Would you like to know:

	How much of your energy bill is due to fouling deposits?
	How long you have before fouling reduces your throughput?
	What you can do to avoid throughput losses caused by fouling?
	The optimal schedule for cleaning your preheat exchangers?
	The cost benefit of cleaning strategic exchangers between cleaning cycles?

To answer the above questions, Linnhoff March offers fouling monitoring and cleaning cycle optimisation for refineries.

Fouling monitoring service

Heat exchanger fouling profile

Fouling petroleum deposits in the preheat train gradually reduce heat transfer coefficients and thus your heat recovery. This means that the furnace inlet temperatures will drop over time causing energy costs to increase. Failure to correct this in time may cause a bottleneck in the furnace and could lead to even more costly losses in throughput. Fouling factors and trends, once determined accurately, can help to avoid these costs.

Heat transfer coefficients calculated from one data set can be misleading, because losses in heat transfer are usually due to a combination of factors (e.g. heavier crude). To filter out these interfering effects, you need rigorous calculations, taking into account varying feed and product compositions, etc. Long-term trending also ensures that errors are minimised.

Linnhoff March’s fouling monitoring service involves rigorous calculation of fouling factors, using plant data obtained from a site data historian. This is done in two steps:

	Data reconciliation to identify gross errors in measured flowrates and temperatures
	Use of reconciled properties to determine fouling factors

Results are presented in spreadsheet format.

Deliverables

Deliverables from this service are:

Reconciled measurements

These are useful for accounting purposes and for other process engineering studies such as Pinch Analysis.

Fouling trend

Similar to the one shown above, which can be used to assess the impact of fouling inhibitors and operational changes (crude mixes, varying flowrates, etc) on fouling rates.

The Cost of Fouling

The cumulative loss in heat recovery and process throughput will be quantified.

Cleaning cycle optimisation

The cumulative costs of fouling in terms of energy and throughput may justify additional cleaning of strategic exchangers or overall shorter cleaning cycles. The optimum cleaning cycle for individual exchangers or for the entire network can be determined by using established fouling trends and trading off the cost of fouling against the cost of cleaning.

Deliverables are:

	Cleaning strategy to overcome bottlenecks in furnace, condensers and pumparounds
	Cleaning strategy to maximise heat recovery and minimise fuel requirement

Why use Linnhoff March?

Trend calculations involve the handling of large amounts of data in a number of simulation programs. Setting up and carrying out these calculations is very time-consuming and leaves considerable room for human-error. Moreover, commercial programs for network simulation are unable to incorporate certain process features, such as pump-around flowrate limits, to accurately assess the cost of fouling.

It takes experienced engineers to incorporate the specific features of your crude unit into the analysis. Even for a standard unit, setting up the necessary data transfers and calculations requires significant effort. Linnhoff March has experienced process and development engineers who are familiar with all these issues.

Linnhoff March uses SimSci’s DATACON for data reconciliation and HEXTRAN for network rating and calculation of fouling factors. These software packages are known for their reliable physical properties databank for petroleum refining processes and have been used successfully for the studies carried out by Linnhoff March. However, our approach does not depend on the simulation packages and also works with other software that will perform these tasks.

Above all, Linnhoff March's knowledge of the interactions in refinery heat exchanger systems is second to none.