OPTIMASI PENEMPATAN SENSOR PADA UNIT KILANG MINYAK MENGGUNAKAN ALGORITMA GENETIKA
DOI:
https://doi.org/10.53026/prosidingsntem.v5i1.767Keywords:
Optimasi sensor, Algoritma genetika, Kilang minyak, Multi-kriteria, Efisiensi biayaAbstract
Kilang minyak adalah lokasi industri berisiko tinggi yang membutuhkan sistem pemantauan yang dapat diandalkan yang melibatkan penempatan sensor strategis. Penempatan yang tidak efektif dapat menyebabkan blind zone, redundansi berlebihan, dan biaya yang tidak efisien. Menggunakan Algoritma Genetika (GA) dengan pendekatan multi-kriteria, penelitian ini bertujuan untuk mengoptimalkan penempatan sensor pada unit kilang minyak. Dalam pemodelan spasial dua dimensi, fungsi fitness dimaksudkan untuk menggabungkan cakupan area, biaya instalasi, dan penalti redundansi. Python, yang memiliki populasi 80 kromosom dan 200 generasi, menghasilkan dua skenario optimal (1) skenario efisiensi biaya dengan empat sensor aktif mencapai cakupan 100% terhadap sepuluh unit dengan fitness 16,7 dan biaya 16.900 satuan tanpa redundansi; (2) skenario keandalan sistem dengan tujuh sensor dengan fitness 15,3, 1.250 redundansi, dan 21.400 satuan. Metode ini dapat mengurangi biaya instalasi hingga enam puluh persen dibandingkan dengan penempatan manual baseline sambil menghilangkan redundansi yang tidak diperlukan. Framework yang dikembangkan dapat disesuaikan dengan berbagai skala kilang. Penelitian ini memberikan dasar kuantitatif yang fleksibel berdasarkan prioritas efisiensi biaya atau keandalan sistem dalam industri gas dan minyak
References
T. H. Nguyen and J. Kim, “Smart refinery systems: Data-driven optimization and sensor network design,” J. Pet. Sci. Eng., vol. 203, p. 108624, 2021.
S. Savazzi, S. Guardiano, and U. Spagnolini, “Wireless Sensor Network Modeling and Deployment Challengesin Oil and Gas Refinery Plants,” Int.J. Distrib. Sens. Netw., vol. 2020, Article ID 383168, 17 pp., Hindawi Publishing Corporation, 2020.
F. Li, Q. Zhang, and Y. Huang, “Optimization of sensor networks in industrial IoT using genetic algorithms,” IEEE Internet Things J., vol. 9, no. 5, pp. 3856–3868, 2022.
A. H. H. Al Jlibawi, “Optimization of Distribution Control System in Oil Refinery Using Soft Sensors,” in IEEE Conference Proceedings, 2021.
R. Patel and P. Mehta, “Multi-objective genetic algorithm for optimizing sensor placement in refinery process monitoring systems,” Energy AI, vol. 8, p. 100236, 2024.
S. Kumar and R. Singh, “Multi-objective optimization of sensor placement for process monitoring,” Chem. Eng. Res. Des., vol. 147, pp. 283–295, 2019
Y. Jiang and J. Li, “Sensor placement optimization for structural health monitoring using genetic algorithms,” Struct. Control Heal. Monit., vol. 23, no. 10, pp. 1285–1298, 2016.
X. Wang, Z. Chen, and H. Zhao, “Optimal sensor placement for damage detection using improved genetic algorithms,” Mech. Syst. Signal Process., vol. 88, pp. 142–156, 2017.
K. Elango and S. Natarajan, “Optimal deployment of wireless sensor networks using genetic algorithm for industrial applications,” Comput. Electr. Eng., vol. 67, pp. 324– 337, 2018.
Y. Lang et al., “A Novel CFD-MILP-ANN Approach for Optimizing Sensor Layout in Industrial Plants,” J. Process Control, 2025.
Y. Zhao, H. Liu, and J. Hu, “Integration of genetic algorithms with process models for optimal sensor configuration in refinery units,” Energy Reports, vol. 6, pp. 1059–1071, 2020.
Y. Kwon et al., “Development of inferential sensor and real-time optimizer for a vacuum distillation unit by recurrent neural network modeling,” Comput. Chem. Eng., vol. 167, p. 108015, 2022.
M. Ahmed and R. Hassan, “A hybrid genetic algorithm approach for optimal sensor allocation in process industries,” Appl. Soft Comput., vol. 111, p. 107702, 2021.
H. Tian et al., “Optimizing Sensor Placement for Enhanced Source Term Estimation in Chemical Plants,” Processes, vol. 13, no. 3, p. 825, 2025.
D. Kwak et al., “Sensor Placement Optimization for Fenceline Monitoring of Chemical Plants,” in Procedia Manufacturing, 2022.
M. Filipiak et al., “A Review of Optimization Methods for Pipeline Monitoring and Leak Detection,” Energies, vol. 18, p. 3591, 2023.
L. Xu, S. Shi, F. Yan, Y. Huang, and Y. Bao, “Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm,” Sensors, vol. 23, no. 17, p. 7379, 2023.
M. Waqas, L. Jan, M. H. Zafar, R. Hassan, and R. Asif, “A Sensor Placement Approach Using MultiObjective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems,” Sensors, vol. 24, no. 5, p. 1423, 2024.
M. A. Rahman and M. Chowdhury, “Genetic algorithm-based optimization for fault detection sensor networks in oil and gas systems,” Sensors, vol. 23, no. 4, p. 1762, 2023.
B. Priandoko, “Optimalisasi Kinerja Energi Furnace Kilang PPSDM Migas Cepu Menggunakan Algoritma Genetik,” J. Migas Zoom, 2023.
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2025 Prosiding Seminar Nasional Teknologi Energi dan Mineral
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
