Abstract In a nickel-producing multiple hearth furnace, there is a problem associated to the automatic operation of the temperature control loops in two of the hearths, since the same flow of air is split into two branches. A neural model of the post-combustion sub-process is built and served to increase the process efficiency of the industrial furnace. Data was taken for a three-months operating time period to identify the main variables characterizing the process and a model of multilayer perceptron type is built. For the validation of this model, process data from a four-months operating time period in 2018 was used and prediction errors based on a measure of closeness in terms of a mean square error criterion measured through its weights for the temperature of two of the hearths (four and six) versus the air flow to these hearths. Based on a rigorous testing and analysis of the process, the model is capable of predicting the temperature of hearth four and six with errors of 0.6 and 0.3 °C, respectively. In addition, the emissions by high concentration of carbon monoxide in the exhaust gases are reduced, thus contributing to the health of the ecosystem.

中文翻译：

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产镍多床炉燃烧后人工神经网络建模

摘要 在生产镍的多炉床炉中，由于同一气流被分成两个分支，因此存在与两个炉床中温度控制回路的自动操作相关的问题。建立后燃烧子过程的神经模型并用于提高工业炉的过程效率。获取三个月运行时间段的数据，以确定表征该过程的主要变量，并建立了多层感知器类型的模型。为了验证该模型，使用了 2018 年四个月运行时间段的过程数据，并基于通过其权重测量两个炉膛温度的均方误差标准来衡量接近度的预测误差（四和六）与流向这些炉膛的气流。基于对工艺的严格测试和分析，该模型能够预测四号炉和六号炉的温度，误差分别为 0.6 和 0.3 °C。此外，废气中高浓度一氧化碳的排放量减少，从而有助于生态系统的健康。