A semi-supervised linear–nonlinear least-square learning network for prediction of carbon efficiency in iron ore sintering process

Abstract

An iron ore sintering is a large energy-consuming process. The energy mainly comes from the combustion of carbon. Improving the carbon efficiency is beneficial to cost saving and environmental protection. The carbon efficiency has to be predicted before it can be improved. A semi-supervised linear–nonlinear least-square learning network (LLLN) was devised based on the process characteristics for the prediction of the carbon efficiency. First, a new comprehensive carbon ratio (CCR) that takes into account the coke residual was proposed for estimating the carbon efficiency. Then, the process characteristics that are concerned in building the model were presented. They are the existence of linear–nonlinear component and limited labeled samples. After that, a semi-supervised LLLN (SS-LLLN) approach that takes into account the process characteristics was presented for the prediction of the CCR. Last, actual run data was collected to verify the effectiveness of the proposed method. The error distribution, accuracy, and overfitness of an extreme learning machine (ELM), a semi-supervised ELM, an LLLN and an SS-LLLN were compared, which shows the effectiveness of the SS-LLLN.