基于数字孪生物料跟踪和统计模型预测控制的半软焦化电厂煤产量优化全自动煤质控制

IF 0.9 4区材料科学 Q3 Materials Science

Journal of The South African Institute of Mining and Metallurgy Pub Date : 2022-09-20 DOI:10.17159/2411-9717/2002/2022

B. Coetzee, PW. Sonnendecker

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引用次数: 1

摘要

两阶段洗煤过程的质量控制涉及几个复杂的组成部分，需要精确建模，以实现过程的自主控制。第一个目标是开发一种通过洗涤过程跟踪材料的方法，同时确保使用准确的洗涤预测模型。这是通过Grootegeluk 1煤处理厂的数字孪生模型实现的。该模型是对来自工厂历史、地质冲刷表和采矿调度服务器的数据集的操作和组合的合并。然后使用这些信息来控制和设置设备上所有15个模块的处理介质密度，其中10个模块在一次洗涤中，5个模块在二次洗涤中。该控制器已成功实施，并对工厂进行了10天的控制。

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Fully automated coal quality control using digital twin material tracking and statistical model predictive control for yield optimization during production of semi soft coking- and power station coal

The quality control of a two-stage coal washing process involves several complex components that need to be modelled accurately, to enable autonomous control of the process. The first objective is to develop a method to track the material through the washing process, while ensuring accurate washing prediction models are used. This was achieved through a digital twin model of the Grootegeluk 1 coal processing plant. The model is the amalgamation of manipulating and combining of data-sets from the plant historian, geological wash tables, and mining dispatch servers. This information is then used to control and set the processing medium densities of all 15 modules on the plant, 10 modules in the primary wash and 5 modules in the secondary wash. This controller has been successfully implemented and controlled the plant for 10 days.

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来源期刊

Journal of The South African Institute of Mining and Metallurgy 工程技术-矿业与矿物加工

CiteScore

1.50

自引率

11.10%

发文量

审稿时长

4-8 weeks

期刊介绍： The Journal serves as a medium for the publication of high quality scientific papers. This requires that the papers that are submitted for publication are properly and fairly refereed and edited. This process will maintain the high quality of the presentation of the paper and ensure that the technical content is in line with the accepted norms of scientific integrity.