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A Causal Matrix-based Method for Key Process Identification: Case Study in Heavy Medium Coal Preparation

Volume 14, Number 6, June 2018, pp. 1341-1351
DOI: 10.23940/ijpe.18.06.p26.13411351

Qingwen Yuana, Zhaojun Lib, Shun Jiac,d,e, and Zhenzhen Jinc

aDepartment of Finance and Economics, Shandong University of Science and Technology, Jinan, Shandong, 250031, China
bDepartment of Industrial Engineering and Engineering Management, Western New England University, Springfield, Massachusetts, 01119, USA
cDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
dState Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao, 266590, China
eNational Demonstration Center for Experimental Mining Engineering Education (Shandong University of Science and Technology), Qingdao, 266590, China

(Submitted on March 17, 2018; Revised on April 21, 2018; Accepted on May 28, 2018)

Abstract:

In order to identify key processes in coal mining and preparation processes for optimized process control and quality improvement, a modified causal matrix method is proposed to reduce quality defects and avoid unnecessary investments. The limitations of existing key processes identification methods are reviewed with the identified key process identification requirements in the context of the coal mining industry. The Key Process Identification Method of Coal Preparation (CP-KPIM) is proposed based on a redesigned causal matrix method, which considers four aspects, i.e., the systematic production data set, multi-dimension quality measures, the correlation between processes and quality characteristics, and a unified total process criticality assessment method. The implementation steps of CP-KPIM are demonstrated in details through a case study in a heavy medium coal preparation plant. Based on the total process criticality assessments, eleven key processes were identified from a total of fifty-three processes. In addition, the CP-KPIM has demonstrated significant productivity improvement through the process capability analysis.

 

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