Enhancing heat transfer performance has been the main interest in the thermal engineering field. Various enhancement methods have been proposed, including twisted and Multilobe tubes. Nevertheless, no study investigating the enhancement by combining both strategies has been reported. This study is thus conducted to numerically evaluate the turbulent convective heat transfer performance of Newtonian fluid flow in a helical twisted Multilobe tube. The model is validated against experimentally measured data of similar configurations. The effects of Multilobe geometries and Reynolds number were evaluated. The results revealed that combination of twisting and Multilobe profile enhance the secondary flow and, in turn, increases the convective heat transfer performance for straight geometries by up to 6.76%, while the addition of twisting of the tubes has a marginal effect on the heat transfer performance in helical models. Furthermore, the variation of the number of lobes does not lead to significant changes in the heat transfer performance (less than 2% difference). Overall, bilobe cross-section shows superior performance in terms of overall performance when it is combined with a helical tube (1.08) or twisting (1.002) only, while pentalobe cross-section has better performance index in sophisticated flow with both helical tube and twisting of tube (1.037). Additionally, correlations are developed to predict the friction factor and Nusselt number in straight and helical tube. To find optimum configurations, Neural Network (NN) models are developed based on the CFD result. By using multi objectives optimization, it was found that the circular straight pipe configurations with and without a twist are the ones closest to the optimum solutions. Meanwhile, helical pipe without a twist is the closest to the optimum solutions. These observations are aligned with the insights obtained from the CFD analysis.
Recent research has shown the potential of long-chain alcohols such as butanol for biofuel. However, the production quantity of butanol is very low and consumes high energy in its purification process. To solve such a problem, one of the effective approaches is to use acetone-butanol-ethanol (ABE) directly from the fermentation process. This study aims to investigate the effects of ABE-diesel blends in an HCCI-DI engine. It was found that although the presence of oxygen in ABE could theoretically promote complete combustion and eventually decrease HC and CO emissions, other significant factors may have played more dominant roles in affecting the HC and CO emissions. The experimental investigation on an HCCI-DI engine fuelled with ABE also did not reduce PM and Soot emissions. Furthermore, the peak in-cylinder combustion pressure decreased with fluctuating heat release rate. However, their p-V areas were larger than pure diesel fuel, which implied a higher indicated work produced per cycle. The addition of ABE also successfully decreased CO2 and NOx emissions. Moreover, improved engine performance in terms of higher BTE, lower BSFC and EGT were observed. All in all, considering its satisfying improved engine performance; ABE has the potential to become a promising alternative biofuel.
Date palm waste (DPW) is an important waste that is abundantly available in the Middle East. However, this DPW is underutilization in the entire Middle East region. DPW could be a useful source of feedstock to generate sustainable and renewable energy via various thermochemical processes. The core objective of current investigation is to convert the DPW into syngas and the electricity generation from the produced syngas. Furthermore, to configure an integrated system to predict the gas composition and power generation under operating variables of temperature, steam/biomass ratio, compression ratio, and air flowrate. The integrated process simulation model of gasification and power generation model was developed using Aspen Plus V10®. The model consisted of two parts; the production of syngas from steam gasification and the second was the combustion-integrated power turbine system for power generation. The syngas composition at a temperature of 850 °C and steam/biomass of 1.0 was obtained as; H2 37.88 vol%, CO 14.24 vol%, CO2 11.29 vol%, CH4 0.001 vol%. The power generation from the gas turbine increased from 3.2 to 3.6 MW with the increase in temperature. Whereas the total power generation was in the range of 3.2 to 5 MW with an increased steam flowrate of 500–2500 kg/h. Energy analysis shows that the process heat integration of the system is able to fulfill the 78 % utilities requirement within the system. This study provides the potential utilization of DPW for renewable fuel gas and electricity generation through a sustainable route and also provides environmentally friendly disposal of DPW in the Middle East and elsewhere in the world.
