(148a) Optimization of Refinery Preheat Trains: Predictive Maintenance and Operations Improvement

The atmospheric crude distillation unit is the largest energy consumer in the entire refinery thus the efficiency of the pre-heat train, which recovers heat from the hot fractionation products, plays a pivotal role for profitability and sustainability [1]. Fouling, the deposition of unwanted material on thermal surfaces, hinders the ability of the pre-heat train to recover heat and forces operators to burn more fuel at the furnace leading to increasing costs and environmental impact as well as loss of throughput when the firing limit is reached.

In common practice, maintenance decisions such as which heat exchanger to clean and when, are driven by a limited amount of information (typically the calculation of the fouling resistance) and operators’ past experience. In this paper, advanced models [2,3,4] are used to predict fouling in refinery preheat trains and provide actionable information for predictive maintenance and operations improvement. An improved method for the simultaneous optimisation of cleaning schedules and optimal operations of flow splits is presented and its benefits illustrated with an industrial case study. The method, implemented in Hexxcell Studio™, allows to quantify and optimise the interactions between cleaning actions and flow split operations and provides information on i) which is the best heat exchanger to clean based on economic considerations including energy at the furnace, potential loss in production and cleaning costs; ii) the preferred cleaning method (i.e. chemical or mechanical); iii) a full cleaning schedule to be implemented over the next year of operations; iv) an assessment of different production scenarios, v) the optimal operation of flow split for the given cleaning schedule proposed.

Results from the industrial case study presented show that the effect of optimising the flow split fraction simultaneously with the cleaning scheduling can provide over 50% additional savings compared to the case where only the cleaning scheduling is optimised with a fixed flow split fraction.

References

[1] Coletti, F. and Hewitt, G.F. (2014). Crude Oil Fouling, Deposit Characterization, Measurements, and Modeling. Gulf Professional Publishing. ISBN: 9780128012567

[2] Coletti, F. and S. Macchietto (2011). A dynamic, distributed model of shell–and–tube heat exchangers undergoing crude oil fouling. Ind. Eng. Chem. Res. 50 (8): 4515–4533.

[3] Diaz-Bejarano E, Coletti F, Macchietto S, (2016). A New Dynamic Model of Crude Oil Fouling Deposits and Its Application to the Simulation of Fouling-Cleaning Cycles, AIChE J., 62(1):90-107.

[4] Diaz-Bejarano E., F. Coletti, S. Macchietto (2016). Impact of complex layering structures of organic and inorganic foulants on the thermo-hydraulic performance of a single heat exchanger tube – a simulation study. Ind. Eng. Chem. Res. 55 (40), pp 10718–10734.