Using Multi-Objective Optimization To Maximize Multiple Benefits For Sustainable Drainage Design

For many years, drainage design was mainly about providing sufficient network capacity. This traditional approach had been successful with the aid of computer software and technical guidance. However, the drainage design criteria had been evolving due to rapid population growth, urbanisation, climate change and increasing sustainability awareness. Sustainable drainage systems that bring benefits in addition to water management have been recommended as better alternatives to conventional pipes and storages. Although the concepts and good practice guidance had already been communicated to decision makers and public for years, network capacity still remains a key design focus in many circumstances while the additional benefits are generally considered secondary only. Yet, the picture is changing. The industry begins to realise that delivering multiple benefits should be given the top priority while the drainage service can be considered a secondary benefit instead. The shift in focus means the industry has to adapt to new design challenges. New guidance and computer software are needed to assist decision makers. For this purpose, we developed a new decision support system. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. Users can systematically quantify the performance, life-cycle costs and benefits of different drainage systems using the evaluation framework. The optimisation tool can assist users to determine combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will focus on the optimisation component of the decision support framework. The optimisation problem formation, parameters and general configuration will be discussed. We will also look at the sensitivity of individual variables and the benchmark results obtained using common multi-objective optimisation algorithms. The work described here is the output of an EngD project funded by EPSRC and XP Solutions.

Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.

Distribution Of Hydrological Losses For Varying Rainfall And Antecedent Wetness Conditions

Hydrological loss is a vital component in many hydrological models, which are usedin forecasting floods and evaluating water resources for both surface and subsurface flows. Due to the complex and random nature of the rainfall runoff process, hydrological losses are not yet fully understood. Consequently, practitioners often use representative values of the losses for design applications such as rainfall-runoff modelling which has led to inaccurate quantification of water quantities in the resulting applications. The existing hydrological loss models must be revisited and modellers should be encouraged to utilise other available data sets. This study is based on three unregulated catchments situated in Mt. Lofty Ranges of South Australia (SA). The paper focuses on conceptual models for: initial loss (IL), continuing loss (CL) and proportional loss (PL) with rainfall characteristics (total rainfall (TR) and storm duration (D)), and antecedent wetness (AW) conditions. The paper introduces two methods that can be implemented to estimate IL as a function of TR, D and AW. The IL distribution patterns and parameters for the study catchments are determined using multivariate analysis and descriptive statistics. The possibility of generalising the methods and the limitations of this are also discussed. This study will yield improvements to existing loss models and will encourage practitioners to utilise multiple data sets to estimate losses, instead of using hypothetical or representative values to generalise real situations.

Model Configuration And Data Management In The Short-Term Water Information Forecasting Tools

The Short-term Water Information and Forecasting Tools (SWIFT) is a suite of tools for flood and short-term streamflow forecasting, consisting of a collection of hydrologic model components and utilities. Catchments are modeled using conceptual subareas and a node-link structure for channel routing. The tools comprise modules for calibration, model state updating, output error correction, ensemble runs and data assimilation. Given the combinatorial nature of the modelling experiments and the sub-daily time steps typically used for simulations, the volume of model configurations and time series data is substantial and its management is not trivial. SWIFT is currently used mostly for research purposes but has also been used operationally, with intersecting but significantly different requirements. Early versions of SWIFT used mostly ad-hoc text files handled via Fortran code, with limited use of netCDF for time series data. The configuration and data handling modules have since been redesigned. The model configuration now follows a design where the data model is decoupled from the on-disk persistence mechanism. For research purposes the preferred on-disk format is JSON, to leverage numerous software libraries in a variety of languages, while retaining the legacy option of custom tab-separated text formats when it is a preferred access arrangement for the researcher. By decoupling data model and data persistence, it is much easier to interchangeably use for instance relational databases to provide stricter provenance and audit trail capabilities in an operational flood forecasting context. For the time series data, given the volume and required throughput, text based formats are usually inadequate. A schema derived from CF conventions has been designed to efficiently handle time series for SWIFT.

Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design

This paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II.