Smart Surrogate Models for Design and Operation of Efficient and Sustainable Comminution Units

在粉碎方面，计算机模拟通常用于测试单元设计，以确保在各种操作条件下可靠运行。由于磨矿机内部复杂的过程动态，再加上不可避免的进料特性变化，使其变得更加复杂，因此开发一个能够捕捉特定单元独特特征的模型是一项具有挑战性的任务。磨矿机典型的多相流的时空分辨率要求使得数值模拟的计算成本很高，这通常超出了采矿业中许多人的能力范围。为了使磨机的多物理场模拟在设计、优化和控制方面更加实用，仿真技术的范式转变是必要的。这可以通过开发和应用基于混合物理和数据驱动的代理模型来实现，这些模型一方面可以在模型准确性或复杂性之间取得平衡，另一方面可以考虑实际操作。数据分析和机器学习的进步使得构建数据拟合的代理模型成为可能，这些模型可以复制用于其构建的数值结果的行为。智能代理建模利用机器学习的模式识别能力来构建强大的工具，以更少的计算成本来预测系统的行为。在这个研究项目中，将采用一种跨学科的方法，结合机器学习、基于物理的模拟和实时数据分析，来开发一个强大的磨机替代模型。该研究计划的主要目标是开发和验证一个完整的数字复制工厂，它将实时执行，并在可变操作条件下提供前所未有的可见性。将开发数据驱动的智能代理模型，以高精度和更快的执行时间模拟数值模拟的结果。这种独特的基于工程的数据分析方法将利用人工神经网络与监督模糊聚类分析相结合，以确定训练过程中最具影响力的参数，并确定用于训练、校准和验证的数据的最佳划分。提出的研究将大大提高我们的能力，以找到最优参数向量，设计和操作，并评估整个设计空间的磨机的整体行为。这将有助于更好地理解输入和输出参数之间的依赖关系，从而有助于优化研磨操作，减少能源和水的消耗，从而产生显著的经济节约。最后，该项目将有助于HQP在采矿和矿物加工、统计数据分析和可视化、机器学习以及高级过程建模和控制等领域的教育和培训。

In comminution, computer simulations are commonly used to test unit designs to ensure reliable operation under a variety of operating conditions. Due to complex process dynamics inside the grinding mills, which are further complicated by unavoidable variations in ore feed properties, it is a challenging task to develop a model that can capture unique characteristics of a particular unit. The spatial and temporal resolution requirements for multiphase flows typical for grinding mills make numerical simulations computationally expensive, which are often beyond the reach of many in the mining industry. A paradigm shift in simulation technology is needed to make multi-physics simulations of grinding mills more practical for design, optimization, and control purposes. This can be achieved through the development and application of hybrid physics-based and data-driven surrogate models that can strike a balance between model accuracy or complexity on the one hand, and practical operational considerations on the other. Advances in data analytics and machine learning have enabled the possibility of construction of data-fitted surrogate models that can duplicate the behavior of the numerical results used for their construction. Smart surrogate modeling takes advantage of pattern recognition capabilities of machine learning to build powerful tools to predict the behavior of a system with a far less computational cost. In this research program, an interdisciplinary approach combining machine learning, physics-based simulations, and real-time data analytics will be used to develop a robust surrogate model of a grinding mill. The main goal of this research program is to develop and validate a complete digital replica of a mill, which will be executed in real-time and provide unprecedented visibility into the process under variable operating conditions. A data-driven smart surrogate model will be developed to mimic results from numerical simulations with high accuracy and faster execution time. This unique engineering-based data analytics approach will utilize artificial neural networks in conjunction with supervised fuzzy cluster analysis to identify the most influential parameters for the training process and identify the optimum partitioning of the data for training, calibration, and validation. The proposed research will significantly enhance our capability to find optimal parameter vectors, both design and operational, and asses the global behavior of grinding mills over the entire design space. This will lead to improved understanding of dependencies between input and output parameters, which will in turn help optimize grinding operations, decrease energy and water consumption, and therefore produce significant economic savings. Finally, this program will contribute to the education and training of HQP in the fields of mining and minerals processing, statistical data analysis and visualization, machine learning, and advanced process modeling and control.