(314g) Modeling and Optimization of Industrial Ethylene Oxide Reactor for Deploying Real-Time Optimization Control Layer | AIChE

(314g) Modeling and Optimization of Industrial Ethylene Oxide Reactor for Deploying Real-Time Optimization Control Layer

Authors 

Sarrafi, S. - Presenter, Petkim Petrokimya Holding, A.?.
Spatenka, S., Process Systems Enterprise Ltd.
Patel, M., Process Systems Enterprise Ltd.
Arkun, Y., Koc University
Kabak, Y. S., Petkim Petrokimya Holding, A.?.
Avci, A. K., Bo?aziçi University
Ethylene oxide (EO), a precursor to ethylene glycol (EG), is mass-produced in the EO/EG Plant of the Petkim Petrochemical Complex, the sole petrochemical manufacturer of Turkey, via the selective oxidation of ethylene with oxygen using an Ag-based catalyst supported onto α-Al2O3. The EO production process involves the use of two parallel, operating fixed-bed multi-tubular reactors at temperatures and pressures of 240–260 °C and 17–18 bar, respectively. The main reaction is the ethylene epoxidation reaction with the undesirable side reactions being the ethylene and EO combustion reactions, both of which lower the EO product yields and increase CO2 emissions from the plant. The aim of this study is to build a rigorous model in order to optimize the industrial EO reactor system by using the gPROMS Process Builder.

This presentation will highlight the modeling steps for optimizing EO reactor in the EO/EG Plant. The determination of the initial catalyst activity will be discussed in detail. It has been shown that the activity of the commercial catalyst varies through the earlier stage of its lifetime. Thus, the reactor will be modeled according to the Start-of-Run (SOR) conditions since this model will be used for real-time optimization (RTO). The modeling strategies will take into consideration the heat transfer coefficients and pressure drops in the reactor system. A pool boiling model for the organic coolant system, which is very important to predict catalyst bed temperatures, will be addressed. Data reconciliation (DR) on the results of the industrial gas chromatography measurements will be introduced. The model-based DR approach that was used in both parallel reactors will be explained. The connection between the lab-rig model and the industrial reactor model in the gPROMS Process Builder will be elucidated. Finally, optimization strategies for effectively handling critical control parameters in the EO reactor will be discussed.

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