Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations

Koh, Edwin J.Y.; Amini, E.; McLachlan, Geoffrey J.; Beaton, Nick

doi:10.1016/j.mineng.2021.107026

Minerals Engineering

2021

DOI: 10.1016/j.mineng.2021.107026

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Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations

Edwin J.Y. Koh

E. Amini²,

Geoffrey J. McLachlan

et al.

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Cited by 8 publications

References 19 publications

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Leveraging neural networks to estimate parameters with confidence intervals

Mathias,

Weir,

Corbett

et al. 2024

Can J Chem Eng

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This manuscript presents a proof of concept for the estimation of parameters in a bioprocess while providing reliable confidence intervals. Specifically, Bayesian inference is used to estimate the uncertainty in the prediction of a parameter due to the presence of measurement noise in the process. The resultant joint probability distribution is utilized to infer the confidence interval of the resultant estimates. This method is numerically applied using a technique known as nested sampling. This algorithm iteratively samples parameters from a pre‐determined range of values to compare model predictions and obtain a probability density function. One challenge typically associated with this algorithm is in the determination of the prediction error, especially when a high‐fidelity dynamic model is being utilized. For the motivating example in the present manuscript, where a high‐fidelity simulated bioprocess is being considered, the use of the high‐fidelity model provided by Sartorius AG as part of the estimation algorithm poses computational challenges. To overcome this challenge, a universal approximator such as a parameterized neural network is used. This neural network is designed to simulate the results of the first principles model (while also capturing the dependence of the model parameters on the output), and once trained can provide near instantaneous results making the use of nested sampling computationally tractable for the application. Simulation results demonstrate the feasibility and capability of the proposed approach.

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Leveraging neural networks to estimate parameters with confidence intervals

Mathias,

Weir,

Corbett

et al. 2024

Can J Chem Eng

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Study of water quality response to water transfer patterns in the receiving basin and surrogate model

Liu

You

2021

Environ Sci Pollut Res

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Smart manufacturing of nonferrous metallurgical processes: Review and perspectives

Sun

Dai

Huang

et al. 2022

Int J Miner Metall Mater

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Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations

Cited by 8 publications

References 19 publications

Leveraging neural networks to estimate parameters with confidence intervals

Leveraging neural networks to estimate parameters with confidence intervals

Study of water quality response to water transfer patterns in the receiving basin and surrogate model

Smart manufacturing of nonferrous metallurgical processes: Review and perspectives

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