Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid

Auto-detection interpretation model for horizontal oil wells using pressure transient responses

Directional drilling is an excellent option to extend the limited reservoir reach and contact offered by vertical wells. Pressure transient responses (PTR) of horizontal wells provide key information about the reservoirs drilled. In this study multilayer perceptron (MLP) neural networks are used to correctly identify reservoir models from pressure derivative curves derived from horizontal wells. To this end, 2560 pressure derivative curves for six distinct reservoir models are generated and used to design a machine-learning classifier. A single hidden layer MLP network with 5 neurons, trained with a scaled conjugate gradient algorithm, is selected as the best classifier. This smart classifier provides total classification accuracy of 98.3%, mean square error of 0.00725, and coefficient of determination of 0.97332 over the whole dataset. Performance accuracy of the proposed classifier is verified with real field data, synthetically generated noisy PTR, and some signals outside the range initially assessed by the training plus testing data subsets. The developed network can correctly identify the reservoir-flow model with a probability of close to 0.9. The novelty of this work is that it employs a large dataset of horizontal (not vertical) well tests applied to six reservoir-flow models and includes noisy data to train and verify a neural network model to reliably achieve a high-level of prediction accuracy.CIted as: Moosavi, S.R., Vaferi, B., Wood, D.A. Auto-detection interpretation model for horizontal oil wells using pressure transient responses. Advances in Geo-Energy Research, 2020, 4(3): 305-316, doi: 10.46690/ager.2020.03.08    

Silver Nanoparticles Decorated in In Situ Reduced Graphene Oxide Nanohybrids Improved Properties in Poly(vinylidene fluoride)/Poly(methyl methacrylate) Blends

In this paper, reduced graphene oxide decorated with silver nanoparticle (rGO-Ag) nanohybrids were prepared using an environmentally friendly approach and incorporated as reinforcement in poly(vinylidene fluoride)-poly(methyl methacrylate) blends via a melt mixing process. The microstructure of rGO-Ag nanohybrids and its effect on the microstructure, mechanical, thermal, and electrical properties of the PVDF/PMM/rGO-Ag was studied using Fourier transform infrared (FTIR), Raman spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), tensile, thermogravimetric analysis (TGA), and impedance spectroscopy methods. FTIR and TEM analysis confirmed that rGO-Ag successfully synthesized and Ag nanoparticles are located on the rGO surface. The tensile analysis demonstrated that incorporating 1 wt.% of rGO-Ag in PVDF/PMMA blend increases Young’s modulus and strength of nanocomposite up to 31% and 35%, respectively. The Halpin-Tsai model was also used for PVDF/PMMA/rGO-Ag nanocomposites, and the results confirmed that this model works well to predict the tensile modulus. Impedance spectroscopy analysis showed that the presence of rGO-Ag nanohybrids in PVDF/PMMA blend effectively enhanced the conductivity of PVDF/PMMA blend. TGA results demonstrated that the presence of rGO-Ag nanohybrids enhanced the thermal stability of nanocomposites and increased the degradation temperature of PVDF/PMMA/rGO-Ag nanocomposites in the range of 20°C compared to PVDF/PMMA blend

A metaheuristic for the containership feeder routing problem with port choice process

In this paper, we focus on understanding the joint problem of container ship route generation and consolidation center selection, two important sub-problems influencing the effectiveness of the liners shipping industry, which addresses the ship-routing problem. Two different metaheuristics procedures are presented that both consist of two stages: a solution construction phase (either nearest neighborhood with greedy randomize and Clark and Wright with greedy randomize selection) and a solution improvement phase, based on local search. Both metaheuristics are compared in terms of quality of solution, robustness analysis and computing time under variety of instances, ranging from small to large. A thorough comparison evaluation uncovers that both metaheuristics are close-to-each other. An argument in favor of the nearest neighborhood with greedy randomize approach is that it produces better performance than Clark and Wright configuration. Additionally, through sensitivity analysis, we investigate and test two hypotheses in this paper

Application of Green Polymeric Nanocomposites for Enhanced Oil Recovery by Spontaneous Imbibition from Carbonate Reservoirs

This study experimentally investigates the effect of green polymeric nanoparticles on the interfacial tension (IFT) and wettability of carbonate reservoirs to effectively change the enhanced oil recovery (EOR) parameters. This experimental study compares the performance of xanthan/magnetite/SiO2 nanocomposites (NC) and several green materials, i.e., eucalyptus plant nanocomposites (ENC) and walnut shell ones (WNC) on the oil recovery with performing series of spontaneous imbibition tests. Scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDAX), and BET (Brunauer, Emmett, and Teller) surface analysis tests are also applied to monitor the morphology and crystalline structure of NC, ENC, and WNC. Then, the IFT and contact angle (CA) were measured in the presence of these materials under various reservoir conditions and solvent salinities. It was found that both ENC and WNC nanocomposites decreased CA and IFT, but ENC performed better than WNC under different salinities, namely, seawater (SW), double diluted salted (2 SW), ten times diluted seawater (10 SW), formation water (FW), and distilled water (DIW), which were applied at 70 °C, 2000 psi, and 0.05 wt.% nanocomposites concentration. Based on better results, ENC nanofluid at salinity concentrations of 10 SW and 2 SW ENC were selected for the EOR of carbonate rocks under reservoir conditions. The contact angles of ENC nanocomposites at the salinities of 2 SW and 10 SW were 49 and 43.4°, respectively. Zeta potential values were −44.39 and −46.58 for 2 SW and 10 SW ENC nanofluids, which is evidence of the high stability of ENC nanocomposites. The imbibition results at 70 °C and 2000 psi with 0.05 wt.% ENC at 10 SW and 2 SW led to incremental oil recoveries of 64.13% and 60.12%, respectively, compared to NC, which was 46.16%.The publication of this article was funded by the Qatar National Library