Student Publications

Nanocomposite protective coatings for hydrogen-natural gas blending: Mechanism, embrittlement mitigation and barrier performance

To circumvent the carbon intensity of natural gas, blending hydrogen with natural gas has emerged as a promising pathway towards achieving net-zero emissions. Consequently, significant attention has been directed toward understanding the effects of Hydrogen-Natural Gas (H₂-NG) blend on material behaviour and performance, particularly in pipelines and storage tanks. However, hydrogen embrittlement (HE) poses a critical challenge, as hydrogen can diffuse through metals' crystal lattices and severely degrade material properties regardless of the time and length scales. The resulting damage may occur at the atomic or molecular level, making prediction and detection of hydrogen trapping & accumulation mechanisms highly complex. Besides, a comprehensive understanding of the mechanisms governing hydrogen ingress, dissolution, diffusion, dissociation, and trapping in materials is essential for the selection and deployment of materials used in the transportation and storage of H₂-NG blends. Thus, this review paper critically examines the major mechanisms of hydrogen embrittlement, with particular focus on their interaction with various materials commonly used in natural gas infrastructure. Special emphasis is given to the interaction between hydrogen and microstructural features such as voids, dislocations, inclusions, grain boundaries, and cracks, which contribute to embrittlement and material degradation. The paper further explores potential mitigation strategies, with a primary focus on protective nanocomposite coatings and their role in controlling hydrogen ingress. The underlying mechanisms through which these nanocomposite coatings provide protection are also discussed in detail. In addition, special emphasis is placed on nanocomposite matrix composition and the influence of fillers with varying sizes, morphologies, and aspect ratios. The review highlights how the geometry, distribution, and interfacial bonding of fillers, such as nanofibers, nanoplatelets, and particulates, affect the hydrogen barrier performance and overall resistance of nanocomposite coatings. Finally, the paper discusses various coating fabrication and deposition techniques relevant to hydrogen service environments and outlines future research directions for the development of durable hydrogen-resistant materials and coatings.

Kumar, S., & Muthu, S. D. J. (2026). Nanocomposite protective coatings for hydrogen-natural gas blending: Mechanism, embrittlement mitigation and barrier performance. Hydrogen Economy & Engineering, Article 100014. https://doi.org/10.1016/j.hee.2026.100014

Development of a distributive Canadian energy-water-carbon nexus system planning model for accomplishing net-zero carbon emissions by 2050

Energy, water, and carbon are tightly interconnected, and balancing these linkages is critical for Canada's low-carbon transition and sustainable development. A major challenge lies in capturing the socio-economic and environmental (SEE) impacts of green alternatives while addressing regional disparities, interregional interactions, and uncertainties within the energy-water-carbon nexus system. Accordingly, this study develops a Canadian Energy-Water-Carbon Nexus (CEWCN) planning model to support strategic decision-making for achieving net-zero emissions by 2050. Built on a mixed-integer interval chance-constrained programming framework, the model addresses multidimensional uncertainties and regional heterogeneity under multi-risk conditions. Scenario analysis and Sustainable Development Goals assessments are further integrated to examine how climate policies, technological innovation, socio-economic development, and interregional cooperation affect system sustainability. Results indicate that by 2050, renewable energy will supply over 90% of Canada's electricity, primarily from hydropower, wind, and solar. Natural gas with carbon capture and storage (CCS) and small modular reactors are expected to replace conventional natural gas and nuclear. Water consumption will remain highest in Quebec, British Columbia, and Ontario due to hydropower, biomass, and nuclear generation, though total withdrawals will decline by [16–20]% compared to 2030. National emissions are expected to fall sharply, reaching net-zero by 2050 through large-scale renewable deployment and negative emission technologies such as bioenergy with CCS. Moreover, regional carbon cooperation can redistribute abatement efforts, easing mitigation burdens in high-emission provinces and lowering system costs. Overall, the findings offer strategic insights for shaping resilient, low-carbon transition pathways and managing multidimensional risks in the CEWCN system.

Xu, Y., Huang, G., Chen, L., Liu, Y., & Luo, B. (2026). Development of a distributive Canadian energy-water-carbon nexus system planning model for accomplishing net-zero carbon emissions by 2050. Journal of Cleaner Production, 573, 148999.

