(1) the complete title of the papers ------------------------------------------------ [1]. Optimal Calibration Method for Water Distribution Water Quality Model [2]. Water Loss Detection via Genetic Algorithm Optimization-based Model Calibration [3]. Diagnosing error prone application of optimal model calibration [4]. Darwin Calibrator -- Improving Project Productivity and Model Quality for Large Water System [5]. Calibrating Water Distribution Model Via Genetic Algorithms (2) the name, physical mailing address... ----------------------------------------- Zheng Yi Wu Tom Walski, Robert Mankowski Gregg Herrin Robert Gurrieri Mike Tryby Haestad Methods Solution Center Bentley Systems Inc., 27 Siemon Co. Dr. Watertown, CT06795, USA Tel 203-8050562 Paul Sage United Utilities PLC Dawson House, Great Sankey, Warrington, WA5 3LW, United Kingdom Elio F. Arniella Ernesto Gianellaand Envirosoft Engineering & Science, Inc. 4343 Shallowford Road, Suite D-5 Marietta, GA 30062, USA (3) the name of the corresponding author ---------------------------------------- Zheng Yi Wu Haestad Methods Solution Center Bentley Systems Inc., 27 Siemon Co. Dr. Watertown, CT06795, USA Email: zheng.wu@bentley.com (4) the abstract(s) of the paper(s) ----------------------------------- Paper [1]: Optimal Calibration Method for Water Distribution Water Quality Model Abstract A water quality model is to predict water quality transport and fate throughout a water distribution system. The model is not only a promising alternative for analyzing disinfectant residuals in a cost-effective manner, but also a means of providing enormous engineering insights into the characteristics of water quality variation and constituent reactions. However, a water quality model is a reliable tool only if it predicts what a real system behaves. This paper presents a methodology that enables a modeler to efficiently calibrate a water quality model such that the field observed water quality values match with the model simulated values. The method is formulated to adjust the global water quality parameters and also the element-dependent water quality reaction rates for pipelines and tank storages. A genetic algorithm is applied to optimize the model parameters by minimizing the difference between the model-predicted values and the field-observed values. It is seamlessly integrated with a well-developed hydraulic and water quality modeling system. The approach has provided a generic tool and methodology for engineers to construct the sound water quality model in expedient manner. The method is applied to a real water system and demonstrated that a water quality model can be optimized for managing adequate water supply to public communities. Paper [2] Water Loss Detection via Genetic Algorithm Optimization-based Model Calibration Abstract Identifying how much water is being lost from water networks and where the losses are occurring is of great importance to water utilities both for operational and planning reasons as well as for reputation. In this paper, an optimization-based approach is presented for simultaneously quantifying and locating water losses via the process of hydraulic model calibration. The model calibration is formulated as a nonlinear optimization problem that is solved by using a genetic algorithm. The method is developed as an integrated framework of hydraulic simulation and optimization modeling. Case studies are presented to demonstrate how the integrated approach is applied to water loss detection. The results obtained show that the method is effective at detecting water loss as part of the hydraulic calibration of the network model. The accuracy of water loss detection is dependent on the quality of the field observed data and model granularity. However, it has been shown that the approach can be used for reducing the uncertainty of the water loss identification by locating water loss hotspots, which could lead to improved operating revenues at water utilities. Paper [3] Diagnosing error prone application of optimal model calibration Abstract Optimization tools have been developed for automatic calibration of water distribution models. The tools are often based upon a powerful genetic algorithm optimization and seamlessly integrated with hydraulic and water quality modeling systems. It provides the advanced features for engineers and modelers. In spite of the power of the GA modeling, some users report that the optimal calibration tool has not worked effectively for practical model calibration. This paper presents a case study on uncovering the causes of problems in automated calibration and proposes an approach for effectively applying the optimization calibration method. The case study involves a real water distribution system. The model is constructed by experienced modeling engineers and an optimization calibration tool is applied to calibrating the model. However, the initial application of the calibration tool did not make any improvement at all over the uncalibrated model. A detailed analysis has been conducted to diagnose the problems with the model calibration. This study uncovers an error prone application of the optimization-based calibration tool and illustrates effective procedures for applying the calibration tool to a real water distribution model. The procedure and steps have been found efficient at improving model calibration. They may also serve the general guidelines for calibrating water distribution models even without use of optimization. Paper [4]: Darwin Calibrator -- Improving Project Productivity and Model Quality for Large Water System Abstract Over the last 40 years, thousands of technical papers have been published on water distribution optimization. A variety of optimization techniques have been developed. Genetic algorithms have been found the most effective and efficient at solving the optimization problems of model calibration and system design. Darwin Calibrator, based upon a competent genetic algorithm for optimizing a water distribution model for the city of