Novel Cellular Automata Approach to Optimal Water Distribution Network Design

This paper proposes a novel heuristic-based and cellular automata-inspired approach to the optimal design of water distribution networks. The design of water distribution networks is of central importance to the water industry, but many networks cannot be optimally designed by traditional techniques due to their complexity. Genetic algorithms have become a state-of-the-art technique for this purpose but are hampered by the fact that they are population based and require a large number of model evaluations to achieve good solutions. The proposed approach uses a parallel, localist, heuristic-based algorithm to optimally design water distribution networks requiring only a limited number of model evaluations. The algorithm is applied to a well-known simple test network and two real water distribution systems in the U.K. The results indicate that the proposed cellular approach is a viable alternative to genetic algorithm approaches while using only a fraction of the computational time required by its evolutionary counterpart.     

Multiobjective Water Quality Model Calibration Using a Hybrid Genetic Algorithm and Neural Network–Based Approach

This study aims at coupling a hybrid genetic algorithm (HGA) and a neural network (NN) model for the multiobjective calibration of surface water quality models. The HGA is formed as a robust optimization algorithm through combining a real-coded genetic algorithm with a local search method, i.e., the Nelder-Mead simplex method (NMS). The NN model is developed to approximate the input-output response relationship underlying a numerical water quality model, and is then incorporated into the HGA framework, which results in the HGA-NN approach. This approach has the advantage of evaluating the objective function of the calibration model in a more efficient way. The HGA-NN approach is tested in the calibration of a CE-QUAL-W2 model which is set up to simulate the hydrodynamic process and water quality conditions in Lake Maumelle in central Arkansas. It is found that the HGA-NN approach can improve the computational efficiency. However, it does not guarantee the finding of the parameter values with a low objective function value. An adaptive HGA-NN approach is then proposed to improve its performance. In this adaptive approach, both the water quality model and the NN model are incorporated into the HGA framework. They are executed adaptively to evaluate the objective function. The application results demonstrate that the adaptive approach can be applied to the calibration of water quality models.     

Water Distribution Network Renewal Planning

This paper provides an overview of the writers' previous work in formulating a comprehensive approach to the important problem of water distribution network renewal planning, with a particular emphasis on the computing aspects involved. As pipes in a water distribution network age in service, they are characterized by increased frequency of breakage and decreased hydraulic capacity. The resulting service failures incur utility costs for the repair or rehabilitation of the pipe systems and consumer costs for degraded system performance. The challenge to the decision maker is to determine the most cost-effective plan in terms of what pipes in the network to rehabilitate, by which rehabilitation alternative and at what time in the planning horizon, subject to the constraints of service requirements (system reliability, service pressure, etc.) A dynamic programming approach, combined with partial and implicit enumeration schemes, was used to search the vast combinatorial solution space that this problem presents. A computer program was written to implement these concepts. A hydraulic network solver is used by the program to assure the network conformance to hydraulic constraints during the search for a solution. The outcome is a strategy that identifies, for each pipe in the network, the optimal rehabilitation∕renewal alternative and its optimal time of implementation. The significance of this method is in its ability to identify an optimal rehabilitation strategy while considering the deterioration of both structural integrity and hydraulic capacity of the entire network. The best current heuristic method is limited in practical studies to a network of up to 15–20 pipe links. A more efficient heuristic method is required for implementing these principles in a larger-scale water distribution system and is the subject of current research.     

Calibration Assessment and Data Collection for Water Distribution Networks

Calibration of a water distribution network is intended to develop a model that mimics field conditions under a range of demand distributions. In this paper, a three-step calibration procedure is developed that considers the uncertainties in measurement and estimation and provides a measure of the quality of the calibration. The approach can also be used to identify preferable conditions for data collection. The procedure's steps are parameter estimation, calibration assessment, and data collection design. Parameter estimation considers input uncertainty and the resulting uncertainty in model parameters. Calibration assessment analyzes the propagation of the parameter errors on model predictions. The trace of the covariance matrix of the predictive heads is used to measure the model uncertainty. Based on this uncertainty and using a sensitivity-based heuristic analysis, data collection experiments can be designed for systemwide tests and critical pipe for individual pipe tests. An example system is analyzed to select calibration demand conditions.     

