Publications | SustainAI

Selected Articles in Refereed Journals

Ta, S., and Liu, T. A physics-informed modular neural network for yield prediction and operational optimization in industrial hydrocracking. 2026. DOI: 10.1016/j.fuel.2026.140956
Qiang, G., Chen, Y., Rotenberg, D., Kim, E., and Fang, F. CRS-Triage: Confidence- and Reliability-Aware Selective Triage under Incomplete Clinical Evidence. 2026. DOI: 10.48550/arXiv.2608.03862
Ta, S., and Liu, T. Data-Driven Multi-Objective Optimization of an Industrial Hydrotreater Using a Multi-Output Gaussian Process Surrogate. 2026. DOI: 10.1021/acs.iecr.6c00111
Chen, T., Liu, T., Lu, C., Malik, R., and Zhang, J. Fluorescent Hydrogel-Based Strain Sensor with Machine Learning-Augmented Performance. 2026. DOI: 10.1002/aidi.70144
Chen, Y., Qiang, G., Xie, X., Wang, B., Cai, D., Diyan, M., Liu, T., and Fang, F. Personalized Vehicular Health Diagnosis via Large Language Model. 2026. DOI: 10.1109/MCOMSTD.2026.3674904
Ta, S., Samavedham, L., Ray, A., and Liu, T. Physics—Informed neural ordinary differential equations for hydrocracking kinetics modeling and system identification. 2026, 167(1), DOI: 10.1016/j.engappai.2026.113808
Ma, X., Fang, F., Liu, T., and Wang, X. Dynamic Authentication and Granularized Authorization with a Cross-Domain Zero Trust Architecture for Federated Learning in Large-Scale IoT Networks. IEEE Transactions on Network Science and Engineering. 2026. DOI: 10.1109/TNSE.2025.3650377
Liu, T., and Zheng, Y. Editorial: AI-driven green revolution. Green Energy and Resources, 2025, 3(3): 100144 DOI:10.1016/j.gerr.2025.100144
Gomez, M.E.P., Park, E., Zheng, Y., Bassi, A., and Liu, T. Exploring the Application of Artificial Intelligence for Bioelectrochemical Systems: A Review of Recent Research. Green Energy and Resources. , 2025, 3(3): 100141. DOI: 10.1016/j.gerr.2025.100141
Fazlikhani, F., Pang, D., de Jourdan, D., Yue, D., Liu, T., Zhang, B., An, C., Farahani, M., and Zheng, Y. Biodegradable lignin surfactant disperses oil spills with droplet dynamics mapped by AutoDrop algorithm. Colloids and Surfaces A: Physicochemical and Engineering Aspects. DOI: 10.1016/j.colsurfa.2025.138329
Salami, R., Liu, T., Han, X., and Zheng, Y. Machine Learning Application in Thermal CO2 Hydrogenation: Catalyst Design, Process Optimization, and Mechanism Insights. Advanced Powder Materials. DOI: 10.1016/j.apmate.2025.100333
An J., Chen T., Pouri H., Liu T., and Zhang J. Machine Learning-Assisted Development of Conductive Polymers. Polymer. DOI: 10.1016/j.polymer.2025.128684
Hu T., Wang X., Liu, T., and Liu T., A Novel Deep Learning Model for Subway PM2.5 Prediction Using Neighborhood Component Analysis and Convolutional Latent Variables. IEEE Transactions on Instrumentation and Measurement. DOI: 10.1109/TIM.2025.3572997
Zhang K., Liu T., Wang Q., Wei W., Zhang F. and Liu H., Enhancing Water Quality Prediction with Advanced Machine Learning Techniques: An Extreme Gradient Boosting Model Based on Long Short-Term Memory and Autoencoder. Journal of Hydrology. 2024, 644, DOI: 10.1016/j.jhydrol.2024.132115
Zhang W., Yang Y., Akilan T., Wu J., and Liu T., Fast Transfer Learning Method Using Random Layer Freezing and Feature Refinement Strategy. DOI: TCYB.2024.3483068
Zhang W., Yang Y., Wu J., and Liu T., Deep Optimized Broad Learning System for Applications in Tabular Data Recognition. IEEE Transactions on Cybernetics. DOI: 10.1109/TCYB.2024.3473809
Lu Y., Wang J., Liu T., Yoo C., and Liu H., Integration of Dynamic Slow Feature Analysis and Deep Neural Networks for Subway Indoor PM2.5 Prediction. IEEE Transactions on Instrumentation and Measurement. 2024, DOI: 10.1109/TIM.2024.3476591.
Chang T., Liu T., Ma X., Wu, Q., Cheng J., Zhang F. and Liu H. Fault Detection in Industrial Wastewater Treatment Processes Using Manifold Learning and Support Vector Data Description. Industrial & Engineering Chemistry Research. 2024 63 (35), 15562-15574, DOI: 10.1021/acs.iecr.4c00424.
Zhang W., Yang Y. and Liu T., Coarse-to-Fine Target Detection for HFSWR with Spatial-Frequency Analysis and Subnet Structure. IEEE Transactions on Multimedia. DOI: 10.1109/TMM.2024.3453044.
Han J., Liu Y., Li W., Huang F., Liu T, Shen W., Corriou J. and Seferlis P., Modeling greenhouse gas emissions from biological wastewater treatment process with experimental verification: a case study of paper mill. Science of the Total Environment. vol. 924, May 10, 2024. DOI: 10.1016/j.scitotenv.2024.171637.
Zhang W., Wu J., Liu T., and Yang Y., A Two-stage Hierarchical One-Class Classification Structure for HFSWR Ship Target Detection, IEEE Transactions on Geoscience and Remote Sensing. vol. 61, pp. 1-11, 2023, Art no. 5109811, DOI: 10.1109/TGRS.2023.3322112.
Liu T., Pillai A.V.N., Shi J., and Roberts D.J., Stable performance of MFC treating winery wastewater irrespective of seasonal variations, Journal of Environmental Engineering, October 2021. DOI: 10.1061/(ASCE)EE.1943-7870.0001921.
Liu T., Pillai A.V.N., and Roberts D.J., Narrow pH tolerance found for a microbial fuel cell treating winery wastewater, Journal of Applied Microbiology, April 12, 2021. DOI: 10.1111/jam.15102.
Tao E.P., Shen W.H., Liu T., and Chen X.Q., Fault diagnosis based on PCA for sensors of laboratory wastewater treatment process. Chemometrics and Intelligent Laboratory Systems, 2013, 128: 49-55. DOI: 10.1016/j.chemolab.2013.07.012.
Li Y., Chen X.Q., Shen W.H., and Liu T., The retention and drainage of DIP containing different fillers and the nanosized TiO2 and modified starch dual-system, China Pulp and Paper, 2012, 31(5):15-18. http://dx.doi.org/10.11980/j.issn.0254-508X.2012.05.004.
Shen W.H., Liu T., and Chen X.Q., Photocatalytic degradation of dye rhodamine B by nanosized TiO2 colloids, Chinese Journal of Environmental Engineering, 2012, 6(6):1863-1870. http://en.cnki.com.cn/Article_en/CJFDTotal-HJJZ201206018.htm.
Liu T., Ning, L., and Shen W.H., A quick Way to Get Fuzzy Control Query Table and Its Application in Papermaking Wastewater Treatment Process, Paper Science & Technology, 2011 30(4), 78-82.
Liu T., and Shen W.H., A review of Applications of Fault Diagnostic Expert System in Wastewater Treatment, Paper Science & Technology, 2011, 30(2):75-81.
Ning L., Shen W.H., Long Z., and Liu T., Simulation and Simulation Interface Design of Activated Sludge Wastewater Treatment Process, Paper Science & Technology, 2010, 29(6): 138-142.
Dou X.L., Hu J., Yang J., and Liu T., Application of One Kind of Water-Soluble Phenolic Resin on Filter Paper, Paper Science & Technology, 2010, 29 (4): 24-27.

