Response surface modelling and optimisation of biodiesel production from Manilkara Zapota L. seed oil

724-729Biodiesel production from non-edible oils is one of the prominent research avenues being exploited in recent times to achieve energy and environmental sustainability. The aim of this study is to model and optimise the production of biodiesel from the reaction of ethanol with Sapota (Manilkara Zapota L.) seed oil using potassium hydroxide (KOH) as catalyst. A quadratic response surface model has been developed and validated. Analysis of variance (ANOVA) reveals that the model is significant. The standard deviation is 3.76% and the coefficient of determination (R2) is 0.8438. Numerical optimisation reveal that the optimal biodiesel yield of 89.57% can be achieved at an ethanol to oil molar ratio is 6.58, catalyst amount of 1.07 wt% and temperature of 64.77C. Parametric studies reveal that the yield of biodiesel initially increases with increasing ethanol-oil ratio and catalyst amount but drops off gradually beyond the region of optimality. Temperature has a slight positive effect on the process

Metal-organic polyhedra (MOPs) as emerging class of metal-organic frameworks for CO2 photocatalytic conversions : current trends and future outlook

DATA AVAILABILITY : No data was used for the research described in the article.Please read abstract in the article.https://www.elsevier.com/locate/jcouhj2024PhysicsNon

Development of high-performance self compacting concrete using eggshell powder and blast furnace slag as partial cement replacement Gender Implications

This study aimed to examine the properties of self-compacting concrete (SCC) developed using eggshell powder (ESP) and granulated ground blast furnace slag (GGBFS) as partial cement replacement. The coarse aggregate impact value was 21.6% and the water absorption of the fine aggregates was 24 wt%. 10 wt% partial replacement was optimal for flow-ability and workability. SCC with 20 wt% partial replacements had the highest compressive strength at 41.34 kN/mm2 and 42.4 kN/mm2 for ESP and GGBFS respectively after 28 days of curing. SCC with 20 wt% partial replacements had the highest flexural strength at 3.2 kN/mm2 for both ESP and GGBFS after 28 days of curing. From the microstructural analysis, partial replacement with mineral admixtures improved the interfacial interactions between constituents of the concrete and GGBFS SCC gave a better interfacial interaction between the concrete constituents than ESP SCC. In summary, GGBFS had better fresh, hard and microstructural properties than ESP

RSM and ANN modelling of the mechanical properties of self-compacting concrete with silica fume and plastic waste as partial constituent replacement

In this study, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) was used to predict the mechanical properties of self‐compacting concrete (SCC) with silica fume as partial cement replacement and Polyethylene terephthalate (PET) solid waste as partial sand replacement. PET plastic was varied between 0 and 20 wt% while the silica fume was varied between 0 and 40 wt%. The parameters investigated were the compressive strength, tensile strength and impact strength of SCC. The RSM model was fairly accurate (R2 ≥ 0.92) in predicting the mechanical properties. The model was statistically significant (p‐value 0.93) for training, testing and validation. Parity plots revealed that both the ANN and RSM models do not have any prediction bias. However, the ANN model is superior because of its higher accuracy and the use of admixtures enhanced the workability suitability for dataset. The 3D microstructural analysis showed that the interfacial adhesion between the aggregates and the cementitious materials reduced at increased partial replacement leading to a decrease in the strengt

IoT-Enabled Alcohol Detection System for Road Transportation Safety in Smart City

In this paper, an alcohol detection system was developed for road transportation safety in smart city using Internet of Things (IoT) technology. Two Blood Alcohol Content (BAC) thresholds are set and monitored with the use of a microcontroller. When the first threshold is reached, the developed system transmits the BAC level of the driver and the position coordinates of the vehicle to the central monitoring unit. At the reach of the second BAC threshold, the IoT-enabled alcohol detection system shuts down the vehicle’s engine, triggers an alarm and puts on the warning light indicator. A prototype of this scenario is designed and implemented such that a Direct Current (DC) motor acted as the vehicle’s engine while a push button served as its ignition system. The efficiency of this system is tested to ensure proper functionality. The deployment of this system will help in reducing the incidence of drunk driving related road accidents in smart cities

Adsorption mechanism and modeling of radionuclides and heavy metals onto ZnO nanoparticles: a review

Abstract The contamination of environmental waters with heavy metals and radionuclides is increasing because of rapid industrial and population growth. The removal of these contaminants from water via adsorption onto metal nanoparticles is an efficient and promising technique to abate the toxic effects associated with these pollutants. Among metal nanoparticle adsorbents, zinc oxide nanoparticles (ZnONPs) have received tremendous attention owing to their biocompatibility, affordability, long-term stability, surface characteristics, nontoxicity, and powerful antibacterial activity against microbes found in water. In this review, we considered the adsorption of heavy metals and radionuclides onto ZnONPs. We examined the isotherm, kinetic, and thermodynamic modeling of the process as well as the adsorption mechanism to provide significant insights into the interactions between the pollutants and the nanoparticles. The ZnONPs with surface areas (3.93 to 58.0 m2/g) synthesized by different methods exhibited different adsorption capacities (0.30 to 1500 mg/g) for the pollutants. The Langmuir and Freundlich isotherms were most suitable for the adsorption process. The Langmuir separation factor indicated favorable adsorption of all the pollutants on ZnONPs. The pseudo-second-order kinetics presented the best for the adsorption of the adsorbates with regression values in the range of 0.986–1.000. Spontaneous adsorption was obtained in most of the studies involving endothermic and exothermic processes. The complexation, precipitation, ion exchange, and electrostatic interactions are the probable mechanisms in the adsorption onto ZnONPs with a predominance of complexation. The desorption process, reusability of ZnONPs as well as direction for future investigations were also presented

Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming

The aim of this study was to utilise artificial neural network (ANN) and AdaBoost (AB) algorithms to model the synthesis gas composition from the steam reforming of biomass bio-oil. At testing on training data, it was observed that R2 > 0.999 was achieved for both algorithms for all product selectivity indicating a 99.9% capture of data variability. Also, the RMSE values were  0.9 using AB considering hydrogen and carbon dioxide, and using ANN considering methane and carbon monoxide