Dr. Tian Lan | Dalian Maritime University | China
Tian Lan is a dedicated PhD candidate at Dalian Maritime University, specializing in the integration of artificial intelligence with advanced engineering applications. With a strong academic foundation and an emerging research profile, Tian has published several high-quality SCI papers as the first author in respected journals including showcasing a consistent commitment to innovative problem-solving and high-impact scientific contributions. His research focuses on leveraging AI-driven models to enhance performance, sustainability, and reliability across energy systems and marine engineering technologies. Tian has participated in multiple research initiatives that explore intelligent optimization, data-driven prediction, and system modeling, contributing meaningful advancements to interdisciplinary engineering solutions. He works collaboratively with scholars across institutions, engaging in joint studies and technical discussions that strengthen the practical relevance of his work. Tian’s growing academic influence is supported by professional memberships and a presence on international research platforms, reflecting both his credibility and engagement with the scientific community. With continuous involvement in applied research, emerging innovations, and collaborative development, Tian remains committed to advancing artificial intelligence applications and contributing to the broader scientific and engineering landscape through rigorous inquiry and impactful scholarship.
Featured Publications
Tan, X., Wang, D., Sun, P., & Lan, T. (2026). A triad framework for ship carbon reduction: Direct CO₂ measurement, multi-intelligence fusion prediction, and Cauchy-enhanced speed optimization. Applied Ocean Research.
Lan, T., Huang, L., Cao, J., Ma, R., Zhao, H., Ruan, Z., Wu, J., Li, X., & Wang, K. (2025). A pioneering approach for improving ship operational energy efficiency: The quantitative application of deep learning interpretable results. Applied Energy.
Lan, T., Huang, L., Ruan, Z., Cao, J., Ma, R., Wu, J., Li, X., Chen, L., & Wang, K. (2025). Multilevel parallel integration framework for enhancing energy efficiency of wing-assisted ships based on deep learning and intelligent algorithms: Towards a smarter and greener shipping. Applied Energy.
Lan, T., Huang, L., Ma, R., Wang, K., Ruan, Z., Wu, J., Li, X., & Chen, L. (2024). A robust method of dual adaptive prediction for ship fuel consumption based on polymorphic particle swarm algorithm driven. Applied Energy.
Lan, T., Huang, L., Ma, R., Ruan, Z., Ma, S., Li, Z., Zhao, H., Wang, C., Zhang, R., & Wang, K. (2024). A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method. Ocean Engineering.
Han, Z., Lan, T., Han, Z., Yang, S., Dong, J., Sun, D., Yan, Z., Pan, X., & Song, L. (2019). Simultaneous removal of NO and SO₂ from exhaust gas by cyclic scrubbing and online supplementing pH-buffered NaClO₂ solution. Energy & Fuels.
Han, Z., Gao, Y., Yang, S., Dong, J., Pan, X., Lan, T., Song, L., Yan, Z., Sun, D., & Ning, K. (2019). NO removal from simulated diesel engine exhaust gas by cyclic scrubbing using NaClO₂ solution in a rotating packed bed reactor. Journal of Chemistry.