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Our environment is increasingly being endangered by the introduction of greenhouse gases which are\ud continuously produced from gas flaring processes. Currently, total volume of gas flared globally amounts\ud to 100 billion cubic meters (BCM) annually. Nigeria flares about 18.27 BCM and loses approximately $2\ud billion yearly. This statistics indicates the urgent need to conduct research aimed at addressing both the\ud environmental impact of gas flaring and the economic implications. This research studies the economic\ud viability of using gas to wire (GTW) technology as an integral component of gas flare management. The\ud investigation critically evaluates the cost implications and impact of the GTW technology. The research\ud method involves the interview of key experts and practitioners in the field. The interviews are structured\ud to obtain information on the total volume of gas produced, utilised and flared in two major gas and electricity\ud producing firms in Nigeria. The data obtained show that the gas producing company flares about\ud 8.33% of its total production which is in excess of the 6.6 million cubic meters (MCM) utilised daily. This\ud study demonstrates that in the Nigerian oil and gas sector, one unit of gas turbine having 0.93 MCM gas\ud consumption capacity generates 150 MW of electricity daily. It is found in result evaluation that 50 turbines\ud are sufficient to consume an average of 46.5 MCM of gas daily to generate 7500 MW of electricity.\ud Economic analysis shows that there is an annual net profit of £2.68 billion gained from flare prevention\ud and overall environmental protection

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We address highly swirling, confined-bluff-body-flow evolving through a burner; particularly, contributions of the swirling motion to a central-recirculation-zone (CRZ) downstream the injector. Previous studies suggest that flame stability reduces in combustors lacking this zone; careful consideration of the CRZ is thus desirable. We use Reynolds-Averaged-Navier–Stokes (RANS) and Large-Eddy-Simulation (LES) to simulate the flow, and the influence of the swirl device was included in defining the inflow conditions for LES simulations. We use mean velocity profiles and turbulence statistics to test results. There is qualitative agreement between computed and reported experimental data, and we document quantitative differences obtained with the RANS models. LES velocity field results are mostly within 3% of the experimental data, better than the latest reported LES data, reinforcing the suitability of our approach. We took advantage of the quality of the LES mesh, which solves 95.6% of the resolved-turbulence-energy, to present the vorticity structures showing the precessing vortex motion on the CRZ boundaries. Anisotropic states of the Reynolds-stress were characterized with the aid of an anisotropy invariant map, a novelty for this type of burner; the turbulence states considerably vary inside the burner, behaving isotropically in the center of the CRZ, whereas axisymmetric turbulence is predominant in the other areas of the CRZ. The results reinforce the importance of applying appropriate turbulence models and inflow conditions for simulations involving confined-bluff-body-flows in order to capture the main flow fields and structures in the CRZ.

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.