Investigation on effect of cerium oxide additive in waste plastic oil fueled CI engine.

In this study, the effects of incorporating cerium oxide into diesel and WPO blends were investigated to determine the potential of the blend as a fuel additive. The study aimed to assess engine-performance, emission, and combustion properties of the blend. The experiments utilized a single-cylinder diesel engine, and researchers prepared two different blends of WPO with 25% WPO in diesel and 50% WPO in diesel. Cerium oxide was added to these blends at concentrations of 25 ppm and 50 ppm using an ultrasonicator. The results demonstrated that increasing cerium oxide content in the blend (50 ppm) led to reduced CO, HC, and NOx emissions at higher loads. For instance, B50 + 50 ppm exhibited lower CO and NOx emissions, while B25 + 50 ppm demonstrated lower HC and smoke emissions. Furthermore, raising the CeO2 content from 25 ppm to 50 ppm resulted in a 3% increase in brake thermal efficiency. Moreover, cerium oxide positively impacted combustion and performance properties of the blends. Among the tested blends, the B50 + 50 ppm combination showcased the highest brake thermal efficiency, optimal air-fuel ratio, and the lowest specific fuel consumption. In conclusion, employing cerium oxide as a fuel additive in diesel-WPO blends offers a promising approach for realizing a sustainable and environmentally friendly future.

Exploring the Impact of Al2O3 Additives in Gasoline on HCCI-DI Engine Performance: An Experimental, Neural Network, and Regression Analysis Approach.

This study delves into the influence of incorporating alumina (Al2O3) nanoparticles with waste cooking oil (WCO) biofuels in a gasoline engine that employs premixed fuel. During the suction phase, gasoline blends with atmospheric air homogeneously at the location of the inlet manifold. The biodiesel, enhanced with Al2O3 nanoparticles and derived from WCO, is subsequently directly infused into the combustion chamber at 23° before the top dead center. The results highlight that when gasoline operates in the homogeneous charge compression ignition with direct injection (HCCI-DI) mode, there is a notable enhancement in thermal efficiency by 4.23% in comparison to standard diesel combustion. Incorporating the Al2O3 nanoparticles with the WCO biodiesel contributes to an extra rise of 6.76% in thermal efficiency. Additionally, HCCI-DI combustion paves the way for a reduction in nitrogen oxides and smoke emissions, whereas biodiesel laced with Al2O3 nanoparticles notably reduces hydrocarbon and carbon monoxide discharges. Predictive tools such as artificial neural networks and regression modeling were employed to forecast engine performance variables.

Effect of silicon carbide on kerf convergence and irregularity of the surface during abrasive water jet machining of fiber-metal hybrid composites.

The traditional way to machine hybrid composites is hard because they tend to break, have a high retraction, have a high service temperature, and have an uneven surface irregularity. For high-strength fiber/metal composite constructions, alternative machining methods have drawn interest as a solution to these problems. Current research focuses on enhancing the Abrasive Water Jet Machining process by optimizing its variables using a composite material of epoxy reinforced with silicon carbide, stainless steel wire mesh, and Kevlar. The variables assessed are the Nozzle-to-substrate gap (S), the Abrasive discharge molding and different percentages of silicon carbide (SiC) filler (0%, 3%, and 6% by weight), three different types of hybrid laminates (H1, H2, and H3) were produced. The response surface method (RSM) was utilized in this learning, specifically on a central composite design, to calculate and optimize machining variables based on the Kerf convergence ratio (Kt) and Surface irregularity (Ra) as responses. According to the results, the traverse feed velocity, Abrasive discharge proportion, and Nozzle-to-substrate gap are the critical factors in determining Surface irregularity and Kerf convergence width (H1 laminate) for a fiber/metal laminate with 0%, 3% and 6% weight fraction. In the case of a 3% weight fraction H2 laminate, the traverse feed velocity was identified as the primary factor affecting the Kerf convergence ratio. In contrast, traverse feed velocity and Nozzle-to-substrate gap had the most significant influence on Surface irregularity. The findings also indicated that S, followed by Abrasive discharge proportion and traverse feed velocity, are the variables that have the most significant influence when cutting 6 wt% SiC filler particle fiber/metal laminate (H3 laminate). For Surface irregularity, the combination of traverse feed velocity and Nozzle-to-substrate gap had the most significant impact. To validate the optimization results, confirmatory tests was conducted, and the findings were very similar to the experimental values, indicating the accuracy and effectiveness of the optimization process. To better understand the manufacturing processes, a scanning electron microscope was used to examine the morphological features of the machined surfaces, such as delamination, fibre breakage, and fibre pull-out.

