Investigation of the ageing effect on chiller plant maximum cooling capacity using Bayesian Markov Chain Monte Carlo method

Ageing inevitably leads to capacity degradation in a chiller plant. Hence in the life-cycle performance analysis of a chiller plant, ageing always represents a crucial consideration for designers. Ageing is normally quantified using maintenance factor. A conventional analysis recommends that the maintenance factor should be 0.01 for systems that undergo annual professional maintenance, and 0.02 for those that are seldom maintained. However, this recommendation is mainly based on a rule of thumb, and may not be accurate enough to describe the ageing for a given chiller plant. This research therefore proposes a method of identifying the chiller maintenance factor using a Bayesian Markov Chain Monte Carlo method, which can take account of the uncertainties that exist in the estimation of the ageing. Details of the identification will be provided by applying the proposed method to a real chiller plant, and results will be compared with that of the conventional analysis.