Tight oil reservoirs, which contain huge reserve volumes, suffer from low recovery factor. The horizontal drilling and hydraulic fracturing techniques are beneficial but not sufficient enough to boost ultimate oil recovery. Therefore, there is a vital need for an enhanced oil recovery (EOR) technique to bring much oil volume to the surface. Carbon dioxide (CO2) injection, as a proven method for EOR, can be beneficial not only in increasing the oil recovery but also in the reduction of greenhouse gases by the storage of CO2. This chapter first presents active mechanisms during CO2-EOR and then available methods for CO2 injection. Next, the affecting parameters, such as reservoir parameters, fracturing parameters, and operational parameters are discussed. As optimization means a lot in an EOR process, it is reviewed in the next step. The last topic covered in this chapter is the application of molecular dynamic simulation for the CO2-EOR process in tight oil reservoirs.
Flake-like Co3O4 nanoparticles were prepared by a facile hydrothermal synthesis method at 200 °C for 8 h, 10 h, and 12 h and labelled FCo8, FCo10, and FCo12. The electromagnetic properties of the samples were investigated at the X-band frequency. The XRD, RAMAN, TGA, FESEM, and XPS were studied to determine the chemical composition and morphology of the samples. The sample's electromagnetic performance mainly depends on multiple reflections, interfacial polarization, and magnetic loss. The results showed that the flake-like Co3O4 prepared at 200 °C for 12 h (FCo12) exhibits a minimum reflection loss of −42.4 dB at 11.7 GHz and an absorber thickness of 3.0 mm. The effective absorption bandwidth was also below −10 dB. This study provides a facile method to synthesize porous flake-like Co3O4 nanoparticles with excellent properties for effective microwave absorption performance at the X-band frequency.
In the production of biofuels, microalgae represent an emerging class of renewable feedstock that can address the problems associated with the use of the traditional land-based lignocellulosic biomass. The high lipid content of microalgae makes them ideal in producing fatty acid methyl esters (FAME), the main components of biodiesel. In this study, the transesterification of the lipids in Chlorella vulgaris with methanol was performed in-situ using graphene oxide (GO) under microwave irradiation. From FTIR measurements and titration experiments, GO was shown to possess oxygen functional groups that can serve as catalysts in transesterification. Moreover, the catalytic performance of GO in terms of FAME yield was found to be better than conventional metal-based catalysts. Microwave irradiation, on the other hand, was found to offer a more efficient heating than conventional methods by taking advantage of the excellent microwave absorptivity of methanol and the local heating induced on the surface of GO. Furthermore, irradiation of microwave in pulses rather than in a continuous mode was shown to be more cost-effective. It is proposed that the high energy introduced into the biomass at short time intervals facilitated the release of more lipids by more effectively disrupting the algal cell wall. Lastly, operating with a methanol reflux allowed the microwave-irradiated system to be operated at the boiling point of methanol (Tb = 64.7 °C) while providing a higher FAME yield than an operation at 160 °C without reflux. This study presents graphene oxide under microwave irradiation as a green, carbon-based, and sustainable catalyst in the production of biodiesel from microalgae.
It is challenging to predict the mechanical properties of modified asphalt binders because of their complex non-linear viscoelastic behavior. This study evaluates and compares the feasibility of using the response surface methodology (RSM) and machine learning (ML) methods to predict the shear strain, accumulated shear strain, non-recoverable creep compliance (Jnr), and percentage of recovery (%R) of the base binder, nanosilica (NS)-modified, waste denim fiber (WDF)-modified, and NS/WDF composite asphalt binders. The study conducts an extensive investigation using ML algorithms to accurately predict the multiple stress creep recovery (MSCR) rutting parameters for the base and modified asphalt binders. The RSM statistical analysis revealed that the %NS and %WDF significantly affect the shear strain, accumulated shear strain, Jnr, and %R at different levels of shear stress within the 95% confidence interval. Besides, the RSM-based predictive models have correlation coefficients (R²) greater than 0.8 for all responses, indicating an adequate consistency between the predicted MSCR parameters by the developed models and the parameters from the experimental work. Analysis of the ML models shows that the Extreme Gradient Boosting regression (XGB regression) is among the most accurate models for predicting the shear strain and accumulated strain. Of the evaluated ML models, Decision Tree Regression (DTR) shows the best performance in predicting Jnr and %R, with the highest R² of 0.99 and smallest root mean square error (RMSE) of <1%, which indicates its ability to represent the experimental MSCR parameters accurately. Evaluation of the XGB regression and DTR performance shows that the developed ML models outperform the RSM in predicting the MSCR rutting parameters.
