Data-Based Multiobjective Plant-Wide Performance Optimization of Industrial Processes under Dynamic Environments
Abstract
This paper provides a method for automatically selecting optimal operational indices for unit processes in an industrial plant using measured data and without knowing dynamical models of the unit process. A dynamic multiobjective optimization problem is defined to find operational indices that lead to plant-wide production indices close to their target values. A case-based reasoning (CBR) technique is also employed, which uses the stored experience of a human expert to determine appropriate operational indices for given target production indices. The solutions of the optimization problem and CBR technique are combined to form baseline operational indices. The dynamic models of the production indices, however, are time varying and affected by disturbances and online corrections of these baseline operational indices are required. To this end, reinforcement learning (RL) is used to provide a data-driven optimization technique to compensate for disturbances and model approximation errors and variations. The data-driven RL approach is used in two different time scales. The samples of the predicted production indices are used at a fast sampling rate, i.e., at each sample time, and the samples of actual production indices are used at a slower sampling rate, i.e., after each operational run, to correct the baseline operational indices. The effectiveness of this automated decision procedure has been demonstrated by successful implementation of the proposed approach on a large mineral processing plant in Gansu Province, China.
Recommended Citation
J. Ding et al., "Data-Based Multiobjective Plant-Wide Performance Optimization of Industrial Processes under Dynamic Environments," IEEE Transactions on Industrial Informatics, vol. 12, no. 2, pp. 454 - 465, Institute of Electrical and Electronics Engineers (IEEE), Apr 2016.
The definitive version is available at https://doi.org/10.1109/TII.2016.2516973
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Case Based Reasoning; Industrial Plants; Optimization; Reinforcement Learning; Casebased Reasonings (CBR); Data-Based Optimization; Data-Driven Optimization; Different Time Scale; Dynamic Multiobjective Optimization; Mineral Processing Plants; Optimization Problems; Performance Optimizations; Multiobjective Optimization; Plant-Wide Performance Optimization; Reinforcement Learning (RL)
International Standard Serial Number (ISSN)
1551-3203
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Apr 2016
