Universiti Teknologi Petronas

Abstract Energy is inarguably the backbone of the socio-economic development of several countries in the Global North and South. While various energy sources exist, hydrocarbons contribute significantly to the world's energy mix (~80%). Global policymakers have also proposed low-carbon sustainable energy carriers (e.g., hydrogen) production from hydrocarbons to support the ever-increasing energy demand, signifying that increased or steady hydrocarbon production volumes are anticipated in the foreseeable future. Carbonated water injection (CWI) has demonstrated remarkable potential in enhancing oil production while sequestering CO2 as a secondary benefit. However, experimental methods for evaluating CWI performance are labor-intensive, time-consuming, and expensive. Incorrect oil recovery estimates can also compromise the techno-economic assessment of CWI. Against this backdrop, k-Nearest Neighbor (kNN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR) were integrated with nature-inspired optimization techniques: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) to estimate the oil recovery efficiency (or factor) of CWI. The developed models were assessed using statistical measures and joint plots. Subsequently, the influence of the different conditions on oil recovery was ascertained with correlation, permutation importance, and Shapley Additive Explanations (SHAP). The outstanding model was then deployed in conjunction with Monte Carlo simulation to evaluate the uncertainties associated with the independent variables on oil recovery potential and provide insights into the practical implications of the integrated modeling approach. While GBR integrated with GWO and PSO (GBR–GWO and GBR–PSO) emerged as the top-performing models (R2 = 0.90, mean absolute error = 3.35%), kNN and SVR showed significant improvements after optimization, with prediction errors reduced by 37–46% and 46–56%, respectively, demonstrating the effectiveness of metaheuristic tuning for models sensitive to hyperparameters. Most importantly, GBR–GWO provided P10, P50, and P90 recovery estimates for 5,000 randomly generated independent variables, as 57%, 72%, and 86%, respectively, without complex experimental settings, showing the developed model's minimal resource dependency and practical utility. Permutation importance and SHAP analysis revealed that injection rate and salinity/oil density were the most and least impactful variables, respectively. SHAP analysis also indicated that high injection rate, porosity, permeability, and pressure enhance recovery, while low temperature, oil viscosity, and salinity favor oil production. These findings align with experimental outcomes and highlight the potential of the integrated modeling approach to complement laboratory and simulation studies, enabling rapid resource-efficient prediction and optimization of CWI recovery efficiency. The technique could also be adapted in adjacent studies to minimize prediction errors and enhance decision-making.

The efficient utilization of construction waste in road engineering is essential for advancing sustainable infrastructure development. This study explores the application of ceramic waste powder (CWP) in cementitious grout for semiflexible pavement (SFP) surfaces. Cement was partially replaced with CWP at proportions ranging from 15% to 50%, and its effects on SFP performance and environmental impact were evaluated using compressive strength testing, life cycle assessment (LCA), and statistical analysis. The obtained SFP mixtures were assessed for volumetric and mechanical properties through Marshall stability and wheel tracking tests. Experimental findings revealed that 20% cement replacement with CWP was the optimal level, leading to an 80% reduction in rutting depth and increases of 50% and 23% in compressive strength and Marshall stability, respectively, at 28 days of curing. These improvements are attributed to CWP’s superior fluidity in filling the voids within the porous asphalt skeleton and its effective bonding capacity with aggregates, which densifies the microstructure, as confirmed by scanning electron microscopy (SEM) analysis. LCA results indicated that higher CWP replacement levels reduced both global warming potential and fossil fuel depletion, thereby enhancing the grout’s sustainability. Statistical analyses, including quadratic regression, ANOVA (p<0.05), and the Tukey HSD test, confirmed significant improvements in strength properties with CWP incorporation, whereas box plots effectively illustrated data trends and variations. In summary, this study underscores the dual environmental and performance benefits of incorporating CWP in SFP systems and supports its adoption in sustainable road construction aligned with circular economy goals.

Sustainability education aims to enhance environmental knowledge and foster civic engagement through effective pedagogical strategies. The integration of Artificial Intelligence (AI) into education offers a transformative opportunity to support students in understanding sustainability-related opportunities and challenges. By cultivating a global perspective, optimizing resource allocation, and personalising learning experiences, AI-driven approaches can significantly enrich sustainability education and better prepare students for a sustainable future. AI applications in education encompass learning enhancement, individualized instruction, and administrative efficiency, ultimately contributing to more effective teaching and learning processes. This study examines students’ perceptions of AI integration in the classroom in relation to sustainability education within Malaysian higher education institutions. A quantitative case study was conducted with 175 participants from a selected public university in Malaysia. Data were collected through online questionnaires, and the responses were analyzed using SPSS version 27. The findings provide valuable insights for academicians, researchers, universities, and policymakers in formulating strategies to equip future generations with the competencies required to address the complex sustainability challenges of the 21st century and beyond.