The primary objective of this study was to evaluate the effect of GnP on the hydration and mechanical properties of HPC at ambient curing conditions. This research focused on the properties of HPC mixes containing various dosages of GnP [0.00 (control mix), 0.02, 0.05, 0.10, 0.30, and 0.50 % wt.]. The first phase involved monitoring the hydration behavior of GnP-HPC using Fourier-transform Infrared spectroscopy. Another aspect of GnP-HPC was its mechanical behavior (compressive, tensile, flexural, and modulus of elasticity). These responses were evaluated in accordance with ASTM C39, JSCE, ASTM C293, and ASTM C469 at various ages (3, 7, 28, 56, and 90 days). The current data was additionally used to evaluate the reproducibility of the ACI 318, ACI 363, and EC-2 formulas, as well as those proposed in earlier research. This research program also developed formulas for predicting GnP-HPC strength properties using a variety of nano-reinforcement materials, and its effectiveness was evaluated based on present and independent data. Test results show that the incorporation of GnP increased HPC's carbonization degree. Moreover, 0.02 % GnP improved compressive, tensile, and flexural strengths by 20.8, 30.0, and 13.2 %, respectively. Moreover, the modulus of elasticity increased by 21.7 % for the same GnP concentration. It was concluded that the models developed for relationships among various properties of GnP-HPC; modulus of elasticity, tensile strength, and flexural strength was found reliable. Predicted results were obtained up to 90 % of the experimental results in many cases.
Negative disconfirmation will usually lead to switching behaviour and attenuate customers’ repurchase intentions, a behaviour that will undercut businesses’ profitability. Limited research discussed post-purchase behaviour, in general, and how to retain aggrieved customers during the online shopping experience, in particular. This study investigates the observed behavioural outcome of Malaysian customers in online shopping with regard to customers’ future buying decisions who faced disconfirmation during the pandemic. Specifically, this study aims to examine service recovery as a moderator that can potentially alleviate the adverse effect of negative disconfirmation on repurchase intention and switching intention. Online questionnaires were distributed. 331 valid data were collected from customers using Smart PLS 3.2. The results showed that negative disconfirmation is negatively associated with repurchase intention and positively affects the switching intention. The moderating effect of service recovery demonstrated a significant positive impact on switching and repurchase intention. The empirical findings will enrich the literature on service recovery, consumer behaviour, and service management, and provide suggestions for webstores in terms of customers’ engagement that can apt recovery response process after customers’ complaints. Lastly, limitations and future directions are discussed for scholarly attention.
Classification is an essential task for many applications, including text classification, image classification, data classification, and so on. The present study investigates the accuracy of different machine learning classification algorithms with three different data smoothing techniques for gas turbine fault detection and isolation task. The gas turbine performance model was developed by considering variable inlet guide vane and bleed air. Fouling and erosion were injected into all six main components of the gas turbine engine. Faulty and non-faulty data were generated from the developed performance model. Based on sensitivity analysis, 12 measurement parameters and 11,824 data points were selected for the development of a fault detection and isolation model. The faulty and non-faulty data were balanced, smoothed, corrected and normalized. Finally, the classification accuracy of the machine learning techniques was analyzed. The result shows that K-Nearest Neighbours, Neural Network and Decision Tree classifiers exhibited high classification accuracy, about 99% with all three data smoothing techniques. It is also observed that the computation time of Support Vector Machine is higher whereas K-Nearest Neighbours shows the lowest. Finally, the research proves that K-Nearest Neighbours is the best classification technique for gas turbine engine fault detection and isolation application.
