Optimization of Oil Pipeline Operations to Reduce Energy Consumption Using an Improved Squirrel Search Algorithm

Shanbi Peng (), Zhe Zhang, Yongqiang Ji and Laimin Shi
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Shanbi Peng: School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
Zhe Zhang: China Petroleum Pipeline Engineering Corporation, Langfang 065000, China
Yongqiang Ji: China Petroleum & Chemical Co., Ltd. of North Branch, Zhengzhou 450006, China
Laimin Shi: Zhejiang Zheneng Natural Gas Operation Co., Ltd., Hangzhou 310051, China

Energies, 2022, vol. 15, issue 20, 1-19

Abstract: To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions are used to test the performance of MASSA, including unimodal functions and multimodal functions. Compared with the other four algorithms, MASSA has better performance and convergence capabilities. In the case study experiment, an optimization model of the oil pipeline is built, which takes the minimum energy consumption of the whole pipeline as the objective function. Compared with the actual operating conditions, the electricity consumption optimized by MASSA decreases by 399,018.94 kgce, and the thermal energy dissipation decreases by 113,759.25 kgce. The total energy consumption is reduced by 512,778.19 kgce, which is 9.62%. These results indicate that the two improvement strategies are significant, and optimizing the operating parameters can reduce energy consumption.

Keywords: energy optimization; inverter pump; squirrel search algorithm; multi-group co-evolution; adaptive inertia weight (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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