Journal
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
Volume 128, Issue -, Pages 121-127Publisher
ELSEVIER
DOI: 10.1016/j.psep.2019.05.046
Keywords
Transient dispersion; Bayesian Regularization Artificial Neural Network; Particle Swarm Optimization; Artificial Bee Colony; Time-series forecasting
Categories
Funding
- National Key R&D Program of China [2017YFC0804501]
- National Natural Science Foundation of China [51504282]
- Fundamental Research Funds for Innovation Program of Seventh-generation Ultra-Deepwater Drilling Platform [2016[24]]
- Key Research and Development Program of Shandong Province [2018GSF120011]
- Fundamental Research Funds for the Central Universities [16CX02045A]
- Natural Sciences Engineering Council of Canada (NSERC)
- Canada Research Chair (Tier I) Program
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Recently, the Bayesian Regularization Artificial Neural Network (BRANN) approach has been used for flammable cloud estimation in a congested offshore setting. These authors observed that BRANN exhibits lower accuracy under specific release and dispersion scenarios. To improve BRANN's accuracy and robustness, the authors have proposed the integration of the Artificial Bee Colony (ABC) algorithm with the BRANN approach. The new ABC-BRANN approach is tested for a wide range of scenarios. The performance of ABC-BRANN approach is compared with the Particle Swarm Optimization (PSO)-BRANN and BRANN approach. The results demonstrate the proposed ABC-BRANN approach is more accurate and robust. It provides an effective alternative for transient dispersion study in congested areas such as an offshore platform. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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