Optimal Sampling Design Methodologies for Water Distribution Model Calibration

Sampling design (SD) for water distribution systems (WDS) is an important issue, previously addressed by various researchers and practitioners. Generally, SD has one of several purposes. The aim of the methodologies developed and presented here is to find the optimal set of network locations for pressure loggers, which will be used to collect data for the calibration of a WDS model. First, existing SD approaches for WDS are reviewed. Then SD is formulated as a multiobjective optimization problem. Two SD models are developed to solve this problem, both using genetic algorithms (GA) as search engines. The first model is based on a single-objective GA (SOGA) approach in which two objectives are combined into one using appropriate weights. The second model uses a multiobjective GA (MOGA) approach based on Pareto ranking. Both SD models are applied to two case studies (literature and real-life problems). The results show several advantages and one disadvantage of the MOGA model when compared to SOGA. A comparison of the MOGA SD model solution to the results of several published SD models shows that the Pareto optimal front obtained using MOGA acts as an envelope to the Pareto fronts obtained using previously published SD models.     

Water Distribution Network Analysis Using Excel

The analysis of water distribution networks has been and will continue to be a core component of civil engineering water resources curricula. Since its introduction in 1936, the Hardy Cross method has been used in virtually every water resources engineering text to introduce students to network analysis. The technique gained widespread popularity primarily because it is amenable to manual calculation techniques. However, the same subtle elegance that facilitates manual calculations often obscures the primary engineering and physical principles of water distribution systems relative to the nuances of algorithm implementation. Herein, the authors illustrate the application of commonly available spreadsheet software (MicroSoft Excel) to more concisely and effectively solve typical undergraduate network distribution problems using linear theory. Application development is much more efficient and straightforward than the corresponding Hardy Cross implementation enabling students to concentrate upon the engineering system and relevant design issues. The technique presented utilizes commonly available technology and is presented as a supplement to alternatives discussed in recent literature.     

Calibration of Water Distribution Hydraulic Models Using a Bayesian-Type Procedure

Estimating model parameters is a difficult, yet critical step in the use of water distribution system models. Most of the optimization-based approaches developed so far concentrate primarily on efficient and effective ways of obtaining optimal calibration parameter values. At the same time, very little effort has been made to determine the uncertainties (i.e., errors) associated with those values (and related model predictions). So far, this has typically been done using the first-order second moment (FOSM) method. Even though reasonably computationally efficient, the FOSM approach relies on several restrictive assumptions and requires computationally demanding calculation of derivatives. To overcome these limitations, the recently developed shuffled complex evolution metropolis (SCEM-UA) global optimization algorithm is linked to the Epanet2 hydraulic model and used to solve a least-squares-type calibration problem. The methodology is tested and verified on the Anytown literature case study. The main advantage of the SCEM-UA algorithm over existing approaches is that both calibration parameter values and associated uncertainties can be determined in a single optimization model run. In addition, no model linearity or parameter normality assumptions have to be made nor any derivatives calculated. The main drawback of the SCEM-UA methodology is that it could, potentially, be computationally demanding, although this is not envisaged as a major problem with current computers.     

Water Distribution Network Design Using Heuristics-Based Algorithm

Water distribution network that includes supply reservoirs, overhead tanks, consumer demand nodes, interconnecting pipes, lifting pumps, and control valves is the main mode of water supply for majority of the communities especially in urban areas. Supply of required quantity of water and at right time is the primary objective of water distribution network analysis. The analysis of water distribution networks can be broadly classified into design and operation problems and both problems have been the focus of many researchers over the past three decades. In the water distribution network design problems, the target is attaining the cost effective configuration that satisfies the minimum hydraulic head requirement at the demand nodes. In this paper, a new algorithm for design of water distribution network namely “heuristics-based algorithm” which completely utilizes the implicit information associated with the water distribution network to be designed has been proposed and validated with two water distribution networks. It is found that the proposed algorithm performs well for the least-cost design of water distribution networks.     

