Research on soft compensation of the potential drift signal of a pH electrode based on a gated recurrent neural network

As a convenient chemical sensor, the pH electrode is widely used in the measurement of the pH value of water bodies. However, due to structural aging and environmental influences, the pH electrode is prone to drift, which directly results in the inability to obtain accurate measurement results. Based on the above problems, this paper proposes a cascade structure soft compensation model with the gated recurrent unit (GRU) as the main body. The model uses the complete ensemble empirical mode decomposition with adaptive noise with permutation entropy (CEEMDAN-PE) method to obtain the main characteristics of the pH electrode potential drift signal to reduce the interference of noise in the actual measurement environment, and uses its output as the input of the GRU neural network to obtain the prediction results and compensate for the drift signal. This model is called the CEEMDA-PE & GRU (CPG) model. In this paper, the CPG model is compared with the commonly used time series prediction model, and the results show that the prediction effect of this model is better than other models. The root mean squared error, mean absolute error, and mean absolute percentage error of the prediction model are reduced by 60.97%, 65.53%, and 66.55%, respectively. Finally, this paper proposes the concept of the degree of compensation to evaluate the compensation effect. The average degree of compensation of the soft compensation method is above 83%. The results show that the soft compensation method can improve the measurement accuracy of the pH electrode and has good robustness.