The rise of smart edge devices and the growing demand for advanced technologies such as Machine Learning (ML) necessitate an evolution beyond 5G networks. Placing Machine Learning Inference Instances (MLIs) at the edge server can reduce latency but faces memory and processing constraints. By combining Mobile Edge Computing (MEC) and Non-Volatile Memory (NVM), Service Providers (SP) can efficiently deploy MLIs on edge, fog, and cloud servers to reduce latency and improve quality by offloading compute-intensive tasks. To serve large geographies, Unmanned Aerial Vehicles (UAVs) can be leveraged in large-scale sparsely distributed edge, fog, and cloud systems. This paper explores a 3-tier edge-fog architecture with NVM memory and introduces a placement scheme to maximize SP revenue by strategically deploying MLIs on UAV and fog with NVM technology using a stable matching-based method.

The parameters of biodiesel generation from Soybean oil having the molar ratio, reaction duration, and catalyst concentration for constant temperature, were modelled Using response surface methodology. a molar ratio of 6–12, NaOH of 1–2% w/w, time of 30–60 min, and temperature of 35–55 °C. an adaptive neuro-fuzzy inference system (ANFIS) and the response surface methodology-based Box–Behnken experimental design. a significant regression model with an R2 value of 0.9411 was obtained. The ANFIS model was used to link each of the four input factors with the outcome variable (biodiesel yield). In the training, an R2 value of 0.9918 was attained. The findings showed that the constructed models accurately depicted the processes they described.

Biodiesel has evolved as a renewable and environmentally friendly energy source that has the potential to reduce global warming. As a result, this work investigates data-operated machine learning strategy for biodiesel yield estimation via regression. Using Box Behnken design, the researchers looked at time (4–8 minutes), methanol/oil mole ratio (30–50%), volume (100–300 mL), and catalyst concentration (1–2 wt%). Statistical performance gauge showed SVM (Root mean square error (RMSE) = 1.20, R2 =0.91 and Mean square error (MSE) = 1.45) and ANN (R2 = 0.86, RMSE = 1.55, MSE = 2.42) models narrate process with excellent precision differentiate to RSM (R2 = 0.85, RMSE = 1.67, MSE = 2.78). Properties are measured as per standard.

The research work presents the microwave-assisted transesterification process to transform the Karanja oil into biodiesel. The different optimization and modelling techniques were compared and parameters were optimized to obtain the highest yield. Adaptive neuro-fuzzy inference System (ANFIS), Artificial Neural Network (ANN) and Response Surface Methodology (RSM) are assessed in the transesterification of Karanja oil esterified with methanol in the appearance of NaOH as a catalyst with microwave power. The investigation was carried out by considering the time, methanol/oil mole ratio, volume and catalyst concentration parameters and using Box Behnken design for experimentation. Statistical performance gauge showed Root mean square error (RMSE), coefficient of determination (R 2 ) adjusted R 2 , Sum of square error (SSE) and mean square error (MSE). A modified Domestic Microwave used for experimentation, which is functioning at 2450 MHz, the topmost power output of 700W attached with DC motor having 100 rpm for stirring action. The highest yield of 87.34% was extracted with optimized parameters of rection.

The transesterification technique is used to produce methyl esters from jatropha-algae oil. The effect of four variables on higher biodiesel yield, such as molar ratio, reaction temperature, catalyst amount and reaction duration, is calculated, and the process is optimised using Response surface methodology based on Box–Behnken Design. At a molar ratio of 1:10, reaction temperature of 53°C, 0.3% by weight. Percent catalyst, and reaction duration of 172 min, an optimum biodiesel yield of 96% is attained. With a relative error of 4% of the experimental outcomes, the projected optimal conditions were experimentally validated (96%). The ANOVA P value of 0.0001 indicates that the model is significant.KEYWORDS: BiodieselJatropha-algae oilBBDoptimisation Disclosure statementNo potential conflict of interest was reported by the author(s).

Biodiesel has prospective to greatly provide to sustainability of fuels for transportation. Biodiesel manufacturing is complex and nonlinear and consumption processes need the usage of rapid and accurate modelling tools for their design and optimization. The predictive ability of ANFIS approaches has been shown. This work is devoted to a full evaluation and critical discussion of ANFIS technology applications for biodiesel research modelling applications. In biodiesel research, ANFIS technology has been widely employed to model various trans esterification processes. As a result, future research appears to be focused on using ANFIS approaches for real-world biodiesel production monitoring to improve efficiency, feasibility, and sustainability.