Evaluating applicability of Google Trend Score in predicting yard waste collection

This study investigates whether Google trends score (GTS) can serve as a useful predictor of yard waste collection. To examine this, yard waste collection patterns in Winnipeg, Canada, from 2010 to 2024 were analyzed, and two multiple linear regression (MLR) models were developed. The study revealed a positive trend (Slope= 1.80, R2= 0.37) in yard waste per thousand capita (YPC) and a comparable rise in yard waste pickup per thousand capita (PPC). The variations were influenced by precipitation, vegetation and storm incidents. The social and climatic parameters were used to predict yard waste collection. Pearson correlation (|r| < 0.95, p < 0.05) revealed 5 potential predictors, including average relative humidity (ARH), median income (MI), education attainment (EA), GTS, and average wind speed (AWS). The MLR model containing GTS performed better, achieving an R2 of 0.72 with all predictors remaining statistically significant (p < 0.05). GTS increased R2 by 0.17, reduced root mean square error (RMSE) by 1.67, and increased F-statistic by 7.91. Diagnostic tests showed no multicollinearity (Variance inflation factor, VIF=1.25) and no heteroscedasticity (Non-constant variance, NCV p > 0.05). The residuals followed normal distribution (Anderson-Darling p > 0.05). The results show that adding GTS produces an observable improvement in the prediction accuracy of the YPC. It demonstrated that online search interest can serve as a meaningful proxy for public awareness.

Jamil, M. S., Gharaei, A., Palansooriya, K. N., Chowdhury, R., & Ng, K. T. W. (2026). Evaluating applicability of Google Trend Score in predicting yard waste collection. Sustainable Horizons, 19, Article 100195. https://doi.org/10.1016/j.horiz.2026.100195

Field-scale simulation on multi-slug proppant transport in a propagating fracture via modified Perkins-Kern-Nordgren-Carter (PKN-C) model and particle tracking algorithm

During hydraulic fracturing operations in unconventional low-permeability reservoirs, non-Newtonian fracturing fluids are commonly injected with multi-slug proppants of varying particle sizes; however, efficiently representing the coupled evolution of such proppant transport at field-scale remains challenging. In this work, a computationally efficient framework is developed to simulate and evaluate proppant transport dynamics within a propagating fracture by modifying the Perkins-Kern-Nordgren-Carter (PKN-C) model and coupling it with a parcel-based Lagrangian particle-tracking algorithm. The framework incorporates not only empirical correlations derived from laboratory measurements to represent concentration-dependent fluid rheology, hindered settling, and bed-load transport, but also a modified width solution accounting for stress anisotropy and wellbore radius to represent realistic fracture morphology and quantify geometric fracture permeability/conductivity distributions conditioned to multi-slug proppant dynamics. Following its validation against experimental measurements and field-scale observations, a sensitivity analysis of 14 scenarios is performed. Proppant transport is found to be highly path-dependent, and dynamic fluid property scheduling produces more spatially uniform conductivity architectures than constant scheduling within the studied cases. A sequential injection of larger particles followed by smaller ones generates a foundational dune that facilitates transport of subsequent particles, yielding more favourable conductivity architectures than the reverse sequence within the studied parameter ranges. Overall, the proposed framework enables field-scale evaluation of multi-slug proppant transport dynamics and spatially variable conductivity in a propagating fracture by addressing several limitations (e.g., a single particle size, constant fluid viscosity, isotropic formation stress and fixed fracture dimensions) that were normally assumed with conventional models.

Wang, Y., Ding, Y., & Yang, D. (2026). Field-scale simulation on multi-slug proppant transport in a propagating fracture via modified Perkins-Kern-Nordgren-Carter (PKN-C) model and particle tracking algorithm. Computers and Geotechnics, 200, 108443.

