A Computationally Efficient Surrogate-Based Reduction of a Multiscale Comill Process Model

Metta, Nirupaplava; Ramachandran, Rohit; Ierapetritou, Marianthi G.

doi:10.1007/s12247-019-09388-2

Cited by 12 publications

(5 citation statements)

References 81 publications

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“…This approach has been applied in flotation [158], thickening [159,160], and comminution [161], among others. Usually, these works are based on CFD models, which consider several complex phenomena involved in the studied process.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

2019

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Multiphase systems are important in minerals processing, and usually include solid–solid and solid–fluid systems, such as in wet grinding, flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization in multiphase systems, are analyzed. Different scales of size and time are included, and therefore, the analysis includes modeling at the molecular level (molecular dynamic modeling) and unit operation level (e.g., computational fluid dynamic, CFD), and the application of optimization for the design of a plant. New strategies for the modeling, design, and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The integration of different modeling techniques such as CFD with discrete element simulation (DEM) and response surface methodology (RSM) with artificial neural networks (ANN) is included. The paper finishes with tools to study the uncertainty, both epistemic and stochastic, based on uncertainty and global sensitivity analyses, which is present in all mineral processing operations. It is shown that all of these areas are very active and can help in the understanding, operation, design, and optimization of mineral processing that involves multiphase systems. Future needs, such as meso-scale modeling, are highlighted.

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Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

2019

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“…Mathematical modeling of ball mills has been extensively explored over the last 70 years, starting from empirical correlations and semiempirical population balance models, , to high-fidelity discrete element method (DEM) models . First proposed by Cundall and Strack, DEM models have received considerable attention due their ability to describe the complex kinematics of moving entities and thus have been successfully used across various applications including the development of kinetic models for mechanochemical reactions. , In DEM, the position and energetics of each discrete entity are evaluated over short time scales considering the effect of the surrounding population and geometry to the forces acting on each entity. , Parameters such as the geometry, material properties, and processing conditions are necessary to develop accurate digital-twin models . Thus, DEM simulations provide a means to obtain a first-principles understanding of the ball milling grinding efficiency and the influence of mechanical factors on the performance of mechanochemical reactions.…”

Section: Introductionmentioning

confidence: 99%

“…To address this, surrogate models have been successfully employed to translate DEM process inputs to outputs. − Rogers and Ierapetritou used a Kriging surrogate model to represent velocity profiles from DEM simulation in blending application. In Metta et al, mechanistic data obtained from DEM simulations were mapped using Kriging and artificial neural network (ANN) surrogate models for a milling process, while in Barrasso et al, collision frequency from the DEM simulations are used as inputs into a population balance model using an ANN surrogate framework. The main benefit of developing surrogates is that once trained, they can be used to interpolate simulation results for various operating conditions for which the expensive simulations were not run (in this case DEM).…”

Section: Introductionmentioning

confidence: 99%

“… 39 , 50 In DEM, the position and energetics of each discrete entity are evaluated over short time scales considering the effect of the surrounding population and geometry to the forces acting on each entity. 40 , 51 Parameters such as the geometry, material properties, and processing conditions are necessary to develop accurate digital-twin models. 52 Thus, DEM simulations provide a means to obtain a first-principles understanding of the ball milling grinding efficiency and the influence of mechanical factors on the performance of mechanochemical reactions.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations

Anglou,

Chang,

Bradley

et al. 2024

ACS Sustainable Chem. Eng.

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Developing efficient and sustainable chemical recycling pathways for consumer plastics is critical for mitigating the negative environmental implications associated with their end-of-life management. Mechanochemical depolymerization reactions have recently garnered great attention, as they are recognized as a promising solution for solvent-free transformation of polymers to monomers in the solid state. To this end, physics-based models that accurately describe the phenomena within ball mills are necessary to facilitate the exploration of operating conditions that would lead to optimal performance. Motivated by this, in this paper we develop a mathematical model that couples results from discrete element method (DEM) simulations and experiments to study mechanically-induced depolymerization. The DEM model was calibrated and validated via video experimental data and computer vision algorithms. A systematic study on the influence of the ball-mill operating parameters revealed a direct relationship between the operating conditions of the vibrating milling vessel and the total energy supplied to the system. Moreover, we propose a linear correlation between the high-fidelity DEM simulation results and experimental monomer yield data for poly(ethylene terephthalate) depolymerization, linking mechanical and energetic variables. Finally, we train a reduced-order model to address the high computational cost associated with DEM simulations. The predicted working variables are used as inputs to the proposed mathematical expression which allows for the fast estimation of monomer yields.

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“…In the pharmaceutical field, ANN has been used to predict the amounts of active pharmaceutical ingredients in the dosage [12,13,16]. Recently, some researchers have extended ANN for predicting the parameters in the industrial production scale [17][18][19][20][21][22][23]. Meanwhile, the SVR model is a generalization of a well-known classification algorithm, called support vector machine (SVM), for prediction using kennel transformations and a series of linear equations to separate data by attribute.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Support Vector Regression Modeling for Prediction of Mixing Time in Wet Granulation

2021

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Purpose Every successful pharmaceutical product requires a rapid and reliable scale-up process from small laboratory quantities to a large commercial production to ensure the qualities of products. Despite best efforts, a pharmaceutical company must incur losses in material costs and working-hours before achieving optimal process parameters. One possible approach to shorten this tedious process is to leverage knowledge and experience using a computational algorithm that predicts the process parameters using related data. Method This study aimed to demonstrate the approach of embedded successful material attributes and process parameters of wet granulation as inputs into an artificial neural network (ANN) model and support vector regression (SVR) model to predict the total number of the impeller revolution for dry mixing and wet massing to produce the granules with the same qualities. ResultsThe SVR model performed better for prediction with RMSEs of 3.2552-4.0066. Using the SVR model using Gaussian kernel, which had the least RMSE, in the training stage and validation stage gave the values of MAPE of 17.12% and 12.32%, respectively. Conclusion The model can be implemented for the prediction of the number of impeller revolution by using the proposed parameters.

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A Computationally Efficient Surrogate-Based Reduction of a Multiscale Comill Process Model

Cited by 12 publications

References 81 publications

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations

Artificial Neural Network and Support Vector Regression Modeling for Prediction of Mixing Time in Wet Granulation

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