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KPIM of Gas Transportation: Robust Modification of Gas Pipeline Equations Studies of the flow conditions of natural gases in pipelines have led to the development of complex equations for relating the volume transmitted through a gas pipeline to the various factors involved, thus deciding the optimum pressures and pipeline dimensions to be used. From equations of this type, various combinations of pipe diameter and wall thickness for a desired rate of gas throughput can be calculated. This research work presents modified forms of the basic gas flow equation for horizontal flow developed by Weymouth and the basic gas flow equation for inclined flow developed by Ferguson. The modified equations incorporate non-iterative forms of the Colebrook-White friction factor into the original forms of the Weymouth's and Ferguson's equations. These modified equations thus eliminate the need for iteration in predicting the flow rate of gas through pipelines as is the case with their original forms when the Colebrook-White friction factor is used. The modified equations also have a wider range of application since the Colebrook-White friction factor is valid for turbulent gas flow as well as for gas flow in a transition zone. On comparing the results it can be seen that the modified Ferguson's equation gives a more accurate prediction of gas flow rates because it takes the pipeline elevation into account. Lower deviations from measured gas flow rates were observed with the modified Ferguson's equation than with the modified basic gas flow equation. The deviations observed using the modified Ferguson equation were found to range from -0.16% to +3.21%. Conclusively, these less cumbersome newly developed equations with high degree reliability will be useful in predicting the rates of gas flow for a wide range of its conditions, pipeline elevation and pipeline lengths.

The experimental study investigated the energy and exergy performance of a domestic refrigerator using eco-friendly hydrocarbon refrigerants R600a and LPG (R290/R600a: 50%/50%) at 0, 0.05, 0.15 and 0.3wt % concentrations of 15nm particle size of TiO2 nanolubricant, and R134a. The effects of evaporator temperature on power consumption, coefficients of performance, exergetic efficiency and efficiency defects in the compressor, condenser, capillary tube and evaporator of the system were examined. The results showed that LPG + TiO2 (0.15wt %) and R600a + TiO2 (0. 15wt %) had the best of performances with an average of 27.6% and 14.3% higher coefficient of Performance, 34.6% and 35.15% lower power consumption, 13.8% and 17.53% higher exergetic efficiency, a total exergetic defect of 45.8% and 64.7% lower compared to R134a. The exergetic defects in the evaporator, compressor, condenser, and capillary tube were 38.27% and 35.5%, 49.19% and 55.56%, 29.7% and 33.7%, 39.1% and 73.8% lower in the system when compared to R134a respectively. Generally, the refrigerants with nano-lubricant mixture gave better results with an appreciable reduction in the exergy defect in the compressor than the pure refrigerants, and LPG + TiO2 (0. 15wt %) gave the best result in the refrigeration system based on energy and exergy analysis.

Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based blackbox models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input-output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.

Accurate forecasting of condensate well deliverability usually requires good knowledge of the gas condensate vapor – liquid properties. Condensate well deliverability is particularly important as it impacts downstream issues such as the number of wells required, surface gas handling facilities, drilling schedules and income from gas sales contracts. A new approach for forecasting viability of gas condensate wells and calculating condensate gas ratio (CGR), using simpler techniques is presented. The calculation uses a volumetric balance model for reservoir system, standardized and modified correlations, equation of state and a vapor-liquid equilibrium technique. The technique has been extended to include mass transfer and also to allow for the changes in produced fluid composition due to the formation of the condensate bank. The approach will provide a useful tool for rapid forecasts of condensate well performance, for examining the effects of condensate blockage in different well types or for studying sensitivities. It is also valuable where simple models of condensate reservoir performance are required for use in integrated studies.