Advanced modelling and simulation of water distribution systems with discontinuous control elements

Water distribution systems are large and complex structures. Hence, their construction, management and improvements are time consuming and expensive. But nearly all the optimisation methods, whether aimed at design or operation, suffer from the need for simulation models necessary to evaluate the performance of solutions to the problem. These simulation models, however, are increasing in size and complexity, and especially for operational control purposes, where there is a need to regularly update the control strategy to account for the fluctuations in demands, the combination of a hydraulic simulation model and optimisation is likely to be computationally excessive for all but the simplest of networks. The work presented in this thesis has been motivated by the need for reduced, whilst at the same time appropriately accurate, models to replicate the complex and nonlinear nature of water distribution systems in order to optimise their operation. This thesis attempts to establish the ground rules to form an underpinning basis for the formulation and subsequent evaluation of such models. Part I of this thesis introduces some of the modelling, simulation and optimisation problems currently faced by water industry. A case study is given to emphasise one particular subject, namely reduction of water distribution system models. A systematic research resulted in development of a new methodology which encapsulate not only the system mass balance but also the system energy distribution within the model reduction process. The methodology incorporates the energy audits concepts into the model reduction algorithm allowing the preservation of the original model energy distribution by imposing new pressure constraints in the reduced model. The appropriateness of the new methodology is illustrated on the theoretical and industrial case studies. Outcomes from these studies demonstrate that the new extension to the model reduction technique can simplify the inherent complexity of water networks while preserving the completeness of original information. An underlying premise which forms a common thread running through the thesis, linking Parts I and II, is in recognition of the need for the more efficient paradigm to model and simulate water networks; effectively accounting for the discontinuous behaviour exhibited by water network components. Motivated largely by the potential of contemplating a new paradigm to water distribution system modelling and simulation, a further major research area, which forms the basis of Part II, leads to a study of the discrete event specification formalism and quantised state systems to formulate a framework within which water distribution systems can be modelled and simulated. In contrast to the classic time-slicing simulators, depending on the numerical integration algorithms, the quantisation of system states would allow accounting for the discontinuities exhibited by control elements in a more efficient manner, and thereby, offer a significant increase in speed of the simulation of water network models. The proposed approach is evaluated on a number of case studies and compared with results obtained from the Epanet2 simulator and OpenModelica. Although the current state-of-art of the simulation tools utilising the quantised state systems do not allow to fully exploit their potential, the results from comparison demonstrate that, if the second or third order quantised-based integrations are used, the quantised state systems approach can outperform the conventional water network simulation methods in terms of simulation accuracy and run-time

Pump schedules optimisation with pressure aspects in complex large-scale water distribution systems

This paper considers optimisation of pump and valve schedules in complex large-scale water distribution networks (WDN), taking into account pressure aspects such as minimum service pressure and pressuredependent leakage. An optimisation model is automatically generated in the GAMS language from a hydraulic model in the EPANET format and from additional files describing operational constraints, electricity tariffs and pump station configurations. The paper describes in details how each hydraulic component is modelled. To reduce the size of the optimisation problem the full hydraulic model is simplified using module reduction algorithm, while retaining the nonlinear characteristics of the model. Subsequently, a nonlinear programming solver CONOPT is used to solve the optimisation model, which is in the form of Nonlinear Programming with Discontinuous Derivatives (DNLP). The results produced by CONOPT are processed further by heuristic algorithms to generate integer solution. The proposed approached was tested on a large-scale WDN model provided in the EPANET format. The considered WDN included complex structures and interactions between pump stations . Solving of several scenarios considering different horizons, time steps, operational constraints, demand levels and topological changes demonstrated ability of the approach to automatically generate and solve optimisation problems for a variety of requirements

Modelling and simulation of water distribution systems with quantised state system methods

The work in this paper describes a study of quantised state systems in order to formulate a new framework within which water distribution systems can be modelled and simulated. In contrast to the classic time-slicing simulators, depending on the numerical integration algorithms, the quantisation of system states would allow accounting for the iscontinuities exhibited by control elements in a more efficient manner, and thereby, offer a significant increase in speed of the simulation of water network models.The proposed approach is evaluated on a case study and compared against the results obtained from the Epanet2 simulator and OpenModelica

Online Simplification of Water Distribution Network Models

This paper presents an implementation of the simplification algorithm of water distribution network (WDN) models for the purpose of inclusion to the online optimisation strategy for the energy and leakage management in WDN, formulated within a model predictive control framework. The advantage of the online model reduction is adaptation to abnormal situations and structural changes in a network. The implementation was carried out with the utilisation of nowadays parallel programing techniques to distribute the simplification tasks across multiple CPU treads. This resulted in significant reduction of the computational time required for the simplification process of the large–scale WDN models. The authors also highlighted a problem of the energy distribution when the reduced and original models were compared

Neighbouring Link Travel Time Inference Method Using Artificial Neural Network

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods

Couch-based motion compensation: modelling, simulation and real-time experiments

Abstract The paper presents a couch-based active motion compensation strategy evaluated in simulation and validated experimentally using both a research and a clinical Elekta Precise Table™. The control strategy combines a Kalman filter to predict the surrogate motion used as a reference by a linear model predictive controller with the control action calculation based on estimated position and velocity feedback provided by an observer as well as predicted couch position and velocity using a linearized state space model. An inversion technique is used to compensate for the dead-zone nonlinearity. New generic couch models are presented and applied to model the Elekta Precise Table™ dynamics and nonlinearities including dead zone. Couch deflection was measured for different manufacturers and found to be up to 25 mm. A feed-forward approach is proposed to compensate for such couch deflection. Simultaneous motion compensation for longitudinal, lateral and vertical motions was evaluated using arbitrary trajectories generated from sensors or loaded from files. Tracking errors were between 0.5 and 2 mm RMS. A dosimetric evaluation of the motion compensation was done using a sinusoidal waveform. No notable differences were observed between films obtained for a fixed- or motion-compensated target. Further dosimetric improvement could be made by combining gating, based on tracking error together with beam on/off time, and PSS compensation.</jats:p

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link

Water Advisory Demand Evaluation and Resource Toolkit

The purpose of this feasibility study is to determine if the application of computational intelligence can be used to analyse the apparently unrelated data sources (social media, grid usage, traffic/transportation and weather) to produce credible predictions for water demand. For this purpose the artificial neural networks were employed to demonstrate on datasets localised to Leicester city in United Kingdom that viable predictions can be obtained with use of data derived from the expanding Internet-of-Things ecosystem. The outcomes from the initial study are promising as the water demand can be predicted with accuracy of 0.346 m3 in terms of root mean square error

Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas

The file attached to this record is the author's final peer reviewed version.Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS syste