###### Abstract
There is no logical correlation between these two kinds of tests, which makes it difficult to learn the association rules and master the adjustment law, as shown in Figure 1. The number of thrusters in one batch was around 120, and only about 40 thrusters (represented by green circles) were successful in the firing tests. Therefore, more than 60% of thrusters needed to correct the orifice size after the firing test, but those results after correction cannot be further verified by the firing test. That is, the adjusted thrusters, after the tests, launched to the space orbit still lacked the final mixture ratio verification. In particular, there was no direct logical bridge between the liquid-flow test and real work condition. This might lead to a huge bias in the estimation of the amount of propellant remaining for spacecrafts on orbit. In the conventional approach to the mixture ratio adjustment, the mixture ratio is predicted by using the flow rate data in the liquid-flow test with a coefficient correction based on experience. Figure 1 shows the mixture ratio data of a batch of thrusters in the firing tests after orifice adjustment. The data came from the flow rate test, and alcohol was used as the test medium. In the test, under the design inlet pressure, the flow rates of the NTO and MMH channels were measured, respectively, in order to achieve the standard mixing ratio during the firing test. The three straight lines in Figure 1 are the contour lines of the ratios of the alcohol flow rate deviation between the NTO flow channel and the MMH flow channel. It can be seen that the number of successful cases with an accepted mixture ratio was much less than those that deviated, and the regularity was also not on the logical track. Moreover, there was no significant difference in the impact of the orifice adjustment. It can be concluded that when the A value is large, the proportion of high mixture ratio goes up, otherwise, there are more thrusters with a low mixture ratio. However, this only \begin{table}
\begin{tabular}{c c c} \hline
**Process Index** & **Work Condition** & **Mixture Ratio Range** \\ \hline
1 & Orbit transfer attitude control & 1.66 \\
2 & Attitude control on orbit & 1.647\(\sim\)1.649 \\
3 & Station keeping on orbit & 1.6561\(\sim\)1.6638 \\ \hline \end{tabular}
\end{table} 
Table 1: The mixture ratio range of the attitude control thruster under typical work conditions. reflects a qualitative trend, but cannot be quantitatively predicted. It is inferred that there must be some other hidden factors that need to be explored to influence the final results. The BP neural network is a machine learning method based on a multi-layer neural network [24; 25]. It can fit all the multi-dimensional nonlinear parameter mapping relations through adaptive weight allocation and the activation function and also has a strong fitting ability with an arbitrary complex pattern. Therefore, this feedforward neural network is consequently used to learn the historical data and analyze the weight of factors that determine the deviation of the mixture ratio. In this way, the influence parameters of each thruster can be processed into sequence data according to the weight value. After this, RNN extracts the features with the highest weight through a \"many-to-one\" structure [26; 27], which can effectively solve the problem of sequence classification. The BP method is used to select and sort the test features after the weight comparison of the influence of features on the mixing ratio, and then the RNN method is used to predict the mixing ratio, so was called the BP-RNN chain method, as proposed in this study. The high-precision prediction of the mixture ratio based on a BP-RNN chain method can be realized according to the historical data training, in order to expectedly adjust the orifice size in advance and improve the qualified rate of the thrusters' mixture ratio in one time in the firing test.