(537e) Simultaneous Optimization of Flow Distribution and Cleaning Schedule for Heat Exchanger Networks Subject to Maintenance Constraints

Fouling of heat exchangers is one of the most common and unavoidable problems faced by process industries (Georgiadis and Papagorgiou, 2000). The deposition of foulants results in the gradual reduction of the overall heat transfer rate and increased pressure drop in each heat exchanger, which affects the thermal and hydraulic performance of the entire heat exchanger network (HEN). This results in increased pumping cost and extra thermal load on the utility system. Since the presence of foulants cannot be prevented entirely, heat exchangers must be cleaned occasionally to restore their effectiveness and avoid critical failures. The costs from additional pumping power and utility requirements, cleaning, and related business losses translate to millions of dollars every year for the process industries (Macchietto et al., 2011, Müller-Steinhagen et al., 2005). This paper seeks to propose the methodology which gives optimal cleaning schedules and operating conditions for the HENs with minimum maintenance interventions.

There are two broad fouling mitigation approaches for HENs: (1) at the design or retrofit stage, the geometry of the heat exchanger can be optimized; and (2) at the operation stage, where operational variables, such as flow distribution in HEN and cleaning schedules can be optimized (Rodriguez and Smith, 2007). This work is targeted at existing HENs and hence falls under the latter type. Several studies have been carried out in this category, formulating it as an optimization problem and considering the operating variables and cleaning activities as continuous and binary variables, respectively. The independent and simultaneous consideration of these variables in an optimization framework results in nonlinear programming (NLP) and mixed-integer nonlinear/linear programming problems (MINLP / MILP), respectively. For instance, the optimization of cleaning schedules has been formulated as MINLP and MILP problem (Georgiadis et al.,2000, Smaili et al.,2002, Ishiyama et al., 2011 and Diaby et al., 2016). Similarly, the optimization of flow rates alone has been formulated as an NLP problem (Assis et al., 2015 and da Silva et al.,2015). The simultaneous consideration of cleaning schedules and flow distribution also have been carried out by several authors (Rodriguez and Smith, 2007, Ishiyama et al., 2010 and Santamaria and Macchietto, 2020).

Although all these studies have shown the potential to save significant costs due to fouling, it is important to note that the realization of the cost saving is only possible if the optimal operating conditions and cleaning schedules are properly implemented. The proper implementation of an optimal cleaning schedule often becomes difficult due to the limited resources such as maintenance budget and personnel. In such cases, cleaning activities are shifted towards suboptimal schedules resulting in higher costs. Therefore, it is necessary to consider the constraints of available maintenance resources. To our knowledge, the resource limitations have not been previously considered in HEN fouling mitigation studies.

This work addresses this gap by modeling maintenance activities with resource limitations. Further, the concept of grouped maintenance, wherein the initial preparatory costs shared across all units within a group, is incorporated (Do et al., 2015). Heat exchangers can be grouped based on the similarity of the type of cleaning activities (mechanical or cleaning methods) or according to their locations. In the present work, we first propose a MINLP formulation for simultaneous optimization of flow distribution and grouped cleaning schedules of the heat exchangers in HENs. Grouping of cleaning activities is modeled through linear constraints based on the type of cleaning required for the heat exchangers. The period for the cleaning of all heat exchangers is assumed to be two weeks. Optimization can be performed using stochastic optimization techniques such as genetic algorithm. The proposed methodology has been evaluated on two HEN case studies with three and six heat exchangers, respectively. Our results demonstrate that the grouping of the maintenance activities results in significant savings. The proposed methodology and the results from the two case studies will be presented in this paper.

References

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