Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 106, Issue -, Pages 609-620Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2017.07.011
Keywords
Design and control; Expected improvement; Gaussian process; Probabilistic modeling
Funding
- Ministry of Science and Technology, R.O.C. [MOST 104-2811-E-033-009, MOST 105-2811-E-033-007, MOST 103-2221-E-033-068-MY3]
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The simultaneous design and control aims to achieve economic profits and smooth operation of the process even under uncertainties. However, the over-estimation of the uncertainties leads to conservative design decisions. Because of the disturbance inputs, the cost is not easily evaluated. Unlike the past work of design and control, the proposed probabilistic approach framework directly uses the Gaussian process (GP) model to represent the uncertainty in the input. The GP model that acts as the cost function model is trained by an iterative approach. The variability can be evaluated statistically by the GP model. In addition, the expected improvement optimization is employed to select the representative data, so no redundant data are used in the modeling. The expected improvement searches for the most probable operating condition for improvement based on the predictive distribution from the GP model. The applicability of the proposed method is tested on a mixing tank. (C) 2017 Elsevier Ltd. All rights reserved.
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