Abstract:Inner coatings have emerged as a novel technique to prevent the deposition of paraffin, wax, scale, and corrosion of pipelines during oil production and transport. Few studies addressed this technique for preventing asphaltene deposition. In this study, two superhydrophobic inner coatings, including polytetrafluoroethylene (PTFE) coating and nanosilica coating, were fabricated on metal surfaces and the asphaltene deposition on these coated surfaces was examined. A model oil solution was prepared using asphalte… Show more
“…However, one of the study's limitations was the life cycle assessment, reclaimed materials, and life cycle cost analysis for design innovation [52][53][54][55]. As mentioned, case studies on the specific application domains would be essential for the validation of the real-life performance as earlier investigated in the case of pavements [56][57][58][59][60]. Nevertheless, using novel machine learning methods such as hybrids and ensembles would be essential for future research for improving the model performance [61,62].…”
Sustainable construction requires high-strength cement materials that additives with silica content could provide the requirements as well. In this study, the effect of the micro and nano-size of silica on the compressive strength of cement paste using different mathematical approaches is investigated. This study compares the strength of preferentially replaced cement pastes with microsilica (MS) and nanosilica (NS) incorporation by proposing several mathematical models. In this study, 205 data were extracted from the literature and analyzed. The modeling processes considered the most significant variables as input variables that influence the compression strength, such as curing time, which ranged between 3 and 90 days, the water-cement ratio, which varied between 0.4 and 0.85, and NS ranged between 0 and 15%. MS ranged between 0 and 40% based on the weight of cement. In this process, the compressive strength of cement paste modified with NS and MS was modeled using four different models, including the Linear Regression Model (LR), Nonlinear Model (NLR), Multi-Logistic Regression Model (MLR), and artificial neural network (ANN). The efficiency of the suggested models was evaluated using different statistical assessments, such as the Root Mean Squared Error (RMES), the Mean Absolute Error (MAE), Scatter Index (SI), Objective value (OBJ), and coefficient of determination (R2). The findings revealed that the ANN model conducted better performance for predicting compressive strength for cement paste than the other models based on the statistical assessment. In addition, based on the statistical assessment of the sensitivity of parameters, NS had more of an effect on the compressive strength of cement paste, with 6.3% more than MS.
“…However, one of the study's limitations was the life cycle assessment, reclaimed materials, and life cycle cost analysis for design innovation [52][53][54][55]. As mentioned, case studies on the specific application domains would be essential for the validation of the real-life performance as earlier investigated in the case of pavements [56][57][58][59][60]. Nevertheless, using novel machine learning methods such as hybrids and ensembles would be essential for future research for improving the model performance [61,62].…”
Sustainable construction requires high-strength cement materials that additives with silica content could provide the requirements as well. In this study, the effect of the micro and nano-size of silica on the compressive strength of cement paste using different mathematical approaches is investigated. This study compares the strength of preferentially replaced cement pastes with microsilica (MS) and nanosilica (NS) incorporation by proposing several mathematical models. In this study, 205 data were extracted from the literature and analyzed. The modeling processes considered the most significant variables as input variables that influence the compression strength, such as curing time, which ranged between 3 and 90 days, the water-cement ratio, which varied between 0.4 and 0.85, and NS ranged between 0 and 15%. MS ranged between 0 and 40% based on the weight of cement. In this process, the compressive strength of cement paste modified with NS and MS was modeled using four different models, including the Linear Regression Model (LR), Nonlinear Model (NLR), Multi-Logistic Regression Model (MLR), and artificial neural network (ANN). The efficiency of the suggested models was evaluated using different statistical assessments, such as the Root Mean Squared Error (RMES), the Mean Absolute Error (MAE), Scatter Index (SI), Objective value (OBJ), and coefficient of determination (R2). The findings revealed that the ANN model conducted better performance for predicting compressive strength for cement paste than the other models based on the statistical assessment. In addition, based on the statistical assessment of the sensitivity of parameters, NS had more of an effect on the compressive strength of cement paste, with 6.3% more than MS.
