Design and Fabrication of Automated Sieve Machine Using Scotch-Yoke Mechanism

As technology increases rapidly in a far better way, there is a need for automation in every field for better future. The growth in construction field in our country has seen extreme levels over the decade. So, to improve further, we need to reduce the manual work. A sand sieving machine has the function to sieve sand and stone that is mixed together. It gives sand with different grade at higher efficiency than manual work. Using machine mechanism driven by electricity power, we can reduce the time to sieve.

Smart Traffic Management for Collision Avoidance Using V2V

The main objective of this project is to connect the vehicles together and avoid accidents by using V2V Communication. The vehicles are to be connected together by means of DSRC algorithm which is used for transceiving alert messages among the connected vehicles, in case of any emergency situation such as accidents. The Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behaviour. An increase in the infrastructure growth is a possible solution but turns out to be costly in terms of both time and effort. Various applications that target transport efficiency could make use of the vast information collected by vehicles: safety, traffic management, pollution monitoring, tourist information, etc.

Comparison of RSM and ANN Optimization Techniques and Modeling of Ultrasonic Energy Assisted Trans-esterification of Salviniaceae Filiculoides Oil Blended to Biodiesel

For the transesterification of biodiesel from Azolla oil, the safe and successful use of feed stocks is a very significant prerequisite. It is of high importance to determine the optimal reaction parameters to maximize the yield of low-cost biodiesel generated from Azolla oil. Ultrasonic energy was used in this work for the development of biodiesel from Azolla oil catalyzed by the KOH catalyst under different conditions. The effect on the transesterification of Azolla Oil to biodiesel of four reaction parameters, namely the methanol/Azolla oil molar ratio (A), KOH catalyst concentration (B), reaction time (C) and reaction temperature (D) were considered. In order to optimize the effects of reaction parameters for the transesterification of Azolla oil to biodiesel, response surface methodology (RSM) based on central composite rotatable design (CCRD) is applied. To obtain a good correlation between the input reaction parameters and the output response parameter (FAME yield) from Azolla oil to biodiesel, an artificial neural network (ANN) model with two feed-forward back-propagation neural-network architecture Multilayer Perceptron Network (MLP) and Radial Basis Function Network (RBFN) was developed. With the experimental information obtained from the RSM model, the built ANN models were trained and evaluated. Absolute Average Deviation (AAD), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination were statistically compared with the predictive capacity of both RSM and ANN models (R2). The statistical analysis showed that the measured FAME yield from both the RSM and ANN models was able to predict the FAME yield, and the findings limited the ANN model to the much more reliable FAME yield prediction compared to the RSM model.