A simplified methodology to optimize the cooling tower approach temperature control schedule in a cooling system. (1st November 2019)

• Record Type: Journal Article
• Authors: Liao, Jiajun; Xie, Xie; Nemer, Hala; Claridge, David E.; Culp, Charles H.
• Is Part Of: Energy conversion and management. Volume 199(2019)
• Publication Date: 2019-11-01
• Subjects: Approach temperature reset schedule -- Cooling tower -- Condenser water leaving temperature -- Building cooling system; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.111950
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: The effectiveness model by Braun is proven less complicated but accurate for cooling tower modeling. A methodology is provided to generate an optimized approach temperature reset schedule. A case study shows that applying the proposed optimal control saves about 15.6% of the chiller plant power consumption. Abstract: This paper presents a comprehensive methodology helpful to engineers and plant managers for designing and achieving optimized operating conditions for cooling systems. The most widely accepted and utilized theories, such as the Merkel and Poppe models, has brought deviation of modeling performance or computational burden. Thus, the effectiveness cooling tower model by Braun is adopted to model the cooling tower performance with field measurements. A typical electric chiller model based on the condenser entering temperature is applied from the EnergyPlus Engineering Reference. These two models are integrated to determine the optimal operation for the cooling system. Correlations between the power consumption of chillers and cooling tower fans and approach temperatures are analyzed to determine the "near-optimal" operation. Furthermore, an optimized approach temperature reset schedule is evaluated in a case study. About 15.6% of the actual power consumption can be saved for the 68 sampled days with the proposed optimal control.