Water consumption peak demand factor: Effectiveness analysis with different approaches

Résumé

The design of the water distribution networks in a given region must guarantee 24-hour supply, meeting the times of greatest demand, defined as factors of peak consumption. Thus, it is important that water reaches users effectively, ensuring adequate quantity and quality for carrying out daily activities. The premise then is the average flow demanded by the population, and the fluctuations that may occur from this value, weighted from the dimensionless peak coefficients K₁ (coefficient of the day of greatest consumption) and K₂ (coefficient of the hour of greatest consumption). In this paper, these coefficients are calculated from both water consumption data and from the application of empirical equations. These values were compared with those suggested by the NBR 12218/2017 standard, which suggests K₁ = 1.2 and K₂ = 1.5 in the absence of water consumption data. However, some surveys that assessed water consumption in three regions based on water consumption data for three to four years indicated that the peak coefficients recommended by the standard may lead to undersizing of the supply network. In one of the cases assessed, the values of K₁ and K₂ respectively corresponded to 2.19 and 4.95. Results of two studies previously developed at the Faculty of Civil Engineering, Architecture and Urbanism of the State University of Campinas (FEC-UNICAMP) were used for the calculation based on water consumption data. These studies defined the peak water consumption coefficients for the same three regions examined by the present research: Parque Jambeiro and Parque Oziel, located in the city of Campinas (third most populous city in the State of São Paulo, with an estimated 1,204,703 inhabitants in 2019), and Jardim América II, located in the municipality of Várzea Paulista, in the interior of the State of São Paulo, based on data provided by the water provider. The analyses carried out considered that the peak water consumption factor Cp is given by the product of K₁ and K₂. Seven empirical equations, available in the specific literature and developed in different locations, were used. As the empirical equations were developed in different regions, the average of the results obtained through these equations was used in order to reduce the existing uncertainties, related, for example, to the socioeconomic profile and climate, parameters that vary according to the region of study. As a result, this research shows that in all the neighborhoods observed, the normative suggestion for the Cp value was below those obtained by applying the empirical equations in all the years used to calculate the coefficient. So, there was no year in which the normative reference was sufficiently adequate to describe water consumption. Furthermore, the evaluation using water consumption data resulted in a peak coefficient equivalent to 240% of the normative suggestion (2.4 times higher) whereas the empirical equations suggest the adoption of a value corresponding to 200% of the indicated value by the standard (twice as high). It was also found that the computation of Cp through empirical equations resulted in values 1.55 times higher than those obtained from the water consumption data. As the calculation of the design flow depends directly on the peak consumption coefficient, the use of smaller values leads to lower design flow and, consequently, to undersized network diameters. As a direct consequence, there are greater head losses at times of higher flow. This situation should result in a lack of water in peak consumption days and times, so as not to serve the population continuously, which is a premise of the public supply system. Future research may focus on expanding the number of regions evaluated in the comparison between different ways to calculate the peak water consumption coefficients, and it is also possible to explore the evaluation of water consumption data to determine the Cp value and the subsequent correlation with local factors such as resident population, supplied area, typical climate, among other factors, adding value to the results already existing in the specific literature, and also expanding the knowledge related to the dimensioning of water distribution networks.