摘要
Predicting the best shutdown time of a steam ethylene cracking furnace in industrial practice remains a challenge due to the complex coking process. As well known, the shutdown time of a furnace is mainly determined by coking condition of the transfer line exchangers （TLE） when naphtha or other heavy hydrocarbon feedstocks are cracked. In practice, it is difficult to measure the coke thickness in TLE through experimental method in the complex industrial situation. However, the outlet temperature of TLE （TLEOT） can indirectly characterize the coking situation in TLE since the coke accumulation in TLE has great influence on TLEOT. Thus, the TLEOT could be a critical factor in deciding when to shut down the furnace to decoke. To predict the TLEOT, a paramewic model was proposed in this work, based on theoretical analysis, mathematic reduction, and parameters estimation. The feasibility of the proposed model was further checked through industrial data and good agreements between model prediction and industrial data with maximum deviation 2% were observed.
Predicting the best shutdown time of a steam ethylene cracking furnace in industrial practice remains a challenge due to the complex coking process. As well known, the shutdown time of a furnace is mainly determined by coking condition of the transfer line exchangers (TLE) when naphtha or other heavy hydrocarbon feedstocks are cracked. In practice, it is difficult to measure the coke thickness in TLE through experimental method in the complex industrial situation. However, the outlet temperature of TLE (TLEOT) can indirectly characterize the coking situation in TLE since the coke accumulation in TLE has great influence on TLEOT. Thus, the TLEOT could be a critical factor in deciding when to shut down the furnace to decoke. To predict the TLEOT, a parametric model was proposed in this work, based on theoretical analysis, mathematic reduction, and parameters estimation. The feasibility of the proposed model was further checked through industrial data and good agreements between model prediction and industrial data with maximum deviation 2% were observed.
基金
Supported by the Major State Basic Research Development Program of China (2012CB720500)
the National Natural Science Foundation of China (U1162202, 21276078)
the National Science Fund for Outstanding Young Scholars (61222303)
the Shanghai Key Technologies R&D Program (12dz1125100)
the Shanghai Leading Academic Discipline Project (B504)
