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A hot metal temperature predictor based on hybrid decision tree techniques

机译：基于混合决策树技术的铁水温度预报器

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HEAT level control (HLC) is one of the important elements for operating an iron-making blast furnace (BF). The goal of HLC is to maintain the hot metal temperature (HMT) as close to a preset aim as possible. HMT is an important indicator of both the product quality and fuel efficiency, and is measured from tapped out liquid iron. For instance, high values of HMT mean unnecessary fuel consumption together with sub-optimal hot metal chemistry, whilst low values of HMT may indicate insufficient fuel consumption, which may consequently lead to dangerous situation of freezing the slag inside the BF. Once an aim HMT is decided, based on production and plant constraints, several inputs of the BF can be adjusted by operators to control this HMT. However, any change of the inputs requires time to take effect, due to inherent time lags, and the best practice control relies heavily on an operator’s experience and judgment. This is due to the complexity of the numerous BF processes, which essentially can be described as highly non-linear, stochastic and nonstationary in nature. The goal of this work is to employ a data mining approach to analyse the BF system and build a dynamical modelling system, which will generate a set of understandable symbolic rules for prediction of HMT changes. The model is regularly generated by decision tree applications, See5 and Cubist, in order to adapt any significant contextual variations of the BF. This rule based system can help the operators increase the BF’s efficiency by making timely control adjustments with the goal of minimising variation of HMT with time. These models also provide a corpus of driving factors that can also be analysed by domain experts as an objective knowledge source.
机译：加热液位控制（HLC）是操作炼铁高炉（BF）的重要元素之一。 HLC的目标是保持铁水温度（HMT）尽可能接近预设目标。 HMT是产品质量和燃油效率的重要指标，并且是通过挖掘出的铁水进行测量的。例如，高的HMT值意味着不必要的燃料消耗以及次优的铁水化学成分，而低的HMT值可能表明燃料消耗不足，因此可能导致冻结高炉内炉渣的危险情况。一旦确定了目标HMT，根据生产和工厂的限制，操作员可以调整高炉的多个输入以控制此HMT。但是，由于固有的时滞，输入的任何更改都需要时间才能生效，并且最佳实践控制很大程度上取决于操作员的经验和判断。这是由于许多高炉过程的复杂性，本质上可以将其描述为高度非线性，随机和非平稳的过程。这项工作的目标是采用一种数据挖掘方法来分析高炉系统，并建立一个动态建模系统，该系统将生成一组可理解的符号规则来预测HMT变化。该模型由决策树应用程序See5和Cubist定期生成，以适应BF的任何重大上下文变化。这种基于规则的系统可以通过及时进行控制调整来帮助操作员提高BF的效率，从而最大程度地减少HMT随时间的变化。这些模型还提供了一系列驱动因素，领域专家也可以将其作为客观知识源进行分析。

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