Renovation and Upgrading of the Urban Water Distribution Systems by Multi-Objective Optimization Approach (Case Study: Part of Zahedan Distribution Network)

Pipe wear in urban water supply networks is one of the most important challenges faced by water authorities. Older pipes must be replaced by new ones once they reach the end of their service life or are no longer useful due to a rise in demand and changes in surface roughness. Budget limits are one of the most common reasons for the failure of renovation and upgrade plans in water supply networks. In the traditional approach, the whole renovation is done in one phase and does not take into account the growth and development of the system. Therefore, uncertainties due to unforeseen changes are not considered. The present study addresses the renovation and upgrade of a part of the water supply network in Zahedan city via a novel approach. This approach involves dividing the plan into phases and redesigning some of the pipes during 5-year phases based on budget partitioning. To improve the hydraulic performance of the network, a multi-objective simulation-optimization model was developed with the pipe replacement cost and the water supply network reliability as the objective functions that must be minimized and maximized, respectively. For this purpose, the EPANET simulator model was combined with the Gray Wolf Optimization (GWO) algorithm in MATLAB software. The model execution results in each Phase were presented in the form of a Pareto front between the objective functions. This allows the company to renovate the water supply network based on the budget in each phase. After optimization in phase 1, the results were presented as a Pareto front between the network reliability coefficient and the reconstruction cost. One of the optimal answers on the chart was selected as the final design. In this optimal design, 22 pipes at a cost of 276 million Tomans and a reliability coefficient of 48.2% were replaced. After applying changes in the diameter of the pipes according to the design, the first phase was optimized as the basic model of the second phase. In phase 2, 32 pipes were selected and renovated. Similarly, after the end of Phase 4, it was found that the network reliability has increased significantly (150%) and the pressure of all network nodes was within the allowable range. The results indicate that the used approach can considerably increase the reliability of the network in addition to appropriately managing the renovation budget

Reducing water conveyance footprint through an advanced optimization framework

This study investigates the optimal and safe operation of pumping stations in water distribution systems (WDSs) with the aim of reducing the environmental footprint of water conveyance processes. We introduced the nonlinear chaotic honey badger algorithm (NCHBA), a novel and robust optimization method. The proposed method utilizes chaotic maps to enhance exploration and convergence speed, incorporating a nonlinear control parameter to effectively balance local and global search dynamics. Single-objective optimization results on a WDS show that NCHBA outperforms other algorithms in solution accuracy and convergence speed. The application of the proposed approach on a water network with two variable-speed pumps demonstrated a significant 27% reduction in energy consumption. Expanding our focus to the multi-objective optimization of pump scheduling programs in large-scale water distribution systems (WDSs), we employ the non-dominated sorting nonlinear chaotic honey badger algorithm (MONCHBA). The findings reveal that the use of variable-speed pumps not only enhances energy efficiency but also bolsters WDS reliability compared to the use of single-speed pumps. The results showcase the potential and robustness of the proposed multi-objective NCHBA in achieving an optimal Pareto front that effectively balances energy consumption, pressure levels, and water quality risk, facilitating carbon footprint reduction and sustainable management of WDSs

A Comparative Study on the Efficiency of Reliability Methods for the Probabilistic Analysis of Local Scour at a Bridge Pier in Clay-Sand-Mixed Sediments

In this work, the performance of reliability methods for the probabilistic analysis of local scour at a bridge pier is investigated. The reliability of bridge pier scour is one of the important issues for the risk assessment and safety evaluation of bridges. Typically, the depth prediction of bridge pier scour is estimated using deterministic equations, which do not consider the uncertainties related to scour parameters. To consider these uncertainties, a reliability analysis of bridge pier scour is required. In the recent years, a number of efficient reliability methods have been proposed for the reliability-based assessment of engineering problems based on simulation, such as Monte Carlo simulation (MCS), subset simulation (SS), importance sampling (IS), directional simulation (DS), and line sampling (LS). However, no general guideline recommending the most appropriate reliability method for the safety assessment of bridge pier scour has yet been proposed. For this purpose, we carried out a comparative study of the five efficient reliability methods so as to originate general guidelines for the probabilistic assessment of bridge pier scour. In addition, a sensitivity analysis was also carried out to find the effect of individual random variables on the reliability of bridge pier scour

An enhanced binary dragonfly algorithm based on a V-shaped transfer function for optimization of pump scheduling program in water supply systems (case study of Iran)

With the continual growth of population and shortage of energy resources, the optimal consumption of these resources is of particular importance. One of these energy sources is electricity, with a significant amount being used in pumping stations for water distribution systems (WDS). Determining the proper pumping schedule can make significant savings in energy consumption and particularly in costs. This study aims to present an improved population-based nature-inspired optimization algorithm for pumping scheduling program in WDS. To address this issue, the binary dragonfly algorithm based on a new transfer-function coupled with the EPANET hydraulic simulation model is developed to reduce the energy consumption of pumping stations. The proposed model was firstly implemented and evaluated on a benchmark test example, then on a real water pumping station. Comparison of the proposed method and the genetic algorithm (GA), evolutionary algorithm (EA), ant colony optimization (ACO), artificial bee colony (ABC), particle swarm optimization (PSO), and firefly (FF) was conducted on the benchmark test example, while the obtained results indicate that the proposed framework is more computationally efficient and reliable. The results of the real case study show that while considering all different constraints of the problem, the proposed model can decrease the cost of energy up to 27% in comparison with the current state of operation.publishe

Risk-Based Design Optimization of Contamination Detection Sensors in Water Distribution Systems: Application of an Improved Whale Optimization Algorithm

In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO). In the proposed hybrid algorithm, the SCSO operators help to find the global optimum solution by preventing the WOA from becoming stuck at a local optimum point. The effectiveness of the WOA-SCSO algorithm was evaluated using the CEC′20 benchmark functions, and the results showed that it outperformed other algorithms, demonstrating its competitiveness. The WOA-SCSO algorithm was finally applied to optimize the locations of CWSs in both a benchmark and a real-world WDS, in order to reduce the risk of contamination. The statistically obtained results of the model implementations on the benchmark WDS showed that the WOA-SCSO had the lowest average and standard deviation of the objective functions in 10 runs, 131,754 m3 and 0, respectively, outperforming the other algorithms. In conclusion, the results of applying the developed optimization model for the optimal placement of CWSs in the Dortmund WDS showed that the worst-case impact risk could be mitigated by 49% with the optimal placement of at least one sensor in the network. These findings suggest that the WOA-SCSO algorithm can serve as an effective optimization tool, particularly for determining the optimal placements of CWSs in WDSs