Title | A parametric approach to identify synergistic domains of process intensification for reactive separation |
Publication Type | Journal Article |
Year of Publication | 2023 |
Authors | Li, J, Hasan, MMFaruque |
Journal | Chemical Engineering Science |
Volume | 267 |
Pagination | 118337 |
Date Published | mar |
ISSN | 0009-2509 |
Keywords | 9.3 |
Abstract | Process intensification aims to combine multiple tasks within multi-functional units to drastically improve economic, energy or sustainability metrics of a chemical process. Limited work exists to systematically identify the synergistic domains where intensification outperforms its nonintensified counterpart. In this work, we computationally derive the synergistic domains of a reactive separation system. Specifically, we first postulate general models for both intensified and nonintensified systems. We use these models to generate data to train a ReLU-type artifical neural network (ANN). The trained ReLU-NN model is formulated as a multi-parametric mixed-integer linear program (mp-MILP), and the critical regions of this mp-MILP define the synergistic feasible domains of intensification. We have derived these synergistic domains of vapor–liquid equilibrium (VLE)-based reactive separation for several industrial applications. These synergistic domains enable quick screening of properties that favor intensification. |
URL | https://www.sciencedirect.com/science/article/abs/pii/S0009250922009228 |
DOI | 10.1016/j.ces.2022.118337 |