Home / Current Issue / Paper 1705128
A Fuzzy Logic-Based Approach for Selecting the Optimal Crusher Model
Subject area: Science,Engineering and Technology · Area of research: Engineering
Abstract
This scientific journal presents a fuzzy logic-based approach for selecting the optimal model that minimizes the power rating, given the product size, feed size, and capacity. The study utilizes the `skfuzzy` library in Python to implement the fuzzy logic system. A dataset containing various model samples along with their corresponding feed size, product size, capacity, and power rating is loaded from a CSV file. Fuzzy membership functions are defined for the input variables: feed size, product size, and capacity, as well as the output variable: power rating. Fuzzy rules are established to determine the relationship between the input and output variables. The fuzzy control system is created and simulated to evaluate the power rating for each data sample. The model with the lowest power rating is identified as the optimal
Keywords
fuzzy logic, optimal model, feed size, product size
References
[1] Mckinney, W., (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9 th Pyton in Science Conference, pp.51-56.
[2] Petrica Vizureanu (2019). Introductory Chapter: Enhanced Expert System. A Long-Life Solution. 83881-886-9 ISBN 978-1-83881 887-6 (E-Book)
[3] Reback, J., J. Mckinney, (2021). pandas- dev/pandas: Pandas zenodo.
[4] Smith, J., & Johnson, A. (2018). Expert Systems in Mining: A Comprehensive Review. Mining Engineering Journal, 25(3), 45-56.
[5] Svedensten, P., Evertsson, M., (2004). Crushing Plant Optimization Via a Genetic Evolutionary Algorithm, Minerals Engineering, Vol. 18, Pp. 473-479.
[6] Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson.
[7] Utley, R.W., (2003). Selection and Sizing of Primary Crushers, Mineral Processing PlantDesign, Practice, And Control – Vol. 2, (Eds: A.L. Mular, D.J. Barratt, D. N. Halbe), SME, Pp. 584-605.
[8] Jain, R., & Gupta, S. (2019). Optimization Techniques for Crushing Plant Design. International Journal of Mineral Processing, 125, 109-123.
How to cite this paper
@article{1705128,
author = {Ogeleka Stephen Chike, Ebenezer Oyedele Ajaka},
title = {A Fuzzy Logic-Based Approach for Selecting the Optimal Crusher Model},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {4},
pages = {281-283},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705128.pdf},
abstract = {This scientific journal presents a fuzzy logic-based approach for selecting the optimal model that minimizes the power rating, given the product size, feed size, and capacity. The study utilizes the `skfuzzy` library in Python to implement the fuzzy logic system. A dataset containing various model samples along with their corresponding feed size, product size, capacity, and power rating is loaded from a CSV file. Fuzzy membership functions are defined for the input variables: feed size, product size, and capacity, as well as the output variable: power rating. Fuzzy rules are established to determine the relationship between the input and output variables. The fuzzy control system is created and simulated to evaluate the power rating for each data sample. The model with the lowest power rating is identified as the optimal },
keywords = {fuzzy logic, optimal model, feed size, product size},
month = {October},
}