Publications

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Journal Papers

2025

• Pilario, K.E. and Wu, Z. (2025). Fast mixed kernel canonical variate analysis for learning-based nonlinear model predictive control. Chemical Engineering Research and Design. Vol. 219 (July 2025), 19-33. [Link] [Code]
• Abiog, J.D., Cacal, V.C.C., Cadiz, K., Pilario, K.E. (2025). Long-term Solar and Wind Energy Forecasting using Gaussian Processes with Tree-Based Multiple Kernel Search. Chemical Engineering Transactions, 116, 817-822. [Link].
• Khan, U., Pao, W., Pilario, K.E., Sallih, N., Sohail, M., Azam, H. (2025). Real-Time Automatic Flow Regime Classification and Mapping for Vertical Pipes Using Dynamic Pressure Signals. International Journal of Multiphase Flow. Vol. 189, 105252. [Link]
• Gojo Cruz, J.I., de Vera, J.M.L., Pilario, K.E. (2025). Machine learning-driven analysis of agro-climatic data for temperature modeling and forecasting in Philippine urban areas. Urban Climate. Vol. 60, 102339. [Link]

2024

• Pilario, K.E., Escober, E.J., de los Reyes V, A., Espino, M.P. (2024). Robust Prediction of Chlorophyll-a from Nitrogen and Phosphorus Content in Philippine and Global Lakes Using Fine-Tuned, Explainable Machine Learning. Environmental Challenges. Vol. 17, 101056. [Link] [Code]
• Pilario, K.E. (2024). Teaching classical machine learning as a graduate-level course in chemical engineering: An algorithmic approach. Digital Chemical Engineering, in Special Issue: Emerging Stars in Digital Chemical Engineering II, 100163. [Link] [Code]
• Khan, U., Pao, W., Pilario, K.E., Sallih, N. (2024). Flow Regime Classification using Various Dimensionality Reduction Methods and AutoML. Engineering Analysis with Boundary Elements. Vol. 163, 161-174. [Link]
• Abantao, G., Ibanez, J.A., Bundoc, P.E.D.C., Blas, L.L.F., Penisa, X.N., Esparcia, E.A., Castro, M., Buendia, R.V., Pilario, K.E.S., Tio, A.E.D., Cruz, I.B.N., Ocon, J.D., Odulio, C.M.F. (2024). Reconceptualizing Reliability Indices as Metrics to Quantify Power Distribution System Resilience. Energies. Vol. 17(8), 1909. [Link]
• Abantao, G., Ibañez, J.A., Bundoc, P.E.D.C., Blas, L.L.F., Penisa, X.N., Esparcia, E.A., Castro, M., Pilario, K.E.S., Tio, A.E.D., Cruz, I.B.N., Ocon, J.D., Odulio, C.M.F. (2024). Utility-Scale Grid-Connected Microgrid Planning Framework for Sustainable Renewable Energy Integration. Energies. Vol. 17(20), 5206. [Link]
• Hipolito, A.N., Palmero, M.A., Ordillo, V.Z., Shimizu, K, Putungan, D.B., Santos-Putungan, A.B., Ocon, J.D., Watanabe, S., Pilario, K.E.S., Padama, A.A.B. (2024). O-and OH-induced dopant segregation in single atom alloy surfaces: A combined density functional theory and machine learning study. Computational Materials Science. Vol. 232, 112607. [Link]
• Gnetchejo, P.J., Daniel, M.W., Dadje, A., Salome, N.E., Pilario, K.E., Pierre, E., Chen, Z. (2024). A new approach based on modified social network search algorithm combined with dichotomy method for solar photovoltaic parameter estimation. International Journal of Ambient Energy. Vol. 45(1), 2281611. [Link]
• Ordillo, V.Z., Shimizu, K., Putungan, D.B., Santos-Putungan, A.B., Watanabe, S., de Leon, R.L., Ocon, J.D., Pilario, K.E.S., Padama, A.A.B. (2024). Two-stage feature selection for machine learning-aided DFT-based surface reactivity study on single-atom alloys. Modelling and Simulation in Materials Science and Engineering. Vol. 32(6), 065003. [Link]

