Ali Haghighi

Optimization of Conventional Rule Curves Coupled with Hedging Rules for Reservoir Operation

As a common approach to reservoir operating policies, water levels at the end of each time interval should be kept at or above the rule curve. In this study, the policy is captured using rationing of the target yield to reduce the intensity of severe water shortages. For this purpose, a hybrid model is developed to optimize simultaneously both the conventional rule curve and the hedging rule. In the compound model, a simple genetic algorithm is coupled with a simulation program, including an inner linear programming algorithm. In this way, operational policies are imposed by priority concepts to achieve the optimal water allocation and the target storage levels for reservoirs. As a case study, a multipurpose, multireservoir system in southern Iran is selected. The results show that the model has good performance in extracting the optimum policy for reservoir operation under both normal and drought conditions.

Detection of Leakage Freshwater and Friction Factor Calibration in Drinking Networks Using Central Force Optimization

Inverse Transient Analysis (ITA) is a powerful approach for leak detection and calibration of friction factors in pressurized pipes. Through this method, a transient flow is initiated and pressures are measured somewhere in the system. Then, a nonlinear programming (NLP) problem with a least-squares criterion objective function is developed to minimize discrepancies between the measured and calculated pressures at measurement sites. Solving the raised NLP results in the problem’s unknowns being leakage specifications and pipe friction factors. For this purpose, various optimization techniques may be utilized. This issue is a major challenge for ITA-based methods. The present work aims at applying the new method of Central Force Optimization (CFO) to the problem of ITA. CFO is a deterministic metaheuristic inspired by gravitational kinematics in which small objects in space are dragged by bigger ones. Herein, the concept and main structure of CFO are represented as well as of CFO. A reference pipe-network is considered to be solved using the ITA equipped with CFO. The results are then discussed compared to the previous works. It is concluded that CFO is easy to implement, computationally efficient and has a remarkable performance in solving leak detection problem.

Optimization of Sewer Networks Using an Adaptive Genetic Algorithm

This work aims at introducing an optimization model to design sewer networks. The approach specially focuses on handling the nonlinear and discrete constraints of the problem. For this purpose, an adaptive genetic algorithm is developed so that every chromosome, consisting of sewer diameters and slopes and pump indicators, is a feasible design. The binary chromosomes are freely generated and then decoded to feasible design alternatives following a sequential design-analysis algorithm. The adaptive decoding strategy is set up based on the open channel hydraulics and sewer design criteria. Through the proposed method, all the sewer system’s constraints are systematically satisfied. Consequently, there is neither need to discard or repair infeasible chromosomes nor to apply penalty factors to the cost function. A benchmark sewer network from the literature is considered to be designed using the proposed approach. The obtained results are then discussed and compared with the previous works. It is found that the adaptive constraint handling method computationally makes the optimization more efficient in terms of speed and reliability.

Uncertainty analysis of water supply networks using the fuzzy set theory and NSGA-II

Abstract This work introduces an approach for taking into account the uncertainty of pipe friction coefficients and nodal demands in the hydraulic analysis of water supply networks. For this purpose, uncertainties are represented by fuzzy numbers and incorporated into the network׳s governing equations. Input uncertainties are spread out on the network and influence its hydraulic responses, including pipe velocities and nodal pressures. To estimate the responses׳ uncertainty, input fuzzy numbers are discretized in some levels of membership function. Then, a multiobjective optimization problem is developed for each level to find the extreme values of the node pressures and pipe velocities. The raised problem is solved using the method of Non Dominated Sorting Genetic Algorithm (NSGA-II) coupled to the network hydraulic simulation model. The proposed approach is applied to an example and a real pipe network. It is found that small uncertainties in input variables can significantly influence the network׳s responses as well as its performance reliability. It is also concluded that NSGA-II has a great role in solving the problem systematically, and improves the computational efficiency of the whole process of network fuzzy analysis.

Deterministic Integrated Optimization Model for Sewage Collection Networks Using Tabu Search

AbstractThis paper presents an integrated optimization model for designing sewage collection networks. The layout configuration is designed using the loop-by-loop cutting algorithm. Then, the network with a given layout is hydraulically designed to determine sewer diameters, installation depths, and pump specifications. In both design steps, all technical constraints and criteria are systematically satisfied. Thereby, the optimization of sewer systems becomes totally unconstrained for the applied optimization solver. In this problem, the objective function is the network’s construction cost and the decision variables are the parameters of layout generation and sewer specifications. For optimization of the cost function, the tabu search (TS) method as a deterministic combinatorial metaheuristic is developed and coupled to the design solvers. The proposed scheme is able to search adaptively in feasible parts of the problem’s decision space as well as to solve the two subproblems of layout generation and sew...