As the world's population, urbanization, and industrialization grow, so does the production of greenhouse gases (GHGs). Considering the harmful impact of these gases on the environment and livelihood, capturing them is necessary to reduce their levels in the atmosphere. Conventional solvents for capturing greenhouse gases, mainly CO2, CH4, and N2O, are toxic, expensive, and result in the generation of additional waste. To overcome these limitations, Ionic liquids (ILs), a class of “green solvents,” are a sustainable alternative for greenhouse gas capture because of their excellent properties. The only restriction is that screening for millions of ILs is time-consuming and inconvenient. Conductor-like screening model for real solvents (COSMO-RS) is an efficient technique to prescreen ILs. This study outlines eight cations and thirty anions, forming 240 ILs combinations for each GHGs CO2, CH4, and N2O. All in total, 720 ILs combinations were screened. COSMO-RS results suggest that phosphonium, choline, and ammonium cations, electronegative and food-grade anions such as [F⁻], [SO4²⁻], [Gly], [Lys] will be suitable for capturing greenhouse gases.
The global depletion of fossil fuel reserves and associated environmental crisis have led researchers to explore microalgal biomass which has been proven could be a promising potential as a renewable energy feedstock. Biofuels such as biodiesel, bioethanol and hydrogen can be produced from microalgae. Indeed, microalgae are best employed for hydrogen generation because the microalgal cells have high growth rate, can grow in diverse habitats and non-arable lands, can solve the fuel versus food conflicts as well as can capture and assimilate the atmospheric carbon dioxide. Hydrogen that is produced from microalgae is a clean and sustainable option to replace or complement the fossil fuel demands. The combustion of hydrogen produces only water and no greenhouse gases emission which will assuage the untoward effect on the environment. Moreover, it can also be used directly to generate electricity in fuel cells and engines. Another sector that is looking into hydrogen as a replacement for fossil fuels, is transportation inclusive of the aviation industry. The use of hydrogen as an energy carrier in airplanes offers several advantages, the burning of hydrogen in the jet engines would produce water vapour which will eliminate carbon-related emissions. However, this also comes with limitations such as new designs and larger tanks for hydrogen storage. Besides, the mechanisms of hydrogen production from microalgae are also sporadic and not well documented systematically, pre-empting researchers from exploring this new energy source inclusively. The factors affecting the microalgal hydrogen production are as well essential, but still poorly conceived, leading to the low outputs in terms of hydrogen yields from microalgae. Accordingly, this article reviews various mechanisms and methods employed for producing hydrogen from microalgae as well as the pre-treatment procedures for enhancing the rates of hydrogen production from microalgae. Furthermore, the enhancement of hydrogen yields through state-of-the-art techniques and genetic engineering the microalgal strains are also unveiled to materialize the hydrogen production at an industrial scale. This review intents to shift the paradigm from typical hydrocarbon biofuels to green hydrogen adoption, hastening the carbon neutrality target that benefits the natural environment the most.
Adsorption is an attractive process for wastewater treatment, owing to its technical simplicity and ease of implementation. However, the adsorption process is often challenged by unoptimized efficiency, especially when the high-performing adsorbents are compacted into a packed bed column design. Herein, an electrospun nanofibrous porous scaffold is rationally designed to fractionate packing and reduce hydraulic resistance of a chromium benzene dicarboxylate-based metal–organic framework (MIL-101(Cr)) used as an adsorbent for the removal of anionic dyes. The MIL-101(Cr) adsorbent was in situ loaded via a spray-assisted method onto the electrospun scaffold in an alternating spray-and-spin fashion to deconstruct the packing and offer fractionated loading of the adsorbent materials. As compared to its unfractionated counterpart, well-fractionated MIL-101(Cr) exhibited an order of magnitude higher adsorption capacity over time with high dye removal towards Methyl Orange (MO), Acid Fuchsine (AF,) and Rose Bengal (RB). In a single pass filtration experiment, the PAN/MOF(50) ESNF-AS scaffold performed at 8808, 5066, and 7574 Lm⁻²h⁻¹bar⁻¹ water permeabilities at 2 psi with exceptional > 99 % dye removal for MO, AF, and RB dye, respectively. In addition, the spent MIL-101(Cr) adsorbent in the electrospun scaffold was able to be regenerated by alkaline and acid washing and showed good recyclability, suggesting the chemical and structural robustness of the scaffold design. This approach is highly versatile and can be adopted on different adsorbents to target the removal of different contaminants from wastewaters for a more sustainable future.
