作为选矿厂电路行为分析工具的人工智能

IF 1.6 4区工程技术 Q3 ENGINEERING, CHEMICAL

Chemie Ingenieur Technik Pub Date : 2025-06-17 DOI:10.1002/cite.70000

Johannes Müller, Julius Ortmanns, Dr.-Ing. Felix Heinicke, Prof. Dr.-Ing. Holger Lieberwirth

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引用次数: 0

摘要

矿物加工厂的生产关键数据通常被设计为具有物料再循环的回路，受到许多影响因素的影响。为了使工厂的运行达到最优状态，识别具有重要意义的因素是很重要的。在这项研究中，人工神经网络作为一个额外的工具，以特征重要性分析的方式对加工厂进行审计。只要有足够的工艺数据，所提出的方法可以独立于特定的工厂设计。此外，还讨论了典型钾肥压实回路分析的具体结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Artificial Intelligence as Analysis Tool of the Circuit Behavior of Mineral Processing Plants

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Artificial Intelligence as Analysis Tool of the Circuit Behavior of Mineral Processing Plants

Production key figures of mineral processing plants, often designed as circuits with recirculation of material, are subject to a high number of influencing factors. In order to set up plant operation in an optimal way, identifying factors with high significance is important. In this study, an artificial neural network is employed as an additional tool for such processing plant audits by means of feature importance analysis. The presented method is applicable independently of the specific plant design, wherever sufficient process data is available. Furthermore, specific outcomes of the analysis of an exemplary potash compaction circuit are discussed.

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来源期刊

Chemie Ingenieur Technik 工程技术-工程：化工

CiteScore

3.40

自引率

15.80%

发文量

601

审稿时长

3-6 weeks

期刊介绍： Die Chemie Ingenieur Technik ist die wohl angesehenste deutschsprachige Zeitschrift für Verfahrensingenieure, technische Chemiker, Apparatebauer und Biotechnologen. Als Fachorgan von DECHEMA, GDCh und VDI-GVC gilt sie als das unverzichtbare Forum für den Erfahrungsaustausch zwischen Forschern und Anwendern aus Industrie, Forschung und Entwicklung. Wissenschaftlicher Fortschritt und Praxisnähe: Eine Kombination, die es nur in der CIT gibt!