Research on a soft-measurement model of gasification temperature based on recurrent neural network

Abstract

Abstract Gasification temperature measurement is one of the most challenging tasks in an entrained-flow gasifier and often requires indirect calculation using the soft-sensor method, a parameter prediction method using other parameters that are more easily measurable and using correlation equations that are widely accepted in the gasification field for the temperature data. Machine learning is a non-linear prediction method that can adequately act as a soft sensor. Furthermore, the recurrent neural network (RNN) has the function of memorization, which makes it capable of learning how to deal with temporal order. In this paper, the oxygen–coal ratio, CH4 content and CO2 content determined through the process analysis of a 3000-t/d coal-water slurry gasifier are used as input parameters for the soft sensor of the gasification temperature. The RNN model and back propagation (BP) neural network model are then established with training-set data from gasification results. Compared with prediction set data from the gasification results, the RNN model is found to be much better than the BP neural network based on important indexes such as the mean square error (MSE), mean absolute error (MAE) and standard deviation (SD). The results show that the MSE of the prediction set of the RNN model is 6.25°C, the MAE is 10.33°C and the SD is 3.88°C, respectively. The overall accuracy, the average accuracy and the stability effects are well within the accepted ranges for the results as such.