Fouling and cleaning analysis of a heat exchanger network
Analysis of a complex heat exchanger network employs a digital twin model to establish a programme for fouling and cleaning.
Honeywell Connected Enterprise
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Maintaining good performance in a heat exchanger network is a major part of any energy efficiency programme. This article presents a fouling and cleaning analysis of a complex heat exchanger network in a crude preheat train, mitigating fouling effects through improved cleaning schedules. Various fouling conditions are simulated in Honeywell’s UniSim Design software to account for heat exchanger interactions and identify critical fouling effects that need to be monitored effectively. In addition, the simulation results are paired with economic factors, other asset conditions and operational scenarios to optimise cleaning schedules that can result in overall economic savings. The findings from this analysis also provide valuable insights for maintenance scheduling, turnaround planning, and process improvement.
For improved process energy efficiency, it is imperative to recover as much heat as possible from the product stream back into the feed stream to minimise the use of fresh energy. However, heat exchangers in oil refineries are at high risk of fouling, which significantly reduces their thermal performance over time. Fouling in oil and refinery plants consumes an extra 0.2 quad (0.2 x 1015 BTU) of energy annually.1 The annual loss attributable to heat exchanger fouling in the US and UK together is $16.5 billion.2,3 Heat exchanger fouling has a direct impact on plant profitability and is one of the costliest problems facing the refining and chemical industries today.
To combat fouling, heat exchangers must be periodically removed from service and cleaned if bypass valves are available. Frequent cleaning substantially increases cleaning expenditures. However, cleaning too infrequently has the potential to increase energy consumption due to the limited heat transfer capabilities of fouled heat exchangers. In both cases, operating costs rise. Therefore, there is a trade-off between heat exchanger cleaning costs and fouling costs (see Figure 1).
Many refineries conduct heat exchanger 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 worst fouled heat exchangers. However, a heat exchanger is not necessarily worth cleaning simply because it is heavily fouled. In the 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. In a complex heat exchanger network, the criticality of individual heat exchanger fouling depends on the arrangement of the heat exchanger network, the size of a heat exchanger, and the available heat transfer driving force, which is best understood through simulation and analysis at a network level. Thus, in addition to individual heat exchanger monitoring, it is also necessary to analyse knock-on effects throughout the heat exchanger network.
Management of heat exchanger fouling consists of three elements: fouling prevention through modification of the process stream and a heat exchanger’s surface properties during the design stage; fouling detection through effective on-line monitoring; and fouling mitigation through fouling analysis and optimal cleaning schedules.
The most effective cleaning programme for a heat exchanger network is based on identifying and monitoring critical fouling heat exchangers, coupled with models that account for heat exchanger interactions, energy and cleaning costs to obtain overall savings. This analysis helps a refiner to make an informed decision about which heat exchanger to clean and when is the best time to perform cleaning operations. It provides solutions to identify critical heat exchange in terms of fouling impact, cleaning prioritisation and developing long-term optimal cleaning cycles.
Figure 2 is a block flow diagram of a solution workflow of this analysis. UniSim Design simulates a rigorous digital twin model of a heat exchanger network and serves as the main calculation engine at the back end to account for interactions in the heat exchanger network. The grey boxes represent inputs required for constructing a digital twin model and performing scenario analysis, which include a heat exchanger network process flow diagram (PFD), a piping and instrumentation diagram (P&ID), heat exchanger datasheets, performance monitoring, and prediction. Yellow boxes represent the various analysis outputs that help operators to make informed decisions about heat exchanger cleaning.
This solution does not require investment in new equipment. It is a software analysis using existing design data for a heat exchanger network and historical process data and focuses on improving decisions on cleaning schedules through rigorous simulation of the heat exchanger network, fouling effect analysis, cleaning benefit analysis, fouling predictions, a what-if analysis for different operational constraints and scenarios, and a comprehensive economic analysis.
The crude unit preheat train is a major factor in a refinery’s business operation and is used as an example of a complex heat exchanger network in the following discussion.
Results and discussion
Fouling effect analysis
In the crude preheat train, as heat exchangers foul over time the furnace coil inlet temperature decreases due to inefficient heat recovery from fouled heat exchangers. To maintain a constant furnace outlet temperature, the required furnace duty increases. However, the loss of heat recovery that has to be made up by the furnace is often less than 100% for most of the heat exchangers in the network. Loss of heat recovery in upstream heat exchangers means higher hot side temperature, bigger log mean temperature difference (LMTD) and more heat transfer driving force available for subsequent heat exchangers, and thus increases heat recovery in downstream heat exchangers in the network.
In the fouling effect analysis, the fouling condition of heat exchangers in the network is compared at a fouling percentage of 50% and 80%, respectively. The fouling percentage is defined by Equation 1:
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