Abstract Modern mining industry thrives for energy-efficient, clean and sustainable mining processes. The cemented paste backfill (CPB) technology, which may constitute 25–30% of the total mining cost, is no exception. One of the major bottlenecks for the current CPB design is that different steps were considered separately. No integrated design frameworks have been proposed, hindering the selection of the optimal CPB processing parameters. Towards this end, this study introduces an integrated and intelligent design framework for CPB (IIDF_CPB). The efficiency and accuracy of the proposed IIDF_CPB rely on two important parts. For one thing, robust machine learning (ML) modelling from constituent materials/processing parameters to performance indicators is established. Accurate ML modelling can save lots of time and substantially reduce the number of lab experiments. For another, IIDF_CPB is inherently a multi-objective optimization problem where two or more objectives need to be optimized simultaneously. The methodology of IIDF_CPB is presented and its feasibility is validated using a comprehensive case study. In the case study, ML modelling is conducted using a hybrid method that combines gradient boosting regression tree (GBRT) and particle swarm optimization (PSO). The non-dominated sorting genetic algorithm II (NSGA-II) is employed to maximize two conflicting performance indicators, namely slump and unconfined compressive strength at 28 days (28-UCS). The case study shows that the GBRT-PSO is robust in the slump and 28-UCS predictions. The average correlation coefficient between experimental and predicted outputs is 0.970 for slump and 0.991 for UCS. NSGA-II is effective in the concurrent optimization of slump and 28-UCS, which determines the Pareto front and maintains the diversity of non-dominated points.

中文翻译：

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水泥浆回填一体化智能设计框架：稳健机器学习建模与多目标优化相结合

摘要 现代采矿业因节能、清洁和可持续的采矿过程而蓬勃发展。水泥浆回填 (CPB) 技术可能占总采矿成本的 25-30%，也不例外。当前 CPB 设计的主要瓶颈之一是分别考虑不同的步骤。没有提出集成设计框架，阻碍了最佳 CPB 处理参数的选择。为此，本研究介绍了一种集成的智能 CPB 设计框架（IIDF_CPB）。所提出的 IIDF_CPB 的效率和准确性取决于两个重要部分。一方面，建立了从组成材料/加工参数到性能指标的稳健机器学习 (ML) 建模。准确的 ML 建模可以节省大量时间并大大减少实验室实验的数量。另一方面，IIDF_CPB 本质上是一个多目标优化问题，需要同时优化两个或多个目标。介绍了 IIDF_CPB 的方法，并使用综合案例研究验证了其可行性。在案例研究中，ML 建模是使用结合梯度提升回归树 (GBRT) 和粒子群优化 (PSO) 的混合方法进行的。非支配排序遗传算法 II (NSGA-II) 用于最大化两个相互冲突的性能指标，即 28 天 (28-UCS) 的坍落度和无侧限抗压强度。案例研究表明，GBRT-PSO 在暴跌和 28-UCS 预测中是稳健的。实验和预测输出之间的平均相关系数对于坍落度为 0.970，对于 UCS 为 0.991。NSGA-II在slump和28-UCS的并发优化中是有效的，它决定了Pareto前沿并保持了非支配点的多样性。