油砂
环境科学
控制(管理)
石油工程
工艺工程
地质学
计算机科学
材料科学
工程类
沥青
人工智能
复合材料
作者
Shima Khatibisepehr,Biao Huang,Elom Ayih Domlan,Elham Naghoosi,Yu Zhao,Yu Miao,Xinguang Shao,Swanand Khare,Marziyeh Keshavarz,Enbo Feng,Fangwei Xu,Aris Espejo,Ramesh Kadali
摘要
Abstract Oil sands development is both a costly and technically complex business with potential concerns in land use, water consumption and greenhouse gas emissions. Therefore, it is of practical interest to further investigate novel techniques to improve profitability while diligently maintaining environmental compliance. Our approach for finding solutions to achieve this objective is to develop innovative strategies for advanced monitoring, optimisation and control of plant operations. Development of reliable process models is a key requirement for investigating the behaviour of complex systems. Such descriptive models can help to improve analysis, simulation, optimisation, design, control and operation of process systems at both micro and macro levels. This paper presents a summary of some of the successful applications focussed on development and implementation of inferential process models, also known as soft sensors for oil sands processes.
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