A new continuous catalytic reforming model was configured by using a molecule-based reactor module. Themodel was based on the Sinopec Research Institute of Petroleum Processing Co., Ltd. continuous catalytic reformer ...A new continuous catalytic reforming model was configured by using a molecule-based reactor module. Themodel was based on the Sinopec Research Institute of Petroleum Processing Co., Ltd. continuous catalytic reformer fullmodel, and was reduced to a size of 157 naphtha molecules (C1−C12) that underwent 764 reactions. The new model inheritedthe advantages of the original model, and had better solving performance and flexibility owing to support by the AspenHYSYS environment. Typical commercial plant data were selected for model validation, which showed advantages in theaccuracy of detailed predictions and the range of its application. In addition, the solving time was reduced from minutes toseconds. Therefore, the simplified model proved to be feasible for industrial application.展开更多
In this work,Digital Twins based on Neural Networks for the steady state production of styrene were generated.Thus,both the Aspen Technology AI Model Builder(alternative 1)and a homemade MS Excel VBA code connected to...In this work,Digital Twins based on Neural Networks for the steady state production of styrene were generated.Thus,both the Aspen Technology AI Model Builder(alternative 1)and a homemade MS Excel VBA code connected to Aspen HYSYS and Aspen Plus(alternative 2)were used with this same aim.The raw data used for generating the Digital Twins were obtained from process simulations using Aspen HYSYS and/or Aspen Plus,which were connected through a recycle-like stream via automation for solving the entire simulation flowsheet.Aspen HYSYS was used for solving the pre-heating,reaction,and stabilization sections of the process whereas Aspen Plus ensured the computing of the separation and purification columns.Both alternatives led to an excellent prediction showing the capability of creating Digital Twins from and for process simulation.展开更多
基金The authors acknowledge collaboration with and support from AspenTech via the National Key R&D Program of China(2021YFA1501201).
文摘A new continuous catalytic reforming model was configured by using a molecule-based reactor module. Themodel was based on the Sinopec Research Institute of Petroleum Processing Co., Ltd. continuous catalytic reformer fullmodel, and was reduced to a size of 157 naphtha molecules (C1−C12) that underwent 764 reactions. The new model inheritedthe advantages of the original model, and had better solving performance and flexibility owing to support by the AspenHYSYS environment. Typical commercial plant data were selected for model validation, which showed advantages in theaccuracy of detailed predictions and the range of its application. In addition, the solving time was reduced from minutes toseconds. Therefore, the simplified model proved to be feasible for industrial application.
基金V.R.F.thanks to the Aspen Technology Inc.the possibility to participate in the training course“EHM 101:Introduction to Aspen Hybrid Models for Engineering”,where,during the trial time available for AIMB he carried out the case presented in the current paper.
文摘In this work,Digital Twins based on Neural Networks for the steady state production of styrene were generated.Thus,both the Aspen Technology AI Model Builder(alternative 1)and a homemade MS Excel VBA code connected to Aspen HYSYS and Aspen Plus(alternative 2)were used with this same aim.The raw data used for generating the Digital Twins were obtained from process simulations using Aspen HYSYS and/or Aspen Plus,which were connected through a recycle-like stream via automation for solving the entire simulation flowsheet.Aspen HYSYS was used for solving the pre-heating,reaction,and stabilization sections of the process whereas Aspen Plus ensured the computing of the separation and purification columns.Both alternatives led to an excellent prediction showing the capability of creating Digital Twins from and for process simulation.
