نشریه سوخت و احتراق
سال هفتم شماره 1 (پیاپی 13، بهار و تابستان 1393)

Development of kinetic model for new fuels requires optimization of rate parameters of chemical reactions and for this purpose, genetic algorithm (GA) has great capabilities. Since the GA population and breeding parameters (e.g. population, crossover probability, and mutation probability) deeply affect the approach to the optimum point and convergence rate toward it, in this paper the effects of those GA parameters in the optimization of a valid kinetic model for combustion of methane/air, as base mechanism, within perfectly stirred reactors studied and then by using statistical analysis the optimum GA parameters have been determined. The mutation probability has the greatest effect with the optimum value of 0.001and then population stands in the next place with optimum value of 16. In order to validate the optimized model, it was used in the simulation of a premixed flame where concentration profile of selected species match perfectly with those of the original model.

One of the best strategies for using natural gas in diesel engines is dual fuel technology. In dual fuel engines, UHC and CO emissions increase and engine thermal efficiency decreasesat part loadsdue to the lack of suitable flame propagation in the combustion chamber. In this paper, the effect of increasingthe amount of diesel fuel on combustion, performance and emission characteristics of dual fuel engine was studied while keeping the total amount of energy input per cycle constant at part load condition. KIVA-3V codewas used to simulate the in-cylinder eventat closed part of engine cycle. Results show that the in-cylinder pressure historiesand emissions values ​​predicted by the models are in good agreement with thecorresponding experimental data. Also it can be resulted that at part loads,the unburned methane is remained in the most remote areas from diesel fuel injectors such as the bottom of the piston bowl and in four directions wherediesel fuel is not injected. By injection of diesel fuel into the combustion chamber, a fuel-rich zone is formed in the upper areas of combustion chamber and in four directions of injection in which the bulk of CO is formed in these regions due to the lack of oxygen for complete combustion. Hence by increasing the amount of diesel fuel at part loads, diffusion flame penetration of diesel fuel is increased into the combustion chamber and then natural gas combustion is improved.Also, the percentage of natural gas in the mixture that charged in intake stroke is reducedand consequently more oxygen enterscombustion chamber. This excess oxygen in the fuel-rich regions leads to complete combustion. Hence, on the top of the combustion chamber, formation of CO decreaseswhileformation of NO is slightly increased.

A large eddy simulation (LES) of premixed flame-turbulence interaction is performed with special emphasis on computing the instantaneous chemical species reaction rates with the recently developed approach of artificial neural networks (ANNs) for chemical kinetics. Training of the neural network is based on an independent flame study using linear eddy mixing technique. An analysis of computational performance, considering CPU time and a comparison between the performance of artificial neural network technique and other conventional methods is used to represent the chemical kinetics such as direct integration (DI) - and the ability of neural networks to model the highly non-linear and stiff chemistry ODEs is illustrated. The sub-grid combustion model of the LES is based on a linear eddy mixing model while a skeletal multi-species, multi-step chemical kinetic mechanism is applied for the combustion. A feed-forward, multi-layer architecture is chosen for the neural network and the training algorithm is based on a back-propagation gradient descent rule with adaptive learning rate and individual momentum factors for the weight coefficients. The flow field distribution and the flame characteristics obtained by LES with neural network based chemical kinetics tabulation, are in reasonable agreement with previous direct numerical simulation (DNS) study of the flame. The results show if the neural network is trained accurately, it can predict the instantaneous chemical species reaction rates in LES framework.

In oil refining industries, sulfur in the petroleum derivatives is removed by a hydrodesulfurization process. In general, the traditional hydrodesulfurization catalysts are CoMo/Al2O3 and NiMo/Al2O3. Addition of secondary promoter, phosphorus, is proposed as useful solution for increasing catalyst activity and production of standard fuels. In this research, a series of NiMoP/Al2O3 nanocatalysts with different loadings of phosphorus were synthesised with impregnation method and was evaluated for catalytic hydrodesulfurization (HDS) of thiophene (as a model sulfur component) in the atmospheric pressure. The synthesized nanocatalysts were characterized by XRD, FESEM, BET and FTIR techniques. The XRD results confirmed high dispersion of particles on the surface of γ-Al2O3 support. The FESEM images showed destroying of agglomerate and appropriate distribution of nanocatalyst particles due to application of optimal level of phosphorus. The specific surface area of the samples was decreased with increasing phosphorus content. The FTIR analysis revealed that the use of phosphorus promoter increased the distribution of the active phase, generated more active sites and decreased production of nickel spinel. The results of the catalytic activity for thiophene HDS reaction indicated that the synthesized nanocatalyst with phosphorus content of 0.8 wt% had the highest activity and was able to remove thiophene from the initial solution to less than 100 ppm. These results could be addressed by increasing acidity and excellent structural properties of the NiMoP/Al2O3 nanocatalyst with phosphorus loading.

A fully kinetic model using selective non catalytic reduction (SNCR) of NOx has been used to reduce the NO in the exhaust of furnaces and incinerators. Ammonia and urea has been applied as reagents in the model. In considered method a reduced mechanism has been used and the results have been compared with the results of the experimental and numerical studies in the references. The comparison reveals good agreements between the results. The optimum concentration of injected reagent has been achieved for specified operating conditions and reagent slip. Comparing the results of the model by two reagents, ammonia and urea, the optimum concentration of ammonia is obtained in lower temperatures than the urea one. Moreover, using lower concentrations of NH3 results higher reduction efficiency of NO in comparison with urea. In addition, in the same concentrations of reagent the reduction efficiency of ammonia is higher. Also, the destruction of increasing the temperature of injection in the case of ammonia as a reagent is much higher than the urea one. Increasing the temperature will decrease the reduction of NO severely, but in the case of urea as a reagent the destruction of increasing the temperature is lower.