Rehabilitation of a water distribution system using sequential multiobjective optimization models

Identification of the optimal rehabilitation plan for a large water distribution system (WDS) with a substantial number of decision variables is a challenging task, especially when no supercomputer facilities are available. This paper presents an initiative methodology for the rehabilitation of WDS based on three sequential stages of multiobjective optimization models for gradually identifying the best-known Pareto front (PF). A two-objective optimization model is used in the first two stages where the objectives are to minimize rehabilitated infrastructure costs and operational costs. The optimization model in the first stage applies to a skeletonized WDS. The PFs obtained in Stage 1 are further improved in Stage 2 using the same two-objective optimization problem but for the full network. The third stage employs a three-objective optimization model by minimizing the cost of additional pressure reducing valves (PRVs) as the third objective. The suggested methodology was demonstrated through use of a real and large WDS from the literature. Results show the efficiency of the suggested methodology to achieve the optimal solutions for a large WDS in a reasonable computational time. Results also suggest the minimum total costs that will be obtained once maximum leakage reduction is achieved due to maximum possible pipeline rehabilitation without increasing the existing tanks

Sequential multi-objective evolutionary algorithm for a real-world water distribution system design

This paper presents a methodology based on a three-stage multi-objective optimization model for solving the problem of Battle of Background Leakage Assessment for Water Networks (BBLAWN) at WDSA2014 conference. At the first stage, the optimal design of pipeline rehabilitation, pump scheduling and tank sizing is formulated and solved on the skeletonized network by a optimizing (1) the costs of pipes, pumps and tank upgrading and (2) the cost of water losses and energy. Three optimal solutions are used for a second optimisation step on the full network (i.e. not skeletonised). The third optimisation step is then performed starting from second stage optimal solutions considering the three objectives of the original proble

Estimation of peak outflow in dam failure using neural network approach under uncertainty analysis

This paper presents two Artificial Neural Network (ANN) based models for the prediction of peak outflow from breached embankment dams using two effective parameters including height and volume of water behind the dam at the time of failure. Estimation of optimal weights and biases in the training phase of the ANN is analysed by two different algorithms including Levenberg—Marquardt (LM) as a standard technique used to solve nonlinear least squares problems and Imperialist Competitive Algorithm (ICA) as a new evolutionary algorithm in the evolutionary computation field. Comparison of the obtained results with those of the conventional approach based on regression analysis shows a better performance of the ANN model trained with ICA. Investigation on the uncertainty band of the models indicated that LM predictions have the least uncertainty band whilst ICA’s have the lowest mean prediction error. More analysis on the models’ uncertainty is conducted by a Monte Carlo simulation in which 1000 randomly generated sets of input data are sampled from the database of historical dam failures. The result of 1000 ANN models which have been analysed with three statistical measures including p-factor, d-factor, and DDR confirms that LM predictions have more limited uncertainty band

Resilience-based performance assessment of water-recycling schemes in urban water systems

Water reuse schemes in urban water system are assessed in this paper against a number of hydraulic performance indicators. A city metabolism model, WaterMet2, is used to evaluate the performance of water reuse schemes. A multi-objective evolutionary algorithm is employed to identify Pareto optimal solutions for the following three objectives: resilience, reliability and total cost. The demonstration of the suggested approach on a real-world case study show the importance of using the resilience index for determining the appropriate schemes. The results suggest, in the case analysed here, the rainwater-harvesting scheme plays a significant role for improvement of resilience index

WaterMet2: a tool for integrated analysis of sustainability-based performance of urban water systems

This paper presents the "WaterMet2" model for long-term assessment of urban water system (UWS) performance which will be used for strategic planning of the integrated UWS. WaterMet2 quantifies the principal water-related flows and other metabolism-based fluxes in the UWS such as materials, chemicals, energy and greenhouse gas emissions. The suggested model is demonstrated through sustainability-based assessment of an integrated real-life UWS for a daily time-step over a 30-year planning horizon. The integrated UWS modelled by WaterMet2 includes both water supply and wastewater systems. Given a rapid population growth, WaterMet2 calculates six quantitative sustainability-based indicators of the UWS. The result of the water supply reliability (94%) shows the need for appropriate intervention options over the planning horizon. Five intervention strategies are analysed in WaterMet2 and their quantified performance is compared with respect to the criteria. Multi-criteria decision analysis is then used to rank the intervention strategies based on different weights from the involved stakeholders' perspectives. The results demonstrate that the best and robust strategies are those which improve the performance of both water supply and wastewater systems

Pipe failure prediction in water distribution systems considering static and dynamic factors

