(166a) Heat Exchanger Fouling and Cleaning Analysis for Complex Network

Heat exchanger fouling has a direct impact on plant profitability. To combat fouling, heat exchangers must be periodically removed from service and cleaned. Frequent cleaning substantially increases cleaning expenditures. However, cleaning too infrequently has the potential to increase energy consumption due to limited heat transfer capabilities of the fouled heat exchangers. In both cases, operating cost goes up.

Many refineries conduct cleaning on a pre-determined schedule. Some only perform cleaning when operating problems, such as hydraulic restrictions or unacceptably high rundown temperatures, become apparent. Others do periodic monitoring of individual heat exchanger performance to identify the most badly fouled heat exchangers. However, a heat exchanger is not necessarily worth cleaning simply because it is heavily fouled. In case of a crude unit, heat exchangers at the front-end of the preheat train have very limited impact on the temperature of the crude oil by the time it reaches the atmospheric heater, as a loss of heat recovery in an upstream heat exchanger is usually offset by increases in heat recovery in downstream heat exchangers. Hence, in addition to individual heat exchanger monitoring, it is also necessary to analyze the knock-on effects throughout the heat exchanger network.

This paper presents heat exchanger fouling and cleaning analysis of a crude preheat train with the objective of mitigating fouling effects through improved cleaning schedules. In this analysis, rigorous heat exchanger network simulation of a crude preheat train at various fouling conditions is performed in UniSim to account for heat exchanger interactions and identify critical heat exchangers in terms of fouling effects that need to be monitored closely. In addition, UniSim simulation results are coupled with economic factors, other asset conditions and operational scenarios to optimize cleaning schedules resulting in overall economic savings (OPEX reduction). The findings from this analysis also provide valuable insights for maintenance scheduling, turnaround planning, and process improvement.