Books

Leiviska K., Shen W.H., Li J., Liu H.B., Li C.C., Liu T., and Tong X. Papermaking process and maintenance management, Beijing: China Light Industry Press, ISBN: 9787518415052, 2017. Print.

Conference Presentations:

Data-Driven Multi-Objective Optimization of an Industrial Hydrotreating Process, The 18th Annual Conference of the Global Academy of Chinese Chemical Engineering Scholars (GACCE 2026), Shanghai, China, 2026/08
Reliable AI for Sustainable Fossil Fuel Production, The International Energy Conference 2026 (2026 IEC), Regina, Canada, 2026/07.
Embedding Physical Structure into Machine Learning for Chemical Engineering: A Case Study in Industrial Hydroprocessing, Symposium on Chemical Engineering and AI (GCCES 2026), Ningbo, China, 2026/06.
Ta, S; Liu, T; Samavedham, L; and Ray, A. (2025). Noise-Robust Physics-Informed Neural ODEs for Hydrocracking Kinetics and Parameter Identification, Indian Chemical Engineering Congress (IIChE CHEMCON 2025), Tamilnadu, India. 2025/12
Ta, S., Liu, T; Samavedham, L; and Ray, A. (2025). Surrogate Modelling and Optimization of an Industrial Hydrotreater Unit Using Multitask Gaussian Processes. Indian Chemical Engineering Congress (IIChE CHEMCON 2025), Tamilnadu, India. 2025/12
Ta, S; Samavedham, L; Ray, A; and Liu, T. (2025). Learning Hydrocracking Reaction Dynamics via Physics-Informed Neural ODEs: Reaction- and Parameter-Based Approaches for Interpretable Kinetic Model Identification. Canadian Chemical Engineering Conference (CSChE 2025), Montreal, Canada. 2025/10
Ta, S; Liu, T; Samavedham, L; and Ray, A. (2025). Neural Ordinary Differential Equations for Hydrocracking Kinetics: A Data-Driven and Interpretable Approach to Reaction Modeling, WCCE12, Beijing, China, 2025/07.
Ta, S; Ray, M B; Liu, T; and Valipour, R. (2025). Physics-Informed LSTM for Great Lakes Surface Temperature Modeling, Water & Environment Student Talks (WEST) Conference 2025, Vancouver, Canada. 2025/06.
Pardo Gomez, M; and Liu, T. (2025). Artificial Intelligence-Augmented Modeling of Microbial Fuel Cells for Sustainable Wastewater Treatment and Energy Recovery, Water & Environment Student Talks (WEST) Conference 2025, Vancouver, Canada. 2025/06.