Publication: Optimization of carbon capture and storage transportation networks in Thailand using the artificial hummingbird algorithm
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Issued Date
2025-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105017402772
Journal Title
Scientific Reports
Volume
15
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.15 No.1 (2025)
Suggested Citation
Promsuk N., Daraneesrisuk J., Thongrueang N., Suetrong N. Optimization of carbon capture and storage transportation networks in Thailand using the artificial hummingbird algorithm. Scientific Reports Vol.15 No.1 (2025). doi:10.1038/s41598-025-18312-3 Retrieved from: https://hdl.handle.net/20.500.14740/50579
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Abstract
Carbon Capture and Storage (CCS) is increasingly recognized as a vital technology for mitigating carbon dioxide (CO<inf>2</inf>) emissions by capturing CO<inf>2</inf> and storing it deep underground, thereby preventing its release into the atmosphere. Effective deployment of CCS projects requires the optimal matching of CO<inf>2</inf> emission sources with storage sinks, as well as efficient routing through onshore and offshore transportation networks. This research focuses specifically on truck-based transportation, a practical and flexible delivery mode given the current infrastructure conditions in Thailand. The optimization problem is formulated as a Capacitated Vehicle Routing Problem (CVRP), aiming to minimize total travel distance while meeting CO<inf>2</inf> transportation capacity constraints. Due to the NP-hard nature of the problem, traditional optimization techniques often encounter limitations. To address this, a constrained K-means clustering algorithm is first applied to perform source–sink matching, grouping CO<inf>2</inf> emission sources based on proximity and capacity compatibility with designated storage sinks, i.e., the Lampang, Nong Bua, Suphanburi, and Kra basins. The clustering process incorporates two key constraints to ensure feasibility and efficiency. Subsequently, the Artificial Hummingbird Algorithm (AHA), inspired by the foraging behavior of hummingbirds, is applied for route optimization. AHA is particularly effective for complex optimization problems due to its strong global search capabilities. The model is applied to a case study in Thailand, taking into account the country’s unique geographical and infrastructural features. The objective function aims to minimize travel distance, thereby improving both the cost-effectiveness and environmental sustainability of CCS deployment. Results demonstrate that AHA significantly outperforms Dijkstra’s and Particle Swarm Optimization (PSO) algorithms, achieving approximately a 13% reduction in travel distance and a 15% decrease in the number of vehicles required within the Suphanburi Cluster. These findings provide valuable insights for enhancing CCS implementation in Thailand and other regions with similar characteristics.