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首页> 外文期刊>International journal of green energy >Predictive study of ultra-low emissions from dual-fuel engine using artificial neural networks combined with genetic algorithm
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Predictive study of ultra-low emissions from dual-fuel engine using artificial neural networks combined with genetic algorithm

机译：用人工神经网络与遗传算法相结合双燃料发动机超低排放的预测研究

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Many degrees of freedom on engine operating parameters limit the optimizing of engine managements for the sake of simultaneously complying with emission regulations and energy economy requirements. Adaptive neuro-fuzzy inference system (ANFIS) is the combination of neural network and fuzzy logic, able to solve nonlinear problems those do not have algorithmic solutions and cannot be modeled mathematically, thus eliminating the limitations of classical approaches. In this study, ANFIS was employed to map the relationships between controlled boundaries and engine performances. A total number of 80 experimental data on dual-fuel diesel engine were selected for training and testing the ANFIS model which has six input variables (diesel fuel injection timing, gasoline premixed ratio, rate of exhaust gas recirculation, indicated mean effective pressure, and the timings of 10% and 50% of total heat release) within a wide validity ranges of engine operating parameters and four outputs of engine emissions and performance. Then, the ANFIS outputs were used to evaluate the objective functions of the optimization process, which was performed with a genetic algorithms (GA) multi-objective optimizing approach. Finally, the Pareto-optimal sets were plotted with minimized NOx as well as soot emissions within the imposed constraints of pressure rise rate and efficiency. This paper studied the feasibility of using ANFIS in combination with GA to optimize the diesel engine settings so that the optimal engine performance and emission behavior would be obtained. The characteristics of the optimal solutions were ultimately explored by sensitivity analysis.
机译：发动机运行参数的许多程度的自由度限制了发动机管理的优化，以便同时遵守排放法规和能源经济要求。自适应神经模糊推理系统（ANFIS）是神经网络和模糊逻辑的组合，能够解决不具有算法解决方案的非线性问题，不能在数学上建模，从而消除了经典方法的局限性。在这项研究中，使用ANFI来映射受控边界和发动机性能之间的关系。选择有关双燃料柴油发动机的80个实验数据的总数用于训练和测试具有六种输入变量的ANFIS模型（柴油燃料喷射正时，汽油预混比，废气再循环率，表示平均有效压力，以及在发动机运行参数的宽效范围内和发动机排放和性能的四个输出中的10％和总热量释放的时间。然后，使用ANFIS输出来评估利用遗传算法（GA）多目标优化方法进行的优化过程的目标函数。最后，绘制了帕肌型最佳集合，最小化NOx以及压力上升率和效率的施加约束内的烟灰排放。本文研究了使用ANFI与GA结合优化柴油发动机设置的可行性，以便获得最佳发动机性能和排放行为。最终通过敏感性分析探索最佳解决方案的特征。

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