Seismic surveying has continuously evolved, transitioning from early mechanical seismoscopes to digital, multi-channel geophone arrays that defined the industry standard since 1970s. digital seismic reflection. Today, the field is undergoing a similar shift, with conventional inertial-based systems being challenged or complemented by Distributed Acoustic Sensing (DAS) systems. This paper examines the two primary seismic systems, such as geophones/accelerometers and DAS systems by focusing on their core principle and transduction systems. Methodologically, we employ a comparative performance framework based on key metrics including sensitivity, calibration, data volume, and spatiotemporal. Furthermore, a systematic review of experimental field trials from 2010 to 2025 is conducted across eight major seismic applications to ascertain practical differences. Results reveal that while traditional sensors excel in low-frequency sensitivity and discrete three-component recording, DAS offers superior spatiotemporal resolution and broadband frequency capabilities. Recent advancements in enhanced backscattering fibers (20 m gauge length) have demonstrated the potential to lower the noise floor of DAS systems to approximately 0.15 $$p\varepsilon /\surd Hz$$, significantly improving sensitivity to high-fidelity microseismic events. Field trial analysis shows heavy concentration of hybrid applications in borehole imaging, with emerging but limited use in environmental sensing, mineral exploration, and pipeline security. We also explore the potential of integrated sensing and quantum networks (ISAQN) to mitigate current signal-to-noise limitations. The study concludes that future of seismic acquisition depends on the continuous integration of these technologies via hybrid systems, leveraging the robust compartments of traditional sensors along the extensive coverage of DAS to overcome their respective uniaxial, low-frequency, and spatiotemporal limitations.

Fulvic acid (FA) is the main active component of humic substances (HS) due to its molecular properties, solubility, reactivity, and essential role in enhancing soil health and supporting plant growth. It forms through the breakdown of organic matter and can be sourced from various natural origins, including low-rank coal, peat, and soil. The extraction of FA from low-rank coal has attracted scientific interest because of its low cost and broad applications in agriculture, environmental cleanup, and medicine. However, extracting FA from natural sources is challenging because yields and quantities are often limited. Moreover, the quality of extracted FA depends on the extraction method, with different techniques offering varying efficiencies, requiring a detailed understanding of the process. This review explores FA's diverse properties and extraction methods, emphasizing its potential as a sustainable agent for environmental remediation and as a nutraceutical. FA possesses promising capabilities to influence the geochemical behavior of metals through adsorption, complexation, and redox reactions, helping to reduce toxicity and remove contaminants such as lead (Pb), chromium (Cr), and mercury (Hg). Its amphiphilic nature also enhances its effectiveness in agriculture by supporting crop development and soil vitality, while exerting immunomodulatory effects on human health. Various chemical and biological extraction methods for FA are thoroughly reviewed, highlighting challenges in achieving efficient industrial-scale production. Despite its potential, variability in quality due to differences in raw materials and extraction techniques necessitates further research to fully realize FA's capabilities for environmental cleanup, sustainable agriculture, and health benefits. This review offers an overview of the current state of FA research and its potential in technological development.

ABSTRACT The present work characterises coal‐bearing deposits of the Mid‐Cretaceous Hoiho Formation, located in the offshore Great South Basin, New Zealand, through a multidisciplinary approach involving geochemical analyses, biomarker evaluation, stable carbon isotope measurements, and one‐dimensional basin modelling. This study aims to characterise the organic matter (OM) present within the Hoiho coal facies and elucidate the mechanisms responsible for gas generation. Biomarker analysis combined with carbon isotope data was employed to assess the sources of OM and reconstruct the depositional environment conditions. The Hoiho coals exhibit high total organic carbon (TOC) and sulfur (S) concentrations, indicating marine‐influenced coaly facies. The high TOC and S result in high TOC/S ratios and further suggest that the Hoiho coal facies was deposited in high‐oxygenated environments. This interpretation is further reinforced by the specific biomarker ratios, such as a high pristane/phytane ratio of more than 3 and a low dibenzothiophene/phenanthrene ratio. Hence, the elevated organic carbon, with TOC content reaching up to 79.8 wt. % in the Hoiho coal facies cannot be solely attributed to low‐oxygen depositional settings. Molecular biomarker signatures combined with carbon isotopic data suggest that the OM in the Hoiho coal facies is primarily derived from terrigenous sources and accumulated in fluvial to fluvio‐deltaic environments, as indicated by high T m / T s and C 24 Te/C 26 Ti ratios, along with the predominance of C 29 over its C 27 and C 28 counterparts. This high abundance of terrigenous plants in the studied coal facies indicates that hydrogen‐poor Type III kerogen was the original OM input during deposition. This finding is consistent with the low hydrogen index (HI) values, between 111 and 292 mg HC/g TOC, indicative of predominantly Type III kerogen for mainly gas generation potential. Kerogen transformation modelling reveals that approximately 10% to 54% of the primary Type III kerogen underwent cracking to generate gas from the Late Eocene to present, corresponding to the wet gas generation window, with Easy %Ro values up to 1.72%. Consequently, the Mid Cretaceous Hoiho coal facies can be considered as a promising target for gas development within the deeper stratigraphic intervals of the offshore Great South Basin.