Assessment of Destructive and Nondestructive Analysis for GGBS Based Geopolymer Concrete and Its Statistical Analysis.

Geopolymer is the alternative to current construction material trends. In this paper, an attempt is made to produce a sustainable construction composite material using geopolymer. Ground granulated blast furnace slag (GGBS)-based geopolymer concrete was prepared and tested for different alkaline to binder ratios (A/B). The effect of various temperatures on compressive strength properties was assessed. The cubes were exposed to temperature ranging from 50 to 70 °C for a duration ranging from 2 to 10 h, and the compressive strength of the specimens was analyzed for destructive and non-destructive analysis and tested for 7, 28, and 90 days. The obtained compressive strength (CS) results were analyzed employing the probability plot (PP) curve, distribution overview curve (DOC), probability density function (PDF), Weibull, survival, and hazard function curve. Maximum compressive strength was achieved for the temperature of 70 °C and an A/B of 0.45 for destructive tests and non-destructive tests with 44.6 MPa and 43.56 MPa, respectively, on 90 days of testing. The survival and hazard function curves showed incremental distribution characteristics for 28 and 90 days of testing results with a probability factor ranging from 0.8 to 1.0.

Optimization of Alkaline Activator on the Strength Properties of Geopolymer Concrete.

This study investigates the effects of red mud on the performance of geopolymer concrete in regard to fresh and mechanical properties. Red mud was used as a binder, and GGBS replaced the binder. Different proportions of red mud ranging from 0 to 30% with an interval of 2% and activator agents such as KOH and K2SiO3 for various alkaline-to-binder ratios such as 0.30, 0.40, and 0.50 were used; their effect on the fresh and mechanical properties of geopolymer concrete were the focusing parameter on the current study. Fresh properties such as setting time, slump, compaction factor, and vee-bee consistometer test, and mechanical properties such as compressive strength, split tensile strength, flexural strength, modulus of elasticity, and impact energy were studied. ANOVA and radar plot analysis were studied for various alkaline to binder (A/B) compressive strength results tested for 7 to 90 days. The increase of red mud quantity caused the decline of workability, but there was continuous enhancement of mechanical properties of GPC up to a specific limit. An alkaline-to-binder ratio of 0.4 shows excellent results compared with other ratios at ambient conditions for strength properties. ANOVA and radar plot reveal that A/B of 0.40 for 90 days shows excellent results compared with other ratios, and CS values vary in a linear manner.

Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries.

BACKGROUND AND OBJECTIVE: Streptococcus mutans is the primary initiator and most common organism associated with dental caries. Prediction of post-Streptococcus mutans favours in the selection of appropriate caries excavation method which eventually results in meliorate caries-free cavity preparation for restoration. The objective of this study is to predict the post-Streptococcus mutans prior to dental caries excavation based on pre- Streptococcus mutans using iOS App developed on Artificial Neural Network (ANN) model. METHODS: For the current research work, children with occlusal dentinal caries lesion were chosen, 45 primary molar teeth cases were studied. Caries excavation was done with carbide bur, polymer bur and spoon excavator. The colony forming units for pre and post-Streptococcus mutans were recorded, data emanating from clinical trials was employed to develop the ANN models. ANN models were trained, validated and tested with the registered clinical data using different ANN architectures. RESULTS: Feedforward backpropagation ANN model with an architecture of 4-5-1, predicts post-Streptococcus mutans with an efficiency of 0.99033, mean squared error and mean absolute percentage error for testing cases were 0.2341 and 4.967 respectively. CONCLUSIONS: Caries excavation methods and pre-Streptococcus mutans are feed as inputs, while post-Streptococcus mutans as targets to develop ANN model. Based on the developed ANN model, an ingenious iOS App was developed, the global clinician may utilize the App to meticulously predict post-Streptococcus mutans on iPhone based on pre-Streptococcus mutans, which in turn aids in decision making for the selection of caries excavation method. This study manifests the potential application of iOS App with built-in ANN model in efficiently predicting the post-Streptococcus mutans. Also, the study extends scope for applications of iOS App with built-in ANN models in clinical medicine.