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.

中文翻译：

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用于预测铁矿石烧结过程中碳效率的半监督线性-非线性最小二乘学习网络

摘要 铁矿石烧结是一个耗能大的过程。能量主要来自碳的燃烧。提高碳效率有利于节约成本和保护环境。必须先预测碳效率，然后才能提高碳效率。半监督线性-非线性最小二乘学习网络（LLLN）是基于过程特征设计的，用于预测碳效率。首先，提出了一种新的综合碳比（CCR），它考虑了焦炭残留，用于估算碳效率。然后，介绍了与建立模型有关的过程特征。它们是线性-非线性分量的存在和有限的标记样本。之后，提出了一种考虑过程特征的半监督 LLLN (SS-LLLN) 方法来预测 CCR。最后，收集实际运行数据以验证所提出方法的有效性。对极限学习机（ELM）、半监督 ELM、LLLN 和 SS-LLLN 的误差分布、精度和过拟合进行了比较，显示了 SS-LLLN 的有效性。