Automatic generation method of a chemical reactor network for predicting NOx emissions
GAO Qiaodong1,2, LEI Fulin1,2, ZHANG Zhedian1,2
1. Key Laboratory of Advanced Energy and Power, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China; 2. School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：[Objective] In the design and development of combustion chambers, fast and reliable emission prediction tools are needed. One of the most extensively utilized technologies today is computational fluid dynamics (CFD) combined with a chemical reactor network (CRN). The manual division of the chemical reactor network is complex, and the calculation of chemical reactor parameters is rough. It can quickly and efficiently divide, build, and solve the CRN to estimate NOx emissions based on the CFD calculation results by developing an automatic generation method for the chemical reactor network. [Methods] The program for the automatic generation of CRN is developed based on Python language, and Cantera is integrated to solve the CRN. The CFD results are obtained by numerically simulating six working conditions of a lean premixed burner and five working conditions of a micromix burner. The CRN automatic generation program divides the combustion chamber domain into different reaction regions based on the division criteria of temperature field, velocity field, and geometric parameters. Meanwhile, when the cells are clustered, the chemical reactor parameters and mass flow rates between the chemical reactors are calculated. The different regions are replaced by reactors, such as the perfectly stirred reactor, and linked by flow controllers. The NOx emissions are obtained by solving the CRN through Cantera and compared with the experimental values. [Results] The parameters of the CRN could be accurately calculated by post-processing the CFD results with the CRN automatic generation program. Under different conditions in the same combustion chamber, the cells could be classified, and the corresponding CRN structure could be generated again by changing the division criteria. The temperature and pressure calculated by the volume-weighted average method and the mass-weighted average method differed in some reactors. However, the NOx values predicted by the two methods were basically identical. The CFD-CRN method predicted NOx emissions more accurately than the Fluent NOx post-processing method. CFD-CRN had a maximum forecast error of 32%, while Fluent NOx post-processing had a maximum prediction error of 96%. The greatest errors in the NOx forecast results of CRN models with different CFD grid numbers and reactor numbers were 5% and 2%, respectively, based on the premise of selecting appropriate division criteria to reasonably build a CRN. The CRN template could be used to predict the NOx emission under nearby working conditions within the acceptable error range. In the lean premixed burner, when heat loss was allocated to the wall area, the NOx values were generally higher than when it was allocated to different reactors according to volume weight. However, the predicted results of the two allocation methods were opposite in the micromix burner. [Conclusions] The CRN automatic generation program may automate the CRN’s division, construction, and solution. Taken temperature, velocity and geometric parameters as the criteria, it can generate well-structured CRN. With fewer grids and reactors, the CRN model can estimate NOx emission accurately. When the combustion temperature is high, considering heat loss and distributing it to different chemical reactors can improve the accuracy of the NOx prediction substantially. The same CRN model may be reused again to accurately predict NOx emissions under similar working conditions.
高桥东, 雷福林, 张哲巅. 预测NOx排放的化学反应器网络自动生成方法[J]. 清华大学学报（自然科学版）, 2023, 63(4): 612-622.
GAO Qiaodong, LEI Fulin, ZHANG Zhedian. Automatic generation method of a chemical reactor network for predicting NOx emissions. Journal of Tsinghua University(Science and Technology), 2023, 63(4): 612-622.
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2023, Vol. 63 