Due to high economic, environmental and social costs resulting from pipe bursts in water distribution systems, development of a reliable and accurate prediction model to assess susceptibility of a pipe to failure is of paramount importance. This paper aims to consider the impact of both static and dynamic factors on pipe failure for long and mid-term predications. Length, diameter and age of pipes are the static and weather is the dynamic factors for the prediction model. To improve the performance of the pipe failure prediction models, the K-means clustering approach is considered. Evolutionary Polynomial Regression (EPR) is used as the pipe failure prediction model. To prepare the database for the prediction model, homogenous groups of pipes are created by aggregating individual pipes using their attributes of age, diameter and soil type. The created groups were divided into training and test datasets using the cross-validation technique. The K-means clustering approach is employed to partition the training data into a number of clusters with similar features based on diameter and age of the pipe groups. An EPR model is developed and calibrated for each data cluster. To predict pipe failures for new (unseen) data, the most suitable cluster is identified and the relevant EPR model is used to obtain the most accurate prediction. The proposed approach is demonstrated by application to a water distribution system in the UK. Comparison of the results shows that the cluster-based prediction model is able to significantly reduce the prediction error of pipe failures. Temperature-related factor is identified as the main dynamic factor influencing the t mid-term prediction of pipe failures. An EPR model is employed to predict the annual variation in the number of failures. Midterm and long-term prediction models are developed to present the relationship between number of pipe failures and temperaturerelated factors for better operation and long term for capital investment respectively

Optimal operation of water distribution systems using a graph theory–based configuration of district metered areas

Optimal operation of large water distribution systems (WDS) has always been a tedious task especially when combined with determination of district metered areas (DMAs). This paper presents a novel framework based on graph theory and optimisation models to design DMA configuration and identify optimal operation of large WDS for both dry and rainy seasons. The methodology comprise three main phases of preliminary analysis, DMA configuration and optimal operation. The preliminary analysis assists in identifying key features and potential bottlenecks in the WDS and hence narrow down the large number of decision variables. The second phase employs a graph theory approach to specify DMAs and adjust their configuration based on similarity of total water demands and pressure uniformity in DMAs. Third phase uses several consecutive single-objective and multi-objective optimisation models. The decision variables are pipe rehabilitation, tank upgrade, location of valves and pipes closure, and valve settings for each DMA. The objective functions are to minimise total annual cost of rehabilitation, water age and pressure uniformity. The proposed methodology is demonstrated through its application to large real-world WDS of E-Town. The results show that the proposed methodology can determine a desirable DMA configuration mainly supplied directly by trunk mains

A comprehensive framework for risk probability assessment of landfill fire incidents using fuzzy fault tree analysis

Landfill fire is the most frequent type of incidents in the waste management complexes. This paper presents a new framework for risk probability evaluation of major fires in landfills using the fuzzy fault tree analysis. The framework starts with construction of the fault tree of landfill fire comprised of 38 basic and 22 intermediate events with the corresponding type of faults under managerial, executive, human, and environmental conditions. Fault tree quantitative analysis is carried out through a combination of fuzzy set theory and experts' judgements to overcome the lack of data limitation. Two new sensitivity analysis approaches are used to identify the critical fault type and critical paths in the fault tree. The proposed framework is demonstrated by its application to a real-world case of a landfill in Iran. The results show the probability of a major "fire incident" is 5.5% in which "fire occurrence" stands for 25% higher than "lack of preparation for controlling fire". In addition, "Waste’s uncontrolled dumping" is recognised as the highest critical event by 6% for probability failure and 24% for importance degree. "Executive fault" also found as the most fault’s critical type by frequency analysis of failure probability. The results also reveal the major impact of the experts’ weights, especially for events related to human or management faults. These results can give decision-makers a profound insight into providing effective intervention strategies for minimising the risk of major landfill fire incidents

Reliability assessment for hybrid systems of advanced treatment units of industrial wastewater reuse using combined event tree and fuzzy fault tree analyses

Advanced treatment units (ATUs) are highly recommended for industrial wastewater reuse in the developing countries especially in arid and semi-arid areas. Reliability of a hybrid treatment system comprised of a number of individual ATUs remains blur due to lack of conceptual framework, collected data or experience in failure performance analsis of these treatment systems. This paper presents a new methodological framework for assessing reliability of hybrid system alternatives in industrial wastewater treatment by using combined event tree analysis (ETA) and fault tree analysis (FTA). The framework comprises three major steps: (1) identification of feasible alternatives; (2) reliability analysis assessment using combined FTA and ETA with fuzzy logic techniques to calculate first failure probability of individual ATUs and then reliability of each hybrid system alternative; (3) prioritisation of alternatives. Failure probability rate of events in FTA is determined by experts’ judgement. The suggested framework is demonstrated through its application to a real case study of wastewater treatment plants of industrial parks in Iran. The results show the highest failure probabilities are reverse osmosis unit with 30% and ozonation unit with 24%, while coagulation and flotation unit has the lowest failure probability of 5.4%. The most reliable alternative of hybrid system is comprised of sand filter + activated carbon + micro filter + ultra-filter + ion exchange with 74.82% reliability. Results in this study also show that selecting ATUs with higher removal efficiencies or rate of acceptable scenarios to form a hybrid ATU system cannot necessarily lead to a more reliable hybrid system without performing suggested FTA and ETA in this paper