| Institution: | 1. School of Safety Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China;2. Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China;1. Singidunum University, Danijelova 32, 11000 Belgrade, Serbia;2. Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech Republic;3. Modern College of Business and Science, AL-Khuwair 133, Muscat, Oman;4. Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia;5. Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt;6. Department of Computational Mathematics, Science and Engineering (CMSE), Michigan State University, East Lansing, MI 48824, USA;1. School of Mechanical Engineering, Xi’an Jiaotong University, 28 West Xianning Road, Xi’an 710049, PR China;2. Department of Fire Protection and Safety, Oklahoma State University, 523 Engineering North, Stillwater, OK 74078, USA;3. School of Chemical Engineering and Technology, Xi’an Jiaotong University, 28 West Xianning Road, Xi’an 710049, PR China;4. Shaanxi Province Boiler and Pressure Vessel Inspection Institute, 30 West Xianning Road, Xi’an 710048, PR China |
| Abstract: | Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system. |