2023

• Khan, U., Pao, W., Pilario, K.E.S., Sallih, N., Khan, M.R. (2023). Two-phase flow regime identification using multi-method feature extraction and explainable kernel Fisher discriminant analysis. International Journal of Numerical Methods for Heat & Fluid Flow. Vol. 34 No. 8, 2836-2864. [Link]
• Abantao, G.A., Ibanez, J.A., Bundoc, P.E.D.C., Blas, L.L., Penisa, X.N., Pilario, K.E.S., Tio, A.E.D., Cruz, I.B.N.C., Ocon, J.D., Odulio, C.M.F. (2023). Generation and Network Planning of Utility-Scale Grid-Connected Microgrids. Chemical Engineering Transactions. Vol. 103, 877-882. [Link]
• Jang, K., Pilario, K.E.S., Lee, N., Moon, I., Na, J. (2023). Explainable Artificial Intelligence for Fault Diagnosis of Industrial Processes. IEEE Transactions on Industrial Informatics. Vol. 21(1), 4-11. [Link]
• Bhagat, S.K., Pilario, K.E., Babalola, O.E., Tiyasha, T., Yaqub, M., Onu, C.E., Pyrgaki, K., Falah, M.W., Jawad, A.H., Yaseen, D.A., Barka, N., Yaseen, Z.M. (2023). Comprehensive review on machine learning methodologies for modeling dye removal processes in wastewater. Journal of Cleaner Production. Vol. 385, 135522. [Link]

2022

• Tomacruz, J.G.T., Pilario, K.E.S., Remolona, M.F.M., Padama, A.A.B, and Ocon, J.D. (2022). A Machine Learning-Accelerated Density Functional Theory (ML-DFT) Approach for Predicting Atomic Adsorption Energies on Monometallic Transition Metal Surfaces for Electrocatalyst Screening. Chemical Engineering Transactions. Vol. 94, 733-738. [Link]
• Pilario, K.E.S., Ching, P.M.L., Calapatia, A.M., and Culaba, A.B. (2022). Predicting Drying Curves in Algal Biorefineries using Gaussian Process Autoregressive Models. Digital Chemical Engineering. Vol. 4, 100036. [Link]
• Grabato, J.R., Pilario, K.E., Micor, J.R.L., and Mojica, E.R.E (2022). Geographical and entomological differentiation of Philippine honey by multivariate analysis of FTIR spectra. Journal of Food Composition and Analysis. Vol. 114, 104853. [Link]
• Roxas II, R.M., Evangelista, M.A., Sombillo, J.A., Nnabuife, S.G., and Pilario, K.E.S. (2022). Machine Learning Based Flow Regime Identification using Ultrasonic Doppler Data and Feature Relevance Determination. Digital Chemical Engineering. Vol. 3, 100024. [Link]
• Pilario, K.E.S., Ibanez, J.A., Penisa, X.N., Obra, J.B., Odulio, C.M.F., and Ocon, J.D. (2022). Spatio-temporal Solar-wind Complementarity Assessment in the Province of Kalinga-Apayao, Philippines using Canonical Correlation Analysis. Sustainability. Vol. 14 (6), 3253. [Link]
• Pilario, K.E.S., Tielemans, A., and Mojica, E.R.E. (2022). Geographical Discrimination of Propolis using Dynamic Time Warping Kernel Principal Components Analysis. Expert Systems with Applications. Vol. 187, 115938. [Link]

2021

• Pilario, K.E.S., Cao, Y., and Shafiee, M. (2021). A Kernel Design Approach to Improve Kernel Subspace Identification. IEEE Transactions on Industrial Electronics. Vol. 68(7), 6171-6180. [Link]
• Eyo, E.N., Pilario, K.E.S., Lao, L., and Falcone, G. (2021). Development of a Real-time Objective Flow Regime Identifier using Kernel Methods. IEEE Transactions on Cybernetics. Vol. 51 (5), 2688-2698. [Link]

2020

• Shittu, A., Mehmanparast, A., Shafiee, M., Kolios, A., Hart, P., and Pilario, K.E.S. (2020). Structural reliability assessment of offshore wind turbine support structures subjected to pitting corrosion‐ fatigue: A damage tolerance modelling approach. Wind Energy. Vol. 23(11), 2004-2026. [Link]
• Pilario, K.E.S., Shafiee, M., Cao, Y., Lao, L., and Yang, S.H. (2020). A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring. Processes. Vol. 8(1), 24. [Link]

