Optimization and Prediction of Karanja oil transesterification with domestic microwave by RSM and ANN

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° 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° 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.

Optimized Cost Model with Optimal Disk Usage for Cloud

M. Aggrawal, N. Kumar, and R. Kumar, “Optimized Cost Model with Optimal Disk Usage for Cloud,” Big Data Anal. Adv. Syst. Comput., pp. 481–485, 2018.

Cloud is a bag full of resources. Using cloud services at an optimal level is required as now cloud is primary technology for deployment over Internet. This is indeed a practice to make use of things efficiently to make cloud a better place. Cloud is providing all computing resources that one may need to compile tasks, but efficiently using of resources can increase the power to accommodate more con- sumers and also consumer can save on cost for the services subscribed. This paper provides a mechanism to increase or decrease the subscription as per the use.

Energy Efficient Host Overloading Detection Algorithm in Cloud Computing

Cloud computing is now a most popular technology of the present generation. Energy efficiency is big aspect to think as the big data center is consuming a lot of energy to run and to serve their customers. Energy efficient algorithm and techniques are required to reduce the carbon emissions. In this paper we have worked for consolidation of Virtual Machine(VM) by detecting over-utilized hosts by using Pattern matching and reduced number of migrations by taking a new approach of Mode Absolute Deviation. It analyzes the historical data of CPU usages to search the usage pattern of CPU and finds the dynamic thresholds values for migration of virtual machine. The work has been carried out in CloudSim and the results in our work has been better than previous work[1] and we are able to save energy and reduce the number of migrations by using our proposed method.