Open AccessResearch on Multi-step Mixed Predictiom Model of Coal Gasifier Furnace Temperature Based on Machine Learning
Author(s) -
Yuchen Zhao,
Zengwei Ma,
Xuefeng Han
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2187/1/012070
Subject(s) - wood gas generator , particle swarm optimization , hyperparameter , coal , computer science , set (abstract data type) , process engineering , algorithm , engineering , waste management , programming language
In order to ensure the safe and stable operation of the coal gasifier, it is particularly important to study a new and reliable method for predicting the temperature of the gasifier. In this paper, the coal gasifier furnace temperature prediction is transformed into a time series prediction problem, and according to the non-linear characteristics of the gasifier furnace temperature data set, the LSTM (Long Short-Term Memory) model in the machine learning algorithm is selected for the gasifier furnace.Then use the PSO (Particle Swarm optimization) algorithm to optimize the hyperparameters of the model. The results show that the LSTM model is feasible but the accuracy is slightly lacking. The prediction accuracy based on the POS-LSTM model is significantly improved. It is of great significance to accurately predict the temperature of the gasifier.