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Multiobjective Bayesian optimization on HPGR

Mellring, Moa LU and Lacinai, Elliot (2025) In Master's Theses in Mathematical Sciences FMSM01 20252
Mathematical Statistics
Abstract
When applying Bayesian optimization (BO) algorithms which utilizes Gaussian processes (GP) models, several components can be adapted to suit different systems and optimization objectives. In this paper, we explore and evaluate a set of such modifications to tailor the algorithm for
a high-pressure grinding rolls (HPGR) system. The system is represented by a steady-state simulator modeling the HPGR machine, used in ore comminution. The primary goal of the optimization is to evaluate the possibility of using Bayesian optimization on the HPGR-system to
generate recommendations for its control settings, and how the BO should be set up and tuned for this purpose. We investigate the effects of different covariance functions, mean functions,... (More)
When applying Bayesian optimization (BO) algorithms which utilizes Gaussian processes (GP) models, several components can be adapted to suit different systems and optimization objectives. In this paper, we explore and evaluate a set of such modifications to tailor the algorithm for
a high-pressure grinding rolls (HPGR) system. The system is represented by a steady-state simulator modeling the HPGR machine, used in ore comminution. The primary goal of the optimization is to evaluate the possibility of using Bayesian optimization on the HPGR-system to
generate recommendations for its control settings, and how the BO should be set up and tuned for this purpose. We investigate the effects of different covariance functions, mean functions, and acquisition functions on optimization performance by testing them on the default system
parameters, and validating the results on time series of varying feed material. Among the tested configurations, the final recommendation for the model settings were using the radial basis function (RBF) covariance and a constant mean function for all objectives and constraints. The
recommended acquisition function was a version of Expected Hypervolume Improvement (EHVI). (Less)
Popular Abstract
How can optimal settings be found for a machine whose inner workings are too complex to model directly? This study shows that Bayesian optimization, a method for exploring unknown “black-box” systems, can reliably guide a key mining machine toward safer, more efficient operation.
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author
Mellring, Moa LU and Lacinai, Elliot
supervisor
organization
course
FMSM01 20252
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Multi-objective optimization, Black-box optimization, Bayesian optimization (BO), Gaussian process (GP), high-pressure grinding rolls (HPGR), covariance function, mean-value function, acquisition function
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMS-3547-2025
ISSN
1404-6342
other publication id
2025:E109
language
English
id
9216380
date added to LUP
2025-12-22 15:12:00
date last changed
2025-12-22 15:12:00
@misc{9216380,
  abstract     = {{When applying Bayesian optimization (BO) algorithms which utilizes Gaussian processes (GP) models, several components can be adapted to suit different systems and optimization objectives. In this paper, we explore and evaluate a set of such modifications to tailor the algorithm for
a high-pressure grinding rolls (HPGR) system. The system is represented by a steady-state simulator modeling the HPGR machine, used in ore comminution. The primary goal of the optimization is to evaluate the possibility of using Bayesian optimization on the HPGR-system to
generate recommendations for its control settings, and how the BO should be set up and tuned for this purpose. We investigate the effects of different covariance functions, mean functions, and acquisition functions on optimization performance by testing them on the default system
parameters, and validating the results on time series of varying feed material. Among the tested configurations, the final recommendation for the model settings were using the radial basis function (RBF) covariance and a constant mean function for all objectives and constraints. The
recommended acquisition function was a version of Expected Hypervolume Improvement (EHVI).}},
  author       = {{Mellring, Moa and Lacinai, Elliot}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Multiobjective Bayesian optimization on HPGR}},
  year         = {{2025}},
}