Wellbore stability in shale is a recurring crisis during oil and gas well drilling. The adsorption of water and ions from drilling fluid by shale, which causes clay swelling, is the primary cause of wellbore instability. Nanomaterials have been a subject of interest in recent years to be an effective shale inhibitor in drilling fluid, intending to minimize clay swelling. This article presents a comprehensive review of the current progress of nanoparticle role in water-based drilling fluid with regards to wellbore stability, reviewing the experimental methods, the effect of nanoparticles in drilling fluid, the mechanism of shale stability and the outlook for future research. This paper employed a systematic review methodology to highlight the progress of nanoparticle water-based drilling fluids in recent years. Previous studies indicated the current trend for drilling fluid additives was nanoparticles modified with surfactants and polymers, which minimize colloidal stability issues and enhance shale stability. A review of experimental methods showed that the pressure transmission test benefits shale stability assessment under reservoir conditions. Parametric analysis of nanoparticles showed that parameters such as concentration and size directly affected the shale stability even in high salinity solution. However, there is a lack of studies on nanoparticle types, with silica nanoparticles being the most popular among researchers. Nanoparticles enhance shale stability via physical plugging, chemical inhibition, and electrostatic interactions between surface charges. To better comprehend the influence of nanoparticles on shale stabilization, it is necessary to evaluate a wider range of nanoparticle types using the proper experimental techniques.
Growing global population increased the energy demand and generation of municipal solid wastes (MSW). MSW can be utilized to produce green renewable fuels via pyrolysis technology. This study investigated the co-pyrolysis of MSW represented by mixtures of food and plastic wastes, in a downdraft pyrolyzer using synthetic flue gas composition. The food wastes in this study included fish and chicken bones, and leftover rice, and plastics included polypropylene and polyethylene (high density and low density) plastics respectively. The effect of pyrolysis temperature and types of feedstocks on the bio-oil yield and quality were determined. Although the highest bio-oil yield was obtained at 400 °C for all feedstocks, GC–MS results indicated major compounds such as fatty acids, esters, amides, nitriles, sugars were more notable at 300 °C. The bio-oil exhibited high water contents due to combustion from the flue gas. Fish bone and plastic mixture has the lowest O/C ratio and the best calorific value of 33.9 MJ/kg compared to the other two feedstocks, however extensive treatments were required to be used as fuel. Overall, bio-oil from this study has the potential to be used as an alternative fuel from co-pyrolysis of food and plastic wastes with further treatments and processing.
The concentration of heavy metals in the environment has increased tremendously with the rapid growth in the human population and urbanization. This is potentially hazardous to humans and the environment in high concentrations. Recently, membrane filtration has received considerable attention for heavy metals removal. In this study, amino acid-based ionic liquid (AAIL) was used as an addictive for electrospun nylon 6,6 nanofiber membrane (NFM) for removal of Fe(III) from synthetic wastewater. The characterizations of NFM/AAIL and its performances on Fe(III) removal were studied and compared with the pure NFM. Based on the results, the NFM/AAIL has a comparably smaller membrane diameter than the NFM, thus resulting in larger pore sizes. Moreover, no distinctive changes in the chemical properties between NFM/AAIL and pure NFM based on the FTIR results. In addition, only 0.16 % of AAIL leached out from the membrane when 500 mL of permeate ran through the membrane. Furthermore, due to the membrane larger pore size and hydrophilicity of AAIL, the pure water and iron permeability of NFM/AAIL was reportedly higher than the pure NFM by 99 % and 98 %, respectively. For adsorption analysis, the interactions of carboxylate groups and amine groups from AAIL with Fe (III) has enhanced the adsorption capacity of NFM/AAIL by 97 % as compared to pure NFM.
