Adaptive Sampling for Surrogate Modelling with Artificial Neural Network and its Application in an Industrial Cracking Furnace-Reference-Cited by-同舟云学术

Adaptive Sampling for Surrogate Modelling with Artificial Neural Network and its Application in an Industrial Cracking Furnace

Published:2016-01-19 Issue:2 Volume:94 Page:262-272
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can. J. Chem. Eng.

Author:

Jin Yangkun¹,Li Jinlong²,Du Wenli¹,Qian Feng¹

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education; East China University of Science and Technology; Shanghai, 200237 China

2. School of Information Science and Engineering; East China University of Science and Technology; Shanghai, 200237 China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemical Engineering

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cjce.22384/fullpdf

Reference40 articles.

1. P. Singh D. Deschrijver T. Dhaene Simulation Conference (WSC) 2013

2. An algorithm for the use of surrogate models in modular flowsheet optimization

3. Process Dynamic Optimization Using ROMeo

4. RTO: An overview and assessment of current practice

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