Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry

By Qingfei Min, Yangguang Lu, Zhiyong Liu, Chao Su, Bo Wang. International Journal of Information Management, 2019

380 citations recorded by the source corpus.

Abstract Digital 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 dynamically 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.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2c103500ca66a6469c53995260f6565a208f4724

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In Industrial and Manufacturing Engineering

Citations in the corpus

The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, background): “Likewise, Min et al. [164] conveyed a rise in the oil yield ratio due to a petrochemical industry DT.”
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, methodology): “In [55], four ML algorithms (Random Forest, AdaBoost, LightGBM, and XGBoost) were able to learn from the equipment’s sensor data and optimise production yield in the petrochemical industry.”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “2020), oil refinery management (Min et al. 2019), risk identification, and city planning (Lu et al.”
All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “design a digital twin framework for the petrochemical industry to optimise the production control [253].”
Emerging opportunities and challenges for the future of reservoir computing (2024, background): “Six domains are 6G 136 – 140 , Next Generation (NG) Optical Networks 92,93,141 – 143 , Internet of Things (IoT) 61,144,145 , Green Data Center 120,146,147 , Intelligent Robots 148 – 150 and AI for Science 151 – 157 and Digital Twins 99,103,158 – 161 .”
Artificial intelligence in operations management and supply chain management: an exploratory case study (2021, methodology): “The Data Collection Infrastructure is designed for the cen-tralisation of collecting real-time and historical data to be used by the model training (Min et al. 2019).”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, methodology): “Typical supervised learning methods used in digital twin include supper vector machine (SVM) [195,201], decision trees [86,93,94], k-nearest neighbors [102], convolutional neural networks (CNN) [103,135,202,206,270] and recurrent neural networks (RNN) [202].”
Digital Twin: Enabling Technologies, Challenges and Open Research (2020, methodology): “The work uses a case study to evaluate the pros and cons of using machine learning and Digital Twins for the petrochemical industry, applying each to an industrial IoT petrochemical factory [35].”
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions (2020, background): “One work that explores the digital twin with machine learning is [53].”