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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]

The combinations of infrared thermal images and artificial intelligence methods have opened new avenues for pushing the boundaries of available testing methods. Hence, in the current study, a novel thermal non-destructive testing method for polyethylene electrofusion joints was combined with k-means clustering algorithms as an intelligent screening tool. The experiments focused on ovality of pipes in the coupler, as well as misalignment of pipes-couplers in 25 mm diameter joints. The temperature responses of each joint to an internal heat pulse were recorded by an IR thermal camera, and further processed to identify the faulty joints. The results represented clustering accuracy of 92%, as well as more than 90% abnormality detection capabilities. [ABSTRACT FROM AUTHOR]

Integrated management can benefit oil-field development and exploitation tremendously. It involves holistic decisions on the order, placement, timing, capacities, and allocations of new well drillings and surface facilities such as manifolds, surface centers, and their interconnections, along with well production/injection profiles. These decisions have profound long-term impacts on field productivity; however, the dynamic nature of oil reservoirs makes them strongly intertwined and highly complex. Hence, a dynamic, holistic, and integrated approach is necessary. Most existing well placement studies ignore surface effects and drilling-rig availability and assume that all wells are opened simultaneously at the beginning of the production horizon. In this work, we extend our previous study [Tavallali Ind. Eng. Chem. Res. 2014, 53 (27), 11033] and develop a mixed integer nonlinear programming (MINLP) approach for addressing such limiting assumptions. We develop a revised outer-approximation algorithm involving two multiperiod, nonconvex MINLPs and several local search strategies. Numerical results for a literature example show significant improvement in the net present value for oil-field development. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new method of C3-C10 analysis was developed by Box-Behnken DoE & multivariable optimization. • The effects of 11 GC input factors on the retention time, peak resolution and height were studied. • Analysis of variance was used to determine the significant variables of GC method. • The new method outperformed an industrial method in terms of resolutions and the retention time. Reducing the Gas Chromatography (GC) run time, and improving the resolution with appropriate peak heights in GC methods are indeed vital goals for any analysis labs. This research was focused on improving an existing empirical method for C3-C10 hydrocarbon analysis in a specific petrochemical complex with many samples per day. The hydrocarbon standards used for calibration were 0.5 W/W% Benzene and 0.5 W/W% 1-Methyl-Cyclopentene, as well as 2 W/W% m-Xylene and 2 W/W% p-Xylene. Moreover, Box-Behnken Design was used to identify the common significant variables between 11 quantitative input factors affecting the retention time, peak resolution and height for each pair. After performing 182 experimental analyses, carrier gas flow rate, initial temperature hold time, oven programming (ramp 1) and oven initial temperature were identified as the common significant variables. The results were used to optimize the method which could increase the resolutions by 50% and 40%, and simultaneously decrease the retention time by 34.2% and 28.3% for both pairs respectively. [ABSTRACT FROM AUTHOR]

CO 2 injection into hydrocarbon reservoirs can simultaneously pave the path for two important processes, namely oil production increase, and CO 2 storage. This combined optimization problem can be referred to as carbon capture and storage - enhanced oil recovery (CCS-EOR) problem. CCS-EOR is a challenging task with many opportunities which should be studied in a timely manner. Moreover, the attitude of the management team towards prioritizing CCS or EOR should be considered. Therefore, in the current study, CCS-EOR problem was addressed by utilizing the dynamic well flow settings (bottom-hole pressure and/or flow rate) as the optimization variables with a weighted objective function. This approach was tested on a case study. Its huge simulation time due to high dimensionality remained a serious challenge. One effective approach was to use the underlying applied knowledge of the system and extract that wisdom to reduce the simulation time in a rational manner. CO 2 breakthrough time was such a knowledge. Therefore, the importance of CO 2 breakthrough time was stressed, and the understanding of the response of the simulator to this event was used to considerably reduce the simulation time in a black box approach. Moreover, a concise sensitivity analysis was performed which helped to schedule the workflow based on the given priority to each process. The optimized case could improve the objective value and gas storage efficiency up to 2.86 % and 12.1 % respectively. The obtained results can be used to analyze a variety of operational matters such as the CO 2 distribution map in the reservoir, and the well settings. • The CO 2 breakthrough time is the prime tool to reduce the total optimization time. • The main benefit of optimization appears after the CO 2 breakthrough. • The optimization controlled the CO 2 injection level. • The idea of "the more injection, the better" should be abandoned. • The GSE has more dominant effect on the final objective value. [ABSTRACT FROM AUTHOR]