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• Thermal pulsing of faultless Electrofusion polyethylene joints is asymmetrically approximated. • The model is experimentally validated by thermography. • PDE constrained parameter estimation & novel geometric coefficients are used to enhance the model. • Thermal efficiency is calculated by using the estimated values of geometric coefficients. Today significantly long distribution pipelines are employed to ensure the availability of natural gas as a clean energy even in the most rural areas. High Density Polyethylene (HDPE) Electrofusion joint has been emerging as the primary means of connection in such HDPE pipelines. Heat transfer has a leading role in the integrity of these joints and clearly a thorough but quick thermal understanding of these joints can be a critical step for effective maintenance and reduction of the associated uncertainties. Therefore, in this study two separate (a primary and a modified) approximate transient heat transfer models are developed to simulate the thermal pulsing of those joints. This pulsing is initiated by converting the electrical into thermal energy within the joint. The modified model is prepared by introducing novel geometric coefficients into the heat source terms by borrowing the idea of skin factors and sphericity coefficients from the petroleum and food engineering domains respectively. Moreover, experimental thermography and TGA are used for validation and design purposes. The results are used to calculate the thermal efficiency of the joint and it was found to be 87.04%. [ABSTRACT FROM AUTHOR]

Highlights • A complementary fault classification stage for Thermal NDT-E of electrofusion PE joints. • Combined heat pulsing, IR imaging, and machine learning for classification accuracy. • The ovality diagnosis was more accurate than unalignment fault. • GLMNet (93.75%) was the best classifier, followed by Random Forests (84.21%) and k-means (70.37%). Abstract The capability of conveniently classifying the fault types in the electrofusion joints can certainly increase the security of polyethylene gas pipelines. Therefore in the current study, we use machine learning to push the horizons of our recent thermal pulsing and IR thermography method, to identify ovality versus unalignment faults. To do so, we extend our experimental IR-thermography data bank and then apply k -means, Random Forests and GLMNet algorithms in a two stage approach. The overall classification accuracy for k -means and Random Forests were 70.37% and 84.21% respectively; GLMNet could successfully outperform the others with a classification accuracy of 93.75%. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Addressing two limitations of our recent idea for NDT of electrofusion PE joints. • A flexible reference simulation to predict the thermal behavior of a faultless joint. • Appropriate pulsing duration at different initial temperatures (300.5–320.5 K). • Successful nondestructive screening of a contaminated joint. Abstract Electrofusion Polyethylene (PE) joints are important elements of the gas distribution networks. Unfortunately they are prone to leakage if not tested thoroughly. Our team has recently proposed a novel noncontact and IR thermal non-destructive test for these PE joints. That is, to induce a controlled heat pulse within the joint via its internal wire, and compare its thermal response with other experimental reference cases. Two gaps exist in this method due to the lack of access to a mathematical model. Firstly the pulsing duration at different initial temperatures are only determined by trial and errors. Secondly, preparing experimental reference thermal images at different joint conditions is considerably costly. Therefore, here we rigorously simulate this pulsing process to tackle both abovementioned shortcomings. We experimentally validate our simulation and report the appropriate pulsing durations between 300 K and 320 K initial temperatures. Moreover, we successfully screen a contaminated joint nondestructively. [ABSTRACT FROM AUTHOR]

• Capillary trapping was investigated for both CO 2 sequestration and EOR. • The high oil swelling factor increased the CO 2 trapping. • The chemical reaction improved the the GSE and FOE. • The produced CO 2 fraction reduced after the breakthrough time. • Coupled optimization increased the objective function to 2.12%. CO 2 trapping in porous media is one attractive sequestration option in which the CO 2 trapped for long periods and also enhanced oil recovery (EOR) simultaneously. The aim of this study is to design a process such that improved both gas storage efficiency and field oil efficiency. Based on the continuous CO 2 flooding results, the residual gas saturation increases up to 10% compared to solution gas injection. Also, the high oil swelling factor and the interfacial tension (IFT) reduction confirmed the changing in curvature of the relative permeability curves which causes pore throat space more available for CO 2 trapping. In addition, the genetic algorithm has successfully improved the gas storage efficiency for 3.2% and field oil efficiency for 7.2% during the full field reservoir simulation. Accordingly, the objective value increased by 2.12% and indicates that the residual and CO 2 capillary trappings in porous media enhanced the security and capacity of storage. [Display omitted] [ABSTRACT FROM AUTHOR]