2019

• Nnabuife, S.G., Pilario, K.E.S., Lao, L., Cao, Y., and Shafiee, M. (2019). Identification of gas-liquid flow regimes using a non-intrusive Doppler ultrasonic sensor and virtual flow regime maps. Flow Measurement and Instrumentation. Vol. 68, 101568. [Link]
• Pilario, K.E.S., Cao Y., and Shafiee, M. (2019). Mixed Kernel Canonical Variate Dissimilarity Analysis for Incipient Fault Monitoring in Nonlinear Dynamic Processes. Computers and Chemical Engineering. Vol. 123 (2019), 143-154. [Link]

2018

• Pilario, K.E.S. and Cao, Y. (2018). Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection. IEEE Transactions on Industrial Informatics. Vol. 14(12), 5308-5315. [Link]

Conference Presentations

2025

• Janer, J.M.S., Pilario, K.E.S. (2025). Surrogate-based Optimization of Anaerobic Digesters for Waste-to-Energy Applications. 86th PIChE (Philippine Institute of Chemical Engineers) National Convention, SMX Convention Center, Davao, 13 September 2025. *Won Best Paper
• Romano, A., dela Pena, E.M., Pilario, K.E.S. (2025). Feature Selection-Driven Supervised Machine Learning for Dissolved Oxygen Prediction Using Multidomain Environmental Parameters in a Major Fishing Lake. 2nd International Conference on Advanced Materials & Sustainable Energy Technologies 2025, Sunway University, Kuala Lumpur, Malaysia, 13–14 November 2025. *Won Best Paper
• Laag, J.E., Pilario, K.E.S. (2025). Machine Learning-Based Deforestation Monitoring and Forecasting Using Sentinel-2 Imagery: A Case Study in Narra, Palawan. Philippine Geomatics Symposium (PhilGEOS 2025), Quezon City, Philippines, 24-25 November 2025.
• Mondejar, L.C., Alamani, B.G., Pilario, K.E.S. (2025). Cocrystal Prediction Enhanced by UMAP Visualization and SMOTE-based Balancing in an Interpretable Machine Learning Framework. 19th Conference of Asian Crystallographic Association (AsCA 2025), Taiwan, 03 December 2025.

2023

• Cabahug, M.K., David, C.P.C., Pilario, K.E.S. (2023). Application of Geographic Information System (GIS) and Logistic Regression Model to Map the Potential Areas of Groundwater Springs. GeoCon 2023. Novotel Manila, Araneta City, Manila.
• Hipolito, A.N., Palmero, M.A., Ordillo, V.Z., Shimizu, K, Putungan, D.B., Santos-Putungan, A.B., Ocon, J.D., Watanabe, S., Pilario, K.E.S., Padama, A.A.B. (2023). Investigating O-and OH- induced dopant segregation in single-atom alloy surfaces using density functional theory and machine learning. Annual Meeting of the Japan Society of Vacuum and Surface Science 2023. [Link]
• Madayag, J.V., Pilario, K.E. (2023). Multi-Kernel Canonical Variate Analysis with Bayesian Optimized Kernel Designs for Nonlinear System Identification. Computer-Aided Chemical Engineering. Vol. 52, 1573-1578. [Link]

2017-2020

• Pilario, K.E.S., Shafiee, M. (2020). Mixed Kernel Functions for Multivariate Statistical Monitoring of Nonlinear Processes. In: Ball, A., Gelman, L., Rao, B. (eds) Advances in Asset Management and Condition Monitoring. Smart Innovation, Systems and Technologies. Vol. 166. Springer, p. 61-67. Springer. [Link]
• Pilario, K.E.S., Cao, Y., Shafiee, M., Lao, L. (2019). Reconstruction-based fault prognosis in dynamic processes using canonical variate analysis. 25th International Conference on Automation and Computing (ICAC). Lancaster University, U.K. IEEE. [Link]
• Pilario, K.E.S., Cao, Y. Shafiee, M. (2019). Incipient Fault Detection, Diagnosis, and Prognosis using Canonical Variate Dissimilarity Analysis. Computer-Aided Chemical Engineering. Vol. 46, 1195-1200. [Link]
• Pilario, K.E.S. and Cao, Y. (2017). Process Incipient Fault Detection using Canonical Variate Analysis. 23rd International Conference on Automation and Computing (ICAC). University of Huddersfield, U.K. IEEE. [Link]

Copyrights

• Palmero, M.A., Hipolito, A.N.P., Padama, A.A.B., Pilario, K.E.S. (2025). INTELLISORP: Artificial Intelligence-Assisted Adsorption Energy Prediction on Multicomponent Alloy Surfaces. 21 February 2025. Philippines.