Modeling and optimization of coal blending and coking costs using coal petrography
Modeling and optimization of coal blending and coking costs using coal petrography
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DOI：
10.1016/j.ins.2020.02.072
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Yan Yuan;Qilin Qu;Luefeng Chen;Min Wu
中科院分区：
文献类型：
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作者：
Yan Yuan;Qilin Qu;Luefeng Chen;Min Wu
焦炭是钢铁工业的重要原料，其质量直接影响钢铁的冶炼。为了提高煤质，降低配煤成本，需要对焦炭质量进行预测，优化配煤方案。针对炼焦配煤过程的特点，提出了一种炼焦配煤过程的建模与优化方法。首先，基于高斯函数和Xgboost-SVR建立了焦炭质量预测模型。该模型有两个组成部分。第一部分分析了配煤炼焦过程的关键特征，利用高斯函数提取了镜质组反射率分布特征。在第二部分中，我们使用Xgboost来选择一个具有代表性的特征子集，然后使用支持向量回归（SVR）来创建一个用于预测煤质的模型。其次，我们建立了一个多约束优化问题来描述配煤成本，并使用改进的粒子群优化算法来求解。最后，我们证明了我们的建模和优化方法的有效性，将其应用到实际的过程数据。这表明，我们提出的方法可以提高预测性能，降低配煤成本。
Coke is an important raw material in the steel industry, and its quality directly influences the smelting of iron and steel. To improve coal quality and reduce coal blending costs, we need to predict the coke quality and optimize the coal blending scheme. In this paper, we propose a modeling and optimization method based on the characteristics of the coal blending and coking process. First, we establish a model for predicting coke quality from coking petrography data, based on Gaussian functions and Xgboost-SVR. The model has two components. In the first part, we analyze the key characteristics of the coal blending and coke process, and extract features of the vitrinite reflectance distribution with Gaussian functions. In the second part, we use Xgboost to select a representative feature subset, and then use support vector regression (SVR) to create a model for predicting coal quality. Next, we formulate a multi-constraint optimization problem to describe the coal blending costs, and solve it using a modified particle swarm optimization. Finally, we demonstrate the effectiveness of our modeling and optimization method by applying it to actual process data. This shows that our proposed method can improve prediction performance and reduce the coal blending costs.