Simulation and optimization of a two-stage ball mill grinding circuit of molybdenum ore

Kwon, Jihoe; Jeong, Jinan; Cho, Heechan

doi:10.1016/j.apt.2016.03.016

Cited by 20 publications

(6 citation statements)

References 25 publications

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“…In fact, several investigators have presented convincing cases for the appropriateness of population balance models for use in scale-up and the optimization of ball mills [7][8][9][10][11][12][13][14]. In most cases, the results confirmed the validity of the linear population balance model for dry grinding and the specific power correlation that was previously observed by Herbst and Fuerstenau [15].…”

Section: Introductionsupporting

confidence: 74%

Optimization of Solids Concentration in Iron Ore Ball Milling through Modeling and Simulation

2019

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Important advances have been made in the last 60 years or so in the modeling of ball mills using mathematical formulas and models. One approach that has gained popularity is the population balance model, in particular, when coupled to the specific breakage rate function. The paper demonstrates the application of this methodology to optimize solids concentration in ball milling of an iron ore from Brazil. The wet grinding experiments were conducted in bench (0.25 m diameter) and pilot-scale mills (0.42 m diameter), and surveys in a full-scale industrial (5.2 m diameter) mill. It is first demonstrated that the successful application of the model required recognizing the non-normalizable nature of the breakage function of the particular ore. It is then demonstrated how the model can be used to predict results of pilot-scale grinding tests under different conditions (overflow/grate discharge) based on data from batch grinding tests. Finally, the model is used to predict the effect of changing solids concentration inside the industrial mill, with good correspondence between the pilot plant and full-scale results, which demonstrated the benefit of reducing solids concentration to values between 76 and 80% in weight for the ore of interest from the 83% that was originally used in the operation.

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Section: Introductionsupporting

confidence: 74%

Optimization of Solids Concentration in Iron Ore Ball Milling through Modeling and Simulation

2019

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“…Modeling and simulation of the cement grinding process are efficient tools for predicting and optimizing the process [7]. Modeling grinding circuits should consider several grinding operational variables such as the mill and separator details to name a few.…”

Section: Related Workmentioning

confidence: 99%

“…This brings the necessity of constructing end-to-end model mapping of the inputs and the outputs of a grinding plant, possibly needing a grey box or black box method approach to solve the problem. One efficient solution to these problems is building data-driven dynamic models for integrated grinding circuits [2,7]. These models are trained only on data collected from inlet and outlet streams of the grinding circuit and result in multiple-input-multiple-output (MIMO) system identification models.…”

Section: Related Workmentioning

confidence: 99%

Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

Danishvar

Daneshvar

Souza³

et al. 2021

Applied Sciences

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Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant.

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“…This work is based on the KG model developed by Austin, Luckie [6,7], and Reid [9]. The basic concept of the models has been used in numerous studies to characterize the grinding properties of various materials with various operating variables in ball mills, in terms of the breakage rate and the breakage distribution [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Error Distribution in the Back-Calculation of Breakage Function Model Parameters via Nonlinear Programming

Kwon

Cho

2021

Minerals

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Despite its effectiveness in determining breakage function parameters (BFPs) for quantifying breakage characteristics in mineral grinding processes, the back-calculation method has limitations owing to the uncertainty regarding the distribution of the error function. In this work, using Korean uranium and molybdenum ores, we show that the limitation can be overcome by searching over a wide range of initial values based on the conjugate gradient method. We also visualized the distribution of the sum of squares of the error in the two-dimensional parameter space. The results showed that the error function was strictly convex, and the main problem in the back-calculation of the breakage functions was the flat surface of the objective function rather than the occurrence of local minima. Based on our results, we inferred that the flat surface problem could be significantly mitigated by searching over a wide range of initial values. Back-calculation using a wide range of initial values yields BFPs similar to those obtained from single-sized-feed breakage tests (SSFBTs) up to four-dimensional parameter spaces. Therefore, by searching over a wide range of initial values, the feasibility of the back-calculation approach can be significantly improved with a minimum number of SSFBTs.

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Simulation and optimization of a two-stage ball mill grinding circuit of molybdenum ore

Cited by 20 publications

References 25 publications

Optimization of Solids Concentration in Iron Ore Ball Milling through Modeling and Simulation

Optimization of Solids Concentration in Iron Ore Ball Milling through Modeling and Simulation

Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

Investigation of Error Distribution in the Back-Calculation of Breakage Function Model Parameters via Nonlinear Programming

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