Intelligent Forecasting on Composite Regeneration Endpoint of DPF in the Vehicle
A mathematical model based on fuzzy least squares support vector machines has been developed to forecast the composite regeneration endpoint of the diesel particulate filter in Vehicle. The results show that the relative error of forecasting model of composite regeneration endpoint is less than 3.5%, and the oxygen concentration of exhaust gas has the biggest effect on the endpoint of composite regeneration, followed by the mass flow rate of exhaust gas, the microwave power, the temperature of exhaust gas and the amount of cerium-based additive.
Analysis and Evaluation of Active Based on MnOx-CeO2 Catalysts in Process of Particle Combustion for Diesel Particulate Filter
In order to reduce effectively the particulate ignition temperature of Diesel Particulate Filter containing MnOx-CeO2 catalysts，the physical and mathematical calculation models of MnOx-CeO2 catalysts were established on Diesel Particulate Filter，and according to bench tests, the activity measurements of the five different concentrations were processed for MnOx-CeO2 catalyst. The results indicated that when concentration is 20ppm, the ignition temperature can be droped about 105°C, activity measurements of Kp、Kpm and ΔKp decrease 10%, 8% and 7% respectively. Meanwhile, regeneration time can be shortened to 7min. It shows that Diesel Particulate Filter which contains concentration of this type not only has higher regeneration rate and level , but also decrease effectively exhaust back pressure.
A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation
By combing the properties of chaos optimization method and genetic algorithm, an adaptive mutative scale chaos genetic algorithm (AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [−1, 1]. Some measures in the optimization algorithm, such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline, were taken to ensure its speediness and veracity in seeking the optimization process. The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision. Furthermore, the average truncated generations, the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally. It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.