This paper presents the identification of a desalination plant model using fuzzy inference techniques, as well as their comparison with the Linear Parameter Variation (LPV) experimental identification. Identification of the plant model has been carried out using the fuzzy C-means clustering (FCM) technique. The identified model was then validated, and the estimated output was compared with the measured output. Both models were obtained with experimental data by running the plant in three different scenarios, with the only variation in the operating point of the waste reuse valve, although the differences are minimal. The results obtained show that the FCM presents the lowest variability in the estimates, the lowest discrepancy between the predicted and observed values.