Constrained operation optimization of a distillation unit in refineries with varying feedstock properties.
| dc.cclicence | CC-BY-NC-SA | en |
| dc.contributor.author | Chen, Qingda | |
| dc.contributor.author | Ding, Jinliang | |
| dc.contributor.author | Yang, Shengxiang | |
| dc.contributor.author | Chai, Tianyou | |
| dc.date.acceptance | 2019-09-19 | |
| dc.date.accessioned | 2019-10-25T10:09:58Z | |
| dc.date.available | 2019-10-25T10:09:58Z | |
| dc.date.issued | 2019-09 | |
| dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | en |
| dc.description.abstract | This paper studies the challenging operational optimization problem of a distillation unit under varying feedstock properties (e.g., density and carbon content). This problem, in which changes in the feedstock properties are incorporated, aims to quickly obtain the operating variables that control the operating condition of the distillation unit. To solve this problem, we first model this operational optimization problem considering the ever-changing feedstock properties and practical technological constraints. Then, we propose an efficient soft-sensing strategy to rapidly measure the feedstock properties. Finally, motivated by the challenges caused by the varying feedstock properties, product yield and tray temperature constraints, we propose an optimization algorithm with global search and self-repair capabilities to optimize the operating variables of the distillation unit. The proposed algorithm integrates the optimization time and survival information of each individual into the proposed mutation strategy to improve its global search capability in the irregular feasible region of the operating variables. Based on the ranking and survival information of each individual, the adaptive strategies of the mutation factor and crossover probability are designed to balance the exploration and exploitation capabilities of the optimization algorithm. Subsequently, we propose an effective correction strategy to correct the infeasible operating variables and improve the optimization efficiency of the algorithm. Computational experiments on practical production data show the accuracy of the soft-sensing model and the superiority of the optimization algorithm for operational optimization of the distillation unit. | en |
| dc.funder | Other external funder (please detail below) | en |
| dc.funder.other | National Natural Science Foundation of China | en |
| dc.identifier.citation | Chen, Q., Ding, J., Yang, S. and Chai, T. (2019) Constrained operation optimization of a distillation unit in refineries with varying feedstock properties. IEEE Transactions on Control Systems Technology, | en |
| dc.identifier.doi | https://doi.org/10.1109/tcst.2019.2944342 | |
| dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/18662 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | 61525302 and 61590922 | en |
| dc.publisher | IEEE | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Operating variables | en |
| dc.subject | distillation unit | en |
| dc.subject | mutation strategy | en |
| dc.subject | adaptive strategies | en |
| dc.subject | correction strategy | en |
| dc.title | Constrained operation optimization of a distillation unit in refineries with varying feedstock properties. | en |
| dc.type | Article | en |