(350b) A Sustainable Framework for Chemical Manufacturing and for the Integration of Advanced Control with Sustainability Assessment

In recent years, with the progress in understanding, quantifying, and improving sustainability, the incorporation of sustainability into chemical process design, optimization and control has become a research highlight in process systems engineering. Many contributions have been made in sustainable process design and multi-objective optimization. However, an efficient framework for the integration of advanced process control with sustainability assessment tools is still missing. The lacking of such a framework can be attributed to the challenges associated with adding sustainability objectives to process controllers, especially for systems characterized by high-dimensional and nonlinear chemical process models. The objective of this presentation is to provide a systematic framework for the optimization and control of chemical manufacturing systems in order to achieve the most sustainable operating points and life cycle inventory in terms of efficiency, environmental, economic and energy aspects that describe sustainability.

This framework is comprised of four main parts: (i) process and sustainability assessment model construction; (ii) multi-objective optimization formulation; (iii) implementation of the advanced control strategy; and (iv) a life cycle inventory generation. For the model construction part, the process systems model is based on existing mathematical models obtained from the literature, while the system sustainability assessment model can be developed based on the GREENSCOPE ^[1] sustainability assessment tool indicators in four categories (economic, environmental, efficiency, and energy). Once the models for process and sustainability assessment are established, the multi-objective optimization problem is formulated based on economic and integrated sustainability impact objectives. The resulting multi-objective nonlinear programming problem is solved using optimization algorithms to generate a Pareto-optimal set. Based on a ranking scheme algorithm for decision making, optimal solutions are selected from the Pareto set and sent to the controller as desired set points. The third step then corresponds to the controller implementation to keep the process system at the chosen optimal sustainable operating points. For this step, an advanced biologically inspired optimal control strategy (BIO-CS) ^[2] is applied to handle the multiple and conflicting objectives. Finally, the fourth step consists of generating a life cycle inventory from the optimized chemical manufacturing stage. Preliminary results associated with the application of the developed approach to a fermentation process example ^[3] considering performance objectives demonstrated the potential of this framework. In this presentation, we will discuss the implementation of the novel framework to automatically take the selected process to the optimal steady state determined by the multi-objective optimization algorithm according to the decision maker.

References:

1. 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.
2. Mirlekar, G.V.; Gebreslassie B.H.; Diwekar U.M.; Lima, F.V.; Design and Implementation of a biomimetic control strategy for chemical processes based on efficient ant colony optimization. Presented at 2015 AIChE Annual Meeting, Salt Lake City, Utah, November 2015.
3. Li, S.; Mirlekar, G.; Ruiz-Mercado, G.; Lima, F.V. Development of chemical process design and control for sustainability. Submitted for Publication.