No abstract

This paper presents a case study of an existing water pipeline with five pumping stations each equipped with five pumps. In order to study the pipeline behavior prior to putting the system into operation, several transient simulations for different scenarios were developed. Results revealed that the most serious situation occurred when a simultaneous failure of the five pumps occur at each station caused by power cut, producing negative pressure waves because the system for control of hydraulic transients of the pipeline was insufficient to suppress downsurge pressures, due to the moment of inertia of all the pumps being erroneously considered during the design stage. The necessity to start supplying water to the population led to attempt an unconventional form of protecting the line against low pressures. The solution was to operate two of the five pumps per plant, and permit air to enter through combination air valves located along the pipeline. Air entrained formed pockets that remained stationary at the air valves locations, acting as air cushions that absorbed the energy of transient pressure waves. Computational simulations were conducted considering that two pumps are in operation at each plant and suddenly these fail simultaneously caused by power failure. The program was verified by comparing the calculated results with those registered during field pressure measurements. It was noticed that the surge modelling results are in good agreement with the measured data; furthermore, these show the air pockets in combination with existing devices for transient control protect the system adequately, avoiding potential damage to the pipeline.

The objective of the present study was to develop a genetic algorithm capable of establishing optimal operating policies for monthly extractions from the three main reservoirs of the Cutzamala System, which supply drinking water to the Mexico City metropolitan area. In previous studies, annual water extraction defined with an annual Z curve in terms of the total water storage in the reservoirs on November 1 was optimized using genetic algorithms. In this study, a percentage of total annual extraction for each reservoir was also optimized, but monthly water extractions were adjusted too, when the water level fell outside the upper or lower limits of guide curves stablished for each reservoir. The capabilities of the genetic algorithms combined with a detailed simulation of reservoirs operation were used to optimize the levels of the guide curves and also to optimize the adjusted monthly programed extractions as linear functions of the difference between the actual storage level at the beginning of each month and the corresponding level of the guide curves. Therefore, 90 parameters were established: four to define the Z curve, two to establish the percentage assigned to each reservoir, 72 to establish the monthly levels of the guide curves and 12 to define the parameters of the linear functions used to adjust the monthly programed extractions when the actual water level exceeds the limits of the guide curves. For each alternative of the 90 parameters, a detailed simulation is done using the last 20 years of hydrological data on the inflow of water to the three main reservoirs, including the net contributions of five diversion dams, and the objective function sought to maximize water delivery to the treatment plant, while penalizing possible spills and deficits in the system is evaluated. The optimal policies found in this research resulted in smaller spills than those that occurred during the historical operation of the reservoir system. Therefore, the optimal monthly operating decisions required for each reservoir are provided by the genetic algorithm.