Longitudinal Dispersion of Pollutants in Natural Streams - The Aggregated Dead-Zone Approach

The Aggregated Dead-Zone (ADZ) model provides a simple dynamic description of pollutant transportation and dispersion in a non-tidal river system and is an alternative to the well-known but more complicated advection-dispersion model. This paper presents the results obtained from the application of the model in a river system. The study shows the general form of functional relationships between the second order ADZ model parameters and stream discharge. In addition, more conventional hydrological analysis has yielded valuable information about the hydraulic characteristics of the reaches and has allowed for useful comparisons between these and the ADZ model parameter

A hands-on approach to teaching system identification using first order plus dead time (FOPDT) modelling of step response data

This paper describes three step response-based system identification methods of increasing complexity, together with a range of exercises that will enhance student understanding of this area in an engaging and practical way. For illustration purposes and practicality, it is assumed that the model to be identified is of the first order plus dead time (FOPDT) type. The first method uses a popular graphical technique, which is easy to understand and apply, but inaccurate when the response data is not ideal. The second uses the Nelder-Mead simplex method, which is a more powerful technique and has the added benefit of introducing undergraduate students to the concepts of numerical optimisation. The third uses an integral equation (IE) algorithm. The latter two methods, which can be readily extended to other model structures and input types, are also demonstrated using experimental data obtained from a tank level control system

System Identification Theory Approach to Cohesive Sediment Transport Modelling

Two aspects of the modelling sediment transport are investigated. One is the univariate time series modelling the current velocity dynamics. The other is the multivariate time series modelling the suspended sediment concentration dynamics. Cohesive sediment dynamics and numerical sediment transport model are reviewed and investigated. The system identification theory and time series analysis method are developed and applied to set up the time series model for current velocity and suspended sediment dynamics. In this thesis, the cohesive sediment dynamics is considered as an unknown stochastic system to be identified. The study includes the model structure determination, system order estimation and parameter identification based on the real data collected from relevant estuaries and coastal areas. The strong consistency and convergence rate of recursive least squares parameter identification method for a class of time series model are given and the simulation results show that the time series modelling of sediment dynamics is accurate both in data fitting and prediction in different estuarine and coastal areas. It is well known that cohesive sediment dynamics is a very complicated process and it contains a lot of physical, chemical, biological and ocean geographical factors which are still not very well understood. The numerical modelling techniques at present are still not good enough for quantitative analysis. The time series modelling is first introduced in this thesis to set up cohesive sediment transport model and the quantitative description and analysis of current velocity and suspended sediment concentration dynamics, which provides a novel tool to investigate cohesive sediment dynamics and to achieve a better understanding of its underlying character

Developing models for the data-based mechanistic approach to systems analysis:Increasing objectivity and reducing assumptions

Stochastic State-Space Time-Varying Random Walk models have been developed, allowing the existing Stochastic State Space models to operate directly on irregularly sampled time-series. These TVRW models have been successfully applied to two different classes of models benefiting each class in different ways. The first class of models - State Dependent Parameter (SDP) models and used to investigate the dominant dynamic modes of nonlinear dynamic systems and the non-linearities in these models affected by arbitrary State Variables. In SDP locally linearised models it is assumed that the parameters that describe system’s behaviour changes are dependent upon some aspect of the system (it’s ‘state’). Each parameter can be dependent on one or more states. To estimate the parameters that are changing at a rate related to that of it’s states, the estimation procedure is conducted in the state-space along the potentially multivariate trajectory of the states which drive the parameters. The introduction of the newly developed TVRW models significantly improves parameter estimation, particularly in data rich neighbourhoods of the state-space when the parameter is dependent on more than one state, and the ends of the data-series when the parameter is dependent on one state with few data points. The second class of models are known as Dynamic Harmonic Regression (DHR) models and are used to identify the dominant cycles and trends of time-series. DHR models the assumption is that a signal (such as a time-series) can be broken down into four (unobserved) components occupying different parts of the spectrum: trend, seasonal cycle, other cycles, and a high frequency irregular component. DHR is confined to uniformly sampled time-series. The introduction of the TVRW models allows DHR to operate on irregularly sampled time-series, with the added benefit of forecasting origin no longer being confined to starting at the end of the time-series but can now begin at any point in the future. Additionally, the forecasting sampling rate is no longer limited to the sampling rate of the time-series. Importantly, both classes of model were designed to follow the Data-Based Mechanistic (DBM) approach to modelling environmental systems, where the model structure and parameters are to be determined by the data (Data-Based) and then the subsequent models are to be validated based on their physical interpretation (Mechanistic). The aim is to remove the researcher’s preconceptions from model development in order to eliminate any bias, and then use the researcher’s knowledge to validate the models presented to them. Both classes of model lacked model structure identification procedures and so model structure was determined by the researcher, against the DBM approach. Two different model structure identification procedures, one for SDP and the other for DHR, were developed to bring both classes of models back within the DBM framework. These developments have been presented and tested here on both simulated data and real environmental data, demonstrating their importance, benefits and role in environmental modelling and exploratory data analysis

Pressure transients in water distribution networks: understanding their contribution to pipe repairs

Drinking water infrastructure functions to provide a service to meet customer demands and health requirements. Pipe repairs are one of the biggest challenges of ageing water infrastructure in the UK and world wide. Pressure transients resulting from sudden interruptions of the movement of the water can be caused by routine value operations. In a single pipeline one extreme event can burst a pipe. However the occurrences and impact of pressure transients in operational water distribution systems were not currently fully understood. This research developed new insights and understanding of pressure transient occurrences and their contribution to observed pipe repair rates. A large scale field monitoring program, including deploying and managing high-speed (100 Hz) instrumentation for 11 months, was designed and implemented to cover 67 district metered areas (DMA) subdivided into 79 pressure zones. In total 144 locations were monitored. The data was analysed using a novel method, termed transient fingerprint. This allowed the identification of discrete pressure transients and their three fundamental components (magnitude, duration and numbers of occurrences) leading to a quantitative interpretation of pressure transients. Evolutionary polynomial regression modelling was used to assess the impact of directly measured pressure transient data in context with static pressure, age, diameter and soil variables on 64 cast iron pipes. The analysis suggested that high magnitude, short duration repeatedly occurring pressure transients can have an adverse effect on the pipes. The extrapolation of pressure transient analysis into 7978 cast iron pipes showed inconclusive results suggesting that more accurate pressure transient data is required for each pipe in the network. Additional analysis carried out on 25 asbestos cement pipes, with actual measurements of pressure transients for each pipe, confirmed an adverse effect of pressure transient on water network observed in cast iron pipes. This research has provided an understanding of the occurrence of pressure transients that has implications on pipe management strategies. Mitigation techniques to locate pressure transient sources based on the project outcomes could be utilised to better manage distribution systems and ultimately reduce future pipe replacements and associated costs