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• Online operating guidelines find the maximum net electrical energy. • Multi-linear model for variable-frequency fans was built. • The cooling capability index was used as a clustering index for the multi-linear model. • The online operating guidelines were demonstrated in this paper. • Two cases of the plant test based on the online operating guidelines were presented. Guidelines for optimal operation strategy of cooling water temperature are presented to find the maximum net electrical energy of a cogeneration system for turbine generators (TGs) and cooling towers (CTs). In this study, a real cogeneration plant in Taiwan, consisting of variable-frequency drive fan-based CTs and a generator, was chosen as a case study. Statistical linear models were used to build TGs and CTs. A multi-linear model was established to approach a nonlinear system in the cooling tower using a physically meaningful index, the cooling capability index, as an indicator of data clustering. The results showed that the cooling capability index was a good clustering index for the CTs. The cooling tower multi-linear model regressed by the clustering data performed well in predicting the cooling water outlet temperature. The simulated case verified that the online operating guidelines were consistent with real plant data and that it has the ability to find the optimal cooling water temperature. Two cases of plant test prove that the online operating guidelines can assist operators in operating cooling water temperature to the maximum net electrical energy. [ABSTRACT FROM AUTHOR]

The increasing economic advantage of replacing traditional two-speed fans with variable-frequency drive (VFD) fans has been gaining popularity in the industry. However, concerns regarding frequent ON/OFF switching and the lack of a well-devised controller have discouraged widespread adoption. In this study, a temperature zone method is proposed to replace the set-point method of fan control. Additionally, the highest output water temperature allowed in the process is set as the upper limit of a zone in order to further conserve energy. Both strategies are comprehensively analyzed for a virtual cooling tower that uses operational data from an existing VFD-fan-based cooling tower system in Taiwan. The results show energy savings of 38% for a 0.75 °C zone without increasing the ON/OFF switching frequency. The proposed strategies were further verified via an on-line field experiment. The proposed methods can be universally and easily applied to any existing cooling tower, and have significant implications for energy conservation if adopted globally. [ABSTRACT FROM AUTHOR]

Highlights: [•] We introduce an index to describe the cooling capability of a cooling tower. [•] The effect of ambient air on cooling capability is studied by statistics analysis. [•] Physical-meaningful and precise-enough model is built by actual operation data. [•] The application to a real cooling tower is promising for energy conservation. [ABSTRACT FROM AUTHOR]

This paper presents a model-based optimization approach to determine operation mode of a set of fans in the cooling tower (CT) by considering turbine generator and CT as an integrated system. The optimization problem is formulated as to maximize the net power output, which is the result of subtracting power consumption of fans in CT from power generated by turbine generator. The operations of both units are characterized by a data-driven method, local model network, which presents a set of local models in the formats of linear equations. Stepwise variable selection is employed to identify key variables as a way of reducing the number of variables and avoiding overfitting. Satisfactory fuzzy c-mean cluster is used to categorize operation data into several groups to build local models. The optimal problem is then solved by linear programming algorithm. A case study conducted in a commercial plant demonstrates that the proposed approach can increase significant net power output. [ABSTRACT FROM PUBLISHER]

Abstract: 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. [ABSTRACT FROM AUTHOR]