Operating Point Estimation for an Absorption Process using Data Clustering Technique

As part of a tri-generation plant, an absorption process provides the means to recover the energy that otherwise would be lost to the environment. Since the overall efficiency relies on the amount of energy recovered in all subsystems, knowing the current performance of the absorption process is vital to proper management of the resources. This paper proposes the use of data clustering technique to estimate the most frequent operating point experienced by the absorption system in a given year. The same technique is applied to identify operating point trajectory of the system over nine years. In order to demonstrate applicability of the proposed approach, an absorption system that is comprised of two 12Ton/hr heat recovery steam generators and two 1250RT double-effect LiBr-H2O steam absorption chillers is considered as a case study. It was observed that data clustering technique is an effective method in establishing the relationships between the supplied heat and the amount of energy recovered by each subsystem. The heat recovery steam generators were identified as operating at part load ratio of about 0.41. The clustering method clearly revealed that both chillers deteriorated in performance. The absorption systems were mostly run at part load ratio of about 0.8. As such, at this operating point the energy demand was by 43% higher than that required for a healthy system. The proposed technique is applicable for performance monitoring, optimization and multi-state reliability studies of the absorption system.