Guayaquil, Ecuador. Guayaquil has a population of 2.3 million and one of the highest growth rates in South America. The genetic algorithm optimization approach significantly increased the productivity on the Guayaquil project. It took approximately 2 hours to set up, execute, and analyze each calibration run, with a total of 40 man-hours for the calibration process. It is estimated that performing a similar level of calibration by human engineer adjustment methods would have taken at least four times as long (i.e. 160 man–hours). Finally, because the Darwin Calibrator automatically evolves and evaluates hundreds of thousands of possible trial calibration solutions (a feat not possible with conventional trial-and-error modeling practice), it efficiently enhances the quality of the modeling work. Paper[5] Calibrating Water Distribution Model Via Genetic Algorithms Abstract Computer models have been built for the simulation of water distribution systems since the mid-1960s. However, a model needs to be calibrated before it can be used for analysis and operational study of a real system. Model calibration is a vitally important, but time consuming task. Over last two decades, several approaches using optimization techniques have been proposed for model calibrations. Although most of the methods can make the model agree with field observations, few are able to achieve a good level of calibration in terms of determining the correct model parameters (pipe roughness coefficients, junction demands and valve settings). The previously developed methods appear to be lacking versatility for users to accurately specify calibration task given real data for a real system. This paper proposes a comprehensive and flexible framework for calibrating hydraulic network model. Calibration tasks can be specified for a water distribution system according to data availability and model application requirements. It allows a user to (1) flexibly choose any combination of the model parameters such as pipe roughness, junction demand and link (pipes, valves and pumps) operational status, (2) easily aggregate model parameters to reduce the problem dimension for expeditious calculation, and (3) consistently specify boundary conditions and junction demand loadings that are corresponding to field data collection. A model calibration is then defined as an implicit nonlinear optimization problem, which is solved by employing a powerful genetic algorithm (GA), a generic search paradigm based on the principles of natural evolution and biological reproduction. Calibration solutions are obtained by minimizing the discrepancy between the model predicted and the field observed values of junction pressures and pipe flows. With this methodology, a modeler can be fully assisted during a calibration process, thus it is possible to achieve a good model calibration with high level of confidence. As a result, calibrated models can be developed for conducting system analysis and operational management. Example application is presented to demonstrate t he efficacy and robustness of the genetic-based methodology for calibrating water distribution model. (5) a list containing one or more of the eight letters ------------------------------------------------------ (B), (C), (D), (E), (F) and (G) (6) a statement stating why the result satisfies that criteria -------------------------------------------------------------- Hydraulic and water quality models have been essential tools for water utilities to effectively managing adequate water supply to communities around the world. Hundreads of millions of dollars have been invested in building the computer model, however, it is essential to ensure that the hydraulic and water quality models are accurately emulating how the real system behave. This is achieved by model calibration, namely estimating a large number of model parameters such as pipe roughness coefficients, node demand, element (pipe, pump and valve) operating status and settings. hundreads of papers have been published over last 40 years, genetic algorithm has been found most robust and effective at improving the accuracy of hydraulic and water quality model for water distribution systems. The outcomes of the research and development have shown: 1. The GA-based calibration methods have outperformed the previously published results; 2. The GA-based water quality model calibration has produced better results than the results of the research project supported by America Water Works Research Foundation; 3. The application of the GA-based calibration to water loss detection has produced the new and useful method of leakage detection with great potential of improving water utility operating revenue; 4. The GA-based calibration approach has provided a better solution methods to this long standing difficult problem, and its applications have produced much better results than the other calibration methods previously developed. (7) a full citation of the papers + links to full text ------------------------------------------------------ [1] Wu, Z. Y. (2006) "Optimal Calibration Method for Water Distribution Water Quality Model.", Journal of Environmental Science and Health Part A, 41:1-16, 2006. [2] Wu, Z. Y. and Sage P. (2006) “Water Loss Detection via Genetic Algorithm Optimization-based Model Calibration” ASCE 8th Annual International Symposium on Water Distribution Systems Analysis, Cincinnati, Ohio, August 27-30, 2006. [3] Wu Z. Y. and Walski T. (2005) “Diagnosing error prone application of optimal model calibration.” International Conference of Computing and Control in the Water Industry, Sept. 5-7 2005, Exeter, UK. [4] Wu, Z. Y., Elio F. A. and Ernesto G. (2004) "Darwin Calibrator--Productivity and Model Quality for Large Water Systesm", Journal of America Water Works Association, Vol. 96, No.10, pp27-34. [5] Wu, Z. Y, Walski, T., Mankowski, R., Herrin G., Gurrieri R. and Tryby, M.(2002) “Calibrating Water Distribution Model Via Genetic Algorithms”, in Proceedings of the AWWA IMTech Conference, April 16-19, Kansas City, MI.