“… Figure 3 SEM images of nanosilica and PTFE coatings on MS substrate. ( a – c ) Nano silica coating 46 . ( d – l ) PTFE coatings includes morphological images of primer layer (large image), and primer and overcoat layers (small image): ( d – f ) Micro Al 2 O 3 —with nano SiO 2 , ( g – i ) Micro glass beads- without nano SiO 2 , ( j – l ) micro glass beads—with nano SiO 2 46 .…”
Section: Resultsmentioning
confidence: 99%
“…Another important point to highlight is that both nanosilica film coating and PTFE coating with micro glass beads in the primer layer and with nano SiO 2 in the overcoat layer (micro glass beads—with nano SiO 2 ) have been used previously for experimental study and modeling of asphaltene deposition on metal surfaces 46 . In that research 46 , the synthesis method of nanosilica film coating and also PTFE coating (micro glass beads—with nano SiO 2 ) were briefly described. The main discussion of that research 46 was the application of the mentioned two types of coatings in reducing asphaltene deposits as one of the heaviest, polar, and most problematic deposits in crude oil.…”
Section: Materials and Experimental Proceduresmentioning
confidence: 99%
“…In that research 46 , the synthesis method of nanosilica film coating and also PTFE coating (micro glass beads—with nano SiO 2 ) were briefly described. The main discussion of that research 46 was the application of the mentioned two types of coatings in reducing asphaltene deposits as one of the heaviest, polar, and most problematic deposits in crude oil. In the present study, in addition to the two coatings used in the previous study, other types of PTFE coating (including micro Al 2 O 3 —with nano SiO 2 and micro glass beads—without nano SiO 2 ) were synthesized and other characteristics of these coatings, such as the thickness of the coatings, water contact angle (WCA) and the sliding angle (SA) of the coatings, AFM parameters, energy dispersive X-ray spectroscopy (EDS) for nanosilica film coating, and the SEM images of the surface morphology of four types of coatings is presented in three different magnifications and in more detail.…”
Section: Materials and Experimental Proceduresmentioning
confidence: 99%
“…Figure 2 shows 3D roughness images for these two coatings. Figure 1 A water droplet on sample with PTFE and nanosilica superhydrophobic coatings: ( a ) nano silica 46 , ( b ) micro Al 2 O 3 —with nano SiO 2 , ( c ) micro glass beads—without nano SiO 2 , ( d ) micro glass beads—with nano SiO 2 46 . …”
Section: Materials and Experimental Proceduresmentioning
Corrosion protection of metals is of paramount importance in different sectors of industry. One of the emerging techniques to prevent or reduce the damaging effects of this phenomenon is to apply superhydrophobic coatings on the susceptible surfaces. In this study, corrosion protection of steel is investigated by fabricating superhydrophobic coatings, using one-step electrodeposition process of nanosilica hybrid film and spraying process of polytetrafluoroethylene (PTFE) on steel surface and also preparation of micro/nano-composite coatings. The anti-corrosion behavior of the nanosilica hybrid film and PTFE coating with two types of microparticles including Al2O3 powder and glass beads in primer layer, and overcoat layer with and without SiO2 nanoparticles is studied. TOEFL polarization and electrochemical impedance spectroscopy (EIS) tests are conducted on coated steel samples to examine their corrosion performance in 3.5 wt% NaCl solution at a temperature of 25 °C. The results showed that the combination of superhydrophobic properties and low conductivity significantly improves the corrosion resistance. Evaluating the effect of adding SiO2 nanoparticles to the overcoat layer in PTFE coating showed that the nanoparticles improve the corrosion resistance of PTFE coatings by sealing up some defects and pores in the coating. Investigation of corrosion resistance of coatings showed that, the corrosion resistance of nanosilica film is lower than that of PTFE coatings. The best sample obtained in this study, namely the PTFE coating with glass beads microparticles in primer layer and SiO2 nanoparticles in overcoat layer, reduced the corrosion rate by nearly 80 times.
A thorough understanding of asphaltene adsorption on clay minerals is particularly important in oil production and contaminated soil remediation using claybased adsorbents. In this paper, we introduced a machine learning approach as a reliable alternative for commonly used adsorption isotherms that suffer from inherent limitations in the prediction of asphaltene adsorption onto clay minerals. Machine learning (ML) models, namely multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), random forest (RF), and committee machine intelligent system (CMIS) combined with two optimizers were used. Experimental data (142 data points for six different clay minerals) was used for the modelling. To improve the accuracy of the smart models, a comprehensive data preparation such as outlier removal and feature selection was carried out. The results showed that relatively all the proposed models predict asphaltene adsorption on clay minerals with acceptable precision. Nevertheless, the MLP model showed superior performance compared with other models in which the overall root mean square error (RMSE) and coefficient of determination (R 2 ) values of 6.72 and 0.93 were obtained, respectively. Finally, the developed MLP model was compared with the well-known adsorption isotherms of Langmuir and Freundlich and exhibited superior performance.
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