(258b) Process Conceptualization in a Carbon-Constrained World: Leveraging Optimization and Systems Engineering to Balance Multiple Objectives
However, despite decades of experience, a heuristic-centric design philosophy can be overly restrictive (scanning a small part of the solution space) and lead to sub-optimal solutions. This is not only due to the highly non-linear nature of the search space but also because of the inherent complexity of handling multiple objective functions (emissions, economics, operational constraints etc.). Advances in computing and process systems engineering offer a route to systematically navigate the design space while accounting for critical design metrics and objectives.
This presentation will discuss the challenges in developing design tools for petrochemical separations focusing on distillation sequences. It builds on the pioneering work of Agrawal and co-workers (1-3) that relies on optimization models incorporating Underwoodâs shortcut method to minimize the vapor duty of a configuration. Furthermore, we discuss approximations that enable comparison of stream temperatures using only information about the composition, component relative volatilities and pressure ratios of the streams, thus making it possible to identify energy efficient distillation configurations.
The approach outlined here has been successfully applied to separation schemes of interest to the petrochemical industry resulting in configurations that not only provide energy savings (reducing emissions and operating costs) but are also operationally feasible. Moreover the capability to provide multiple options gives process developers the ability to adapt quickly to evolving design objectives. These results provide the impetus for further efforts in developing tools that effectively leverage both heuristics and advanced optimization, and usher in a new paradigm in chemical process design.
- Agrawal, R., & Herron, D. M. (1997). Optimal thermodynamic feed conditions for distillation of ideal binary mixtures. AIChE journal, 43(11), 2984-2996.
- Nallasivam, U., Shah, V. H., Shenvi, A. A., Tawarmalani, M., & Agrawal, R. (2013). Global optimization of multicomponent distillation configurations: 1. Need for a reliable global optimization algorithm. AIChE Journal, 59(3), 971-981.
- Nallasivam, U., Shah, V. H., Shenvi, A. A., Huff, J., Tawarmalani, M., & Agrawal, R. (2016). Global optimization of multicomponent distillation configurations: 2. Enumeration based global minimization algorithm. AIChE Journal, 62(6), 2071-2086.