Developing a closing rule curve for valves in pipelines to control the water hammer impacts: Application of the NSGA-II optimization model (original) (raw)
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Optimal operation of pipeline systems using Genetic Algorithm
2009
A Genetic Algorithm (GA) is used in this paper for the optimal operation, result in better solution than the existing one, of the pipeline systems under transient conditions caused by valve closure. Simulation of pipeline system is carried out here by the Implicit Method of Characteristics, a method recently developed and introduced by the authors. This method uses an element-wise definition for all the devices that may be used in a pipeline system. The advantages of this method lie in its capability of considering any arbitrary combination of devices in a pipeline system. The transient simulator is linked to a GA optimizer, which is then used to optimize the operation of a pipeline system under valve closure. One example problem of valve closure is used to test the performance of the proposed model. In this example, the GA is used to obtain the minimum valve closure time such that the pipeline system with predefined characteristics can withstand the induced pressure surge. Two pre-specified closure rules of linear and sinusoidal type were used and their corresponding results are presented and compared. The results clearly emphasize on the applicability of the proposed optimization model to control the water hammer effects by properly managing the valve closure in a pipeline system.
Developing an optimal valve closing rule curve for real-time pressure control in pipes
Sudden valve closure in pipeline systems can cause high pressures that may lead to serious damages. Using an optimal valve closing rule can play an important role in managing extreme pressures in sudden valve closure. In this paper, an optimal closing rule curve is developed using a multi-objective optimization model and Bayesian networks (BNs) for controlling water pressure in valve closure instead of traditional step functions or single linear functions. The method of characteristics is used to simulate transient flow caused by valve closure. Non-dominated sorting genetic algorithms-II is also used to develop a Pareto front among three objectives related to maximum and minimum water pressures, and the amount of water passes through the valve during the valve-closing process. Simulation and optimization processes are usually time-consuming, thus results of the optimization model are used for training the BN. The trained BN is capable of determining optimal real-time closing rules without running costly simulation and optimization models. To demonstrate its efficiency, the proposed methodology is applied to a reservoir-pipe-valve system and the optimal closing rule curve is calculated for the valve. The results of the linear and BN-based valve closure rules show that the latter can significantly reduce the range of variations in water hammer pressures.
Volume 1: Fora, Parts A, B, C, and D, 2003
Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.
2013 IEEE Congress on Evolutionary Computation, 2013
In this paper, a procedure for minimizing the pumping power, the number of pumping stations and the total pipeline mass required for a pipeline project is presented using multiobjective optimization. Two and three-objective optimization cases were considered. The decision variables included the outer diameter, wall thickness, suction pressure and discharge pressure. A novel hybrid multiobjective optimization algorithm combining NSGA-II with a simple front prediction (FP-NSGA-II) is proposed to improve upon the performance NSGA-II. Then, the application of the proposed algorithm to a problem taken from the open literature is presented and analyzed. The resulting Pareto domain was ranked using a cost function. Results indicate that FP-NSGA-II improved significantly convergence, spread and number of non-dominated solutions for the determination of the optimal design for a specified pipeline problem.
NETWORK OPTIMIZATION FOR STEADY FLOW AND WATER HAMMER USING GENETIC ALGORITHMS
The paper presents the water network optimization by selecting the optimal pipe diameters for steady state flow and water hammer. The optimization method used is the Genetic Algorithm (GA). The GA's have been used in solving the water network optimization for steady state conditions. The GA is integrated with the Newton-Raphson program and a transient analysis program to improve the search for the optimal diameters under certain constraints. These include the minimum allowable pressure head constraints at the nodes for the steady state flow, and the minimum and maximum allowable pressure heads constraints for the water hammer caused by the pump power failure. The application of the computer program to a case study shows the suitability of the method to find the least cost in a favorable number of function evaluations. This technique can be used in the first stages of the design of water distribution networks to protect it from the water hammer damages. The technique is very economical as the network design can be achieved without using hydraulic devices for water hammer control.
A Review of Genetic Algorithm Optimization Operations and Applications to Water Pipeline Systems
—Genetic Algorithm (GA) is a powerful technique for solving optimization problems. It follows the idea of survival of the fittest-Better and better solutions evolve from previous generations until a near optimal solution is obtained. GA uses the main three operations, the selection, crossover and mutation to produce new generations from the old ones. GA has been widely used to solve optimization problems in many applications such as traveling salesman problem, airport traffic control, information retrieval (IR), reactive power optimization, job shop scheduling, and hydraulics systems such as water pipeline systems. In water pipeline systems we need to achieve some goals optimally such as minimum cost of construction, minimum length of pipes and diameters, and the place of protection devices. GA shows high performance over the other optimization techniques, moreover, it is easy to implement and use. Also, it searches a limited number of solutions.
Modern Applied Science
One of the major challenges in designing under pressure water transmission pipeline is the system protection against water-hammer pressures due to a pump trip. The best strategy is to use air-chamber; which imposes considerable costs. To mitigate the air-chamber volume, the use of air-inletvalvesis also suggested. Determination of air-chamber volume as well as the type and proper locations of air-inlet valves, aiming at the cost reduction, introduces an optimization problem. To solve this problem, this study exploitsthe central force optimization (CFO) method. Herein, a case study pipeline is optimized using the proposed model based on the CFO and is compared with results of a genetic algorithm (GA) based model. Both methods yielded almost the same results and led to about 30% saving in the system protection cost. However, a comparison between the methods showed that the CFO dramaticallyoutperforms GA in both terms of computational efficiency and reliability of the results.
Evolutionary Computing Techniques for Optimal Pressure Regulation in Water Distribution Networks
Suikōgaku rombunshū, 2003
This paper addresses the problem of appropriate electrical motor valves setting for the pressure regulation of a water distribution networks for specified nodal demands by using both genetic algorithm (GA) and a relatively new concept known as Shuffled Complex Evolution-University of Arizona (SCE-UA). To demonstrate the performance of both techniques, a simple illustrative example of a controlled water distribution networks is presented showing the effectiveness of both algorithms to regulate the pressure at all the network nodes, between upper and lower values and as near as possible to the target values. Regardless the final mathematical solutions of both algorithms which are approximately the same, results show the superiority of SCE-UA technique to reach the optimal solution using less number of function evaluations than GAs. This paper concludes that the SCE-UA algorithm is well suited to deal with water supply networks problems, which provides a rich field for future research.