Optimization of Pump Scheduling Program in Water Supply Systems Using a Self-Adaptive NSGA-II; a Review of Theory to Real Application

The operation of pumps imposes significant costs on a water distribution system for energy supply and pumps maintenance. To derive an optimum pumps scheduling program, this study presents a multiobjective optimization problem with the objective functions of 1- energy cost and 2- the number of pump switches. The optimization of both objective functions together leads to a multiobjective constrained optimization problem. To solve the problem, the Non-Dominated Sorting Genetic Algorithm, version II, (NSGA-II) is coupled to the EPANET hydraulic simulation model. For constraint handling, some modifications are introduced to the standard NSGA-II to make it self-adaptive through which all constraints of the problem are automatically satisfied. Application of the model to a test example and a real pipe network verifies that the proposed scheme is computationally efficient and reliable. Also, optimization of the real pipe network reveals that by a careful pump scheduling program the total number of pump switches even in optimum operations could be decreased by 69% while the energy cost increases at most by 10%.

Simultaneous Optimization of Operating Rules and Rule Curves for Multireservoir Systems Using a Self-Adaptive Simulation-GA Model

AbstractThis study introduces a simulation-optimization model for deriving an operating policy for multireservoir systems. Two adjustable monthly rule curves are introduced to each reservoir in the system. The applied rule curves divide the reservoir volume into three zones in each within-year period. For each zone, a release coefficient is specified to indicate releases from the reservoir as a function of the available storage and time of the year. To obtain optimum rule curves and release rules, a self-adaptive genetic algorithm (GA) is developed to maximize the system’s hydropower production, subject to the system’s physical constraints as well as a desirable reliability to satisfy water demands. To evaluate the objective function and constraints, a simulation model based on the network flow technique and linear programming is developed and coupled to the GA. The model is applied to a real three-reservoir system in Iran and the results are discussed and compared to the standard operation policy (SOP).

Optimization of gated spillways operation for flood risk management in multi-reservoir systems

In this research, a flood control operation policy based on a simulation–optimization model is developed for minimizing flood damage downstream of multi-reservoir systems by using spillway gates. The operation policy is based on a multistage method used to control floods of various magnitudes in reservoirs with gated spillways. In this operation policy, it is assumed that the system has no flood forecast, and thus shapes and sizes of the inflow hydrographs are not prior known. In this method, the rate of flow released in each stage level is decided according to the current reservoir water level and determined in such a way that flood damage risk for downstream areas is minimized while the dams’ safety is maintained. For this purpose, an optimization algorithm is introduced in which the expected annual flood damage downstream of the river system is the objective function and the spillway release discharges in stage levels of spillways gates are the decision variables. A continuous genetic algorithm is utilized to solve this problem. As a case study, the Karun River system including multi-reservoir of Karun 4, Karun 3, Karun 1 and Gotvand is analyzed. Results obtained from the proposed model indicate dramatic decrease in the expected annual flood damage. Comparison of the results with other studies shows that the proposed model has a better ability in flood control and minimization of damage costs.

A Graph Portioning Approach for Hydraulic Analysis-Design of Looped Pipe Networks

This study introduces a new formulation for hydraulic analysis and design of looped pipe networks. The network is partitioned into two sub-graphs so-called the base and the remainders graphs. The base graph is a spanning tree of the network and, the remainders graph includes pipes hypothetically removed from the network to open the loops. The network governing equations of mass and energy conservation are manipulated so that, pipe flows in the remainders and head losses in the base graph are considered as independent variables to calculate pipe diameters directly. The method is applied to two example networks and the results are discussed. The new approach is found to be computationally efficient and useful for design and optimization of pipe networks.

Straightforward Transient-Based Approach for the Creep Function Determination in Viscoelastic Pipes

AbstractThis work introduces a simple and straightforward approach for the creep function determination in viscoelastic pipes on the basis of transient flow analysis. The governing equations are expanded and analytically solved for the first half period of the transient. This solution results in a direct formula for the viscoelastic Joukowsky pressure head as a function of the creep function coefficients which are a priori unknown. Utilizing the measured data, only in the first half water hammer period and the proposed viscoelastic Joukowsky formula, the problem unknowns are determined. To investigate the method’s merits and limitations, two experimental polyethylene pipes are taken into account from the literature. The results show that the proposed approach works very well for long enough pipelines which are completely crept in the half period of the transient flow. The method is found to be computationally efficient and easy to implement in comparison with traditional inverse transient analysis techniques.