Deterministic cost estimation approaches for carbon capture, utilization, and storage: A review

Carbon capture, utilization, and storage (CCUS) is regarded as a connection between traditional, high-emission sectors and future green energy. This study encompasses literature from 2003 to early 2026 to review methodologies and formulae used for cost estimating in the various segments of carbon capture, transport, utilization, and storage. The focus of this study is the deterministic economic assessment metrics (net present value [NPV], internal rate of return [IRR], minimum attractive rate of return [MARR], cost–benefit analysis [CBA], break-even analysis, sensitivity analysis, payback period, and techno-economic assessment) in the CCUS system to assess the viability of projects. This review discusses how different assumptions and scenarios can change the results and make comparisons between CCUS projects difficult. This review indicates that deterministic models are useful for early screening of projects, but their results may be misleading when the project scale, regional conditions, infrastructure availability, and market assumptions are not clearly defined. Although technical innovation and shared infrastructure can reduce the costs, most available research still emphasizes the critical role of carbon taxes and incentives in improving CCUS project feasibility. On the other hand, the results identify that high capital costs, policy uncertainty, the absence of standardized economic evaluation frameworks, and a lack of clear data all hinder the CCUS project development. It is suggested that deterministic approaches and standard key performance indicators (KPIs) with clear input data can help improve the economic evaluation of CCUS projects. However, uncertainty in input parameters could still reduce accuracy, and the need for other estimation tools could arise.

Mohajeri, M., & Azadbakht, S. (2026). Deterministic cost estimation approaches for carbon capture, utilization, and storage: A review. Greenhouse Gases: Science and Technology.

https://doi.org/10.1002/ghg.70043

A review of probabilistic and novel approaches to cost assessment in CCUS projects

Although the renewable energy sector is making progress, there is still a long way to go before green energy can be fully utilized. In this context, Carbon Capture Utilization and Storage (CCUS) technologies can act as a temporary bridge between a high-emission past and a low-emission future. High costs, together with uncertainties and risks related to technology, economics, policy, and regulation, are among the main reasons why global CCUS deployment has not yet reached the targets set by the International Energy Agency. Conventional deterministic methods provide single-point estimates and can offer an initial picture of CCUS projects. However, probabilistic models allow project evaluation while accounting for uncertainty and investment flexibility. In this review, we aim to examine, step by step, how probabilistic economic simulation is carried out in CCUS projects. This review first explains the sources of uncertainty reported in different studies and classifies them into technical, economic, and sociopolitical categories. Then, we examine how uncertain parameters are modeled using stochastic processes, probability distributions, or scenario-based approaches. Finally, the review explains how economic evaluation methods and probabilistic computational techniques are used to simulate the overall CCUS project. Previous studies have discussed CCUS uncertainties and modeling in a fragmented manner; however, this article provides a comprehensive framework for modeling CCUS economics under uncertainty. In recent studies, each study uses specific assumptions. Therefore, one of the main gaps in the recent literature is the lack of a standard benchmark framework that enables comparison between models.

Mohajeri, M., & Azadbakht, S. (2026). A review of probabilistic and novel approaches to cost assessment in CCUS projects. Greenhouse Gases: Science and Technology.

https://doi.org/10.1002/ghg.70046

Enhanced loss of circulation prediction using Sea Lion-optimized machine learning models

Lost circulation is a major drilling challenge that increases cost and non-productive time and is influenced by different parameters. This study develops and evaluates machine learning models for predicting lost circulation severity during drilling operations. Three machine learning algorithms, Decision Tree, K-Nearest Neighbors (K- NN), and Random Forest, were optimized using the Sea Lion Optimization algorithm to enhance prediction accuracy. Feature importance analysis identified azimuth as the most significant predictor, followed by measure depth, average bit RPM, hook load, and torque, indicating that directional and mechanical drilling parameters outweigh mud properties and formation data in predicting fluid loss. Correlation analysis further confirmed strong relationships among these key parameters. The results indicate that azimuth and depth have a stronger correlation with the target parameters. All optimized models demonstrated good performance in classifying fluid loss severity. The Random Forest model achieved the best performance among the evaluated models. The K-NN and Decision Tree models also showed strong performance. Confusion matrix analysis validated all models' capability to reliably distinguish between different fluid loss severity categories (Seepage, Partial, Severe, and Total Loss) on both training and testing datasets. The findings of this study can help to provide an effective approach for predicting lost circulation severity, offering potential for reducing non-productive time, cost, and improving drilling safety.

Hashemi, S. H., Mohajeri, M., Torabi, F., & Azadbakht, S. (2026). Enhanced loss of circulation prediction using Sea Lion-optimized machine learning models. Underground Resources and Engineering.