Comminution circuits can be modelled using phenomenological models of individual unit operations to represent the operation performance. Process optimisation and block model variability analysis require millions of simulations to fully explore process efficiency through mine scheduling which is costly and time consuming. Surrogate modelling is a technique used in process engineering approach of approximating the behaviour of the underlying model by using a model which is computationally more feasible. Neural networks are a form of machine learning which can approximate complicated function mappings of inputs to outputs and are computationally parallelisable.

In this study, a neural network is used as a surrogate model to approximate a copper porphyry mine comminution circuit for faster simulations. The neural network was trained to predict throughput using data generated from the phenomenological models of the comminution circuit. The optimal neural network hyperparameters were determined using an evolutionary algorithm to minimise overfitting. The neural network predicted simulation results 3363 times quicker than phenomenological models with errors of 0.37%, 0.55% and 0.45% across three different test sets. The neural network only required a stratified training sample of 1 in 1000 data to interpolate the rest of the data.

可以使用单个单元操作的现象学模型对粉碎回路进行建模，以表示操作性能。过程优化和块模型可变性分析需要进行数百万次模拟，才能通过成本高昂且耗时的矿山调度来充分探索过程效率。代理建模是在过程工程方法中使用的一种技术，通过使用在计算上更可行的模型来近似底层模型的行为。神经网络是机器学习的一种形式，它可以逼近输入到输出的复杂函数映射，并且在计算上是可并行化的。

在这项研究中，神经网络用作替代模型来近似铜斑岩矿粉碎电路，以加快模拟速度。神经网络经过训练，可以使用从粉碎电路的现象学模型中生成的数据来预测吞吐量。使用进化算法确定最佳神经网络超参数以最小化过度拟合。神经网络预测模拟结果比现象学模型快 3363 倍，在三个不同的测试集上的误差分别为 0.37%、0.55% 和 0.45%。神经网络只需要 1000 个数据中 1 个的分层训练样本来插值其余数据。