Prediction Model and Influencing Factors of CO2 Micro/Nanobubble Release Based on ARIMA-BPNN

Abstract

The quantitative prediction of CO2 concentration in the growth environment of crops is a key technology for CO2 enrichment applications. The characteristics of micro/nanobubbles in water make CO2 micro/nanobubble water potentially useful for enriching CO2 during growth of crops. However, few studies have been conducted on the release characteristics and factors influencing CO2 micro/nanobubbles. In this paper, the factors influencing CO2 release and changes in CO2 concentration in the environment are discussed. An autoregressive integrated moving average and backpropagation neural network (ARIMA-BPNN) model that maps the nonlinear relationship between the CO2 concentration and various influencing factors within a time series is proposed to predict the released CO2 concentration in the environment. Experimental results show that the mean absolute error and root-mean-square error of the combination prediction model in the test datasets were 9.31 and 17.48, respectively. The R2 value between the predicted and measured values was 0.86. Additionally, the mean influence value (MIV) algorithm was used to evaluate the influence weights of each input influencing factor on the CO2 micro/nanobubble release concentration, which were in the order of ambient temperature > spray pressure > spray amount > ambient humidity. This study provides a new research approach for the quantitative application of CO2 micro/nanobubble water in agriculture.