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

用于高效和可持续粉碎装置的设计和运行的智能替代模型

• 批准号: RGPIN-2020-06969
• 负责人: Miskovic, Sanja
• 金额: $ 1.89万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718587
• 关键词: Smart; Surrogate; Models; Design; Operation

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.

在粉碎中，通常使用计算机模拟来测试单元设计，以确保在各种操作条件下可靠地运行。由于磨机内部复杂的过程动态，加之给矿特性不可避免的变化，使其更加复杂，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。磨粉机对多相流的空间和时间分辨率要求使得数值模拟的计算成本很高，这往往超出了采矿行业的许多人的能力范围。 为了使磨机的多物理仿真更实用于设计、优化和控制目的，需要对仿真技术进行范式转换。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，该模型一方面可以在模型精度或复杂性与实际操作考虑之间取得平衡。数据分析和机器学习的进步使构建符合数据的代理模型成为可能，这种模型可以复制用于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来预测系统的行为，而计算成本要低得多。 在这项研究计划中，将使用一种结合了机器学习、基于物理的模拟和实时数据分析的跨学科方法来开发磨机的稳健代理模型。该研究计划的主要目标是开发和验证磨机的完整数字复制品，该复制品将实时执行，并在不同的操作条件下提供前所未有的过程可见性。将开发一个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和有监督的模糊聚类分析来确定对训练过程影响最大的参数，并确定用于训练、校准和验证的数据的最佳分割。 拟议的研究将显著增强我们找到最佳参数向量的能力，包括设计和运行，并评估整个设计空间内磨机的整体行为。这将导致更好地了解输入和输出参数之间的依赖关系，这反过来将有助于优化磨削操作，降低能源和水消耗，从而产生显著的经济节约。最后，该计划将有助于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领域的教育和培训。