(220e) Heat Exchanger Fouling Monitoring and Cleaning Optimization Using Opticlean | AIChE

(220e) Heat Exchanger Fouling Monitoring and Cleaning Optimization Using Opticlean

Authors 

Davis, Jr., J. - Presenter, KBC Advanced Technologies, Inc
Polanco, D. - Presenter, KBC Advanced Technologies, Inc


Fouling petroleum deposits often found in crude unit preheat trains gradually reduce overall heat transfer coefficients, and thus heat recovery into the preheat train. This means that the furnace inlet temperature will drop over time, resulting in an increase in the furnace duty. This directly establishes the heating cost of fouling. In addition, the pressure drop will increase due to a reduction in flow diameter. This is compounded by an increase in velocity (for the same flowrate), resulting in a further increase in pressure drop. It has been common industry practice to clean crude preheat exchangers only when throughputs become constrained due to the increase in pressure drop. At today's fuel gas prices, however, waiting on crude unit rates to be impacted before cleaning is performed results in significant energy lost opportunity. The fact is, proper cleaning performed months before rates are impacted will result in real energy savings, enough to offset the exchanger cleaning costs and even the lost margin incurred due to having to slowdown the units during the several days that a particular exchanger bank is out of service for cleaning. Proper fouling monitoring tools and practices enable the impact of fouling on exchanger performance to be assessed. This assessment must include the determination of not only the proper timing for cleaning, but must be able to specify which exchanger (s) should be cleaned for maximum benefit.

Without being too much of a "sales pitch", this presentation will discuss a new methodology for fouling monitoring and cleaning frequency optimization using a new product by Linnhoff March, a division of KBC Advanced Technologies Inc. known as OptiClean. OptiClean is an Excel-based application that retrieves and reconciles data from the plant data historian, and using a model of the exchanger network, quantifies the effect of each individual exchanger performance on the coil inlet temperature to identify the critical exchangers, seen in the context of the overall network.

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2006 Spring Meeting & 2nd Global Congress on Process Safety
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