Texas A&M University at Qatar

Abstract This study investigates how mechanical and chemical engineering curricula worldwide are adapting to the energy transition by integrating renewable energy, carbon capture and hydrogen technologies, digitalization/AI, and sustainability. A global survey was distributed to 1000+ universities and directed to academic leaders and curriculum committees; 18 early responses were analyzed and benchmarked against a petroleum-engineering study reporting 82% of programs updated in response to energy expansion. Preliminary results indicate adaptation is underway but uneven: 72.2% of programs report partial integration, 22.2% report full integration, and 5.6% report none, with two-thirds of “partial” adopters at ≤25% curriculum coverage. Change occurs within existing structures (no reported department name changes) and spans multiple tiers: 50% cite updates across BSc + MSc + PhD. By level, BSc emphasizes broad exposure, with Sustainability 83.3%, Renewables 77.8%; MSc shows deeper technical expansion, with Sustainability 66.7%, Renewables 66.7%, CCUS/H₂ 66.7%, AI 44.4%; PhD shifts remain selective, with Renewables 38.9%, AI 38.9%, Sustainability 33.3%, with 44.4% reporting no change. These early signals suggest mechanical/chemical programs are directionally aligned with petroleum's transition topics but lag in depth, especially on CCUS/H2 at scale and explicit climate content, highlighting the need for ready-to-adopt materials and cross-level coherence to accelerate reform and better prepare engineers for emerging energy demands.

Electric vehicle charging demand forecasting is crucial for power grid planning in Qatar, as studies predict a significant growth in EVs over the next 8 years. This paper proposes a survey-based methodology to develop hourly probability EV demand profiles to be used in forecasting residential demand. A survey was conducted in Qatar, targeting both fuel-based and electric vehicle drivers, to capture average daily driving distances, at-home durations, and other behavioral factors. The responses from 180 participants were analyzed to develop weekday and weekend demand profiles for summer and winter, employing robust data cleansing, state-of-charge (SoC) calculations, realistic battery specifications, and an assessment of the ambient temperature effect on EV energy consumption. The methodology can be adopted regionally and internationally. Results show high evening peaks and low early-morning demand with seasonal influences on overall demand probabilities. This work contributes to Qatar’s goal of a smooth and reliable transition to widespread EV adoption by demonstrating the feasibility of this transition and delivering a realistic probability EV demand profiles specific to Qatar.

Abstract We demonstrate how a visual digital twin system is used to implement a digital twin for pipeline monitoring. A visual digital twin allows for ingestion of data for calibration and creation of models which enable a digital replica of a real-world physical system for decision support and improving situational awareness. A laboratory experimental pipeline system is used to generate data to drive modelling capabilities in this system. We show how machine learning operations (MLOps) principles are applied in the context of digital twins, forming a sub-study area known as Digital Twin ML Ops (DT MLOps). The intended purpose of the system is to both train operators of the leak detection system in its use and provide high situational awareness and operational readiness to users. We demonstrate how multiple sources of monitoring data from an experimental pipeline setup, as well as simulation can be combined in a visual digital twin system and used for pipeline leak detection and leak plume and flow visual prediction. We show how leak detection and visual leak prediction are visualized in the context of a pipeline twin along with confidence and uncertainty and other explainable elements from machine learning models. We demonstrate how models are tracked using the DT MLOps sub system. The overall system demonstrates a novel combination of real experimental data driving models for both leak detection and the prediction of the associated leak plume and pipeline flow visual assessment imagery. This demonstrates a system which can detect leaks and their locations and also provide operators with assessment of the leak. The system provides provenance through its DT MLOps capabilities. This presented virtual pipeline and leak model, which integrates AI with experimental validation, is not widely available in industry.

A bstract A first study is presented of the cross section for the scattering of same-sign W boson pairs via the detection of a τ lepton. The data from proton-proton collisions at the center-of-mass energy of 13 TeV were collected by the CMS detector at the LHC, and correspond to an integrated luminosity of 138 fb −1 . Events were selected that contain two jets with large pseudorapidity and large invariant mass, one τ lepton, one light lepton (e or μ ), and significant missing transverse momentum. The measured cross section for electroweak same-sign WW scattering is $${1.44}_{-0.56}^{+0.63}$$ times the standard model prediction. In addition, a search is presented for the indirect effects of processes beyond the standard model via the effective field theory framework, in terms of dimension-6 and dimension-8 operators.

Abstract Decarbonizing ammonia production is crucial for reducing industrial greenhouse gas emissions; however, steam methane reforming (SMR) remains the dominant, carbon-intensive pathway. This study proposes a retrofit strategy for large-scale ammonia plants (1,268 tons/day) by replacing the conventional reformer with an advanced dual-reactor system that enables CO₂ utilization and carbon valorization. The novel configuration co-produces synthesis gas and multi-walled carbon nanotubes (MWCNTs), integrating ammonia and CNT production in a single process. Aspen Plus® simulations compare the baseline SMR process with the retrofitted configuration, assessing energy demand, feedstock consumption, CO 2 emissions, and economic performance. The retrofitted system achieves a 76% reduction in lifecycle CO₂-equivalent emissions and a 31% decrease in total energy demand, despite a 2.6-fold increase in methane input. At 25% MWCNT recovery, the Levelized Cost of Ammonia (LCOA) increases to $680.90/ton; however, substantial co-product revenue yields a 3.2-fold increase in Net Present Value (NPV), 56% Internal Rate of Return (IRR), and a 4.5-year payback period. Sensitivity analyses support the robustness of the economic potential, confirming the viability of integrated CNT-ammonia production as a pathway for sustainable, low-carbon manufacturing.

Abstract Digital twins are transforming the digitalization and automation of offshore gas pipeline systems by enabling realtime monitoring, predictive maintenance, and operational efficiency. This study introduces a novel adaptive digital twin framework designed for leak detection and localization in offshore gas pipelines. The framework integrates OLGA-generated synthetic data, validated experimental results, and advanced machine learning (ML) techniques, including transfer learning and ensemble models. The proposed framework achieves a classification accuracy of 98.2% for leak detection, with a mean absolute error (MAE) of 0.11 cm for leak size prediction and a mean absolute percentage error (MAPE) of 3.8% for leak localization. A core innovation of this framework is the calibration methodology, which recalibrates dimensionless nomographs and leak detection correlations for seamless adaptation to new pipeline geometries and operating conditions. Through systematic steps, the calibrated correlations predict leak size and location with high accuracy, leveraging pressure drop and mass flow difference data. Additionally, ML-driven models enable efficient generation of new nomographs for pipelines with varying configurations, enhancing scalability and reducing computational effort. The real-time implementation enables predictions with a latency of less than 2 seconds, significantly outperforming conventional methods in speed and accuracy. Also, the framework’s adaptability, supported by its digital twin visualization and real-time feedback mechanisms, significantly improves pipeline integrity management, operational safety, and environmental protection. The study demonstrates the framework’s robustness in handling complex flow dynamics and offers a scalable solution to enhance the digital transformation of offshore oil and gas operations.