Present study presents the results of methyl esters preparation from Jatropha-Algae oil using transesterification process. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and the response surface methodology (RSM) based Box-Behnken techniques were used for modelling and analysis of different parameters viz molar ratio, temperature, reaction time, and catalyst concentration in biodiesel production process. Significant regression model with R-2 value of 0.9867 was obtained under a molar ratio of 6-12, KOH of 0-2% w/w, time of 60-180 min and temperature of 35-55 degrees C using RSM. The ANFIS model was used to individually correlate the output variable (biodiesel yield) with four input variables with R-2 value of 0.9998. Finally, a study investigating the performance and emissions of a diesel engine fuelled with biodiesel blends (BO, B5, B10 and B20 vol%) has been performed concluding significant reduction of emission. (C) 2021 Elsevier Ltd. All rights reserved.

In this study, Karanja oil was investigated under different operating conditions such as Methanol: oil molar ratio, catalyst, Volume, and time with microwave power to produce the biodiesel. Four factors with three levels BoxBehnken response surface design (BBD) was used to optimize and investigate the effect of process variables on the biodiesel production. ANFIS tool was adopted for modelling and prediction. RSM and ANFIS models describe correlation coefficient of 0.85 and 0.95 respectively. The physico-chemical properties of Karanja oil methyl ester are characterized out using standard methods.

Biodiesel produced from different raw materials is the most effective way to solve problems related to the fuel crisis and environmental problems. In the present study, the methodology of the adaptive neuro-fuzzy inference system (ANFIS) for the modeling and estimation of a process applied to the production of biodiesel from the blend of algae oil. The Gaussian membership function was applied and studied. The results of ANFIS are compared with the actual results obtained through the experiment using the mean root-mean-square error (RMSE) and the determination coefficient (R-square). The results show an improvement in the prediction, accuracy, and capacity of the ANFIS technique for estimation. The statistical characteristics of RMSE were 0.2179 and R-squared was 0.9998 obtained in training. As the ANFIS offers a good estimation, the modeling quality can be applied to the biodiesel production process as well.

Atmospheric pollution is one of the biggest problems all over the world. For this reason, researchers try to find alternative fuels for diesel engine, and biodiesel is the most feasible alternate fuel for diesel engines. In this study, linear regression (LR) and artificial neural network (ANN) used to predict the biodiesel yield produced by transesterification of soybean oil at constant temperature is reported in present work describes. The ANN estimation was done using a Levenberg-Marquardt learning algorithm (trainlm) with log sigmoid (logsig) neural network algorithm with 4 neurons in the hidden layer (3:4:1 topology). The experimental and ANN values were compared for the biodiesel yield. The value R-2 = 0.9899 for ANN and R-2 = 0.4198 for LR. Root mean square errors (RMSE) for ANN and LR are 0.6331 and 3.052, respectively. Results were compared with LR modeling. As a result, ANN gave more accurate results than LR and can be suggested as good a prediction method. It was followed by Fourier transform infrared (FTIR) spectroscopy analysis.

Optimisation and estimation of biodiesel by Karanja oil (KO) was done by response surface methodology (RSM) and artificial neural networks (ANNs) models through domestic microwave heating transesterification. Box-Behnken experimental design was adopted. Four process parameters are methanol/oil mole ratio (30-50%), catalyst concentration (1-2 wt%), volume (100-300 mL) and time (4-8 min). Biodiesel with a yield of 87.34% was obtained using 1.5 wt. % NaOH, 35% methanol to oil molar ratio, 150 mL amount and 5 min of reaction at 700 W power with 100 rpm stirring. The physico-chemical characteristics of KO methyl ester are measured using standard methods. Quality of RSM model is analysed by analysis of variance. ANN tool was adopted for modelling and prediction. Correlation coefficient values were 0.85 and 0.8663 with RSM and ANN, respectively. The concentration of the catalyst, volume, methanol to oil molar ratio and time required producing maximum yield of biodiesel were obtained. The predictive capacities of RSM and ANN are evaluated and compared by statistical parameters, namely, R-2, RMSE, ADJ-R-2 and MSE. Hence, results typify the strength and excellence of ANN over RSM specifically in the transesterification of biodiesel.

Methyl ester production from jatropha-algae oil is conducted through a transesterification process. Consequences of four parameters, the molar ratio (oil:methanol), the reaction temperature, the amount of catalyst, and the reaction time for obtaining a higher yield of biodiesel, are derived, and the process was optimized using the response surface methodology based on the Box-Behnken Design. An optimized biodiesel yield of 96% is achieved at a molar ratio of 1:10, a reaction temperature of 53° C, a 0.3 wt% catalyst, and a reaction time of 172 min. The predicted optimal conditions were experimentally validated with a relative error of 4% of the experimental result (96%). The P value of ANOVA is <0.0001, which shows that the model is significant. Finally, the performance and emissions in a diesel engine coupled with an electricity generator powered by biodiesel blends (B0, B5, B10, and B20% vol.) were investigated, concluding a significant reduction of exhaust gases. The engine was run with numerous blends of biodiesel by changing the brake power from 0 load to 0.5, 1, 1.5, and 2 KW.

