Modeling and optimization of high chromium alloy wear in phosphate laboratory grinding mill with fuzzy logic and particle swarm optimization technique

Kor, Mohammad; Abkhoshk, Emad; Tao, D.; Chen, G.L.; Modarres, Hamidreza

doi:10.1016/j.mineng.2010.04.009

Cited by 19 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 56 datasets were collected and randomly divided into training (40 datasets: 70%), validation (8 datasets: 15%), and training (8 datasets: 15%) datasets. For the milling study, 56 experimental datasets are considered more than enough [17][18][19]. The used datasets split% is common in the literature [7,20,21].…”

Section: Methods-ann Model Predicting Percentage Passing Cumulativementioning

confidence: 99%

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

Otsuki

Jang

2022

ChemEngineering

View full text Add to dashboard Cite

High energy consumption in size reduction operations is one of the most significant issues concerning the sustainability of raw material beneficiation. Thus, process optimization should be done to reduce energy consumption. This study aimed to investigate the applicability of artificial neural networks (ANNs) to predict the particle size distributions (PSDs) of mill products. PSD is one of the key sources of information after milling since it significantly affects the subsequent beneficiation processes. Thus, precise PSD prediction can contribute to process optimization and energy consumption reduction by avoiding over-grinding. In this study, coal particles (−2 mm) were ground with a rod mill under different conditions, and their PSDs were measured. The variables studied included volume% (vol.%) of feed (coal particle), vol.% rod load, and grinding time. Our supervised ANN models were developed to predict PSDs and trained by experimental data sets. The trained models were verified with the other experimental data sets. The results showed that the PSDs predicted by ANN fitted very well with the experimental data after the training. Root mean squared error (RMSE) was calculated for each milling condition, with results between 0.165 and 0.965. Also, the developed ANN models can predict the PSDs of ground products under different milling conditions (i.e., vol.% feed, vol.% rod load, and grinding time). The results confirmed the applicability of ANNs to predict PSD and, thus the potential contribution to reducing energy consumption by optimizing the grinding conditions.

show abstract

Section: Methods-ann Model Predicting Percentage Passing Cumulativementioning

confidence: 99%

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

Otsuki

Jang

2022

ChemEngineering

View full text Add to dashboard Cite

show abstract

“…According to [25], the implementation of fuzzy analysis can be divided in three main steps: Fuzzification of input values, reasoning based on rules, and defuzzification for the system output. According to [26], fuzzification is a process that sorts numeric values in fuzzy sets. The reasoning based on fuzzy rules is easily understandable to humans due to the linguistic terms associated with fuzzy sets [27].…”

Section: Fuzzy Modelingmentioning

confidence: 99%

Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model

Alexandre

Lopes

Dotto

et al. 2018

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The grinding process is situated at the end of the machining chain, where geometric and dimensional characteristics and highquality surface are required. The constant use of cutting tool (grinding wheel) causes loss of its sharpness and clogging of the pores among the abrasive grains. In this context, the dressing operation is necessary to correct these and other problems related to its use in the process. Dressing is a reconditioning operation of the grinding wheel surface aiming at restoring the original condition and its efficiency. The objective of this study is to evaluate the surface regularity and dressing condition of the grinding wheel in the surface grinding process by means of digital signal processing of acoustic emission and fuzzy models. Tests were conducted by using synthetic diamond dressers in a surface grinding machine equipped with an aluminum oxide grinding wheel. The acoustic emission sensor was attached to the dresser holder. A frequency domain analysis was performed to choose the bands that best characterized the process. A frequency band of 25-40 kHz was used to calculate the ratio of power (ROP) statistic, and the mean and standard deviation values of the ROP were inputted to the fuzzy system. The results indicate that the fuzzy model was highly effective in diagnosing the surface conditions of the grinding wheel.

show abstract

“…Fuzzy control theory has been used for more than 50 years and has been widely used in the field of industrial control, achieving good results in different industries. In the field of mining, Mohammad evaluated the potential of fuzzy logic as an alternative method to the traditional statistical regression techniques for predicting the wear rate of high chromium alloy during phosphate grinding, The comparison of a fuzzy model and a regression model showed the superiority of the developed fuzzy model [12]. Ajaya Kumar Pani performed the modeling of a vertical roller mill used for clinker grinding using support vector regression (SVR), fuzzy inference and adaptiveneuro fuzzy inference (ANFIS) techniques [13].…”

Section: Introductionmentioning

confidence: 99%

Mill Feed Control System and Algorithm Based on Python

Zhang

Liú

et al. 2022

Minerals

View full text Add to dashboard Cite

Grinding is an important link in the process of mineral processing. It plays a vital role in mineral processing by optimizing the grinding process, improving the quality of grinding products and ensuring the follow-up operation indicators. In this paper, the Python language, intelligent theoretical control technology and mineral processing were combined to solve the problem of ore feeding control in mineral processing. Using error factor analysis, an extended control algorithm was designed. The NumPy library and data collected from the Yuan YangMou concentrator in China were used to quantitatively analyze the factors affecting the error of electronic belt scales. This paper introduces the use of Kalman filtering for electronic belt scale weight data to reduce the effect of noise and hence reduce errors. The factors affecting the process of mill feeding are also analyzed. The core ideas and methods of fuzzy control theory are summarized, and a Python-based fuzzy controller suitable for the mill feeding process that improves the overall robustness and accuracy of feeding system is implemented.

show abstract

Modeling and optimization of high chromium alloy wear in phosphate laboratory grinding mill with fuzzy logic and particle swarm optimization technique

Cited by 19 publications

References 45 publications

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model

Mill Feed Control System and Algorithm Based on Python

Contact Info

Product

Resources

About