Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling. The operation data is an important basis for state monitoring

optimal control

and fault diagnosis. However

the operating environment of acid production with flue gas is complex and there is much equipment. The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers. Therefore

to solve the problem of abnormal data in the process of acid production with flue gas

a data cleaning method based on improved random forest is proposed. Firstly

an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the dataset. Secondly

an improved random forest regression model is established. Genetic algorithm is used to optimize the hyperparameters of the random forest regression model. Then the optimal parameter combination is found in the search space and the trend of data is predicted. Finally

the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is realized. Results show that the proposed method can accuratel

y eliminate and compensate for the abnormal data in the process of acid production with flue gas. The method improves the accuracy of compensation for missing data. With the data after cleaning

a more accurate model can be established

which is significant to the subsequent temperature control. The conversion rate of SO

2

can be further improved

thereby improving the yield of sulfuric acid and economic benefits.