Statistical multi-model approach for performance assessment of cooling tower

This paper presents a data-driven model-based assessment strategy to investigate the performance of a cooling tower. In order to achieve this objective, the operations of a cooling tower are first characterized using a data-driven method, multiple models, which presents a set of local models in the format of linear equations. Satisfactory fuzzy c-mean clustering algorithm is used to classify operating data into several groups to build local models. The developed models are then applied to predict the performance of the system based on design input parameters provided by the manufacturer. The tower characteristics are also investigated using the proposed models via the effects of the water/air flow ratio. The predicted results tend to agree well with the calculated tower characteristics using actual measured operating data from an industrial plant. By comparison with the design characteristic curve provided by the manufacturer, the effectiveness of cooling tower can be obtained in the end. A case study conducted in a commercial plant demonstrates the validity of proposed approach. It should be noted that this is the first attempt to assess the cooling efficiency which is deviated from the original design value using operating data for an industrial scale process. Moreover, the evaluated process need not interrupt the normal operation of the cooling tower. This should be of particular interest in industrial applications.