Project Network Interdependency Alignment: New Approach to Assessing Project Effectiveness

The engineering and construction industry has evolved to a task-centric approach to evaluating the effectiveness of projects. However, a narrow task-based view of project network logic neglects the coordination of communication and knowledge exchanges across the project organizational network. This paper departs from traditional approaches to introduce a new approach to assessing project effectiveness that focuses on alignment of actual knowledge exchanges with knowledge exchange requirements across task-organization network dyads. A new modeling approach is introduced, called Project Network Interdependency Alignment. Project Network Interdependency Alignment identifies potentially excessive or insufficient communication and knowledge exchanges that can make projects ineffective. The modeling approach is introduced and retrospectively validated by using a building renovation construction project. The case study demonstrates that the approach can provide project managers with the capacity to analyze task and organizational network interdependence on projects and the critical capability to identify misalignments that impede project effectiveness.     

Considering Actual Pipe Connections in Water Distribution Network Analysis

The classical assumption of representing total demand along a pipe as two lumped withdrawals at its terminal nodes is hitherto common. It is a simplification of the network topology which is useful in order to drastically reduce the number of nodes during network simulation. Conversely, this simplification does not preserve energy balance equation of pipes and, for this reason, it is an approximation that could generate significant head loss errors. This paper presents a modification of the global gradient algorithm (GGA) which entails an enhancing of GGA (EGGA) permitting the effective introduction of the lumped nodal demands, without forfeiting correctness of energy balance, by means of a pipe hydraulic resistance correction. The robustness and convergence properties of the algorithm are compared with those of the classical GGA. Furthermore, the effectiveness of EGGA is demonstrated by computing the network pressure status under different configurations of the connections along the pipes of a test network.     

给水管网爆管原因分析及防治对策

从邯钢历年爆管事故统计分析入手,阐述了爆管原因,从管材的选用、排气系统的完善、水锤作用的消除、接口工艺的改进、温度应力的防止、施工质量的提高、给水管道的更新等方面提出了防止爆管的对策。     

Adjusting Nodal Demands in SCADA Constrained Real-Time Water Distribution Network Models

This paper describes the proportional demand method and the target demand method, two techniques for adjusting estimated demands in hydraulic models of water distribution networks to produce solutions that are consistent with available supervisory control and data acquisition (SCADA) data. The two techniques assume that pipe resistances and SCADA data are accurate and that the combination of SCADA data and demand estimates produce overdetermined problems. Nodal demands are regarded as stochastic variables which fluctuate about an estimated mean value. The method of weighted least squares is used to obtain solutions that satisfy all of the constraints imposed by SCADA data with adjusted nodal demands that most closely resemble the estimates. The methods are intended for use in real-time modeling but are limited to quasi-steady state flow. The paper demonstrates the methods on two example problems.     

Calibration of On-Demand Irrigation Network Models

In this study, a new procedure for calibrating on-demand irrigation network models was developed. This procedure used a new objective function called maximum data with a reasonable error (MDRE) for calibrating the network. It was compared with the two more commonly used objective functions in calibration procedures that are the simple least squares (SLS) and the maximum likelihood estimator for the heteroscedastic error case (HMLE). In order to carry out the calibration, a quasi-Newton optimization method was used having as variable the Hazen-Williams head losses coefficient (C). This procedure was applied to an on-demand irrigation network located in Tarazona de La Mancha (Albacete, Spain) where flow and pressure at hydrant level was measured. The calibration procedure using the MDRE objective function was applied considering all the pressure control points simultaneously and the obtained results were compared with the results of considering the pressure control points independently. Therefore, the effect of the location of the pressure control point was studied. Results showed that, when the proposed objective function was used, the root mean squared error (RMSE) comparing the measured and simulated data after calibration was lower than when the SLS or HMLE objective functions were used. The location of the pressure control points throughout the irrigation network could affect the results; therefore, it was more accurate to use all the control points simultaneously than independently in the calibration process.     

Multivariable Regulator Approach to Sewer Network Flow Control

The problem of optimal water distribution to a range of retention reservoirs in an urban sewer network during rainfall events is considered in this paper. The goal of the control actions is the minimization of overflows and eventually the reduction of their polluting impact on receiving waters. A multilayer control structure consisting of an adaptation, an optimization, and a direct control layer, is proposed for the solution of this complex control problem. Several approaches have been proposed with regard to the optimization layer. This paper proposes a linear multivariable feedback regulator that is developed via a systematic design procedure, including a simplified model, a quadratic minimization criterion, and subsequent application of the linear-quadratic optimization method. Inflow predictions are accommodated through feedforward terms in the control law. The control design guidelines facilitate a quick and efficient regulator design for a broad class of sewer network control problems. Simulation tests for a particular large network and various inflow scenarios indicate that significant overflow reductions are achieved by the application of the linear-quadratic regulator.     

