An intelligent control for reducing third-party interference in oil and gas pipeline using Deep Q-Networks (DQN)
Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Agbani, Enugu State, Nigeria.
Research Article
Global Journal of Engineering and Technology Advances, 2024, 18(03), 075–081.
Article DOI: 10.30574/gjeta.2024.18.3.0233
Publication history:
Received on 12 November 2023; revised on 08 March 2024; accepted on 11 March 2024
Abstract:
A nation's economic survival depends on its oil and gas pipelines. They must therefore be carefully inspected in order to enhance their efficiency and prevent product losses during the transportation of petroleum products. They could, however, fail, having negative effects on the environment, the economy, and safety. Therefore, evaluating the pipe's condition and quality would be extremely important. This research work performed an intelligent control for reducing third-party interference in oil and gas pipeline using Deep Q-Networks (DQN). The learning curve shows a steady improvement, indicating that the algorithm progressively learned and improved its performance over time. This observation demonstrates the effectiveness of the DQN algorithm in adapting and optimizing control strategies. Overall, the results of the analysis indicate that the DQN algorithm holds promise for mitigating third-party interference in oil pipelines.
Keywords:
Oil and Gas; Pipelines; Third-Party Interference; Deep Q-Networks (DQN and MATLAB)
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