The free piston engine linear generator (FPELG) is a simple engine structure with few components, making it a promising power generation system. However, because the engine works without a crankshaft, the handling of the piston motion control (PMC) is the main challenge influencing the stability and performance of FPELGs. In this article, the optimal operating parameters of FPELG for maximising engine performance and reducing exhaust gas emissions were studied. Moreover, the influence of adding hydrogen (H2) to compressed natural gas (CNG) fuel on FPELG performance was investigated. The influence of operating parameters on in-cylinder pressure was also analysed. The single-piston FPELG fuelled by CNG blended with H2 was used to run the experiments. The response surface methodology (RSM), including the central composite design (CCD), was used. Then, adequacy models were developed and verified by ANOVA. Three independent factors on seven responses were utilised for optimisation. Results showed that the optimal operating conditions of lambda, ignition velocity, and injection position were 0.96, 0.53 m/s, and 14.9 mm, respectively. The best-predicted values were as follows: indicated mean effective pressure (IMEP) of 7.6 bar, in-cylinder pressure of 27.87 bar, combustion efficiency of 39.64%, CO of 9531.41 ppm, CO2 of 2.4%, HC of 551.75 ppm, and NOX of 113.737 ppm. Furthermore, results showed that the experimental data could be fitted well with the predicted quadratic model.
There are many researches deals with constructing an efficient solutions for real problem having Multi - objective confronted with each others. In this paper we construct a decision for Multi – objectives based on building a mathematical model formulating a unique objective function by combining the confronted objectives functions. Also we are presented some theories concerning this problem. Areal application problem has been presented to show the efficiency of the performance of our model and the method. Finally we obtained some results by randomly generating some problems.
The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
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