Applied Mathematics

In this present investigation, four important process parameters of catalyst concentration, molar ratio, reaction time, and reaction temperature were studied and optimized using Box Behnken assisted response surface method (RSM) and Genetic Algorithm (GA) to achieve the maximum mahua oil biodiesel yield. For this purpose, 27 experiments were conducted randomly based on the design matrix using statistical software MiniTab®2019. A maximum yield of 91.32 % is achieved in RSM, catalyst concentration and reaction time are identified as influence parameters in biodiesel yield. GA modelling show an improvement of 4.96 % in biodiesel yield compared to RSM approach. Both techniques are successfully tested in prediction and modelling the biodiesel yield from mahua oil. The obtained biodiesel from the transesterification process is blended with standard diesel fuel at various proportions (B10 to B90) and tested for different fuel properties. All the biodiesel blends are observed within the limits of international standards of ASTMD-6751 and EN-14214. The results indicate that the chosen models are highly accurate in achieving maximum biodiesel yield and mahua biodiesel is recommended as the best alternative fuel to diesel engines without any major modifications in the engine design.

This paper proposes the Taguchi based optimization technique for the production of biodiesel from palm oil. For this purpose, L9 orthogonal array was successfully used for better yield estimation by using Minitab-18 software. Different process variables like molar ratio, catalyst concentration, reaction time and reaction temperature were studied. A predicted yield of 92.06 % was achieved by regression analysis by maintaining the process variables of molar ratio 5:1 (methanol to oil), 4 grams of catalyst concentration (NaOH), 180 minutes of reaction time and 44°C of reaction temperature. Experimentations were conducted on the same process variables and achieved a yield of 91.65 %. By this it is clear that both experimentation and regression analysis by Taguchi are in good agreement with an error of 0.41 % which may be acclaimed as experimental error. The fatty acid compositions (FAC) were also analyzed and it is found that 37.12 % saturated and 62.88 % unsaturated fatty acids present in the palm methyl ester (PME). By using the FAC of PME the Cetane number was predicted as 55.38. The predicted Cetane number from FAC tally with experimental Cetane number. The PME is characterized for different fuel properties by following the international standards. And it is concluded that catalyst concentration and reaction temperature are the important parameters which influence PME yield, Taguchi based optimization technique will helps in predicting the maximum yield with minimum number of experiments.

Path planning is one of the important directions in the field of intelligent vehicles research. Traditional path planning algorithms generally use Dijkstra algorithm, Breadth-First-Search (BFS) algorithm and A* algorithm. Dijkstra algorithm is a search-based algorithm, which can search to an optimal path, but the disadvantage is too many expansion nodes, which leads to insufficient search efficiency. BFS algorithm is a heuristic search algorithm, which reduces the disadvantage of too many expansion nodes and improves the search efficiency by heuristic function. A* algorithm is a heuristic search algorithm that combines Dijkstra’s algorithm and BFS algorithm, which has higher search efficiency and can search to an optimal path at the same time, but it is still lacking in the search mode and smoothness of the planned route. This paper first introduces the general path planning algorithm, then introduces and analyzes the A* algorithm, and proposes improvement measures for its shortcomings; finally, the executability and effectiveness of the improved algorithm are tested using simulation, and compared with the traditional A* algorithm, and the results show that the improved A* algorithm has good effect on path planning of intelligent vehicles.

To improve the soil compactor ride comfort, a combined control method of Fuzzy and PID control is proposed to control the cab isolation system of soil compactor based on the non-linear vehicle dynamic model. The vibration excitation sources are concerned by the vibrator drum and elastoplastic soil (EPS) interactions in the compression process. The power-spectral-density (PSD) and weighted root-mean-square (RMS) of acceleration responses of both the vertical driver’s seat and pitching cab angle are chosen as the objective functions. The research results show that both the PSD and weighted RMS values of the vertical driver’s seat and pitching cab angle are significantly reduced by using the PID-Fuzzy control under various EPSs in the low-frequency region, especially on the EPS with high-density.