Heat exchanger fouling analytics

Modelling the effects of fouling on the performance of heat exchangers and its impact on product values as well as energy losses

Anukoolan Solutions

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Article Summary

In the case of a heat exchanger, fouling causes substantial energy losses, leading to less efficient heat exchange between the streams. It also increases resistance to fluid flow, resulting in higher pressure drops across the exchanger. As a result of the combination of these effects, the resultant temperature of the process stream is lower than the anticipated value. Furthermore, in a network of exchangers, the downstream exchangers’ behaviour could become counterintuitive. Usually as an upstream exchanger fouls, the driving forces on the downstream exchangers increase, so they could appear to perform better. However, the net effect usually is additional load on downstream furnaces and steam heaters.

In a traditional petroleum refinery, a number of these heat exchangers are connected in series, followed by a fired heater before the crude stream is allowed to enter the atmospheric and vacuum distillation columns. If any of the heat exchangers are fouled, the result will be a lower furnace inlet temperature which would require additional duty in the furnace to achieve the desired column inlet temperature.

The impact of fouling is not limited to furnace duty and energy consumption increase alone. At the end of the preheat train and beyond the furnace is a set of distillation columns which separates the useful products present in crude oil. Considering that everything is interconnected as a network, the column throughput, the condenser duty, and the product flows and compositions are affected by fouled exchangers. The column may no longer produce the desired quantity of useful products, leading to a bigger economic loss as well as a loss of energy. The aim of this article is to highlight the effects of fouling on the performance of heat exchangers, condensers, furnaces, and distillation.

Moreover, if the exchangers are not cleaned, not only is their performance reduced, but it can also lead to a failure of the equipment, resulting in a safety issue in some cases.

A fouling analysis was conducted to evaluate the effect of fouling on the performance of a typical preheat train and distillation columns, as well as to determine the detrimental effect of fouling on the economic value of the products.

Aspen Hysys was used for the simulation of a model required to perform the analysis in conjunction with Aspen Simulation Workbook (ASW) and Virtual Basic for Applications (VBA) in Microsoft Excel.

An equation oriented (EO) model was developed for data reconciliation with the help of an objective function to minimise the offset between plant and model values. Figure 1 shows the schematic of a preheat train with some measurements in their respective places for temperatures and flows.

As the schematic shows, a temperature measurement was added on the crude side (cold outlet) of each exchanger as well as on the product stream side (hot side) for some of the exchangers to control the outlet temperature of the crude stream. A flow measurement was installed on the HGO pumparound stream to monitor and control the effect of the hot side stream in this scenario.

An objective function was created, aiming to minimise the offset between the measured and calculated values for the measurement variables. Equation 1 shows the calculation for the objective function:

where ‘n’ stands for the number of offset value inputs in the objective function.
offset= Tp-Tm             (2)
where Tp and Tm stand for plant and model temperature values respectively.    
offset= Fp-Fm             (3)

where Fp and Fm stand for plant and model flow values respectively.

The duties of the exchangers in this model are reconciled to achieve the goal for this EO model – minimum offset between the plant measurements and model values. In addition to this, we use rigorous heat exchanger capabilities to obtain the fouling factor for each of the exchangers post data reconciliation.
Several cases were run on the existing model to simulate the highest and least fouled conditions for the heat exchangers, and the output values were recorded to further investigate the extreme situations.

A simulation case consisting of the preheat train was attached to atmospheric and vacuum distillation columns to obtain the products. The products and the pumparounds from the column are used in turn back in the preheat train as hot fluids to preheat the crude oil. This model helps in analysing different fouling scenarios and their effect on the performance and economics of the process.

An ideal case with a very low fouling factor for each exchanger was run as well as a case with a high fouling for each exchanger to highlight the contrast between two extreme cases. These cases were run again, side by side, under different conditions and parameters for the furnace duty, condenser duty, and reflux ratio. A price index was appointed to each product and crude oil to calculate the overall profit under different circumstances.

Subsequently, a price amount was also associated with the energy requirements to compare the loss of energy to product flow, thereby establishing the dominant parameter.

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