Abstract:
Fouling is the major cause of inefficiencies in heat exchange equipment with sever effects on industrial operation, economics, environment and health and safety. It is nearly a universal problem in heat exchangers. Current monitoring and mitigation techniques does not incorporate all the aspects of fouling. To mitigate fouling, heat exchangers are cleaned periodically. Currently, cleaning schedule of heat exchange equipment is based on rudimentary discussion rather than holistic approach. In this work, a holistic approach is formulated to predict fouling rate at any given time interval and generate cleaning schedule utilizing optimization technique. Model is used within the hybrid framework of MINLP and priori statistic method. Optimization is carried out by lowering the utility consumption (fuel gas or fuel oil) and maximizing the heat recovery. The proposed system incorporates all three aspects (thermal, hydraulic and transport) of fouling and with less than 1.5% deviation from real case scenario. For a crude oil refinery of 100,000 bbl/day the model has proven to manage the multi-billion-dollar fouling problem by saving U.S. $ 32,622 for every 1oC furnace inlet temperature drop due to fouling and thus eliminated substantial unnecessary cost associated with heat exchange equipment cleaning.
Keywords: Heat exchanger, cleaning schedule, fouling resistance, MINLP, goal programming.
