Thermal performance enhancement of a flat plate solar collector using hybrid nanofluid

Covalent Functionalized-Multi wall carbon nanotubes (CF-MWCNTs) and Covalent Functionalized-graphene nanoplatelets (CF-GNPs) with hexagonal boron nitride (h-BN) were suspended in distilled water to prepare the hybrid nanofluids as working fluids inside the Flat Plate Solar Collector (FPSC). Different concentrations of the hybrid nanoparticles were considered and Tween-80 (Tw-80) was used as a surfactant. The stability and thermophysical properties were tested using different measurement tools. The structural and morphological properties were examined using FTIR, XRD, UV–vis spectrometry, HRTEM, FESEM, and EDX. The thermal efficiency of FPSC were tested under different volumetric flow rates (2 L/min, 3 L/min, and 4 L/min), whereas the efficiency of the collector was determined based on ASHRAE standard 93-2010. As a result, the most thermal-efficient solar collector improved up to 85% with hybrid nanofluid as the absorption medium at 4 L/min flow rate. Increment in nanoparticles’ concentrations enhanced thermal energy gain and resulted in higher fluid outlet temperature

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model?s reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.Scopu

Development of oil formation volume factor model using adaptive neuro-fuzzy inference systems ANFIS

The oil formation volume factor is one of the main reservoir fluid properties that plays a crucial role in designing successful field development planning and oil and gas production optimization. The oil formation volume factor can be acquired from pressure-volume-temperature (PVT) laboratory experiments; nonetheless, these experiments' results are time-consuming and costly. Therefore, many studies used alternative methods, namely empirical correlations (using regression techniques) and machine learning to determine the formation volume factor. Unfortunately, the previous correlations and machine learning methods have some limitations, such as the lack of accuracy. Furthermore, most earlier models have not studied the relationships between the inputs and outputs to show the proper physical behaviors. Consequently, this study comes to develop a model to predict the oil formation volume factor at the bubble point (Bo) using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS model was built based on 924 data sets collected from published sources. The ANFIS model and previous 28 models were validated and compared using the trend analysis and statistical error analysis, namely average absolute percent relative error (AAPRE) and correlation coefficient (R). The trend analysis study has shown that the ANFIS model and some previous models follow the correct trend analysis. The ANFIS model is the first rank model and has the lowest AAPRE of 0.71 and the highest (R) of 0.9973. The ANFIS model also has the lowest average percent relative error (APRE), root mean square error (RMSE), and standard deviation (SD) of -0.09, 1.01, 0.0075, respectively.The authors express their appreciation to the Universiti Teknologi PETRONAS for supporting this work under the YUTP-Grant cost center 015LC0-098. They also especially thank COREOR, Petroleum Engineering, Universiti Teknologi PETRONAS, for supporting this work.Scopu

Investigation of stability, dispersion, and thermal conductivity of functionalized multi-walled carbon nanotube based nanofluid:5th UTP-UMP-UAF Symposium on Energy Systems 2019, SES 2019

In the attempt of preparing multi-walled carbon nanotube (MWCNTs), covalent functionalisation (CF-MWCNTs) were applied. The stable thermal conductivity was measured as a function of temperature. A number of techniques, such as FTIR, FESEM and UV-vis spectrophotometer were employed to characterise both dispersion stability and morphology of functionalised materials. By using ultrasonic test time, the highest stability of nanofluids was achieved at 60 minutes. As a result, the thermal conductivity displayed by CF-MWCNTs was higher than distilled water. In conclusion, improvement in thermal conductivity and stability displayed by CF-MWCNTs was higher, while the best thermal conductivity improvement was recorded at 31%. © 2020 Published under licence by IOP Publishing Ltd

The influence of covalent and non-covalent functionalization of GNP based nanofluids on its thermophysical, rheological and suspension stability properties

Covalent functionalization (CF-GNPs) and non-covalent functionalization (NCF-GNPs) approaches were applied to prepare graphene nanoplatelets (GNPs). The impact of using four surfactants (SDS, CTAB, Tween-80, and Triton X-100) was studied with four test times (15, 30, 60, and 90 min) and four weight concentrations. The stable thermal conductivity and viscosity were measured as a function of temperature. Fourier transform infrared spectroscopy (FTIR), thermo-gravimetric analysis (TGA), X-ray diffraction (XRD) and Raman spectroscopy verified the fundamental efficient and stable CF. Several techniques, such as dispersion of particle size, FESEM, FETEM, EDX, zeta potential, and UV-vis spectrophotometry, were employed to characterize both the dispersion stability and morphology of functionalized materials. At ultrasonic test time, the highest stability of nanofluids was achieved at 60 min. As a result, the thermal conductivity displayed by CF-GNPs was higher than NCF-GNPs and distilled water. In conclusion, the improvement in thermal conductivity and stability displayed by CF-GNPs was higher than those of NCF-GNPs, while the lowest viscosity was 8% higher than distilled water, and the best thermal conductivity improvement was recorded at 29.2%