, 5] is implemented to handle the multiple and conflicting objectives associated with economic and sustainability performance. Finally, LCI data is collected for monitoring of steady-state and transient scenarios. In this presentation, the details on the application results of this novel framework for sustainable process control are discussed, focusing on the tradeoffs between using coal and biomass for the sustainable production of chemicals.

The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. 

References:

  1. Li, S.; Mirlekar, G.; Ruiz-Mercado, G.J.; Lima, F.V. Development of Chemical Process Design and Control for Sustainability. Processes 2016.
  2. Lima, F.V.; Li, S.; Mirlekar, G.V.; Sridhar, L.N.; Ruiz-Mercado, G.J. Modeling and advanced control for sustainable process systems In The analysis, synthesis and design of chemical engineering processes, Ruiz-Mercado, G.J.; Cabezas, H. (eds.), Eds. Elsevier, 2016.
  3. Robinson, P.J.; Luyben, W.L. Simple Dynamic Gasifier Model That Runs in Aspen Dynamics. Ind. Eng. Chem. Res. 2008.
  4. Smith, R.L.; Ruiz-Mercado, G.J.; Meyer, D.E. et al. Coupling Computer-Aided Process Simulation and Estimations of Emissions and Land Use for Rapid Life Cycle Inventory Modeling. ACS Sustainable Chemistry & Engineering 2017.
  5. Mirlekar, G.V.; Li, S.; Lima, F.V. Design and implementation of a biologically-inspired optimal control strategy (BIO-CS) for chemical process control. Submitted for Publication.