南昌有色设计院计算机网络化建设探讨

探讨了南昌有色院如何以网络为支撑，专业CAD技术应用为基础，工程信息管理为核心，工程项目管理为主线，进行计算机网络化建设，使设计与管理初步实现一体化集成应用系统的实施方案。     

New Approach to Bearing Steel Quality Evaluation

Metallurgist - An approach to creating methods for evaluating the quality of bearing steels aimed at the consumer is presented. The most important characteristics that determine bearing steel...     

放矿动力学方程初探

在分析放矿过程中矿岩移动规律的基础上,提出了流动体和固结体概念。建立了矿岩块体力学模型和相应的动力学方程,应用变分原理推导出块体运动轨迹和确定流动体形状,从理论上探讨放矿动力学过程取得了新的进展。     

Spatial System Dynamics: New Approach for Simulation of Water Resources Systems

A new approach called spatial system dynamics (SSD) is presented to model feedback based dynamic processes in time and space. This approach is grounded in control theory for distributed parameter systems. System dynamics and geographic information system (GIS) are coupled to develop this modeling approach. The SSD modeling approach offers a single modeling framework for developing conceptually different models. It also provides the much-needed capability to model feedback based complex dynamic processes in time and space while giving insight into the interactions among different components of the system. The proposed approach is superior to existing techniques for dynamic modeling such as cellular automata and GIS and addresses most of the limitations present in these approaches. The SSD approach can be used to model a variety of physical and natural processes where the main interest is the space–time interaction, e.g., environmental/water resources processes, natural resources management, climate change, and disaster management. The applicability of the proposed approach is demonstrated with an application to flood management in the Red River basin in Manitoba, Canada.     

New Approach: Waste Materials as Sorbents for Arsenic Removal from Water

The sorption of inorganic arsenic species (arsenite and arsenate) from aqueous solutions onto steel-mill waste and waste filter sand, under neutral conditions, was investigated in this study. Additionally, the steel-mill waste material was modified in order to minimize its deteriorating impact on the initial water quality and to meet the drinking water standards. The influence of contact time and initial arsenic concentration was investigated using batch system techniques. To evaluate the application for real groundwater treatment, the capacities of the obtained waste materials were further compared to those exhibited by commercial sorbents, which were examined under the same experimental conditions. Kinetic studies revealed that waste slag materials are the most efficient in arsenic removal, reaching equilibrium arsenic sorption capacities in the range 47.6–55.2?μg/g, while waste filter sand exhibited capacities of 25.4–29.8?μg/g (for an initial arsenic concentration Co = 0.5?mg/L). The higher iron content in the slag materials was considered to be responsible for the better removal efficiencies, and the specific arsenic removal efficiency was estimated to be 220?μgAs/gFe. The specific arsenic removal efficiency of the second active substance found in waste filter sand, manganese, was estimated to be 115?μgAs/gMn. Equilibrium studies revealed the occurrence of both chemisorption and physical sorption processes. All the waste materials exhibited higher performances for As(V). The highest maximum sorption capacity was obtained by waste iron slag: 4040?μg/g for As(V). The waste materials reached the arsenic removal capacities of the examined commercial materials, suggesting the feasibility of their application in real groundwater treatment.     

Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty

This paper proposes a neural network embedded Monte Carlo (NNMC) approach to account for uncertainty in water quality modeling. The framework of the proposed method has three major parts: a numerical water quality model, a neural network technique, and Monte Carlo simulation. The numerical model is used to generate desirable output for training and testing sets, and the neural network is used as a universal functional mapping tool to approximate the input-output response of the numerical model. The Monte Carlo simulation then uses the neural network to generate numerical realizations based on a probabilistic distribution of parameters, thus obtaining a probabilistic distribution of the simulated state variables. By embedding a neural network into the conventional Monte Carlo simulation, the proposed approach significantly improves upon the conventional method in computational efficiency. The proposed approach has been applied to uncertainty and risk analyses of a phosphorus model for Triadelphia Reservoir in Maryland. The results of this research show that the NNMC approach has potential for efficient uncertainty analysis of water quality modeling.