Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a direct influence on the yield, quality, and energy consumption of the ironmaking process. Since iron ore sintering is a very complex industrial process with strong nonlinearity, multivariable coupling, random noises, and time variation, traditional soft-sensor models are hard to learn the knowledge of the sintering process. In this article, a multistep prediction model, called denoising spatial–temporal encoder–decoder, is developed to predict BTP in advance. First, the mechanism analysis is carried out to determine the relevant-BTP variables, and the BTP prediction is defined as a sequence-to-sequence modeling problem. Second, motivated by the random noises of industrial data, a denoising gated recurrent unit (DGRU) is designed to alleviate the impact of noise by adding a denoising gate into the GRU. In this case, the encoder with DGRU can better extract the latent variables of original sequence data. Then, spatial–temporal attention is embedded into the decoder to simultaneously capture the time-wise and variable-wise correlations between the latent variables and the target variable BTP. Finally, the experimental results on the real-world dataset of a sintering process demonstrated that the integrated multistep prediction model is effective and feasible.

中文翻译：

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DSTED：用于烧结过程中烧穿点多步预测的去噪时空编码器-解码器框架

烧结矿是高炉的主要原料，烧穿点（BTP）直接影响炼铁过程的产量、质量和能耗。由于铁矿石烧结是一个非常复杂的工业过程，具有很强的非线性、多变量耦合、随机噪声和时间变化，传统的软传感器模型很难学习到烧结过程的知识。在本文中，开发了一种称为去噪时空编码器-解码器的多步预测模型来提前预测 BTP。首先，进行机制分析以确定相关的BTP变量，并将BTP预测定义为序列到序列的建模问题。其次，受工业数据的随机噪声驱动，降噪门控循环单元 (DGRU) 旨在通过在 GRU 中添加降噪门来减轻噪声的影响。在这种情况下，带有 DGRU 的编码器可以更好地提取原始序列数据的潜在变量。然后，将时空注意力嵌入到解码器中，以同时捕获潜在变量和目标变量 BTP 之间的时间和变量相关性。最后，在烧结过程的真实数据集上的实验结果表明，集成多步预测模型是有效和可行的。时空注意力被嵌入到解码器中，以同时捕获潜在变量和目标变量 BTP 之间的时间和变量相关性。最后，在烧结过程的真实数据集上的实验结果表明，集成多步预测模型是有效和可行的。时空注意力被嵌入到解码器中，以同时捕获潜在变量和目标变量 BTP 之间的时间和变量相关性。最后，在烧结过程的真实数据集上的实验结果表明，集成多步预测模型是有效和可行的。