| 1 |
Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries
Geng ZQ, Zeng RF, Han YM, Zhong YH, Fu H
Energy, 179, 863, 2019 |
| 2 |
A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries
Han YM, Long C, Geng ZQ, Zhu QX, Zhong YH
Energy Conversion and Management, 183, 349, 2019 |
| 3 |
Production capacity analysis and energy optimization of complex petrochemical industries using novel extreme learning machine integrating affinity propagation
Han YM, Wu H, Jia MH, Geng ZQ, Zhong YH
Energy Conversion and Management, 180, 240, 2019 |
| 4 |
A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction
Xu Y, Zhang MQ, Ye LL, Zhu QX, Geng ZQ, He YL, Han YM
Energy, 164, 137, 2018 |
| 5 |
Energy management and optimization modeling based on a novel fuzzy extreme learning machine: Case study of complex petrochemical industries
Han YM, Zeng Q, Geng ZQ, Zhu QX
Energy Conversion and Management, 165, 163, 2018 |
| 6 |
Evaluation of a sequential biotrickling - biofiltration unit for removal of VOCs from the headspace of crude oil storage tanks
Khoramfar S, Jones KD, Boswell J, Ghobadi J, Paca J
Journal of Chemical Technology and Biotechnology, 93(6), 1778, 2018 |
| 7 |
A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries
Gong HF, Chen ZS, Zhu QX, He YL
Applied Energy, 197, 405, 2017 |
| 8 |
Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP
Geng ZQ, Qin L, Han YM, Zhu QX
Energy, 122, 350, 2017 |
| 9 |
Structural decomposition of CO2 emissions from Taiwan's petrochemical industries
Lee CF, Lin SJ
Energy Policy, 29(3), 237, 2001 |
