Qi Yuan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Qi Yuan has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Mingyu Luan
Research Institute of Oil Production Engineering, Xinjiang Oilfield Company, PetroChina, Karamay 834000, China
1 shared publication - Qian Li
Research Institute of Oil Production Engineering, Xinjiang Oilfield Company, PetroChina, Karamay 834000, China
1 shared publication - Xiaoqin Xiong
College of Engineering, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
1 shared publication - Yi-Chang Li
China University of Petroleum-Beijing At Karamay
1 shared publication - Yukun Wang
Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing 102249, People’s Republic of China
1 shared publication - Zhiqiang Wang
Research Institute of Oil Production Engineering, Xinjiang Oilfield Company, PetroChina, Karamay 834000, China
1 shared publication
Latest Publications
- Open Access
A Memory-Guided Hybrid Artificial Bee Colony Algorithm with Variable Neighborhood Search for Green Power Consumption Optimization in Long-Distance Oil Pipelines
This paper addresses high electricity costs in long-distance crude oil transmission systems due to limited renewable energy integration. A mixed-integer linear programming (MILP) model is formulated to maximize renewable energy use and minimize purchased electricity costs, considering pump station and pipeline constraints. To solve this problem, a hybrid artificial bee colony algorithm with variable neighborhood search (HABC-VNS) is proposed, incorporating memory guidance, discrete uniform crossover, and three neighborhood structures. The algorithm is compared with standard ABC, binary PSO, and GA. In two experimental setups (four pumps/eight stations, 24 h horizon), HABC-VNS achieves average total costs of 33,000 CNY and 43,000 CNY, respectively, compared to 61,000–368,000 CNY for the other methods. Average green power integration rates reach 61.6% and 59.6%, outperforming all baselines. The proposed approach provides effective scheduling under strong operational constraints.
Open AccessA Memory-Guided Hybrid Artificial Bee Colony Algorithm with Variable Neighborhood Search for Green Power Consumption Optimization in Long-Distance Oil Pipelines
This paper addresses high electricity costs in long-distance crude oil transmission systems due to limited renewable energy integration. A mixed-integer linear programming (MILP) model is formulated to maximize renewable energy use and minimize purchased electricity costs, considering pump station and pipeline constraints. To solve this problem, a hybrid artificial bee colony algorithm with variable neighborhood search (HABC-VNS) is proposed, incorporating memory guidance, discrete uniform crossover, and three neighborhood structures. The algorithm is compared with standard ABC, binary PSO, and GA. In two experimental setups (four pumps/eight stations, 24 h horizon), HABC-VNS achieves average total costs of 33,000 CNY and 43,000 CNY, respectively, compared to 61,000–368,000 CNY for the other methods. Average green power integration rates reach 61.6% and 59.6%, outperforming all baselines. The proposed approach provides effective scheduling under strong operational constraints.