Joint economic optimization of heat exchanger design and maintenance policy

• Published in: Applied thermal engineering Vol. 31; no. 8; pp. 1381 - 1392
• Main Authors: Caputo, Antonio C., Pelagagge, Pacifico M., Salini, Paolo
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.06.2011; Elsevier
• Subjects: Applied sciences; Cleaning; Devices using thermal energy; Economics; Energy; Energy. Thermal use of fuels; Engineering Sciences; Equipment costs; Exact sciences and technology; Fouling; Genetic algorithm; Heat exchangers; Heat exchangers (included heat transformers, condensers, cooling towers); Heat transfer; Maintenance; Mechanics; Optimization; Physics; Policies; Schedules; Theoretical studies. Data and constants. Metering; Thermics; Cleaning; Fouling; Heat exchangers; Genetic algorithm; Optimization; Economic optimization; Maintenance cost; Minimization; Maintenance; Heat exchanger; Life cycle cost; Flow velocity; Cost analysis; Pressure loss; Investment cost; Heat transfer; Investment; genetic algorithm; cleaning; heat exchangers; fouling; optimization
• DOI: 10.1016/j.applthermaleng.2010.12.033
• ISSN: 1359-4311

In this paper a new approach to shell and tube heat exchanger optimization is presented based on the minimization of the life cycle cost. The method allows the joint optimization of both the equipment design and the cleaning policy. Economic savings resulting from the proposed design procedure are relevant especially when large sized equipment is involved or when many small sized units are installed. At first, a thermal design procedure defines the heat transfer area as well as flow velocities and the fouling rate, given the heat duty of the equipment. The capital investment, pressure losses and the cleaning interval required to reach a maximum allowed fouling resistance are thus calculated. An optimization algorithm is then utilized to determine the optimal values of both geometric design parameters and maximum allowable fouling resistance by pursuing the minimization of a total cost function. The objective function includes capital investment, operational costs related to friction losses, and maintenance costs deriving from the cleaning schedule. The research contribution comes from integrating two optimization tasks which traditionally are carried out in separate ways. In fact, during the design phase the heat exchanger architecture is usually optimized neglecting maintenance expenses, while during the operational phase the heat exchanger architecture is already finalized and maintenance optimization approaches allow only to determine a minimum cost cleaning schedule based on the existing exchanger. In this work, instead, the problems of equipment sizing and cleaning schedule determination are solved simultaneously so that the entire life cycle cost is minimized. Optimization of the objective function is carried out resorting to a genetic algorithm. In the paper the optimal design approach is described and an application example is provided to show the capability of the method.