Application of a Genetic Algorithm to Optimize Purification in the Zone Refining Process

Cheung, T.; Cheung, Noé; Tobar, Carlos Miguel; Caram, Rubens; Garcia, Amauri

doi:10.1080/10426914.2010.520787

Cited by 16 publications

(5 citation statements)

References 35 publications

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“…[ 173 ] They have also used a genetic algorithm with a numerical model for optimizing the operational parameters of zone refining and the computational results are found to be close in agreement with the experiments. [ 174 ] Silva‐Santos et al utilized two swarm AI approaches, which interact with a mathematical model for optimizing the zone refining purification process, and results from the computation agreed well with the experimental data. [ 175 ] Shang et al employed a machine learning model for optimizing the process parameters of a vertical zone refining system and they obtained an RMSE of 0.023 and R 2 value of 0.91 while fitting with the experimental data.…”

Section: Numerical Modeling and Simulation On Zone Refiningmentioning

confidence: 91%

A Review on the Zone Refining Process Technology toward Ultra‐Purification of Gallium for GaAs/GaN‐based Optoelectronic Device Applications

Ghosh,

Mani

2024

Cryst. Res. Technol.

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Ultrapure gallium up to 99.9999%/ 99.99999% (6N/7N) purity level is a highly demanding material needed for the growth of gallium‐based group III–V semiconductor compounds and optoelectronic devices. However, general extraction of gallium from Bayer liquor contains high impurity content and ultra‐purification of the same cannot be accomplished by a single step. Thus, the purpose of this review is to assess various purification processes for the production of ultra‐pure gallium and to critically examine its applications in the optoelectronics industry. Through this research survey, it is found that zone refining of the zone melting process stands tall over other methods in purifying materials even up to 13N. Hence, scientists are adopting detailed mathematical models and simulation tools for designing unique zone refining systems for material purification. Current‐day technology even adopts intelligence methods such as machine learning, which sheds light on the importance of different zone refining parameters that influence the purification process. Here, the practical aspects of zone refining and how the feedback from the theoretical models or performance prediction through intelligence methods can be effectively incorporated into practice have also been emphasized

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Section: Numerical Modeling and Simulation On Zone Refiningmentioning

confidence: 91%

A Review on the Zone Refining Process Technology toward Ultra‐Purification of Gallium for GaAs/GaN‐based Optoelectronic Device Applications

Ghosh,

Mani

2024

Cryst. Res. Technol.

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“…To rapidly find out better network parameters, some studies have used the Taguchi orthogonal array as the basis for network parameter configuration. In the case of applying the Taguchi [6,16,17].…”

Section: Using the Taguchi Methods To Obtain The Optimal Combination Omentioning

confidence: 99%

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Wang¹

2014

Materials and Manufacturing Processes

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The light guide plate (LGP) printing process is used to obtain the optimal optical brightness, which requires the optimization of the LGP printing process parameters. This study analyzed the optical brightness optimization parameter design. This study integrated the back-propagation neural network (BPN) and a revised genetic algorithm (GA) to identify the optimal experimental level combination of the optical brightness parameter design. The obtained optical brightness process capability C pk by the proposed method was 1.73, which was better than the process capability C pk of 1.65 obtained by using the Taguchi method.

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“…In the present work, a genetic algorithm is used to iteratively search for compositions that optimize desired properties as predicted by the neural-network model, both with constraints, optimizing existing alloys, and without constraints, freely searching for novel alloys. GAs have been established for some time as a powerful tool for materials science, 19,20 in particular being frequently used to optimize the properties of existing materials, 21,22 the parameters of processing techniques, 23,24 and to design entirely novel materials. 25,26 GAs have also been applied in the search for novel glass-forming alloy compositions; Bansal et al 27 applied a NN and GA to identify glassy structures in Cu-Zr alloys with good resistance to shear deformation, Sun et al 28 used a GA to search for energetically favoured packing motifs in glassy Cu-Zr and Al-Sm alloys, and Tripathi et al applied genetic programming to identify glass-forming ability criteria.…”

Section: Number Of Constituent Elementsmentioning

confidence: 99%

Evolutionary design of machine-learning-predicted bulk metallic glasses

Forrest

Greer

2023

Digital Discovery

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Application of a Genetic Algorithm to Optimize Purification in the Zone Refining Process

Cited by 16 publications

References 35 publications

A Review on the Zone Refining Process Technology toward Ultra‐Purification of Gallium for GaAs/GaN‐based Optoelectronic Device Applications

A Review on the Zone Refining Process Technology toward Ultra‐Purification of Gallium for GaAs/GaN‐based Optoelectronic Device Applications

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Evolutionary design of machine-learning-predicted bulk metallic glasses

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