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Maximizing margins and optimizing operational conditions for residue fluid catalytic cracking with an artificial intelligence hybrid reaction model
参考中译:基于人工智能混合反应模型的渣油催化裂化边际最大化及操作条件优化


          

刊名：Journal of Advanced Manufacturing and Processing
作者：Eiji Kawai（Refinery, Petrochemical & New Energy Process Engineering Department, Technological & Engineering Division, Chiyoda Corporation）
Hideki Sato（Digital Products Department, Digital Transformation Division, Chiyoda Corporation）
Kazuya Furuichi（Digital Products Department, Digital Transformation Division, Chiyoda Corporation）
Takatsuka Toru（Refinery, Petrochemical & New Energy Process Engineering Department, Technological & Engineering Division, Chiyoda Corporation）
Toshio Yoshioka（Digital Products Department, Digital Transformation Division, Chiyoda Corporation）
刊号：810B0157/I
出版年：2022
年卷期：2022, vol.4, no.3
页码：10118-1--10118-20
总页数：20
分类号：TQ
关键词：AI；Deactivation；Lump model；Machine learning；Optimization；RFCC
参考中译：人工智能；停用；集总模型；机器学习；优化；RFCC
语种：eng
文摘：Because of the recent declining demand for gasoline, the key to making refineries competitive is to maximize the yields of propylene and aromatics by converting heavier feedstock into basic petrochemicals through the residue fluid catalytic cracking (RFCC) process. This study presents an artificial intelligence (AI) hybrid reaction model to optimize the catalyst make-up rate and maximize the product yield in a real-time operation by (1) developing a catalyst activity evaluation method, (2) integrating the catalyst to oil (Cat/Oil) ratio to evaluate the reaction performance, and (3) incorporating the yield prediction model into the latest digital technologies. To this end, the catalyst deactivation function, which uses a deep neural network of the basic machine learning method, was added to the past RFCC reaction model. Under actual operational conditions, this study shows that the AI hybrid reaction model using the catalyst deactivation function can minimize catalyst loss and produce an accurate yield prediction as a production planning support tool.
参考中译：由于最近对汽油的需求下降，使炼油厂具有竞争力的关键是通过渣油催化裂化(RFCC)工艺将更重的原料转化为基础石化产品，从而最大限度地提高丙烯和芳烃的产量。本研究提出了一种人工智能(AI)混合反应模型，通过(1)开发一种催化剂活性评价方法，(2)结合催化剂与油(Cat/Oil)的比例来评价反应性能，以及(3)将产率预测模型融入最新的数字技术中，从而在实时操作中优化催化剂的补充率并最大化产品收率。为此，在过去的重油催化裂化反应模型中加入了催化剂失活函数，该函数采用了深度神经网络的基本机器学习方法。在实际操作条件下，本研究表明，使用催化剂失活函数的AI混合反应模型可以最大限度地减少催化剂损失，并产生准确的产率预测，作为生产计划的支持工具。



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