石油石化行业作为我国重要能源之一，是国民经济发展的重要基础。保障石油石化生产企业的安全高效生产是企业的重要目标。本文针对石油石化流程行业生产过程中存在的工况连续性、复杂多变性问题，引入数字孪生技术，通过构建流程行业的数字孪生物理模型、数字孪生工况模型，搭建数字孪生系统，以期提高企业生产效率，保障企业安全高效运行。研究发现，基于数字孪生系统开展原料组成优化、工艺参数设计与仿真、生产过程建模与优化控制、设备故障诊断与远程运维方面应用的示范性装置，产品质量、经济效益均有较大程度的提升，同时装置能耗有较大降低，在提高工厂生产效率，保障工厂安全高效运行的同时，为企业带来巨大的经济效益。
准确理解并精确预测多相反应器内复杂的流体力学特性、传递现象及反应特征，是过程工程领域的热点方向之一。随着试验测量技术及高性能计算机的快速发展，研究者可以获取高精度的多维瞬态流场数据集。近十年来，机器学习作为一门新兴学科，越来越广泛地应用于数据挖掘、图像识别、智能控制等领域。本文概述了几种常用的机器学习方法（包括神经网络模型、支持向量机模型、决策树模型、聚类算法模型等），总结了机器学习模型的构建过程（包括数据集的建立、特征变量的选择、算法框架的选取、模型参数的调优、模型验证与测试等），综述了机器学习辅助多相反应器中流场本构模型构建、流场图像重构、流型识别、流场关键参数预测及优化、不确定度分析、数字孪生技术平台等方面的应用进展，剖析了机器学习结合多相反应器领域所面临的挑战，展望了机器学习在多相反应器中可能有待拓展的方向。
gital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in petrochemical industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the petrochemical industry. This paper proposes a framework and approaches for constructing a digital twin based on the petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support petrochemical and other process manufacturing industries to dy-namically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the petrochemical industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the petrochemical industry.
Process Intensification Solutions for U.S. Manufacturing
May 3, 2017
SRNL Partners with AIChE and Georgia Tech in the New RAPID Institute
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