Soft measurement method for temperature in multi-component organic waste high-temperature gasifier

The invention relates to a soft measurement method for the temperature in a multi-component organic waste high-temperature gasifier, which comprises the following steps of: selecting a plurality of process variables measured during normal operation of the gasifier, performing data preprocessing, selecting a BP neural network with three layers, training the BP neural network by adopting a level-marquardt algorithm, and calculating the temperature in the gasifier according to the trained BP neural network. And selecting the neural network with the minimum result error in the neural networks with different hidden layer node numbers as a target neural network, and realizing soft measurement of the temperature in the multi-component organic waste high-temperature gasifier by using the obtained neural network. The method effectively overcomes the defects of uncertain and unstable properties of traditional temperature physical measurement and raw material components entering the furnace and the like, can accurately predict the temperature in the multi-component organic waste high-temperature gasifier, and has important application value for temperature regulation and stable operation in the waste treatment process.