Academic Journal
Open Python-Based Simulation and MOPSO Multiobjective Optimization of a Rod Mill–Hydrocyclone–Ball Mill Circuit.
| Τίτλος: | Open Python-Based Simulation and MOPSO Multiobjective Optimization of a Rod Mill–Hydrocyclone–Ball Mill Circuit. |
|---|---|
| Συγγραφείς: | Méndez-Gordillo, Alma Rosa, Arreola-Villa, Sixtos A., Vergara-Hernández, Héctor Javier, Vázquez-Gómez, Octavio, González-Juárez, Julio César, Pacheco-Cedeño, José Sergio |
| Πηγή: | Processes; Aug2026, Vol. 14 Issue 15, p2529, 22p |
| Θεματικοί όροι: | Ball mills, Multi-objective optimization, Python programming language, Machine separators, Grinding machines, Particle swarm optimization |
| Περίληψη: | Comminution–classification circuits are difficult to optimize because hydraulic, granulometric, energy, and economic responses are nonlinearly coupled, while circuit simulation, equipment sizing, simulator benchmarking, and operating optimization are often treated separately. This study aimed to develop an open Python framework for steady-state simulation and five-objective optimization of a rod mill–hydrocyclone–ball mill circuit processing a gold ore. The framework integrates solid and water balances, Rosin–Rammler particle-size reconstruction, comminution and hydrocyclone models, preliminary equipment sizing, explicit feasibility constraints, and Multiobjective Particle Swarm Optimization (MOPSO). Its novelty lies in coupling complete-circuit simulation, simulator-to-simulator benchmarking against USIM PAC®, model-based sizing, convergence diagnostics, and Pareto optimization within one transparent workflow. The benchmark produced zero or below 10 − 3 % errors in solid balances and sizing differences of 1.07%, 8.21%, and 0.00% for the rod mill, ball mill, and hydrocyclone, respectively. Relative to the base case, the joint minimum-water, minimum-energy, and minimum-cost solution reduced specific water consumption by 24.50%, specific grinding energy by 4.24%, specific operating cost by 10.19%, and mass recirculation by 8.80%, while useful recovery decreased slightly from 82.67% to 81.78%. The maximum-recovery solution increased useful recovery to 84.45%, with higher water, energy, and operating-cost requirements. The framework supports reproducible evaluation of resource–recovery trade-offs in grinding–classification circuits. [ABSTRACT FROM AUTHOR] |
| Copyright of Processes is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 196243634 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42456054688 |
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| Items | – Name: Title Label: Title Group: Ti Data: Open Python-Based Simulation and MOPSO Multiobjective Optimization of a Rod Mill–Hydrocyclone–Ball Mill Circuit. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Méndez-Gordillo%2C+Alma+Rosa%22">Méndez-Gordillo, Alma Rosa</searchLink><br /><searchLink fieldCode="AR" term="%22Arreola-Villa%2C+Sixtos+A%2E%22">Arreola-Villa, Sixtos A.</searchLink><br /><searchLink fieldCode="AR" term="%22Vergara-Hernández%2C+Héctor+Javier%22">Vergara-Hernández, Héctor Javier</searchLink><br /><searchLink fieldCode="AR" term="%22Vázquez-Gómez%2C+Octavio%22">Vázquez-Gómez, Octavio</searchLink><br /><searchLink fieldCode="AR" term="%22González-Juárez%2C+Julio+César%22">González-Juárez, Julio César</searchLink><br /><searchLink fieldCode="AR" term="%22Pacheco-Cedeño%2C+José+Sergio%22">Pacheco-Cedeño, José Sergio</searchLink> – Name: TitleSource Label: Source Group: Src Data: Processes; Aug2026, Vol. 14 Issue 15, p2529, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ball+mills%22">Ball mills</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+separators%22">Machine separators</searchLink><br /><searchLink fieldCode="DE" term="%22Grinding+machines%22">Grinding machines</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Comminution–classification circuits are difficult to optimize because hydraulic, granulometric, energy, and economic responses are nonlinearly coupled, while circuit simulation, equipment sizing, simulator benchmarking, and operating optimization are often treated separately. This study aimed to develop an open Python framework for steady-state simulation and five-objective optimization of a rod mill–hydrocyclone–ball mill circuit processing a gold ore. The framework integrates solid and water balances, Rosin–Rammler particle-size reconstruction, comminution and hydrocyclone models, preliminary equipment sizing, explicit feasibility constraints, and Multiobjective Particle Swarm Optimization (MOPSO). Its novelty lies in coupling complete-circuit simulation, simulator-to-simulator benchmarking against USIM PAC<superscript>®</superscript>, model-based sizing, convergence diagnostics, and Pareto optimization within one transparent workflow. The benchmark produced zero or below 10 − 3 % errors in solid balances and sizing differences of 1.07%, 8.21%, and 0.00% for the rod mill, ball mill, and hydrocyclone, respectively. Relative to the base case, the joint minimum-water, minimum-energy, and minimum-cost solution reduced specific water consumption by 24.50%, specific grinding energy by 4.24%, specific operating cost by 10.19%, and mass recirculation by 8.80%, while useful recovery decreased slightly from 82.67% to 81.78%. The maximum-recovery solution increased useful recovery to 84.45%, with higher water, energy, and operating-cost requirements. The framework supports reproducible evaluation of resource–recovery trade-offs in grinding–classification circuits. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Processes is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/pr14152529 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 2529 Subjects: – SubjectFull: Ball mills Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Machine separators Type: general – SubjectFull: Grinding machines Type: general – SubjectFull: Particle swarm optimization Type: general Titles: – TitleFull: Open Python-Based Simulation and MOPSO Multiobjective Optimization of a Rod Mill–Hydrocyclone–Ball Mill Circuit. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Méndez-Gordillo, Alma Rosa – PersonEntity: Name: NameFull: Arreola-Villa, Sixtos A. – PersonEntity: Name: NameFull: Vergara-Hernández, Héctor Javier – PersonEntity: Name: NameFull: Vázquez-Gómez, Octavio – PersonEntity: Name: NameFull: González-Juárez, Julio César – PersonEntity: Name: NameFull: Pacheco-Cedeño, José Sergio IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22279717 Numbering: – Type: volume Value: 14 – Type: issue Value: 15 Titles: – TitleFull: Processes Type: main |
| ResultId | 1 |