Abstract
Superstructure optimization-based process synthesis is generally regarded as theoretically powerful; however, it has not been widely used in practice since it typically results in large-scale non-convex Mixed-Integer Non-Linear Programs (MINLP) which are very hard to solve effectively. To address this limitation, we propose a framework leading to substantially simpler formulations through the replacement of complex first-principle unit models by compact and yet accurate surrogate models. We show how all the relevant variable relationships established by a unit model, can be expressed in terms of a subset of the original model variables. We discuss how this subset of variables can be identified, and we present a method to develop high quality surrogate models through artificial neural networks. Finally, we propose a tailored surrogate model reformulation to incorporate binary variables that allow activation/deactivation of particular units within the superstructure model. An example is presented to illustrate the application of the proposed framework.
Original language | English (US) |
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Pages (from-to) | 1129-1134 |
Number of pages | 6 |
Journal | Computer Aided Chemical Engineering |
Volume | 28 |
Issue number | C |
DOIs | |
State | Published - 2010 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Chemical Engineering
- Computer Science Applications
Keywords
- Process optimization
- Process synthesis
- Surrogate models