Efficient Chlorination Schedule for a Water Distribution Network With Multiple Pumping Stations

Abstract

The present study proposes a chlorination schedule calibrated for the Drinking Water Distribution Network (DWDN) of Buzau  a medium sized city, in South-Eastern Romania. The numerical model of Buzau's DWDN was set up in EPANET, considering the main pipes of the network that interconnect 4 pumping stations and 45 booster stations (viewed here as end-users). The calibration of the numerical model was based on real-time recordings available for January and July 2014. The chlorination scheduling was simulated at each reservoir supplying a pumping station, by a variable injection pattern added to the EPANET DWDN model. The injected amount of chlorine was determined by values of the chlorine concentration obtained at 4 key monitoring points, spread over the network. The simulations were performed over an extended time period of 72 hours. The system behaviour has been analysed for two cases: with all pumping stations in operation, and with the biggest pumping station shutdown. For each case, the proposed schedule corresponds to the injection in the network of less chlorine than the corresponding recorded values for January and July 2014, and yet obtaining at the 4 key points a chlorine concentration variation similar to the recorded one, while the chlorine concentration at the end users was within the admissible range, across the whole DWDN.

Experimental test rig designed to analyse pumping station operation controlled by pressure at different key points

“…Further, the nonlinear system (7) was solved in GNU Octave [4,15], using the built-in function fsolve (also available in MATLAB [16]); fsolve requests a usersupplied initial solution (starting guess), denoted here by a column vector w0 of 31 components w0j. It turned out that the above nonlinear system was not at all sensitive to the initial solution.…”

“…It turned out that the above nonlinear system was not at all sensitive to the initial solution. The final solution w of the system (7), obtained for w0 with all components equal to zero (w0j = 0, for j = 131), is presented in table 3.…”

“…Table 3. Numerical solution of the system (7), for  = 0: wj for j = 131 and available pressure head pj/(g) for j = 2931. For the computed pumped flow rates Qj (j = 13), one must verify if the pumps operate with or without cavitation when running at nominal speed.…”

“…It's worth mentioning that decreasing the diameter of the main suction pipe (with index j = 5) is not an option, because the increase of the head losses on the suction circuit leads to cavitation in pumps: e.g. if the pipe 5 is of DN40 size, with an inner diameter D5 = 29.2 mm, the solution of the system (7) gives Q1 = 0.000845, Q2 = 0.000845 and Q3 = 0.000846 m 3 /s, as well as a minimal head H21 = -9.02 m near the inlet of pump P1; the NPSH available at the pump inlet (9) gives negative values.…”

“…To avoid exceeding pressure at the end-users, one can choose to control the pumps operation within a PS with respect to a PSP value compared to the reading of a pressure sensor positioned at a critical point (monitoring node) within the WDN. For WDNs fed by multiple pumping stations, each PS can be controlled by its own PSP level, compared to pressure values measured at a critical point that is 100% fed by the considered PS [6,7]. The latter control method implies remote-control (i.e.…”