Evidence map›Paper›PMID 39548259›Full record

ArticleScientific reports2024

Optimization of working parameters of high-pressure roller mill based on entropy weight method and response surface method.

Fang Yang, Runze Li, Xiao Wang, Bo Cheng, Ruijie Gu

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Fang YangSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China. yangfanghkd@163.com.
Runze LiSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China.
Xiao WangSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China.
Bo ChengState Key Laboratory of Intelligent Mine Heavy Equipment, Luoyang, 471003, China.
Ruijie GuSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China.

Funding

The Frontier Exploration Project of Longmen Laboratory LMQYTSKT036
6 · The paper itself

Abstract

In order to improve the crushing efficiency of high-pressure roller mill and reduce energy consumption, the optimal parameter combination of high-pressure roller mill is sought, GM160-140 high-pressure roller mill is taken as the research object, and the discrete element simulation calculation model of high-pressure roller mill is established to simulate the crushing process. Taking the roll surface pressure, productivity, and the proportion of discharge particle diameter as the performance evaluation indexes, the simulation data are used to analyze the influence of each working condition parameter of the high-pressure roller mill on its performance law. Through the entropy weighting method to determine the weights of high-pressure roller mill productivity, roll surface pressure, the proportion of discharge particle diameter, and through the extreme difference analysis to get the optimal parameter combination. Minitab software is used to carry out the response surface method (RSM) experimental design, the optimal parameter combinations are derived from the optimization using the response surface apparatus, the optimal parameter combinations derived from the two optimization methods are compared and analyzed, and ultimately it is concluded that the optimization of the response surface is the global optimal, and the optimal parameter combinations are as follows: the roller speed is 2.50 rad/s, the roller gap is 28.87 mm, and the feed size is 31.97 mm, Under the above optimal parameters, the roll surface pressure is 150450.00 N, the productivity is 1268.00t/h, the percentage of the material less than 6 mm is 58.14%, so as to achieve the purpose of optimizing the parameters of the high-pressure roller mill, It is of significance to save resources and respond to the national green development and intelligent development.

Indexed as

Discrete elementEntropy weight methodHigh pressure roller millParameter optimizationResponse surface method

Identifiers

PMID39548259
PMCPMC11568260

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