In this work, the experiments of the transesterification process were carried out on jatropha-algae oil blend and the prediction of the synthesized biodiesel was investigated. The study was divided into two parts. In the first part, a series of experiments were employed practically and in the second part, the prediction is made with the artificial neural network (ANN). The ANN with Levenberg-Marquardt (LM) algorithm was trained with topology 4-10-1. The estimated results were compared with the experimental results. An ANN model was developed based on a back-propagation learning algorithm. An R-square value of the model from ANN was 0.9976. The results confirmed that the use of an ANN technique is quite suitable. The artificial neural network gave acceptable results.

The optimization and transesterification of soybean oil with methanol in the presence of sodium hydroxide as a catalyst was investigated. A low-temperature transesterification process was selected to make the transesterification process more energy efficient. To further improve the production of biodiesel, the experimental design was carried out with the Box-Behnken method. The results were analysed using the response surface methodology. A model was developed to correlate the performance of biodiesel with the parameters of the process, such as the molar ratio, the concentration of the catalyst and the reaction time. The influence of the reaction variables, including; The molar ratio of oil (6: 1-12: 1), temperature (50 degrees C) and catalyst concentration (1-2% by weight) and residence time (30-60 minutes) on the transesterification reaction of the methyl ester of Fatty acid (FAME) were studied. A biodiesel yield of 80.86% with the molar ratio (8:1) was reached using NaOH as catalyst (1.8) in 34 minutes at a temperature of 50 degrees C. It was observed that the catalyst concentration, the reaction time and the molar ratio had a significant effect on the yield of soybean biodiesel.

Biodiesel production from different feedstocks is an effective method of resolving problems related to the fuel crisis and environmental issues. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and the response surface methodology based Box-Behnken experimental design were used to model the parameters of biodiesel production for a jatropha-algae oil blend, including the molar ratio, temperature, reaction time, and catalyst concentration. A significant regression model with an R-2 value of 0.9867 was obtained under a molar ratio of 6-12, KOH of 0-2% w/w, time of 60-180min, and temperature of 35-55 degrees C using response surface methodology (RSM). The ANFIS model was used to individually correlate the output variable (biodiesel yield) with four input variables. An R-2 value of 0.9998 was obtained in the training. The results demonstrated that the developed models adequately represented the processes they described.

The present article elaborates on the various emission characteristics of mahua oil with diesel fuel in a diesel engine at various blending conditions. Experimental investigation results are studied for various parameters such as exhaust emission of carbon monoxide (CO), hydrocarbon (HC), and oxides of nitrogen (NO) gases and exhaust gas temperature. Results show that residual oxygen, CO, HC, and NO emission were the lowest for mahua biodiesel compared with diesel. The experimental results proved that the use of mahua oil biodiesel as fuel in the diesel engine is a viable alternative to diesel fuel. Mahua biodiesel oil may be beneficial in decreasing greenhouse gas emissions without any engine modification. Mahua oil has the possibility of becoming a sustainable fuel source as biodiesel.

ABSTRACT Biodiesel production from different feedstocks is one of the effective ways to anticipate the problems related with fuel crisis and environmental issues. In this study, the response surface methodology (RSM)-based Box–Behnken experimental design (BBD) is used to optimize the parameters of biodiesel production for the blend of Jatropha–algae oil such as molar ratio, temperature, reaction time, and catalyst concentration. A significant quadratic regression model (p < 0.0001) with R2 of 0.9867 was achieved under the condition of molar ratio 6–12%, KOH 0–2%, reaction time 60–180 min, and temperature 35–55°C. The artificial neural network (ANN) with the Levenberg–Marquardt algorithm was also trained in this study with the topology 4-10-1 with a predicted correlation coefficient of 0.9976. From the results, it is also found that the predicted values of yield are in good agreement with the results of RSM correlations.

Today’s the plastic injection moulding process is the widely used manufacturing process for the production plastic products. The main aim of this paper is to study the effect of different parameters on polypropylene work material for a plastic injection moulding process during manufacturing of bottle cover. The optimization of different parameters like melting temperature, injection pressure and cooling time are done which mainly affects the mechanical properties of the bottle cover. The taguchi method, signal to noise (S/N) ratio and analysis of variance (ANOVA) are employed to analyse the effect of process parameters of plastic injection moulding on the tensile strength of the bottle cover of polypropylene material.