The abundance of waste foundry sand (WFS) produced by the foundry industry has become a global issue. As a result, foundry waste management and disposal are getting more complex, necessitating more extensive and inventive efforts. The purpose of this study was to use WFS as a partial replacement to reduce the use of fine aggregate in various concrete mixtures and to evaluate fresh concrete performance such as slump and mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS). WFS was adjusted using the Design-Expert software's Central Composite Design (CCD) tool in Response Surface Methodology (RSM). The optimization process investigated the interaction between WFS ratio and curing days on the mechanical properties of concrete. The responses of the optimization process were the CS, STS, and FS, which were generated by the quadratic regression model created by ANOVA. The WFS was replaced in 10% increments from 0% to 40%. The highest mechanical properties were achieved at 20% replacement and 56 days of curing with a CS of 29.37 MPa, STS of 3.828 MPa, and FS of 8.0 MPa. The quadratic model was suggested for the three responses by RSM, in which the coefficient of determination (R²) ranges from 0.987 to 0.995, showing the model's high significance. Up to a 30% replacement level, the fresh qualities of all substitutes were nearly identical to the control mix. So, 20% replacement is the optimum replacement level, and 30% is the general replacement level. As a result, it can be inferred that WFS can replace 20% of natural fine aggregate in order to obtain normal concrete strength. In contrast, for non-structural concrete, WFS can replace 30% of natural sand, which improves environmental sustainability.
The objective of this study was to evaluate the effect of graphene nanoplatelets (GnP) on the mechanical properties of concrete as well as the flexural performance of reinforced concrete (GnP-RC) beams. In the experimental campaign, several dosages of GnP (0.00%, 0.02%, 0.05%, 0.10%, 0.30%, and 0.50% wt of cement) were included in the concrete mixtures. First, the mechanical properties of concrete (compressive, tensile, flexural, and modulus of elasticity) were studied. A further experimental investigation was conducted on the flexural behavior of GnP-RC beams. The failure mode of beams, crack patterns, moment-curvature relationship, and ductility properties are reported. According to the observed results, GnP addition is capable of significantly improving mechanical properties. By adding 0.02% of GnP, both the compressive and tensile strengths were improved by 20.82% and 30.05%, respectively. Additionally, 0.02% of GnP also enhanced the cracking, yielding, and ultimate loads of beams by 36%, 23%, and 15%, respectively. Further, for the same concentration of GnP, the energy absorption and post-cracking ductility were improved by 25% and 20%, respectively. This report also presents analytical and statistical models for predicting the ultimate moment capacity of RC beams containing nano-reinforcement materials. The models have been demonstrated to be accurate at predicting the present and independent data.
Alternative fuels, such as biodiesel, play an important role in protecting the global environment. Biodiesel obtained from palm oil holds promising applications for compression ignition or diesel engines. However, one major concern associated with the adoption of biodiesel is the degradation and material incompatibility between biodiesel and the existing fuel system. Changes in fuel composition with the introduction of biodiesel often create many problems in which the elastomer is normally used as the fuel hose material in a diesel engine fuel system. This study investigated the effect of palm oil biodiesel blends (B10 and B20) on the nitrile rubber elastomer's physical and mechanical properties, such as mass and volume change, hardness, and tensile strength. Biodiesel blends were found to affect the mechanical properties of the elastomer, causing the fuel hose to swell. After an immersion of the elastomers in the biodiesel blends at room temperature for five weeks, biodiesel properties, such as density and viscosity, were also examined. The density and viscosity were found to increase in the blends with increasing biodiesel content. The result of the study shows that the density and kinematic viscosity increased with the percentage of biodiesel blends. The elastomer increased mass change by 58.1%, the volume change by 58%, the tensile strength by 53.5% and the hardness by 52% with increasing biodiesel blends.
Human detection is an important task in computer vision. It is one of the most impor- tant tasks in global security and safety monitoring. In recent days, Deep Learning has improved human detection technology. Despite modern techniques, there are very few optimal techniques to construct networks with a small size, deep architecture, and fast training time while maintaining accuracy. ReSTiNet is a novel small convolutional neural network that overcomes the problems of network size, detection speed, and accuracy. The developed ReSTiNet contains fire modules by evaluating their number and position in the network to minimize the model parameters and network size. To improve the detection speed and accuracy of ReSTiNet, the residual block within the fire modules is carefully designed to increase the feature propagation and maximize the information flow in the network. The developed approach compresses the well-known Tiny-YOLO architecture while improving the following features: (i) small model size, (ii) faster detection speed, (iii) resolution of overfitting, and (iv) better performance than other compact networks such as SqueezeNet and MobileNet in terms of mAP on the Pascal VOC and MS COCO datasets.
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