Urban flooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the flood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers significant benefits over traditional RTC approaches for flood risk management.

Modelling of wastewater temperatures along a sewer pipe using energy balance equations and assuming steady-state conditions was achieved. Modelling error was calculated, by comparing the predicted temperature drop to measured ones in three combined sewers, and was found to have an overall root mean squared error of 0.37 K. Downstream measured wastewater temperature was plotted against modelled values; their line gradients were found to be within the range of 0.9995–1.0012. The ultimate aim of the modelling is to assess the viability of recovering heat from sewer pipes. This is done by evaluating an appropriate location for a heat exchanger within a sewer network that can recover heat without impacting negatively on the downstream wastewater treatment plant (WWTP). Long sewers may prove to be more viable for heat recovery, as heat lost can be reclaimed before wastewater reaching the WWTP.

A computational network heat transfer model was utilised to model the potential of heat energy recovery at multiple locations from a city scale combined sewer network. The uniqueness of this network model lies in its whole system validation and implementation for seasonal scenarios in a large sewer network. The network model was developed, on the basis of a previous single pipe heat transfer model, to make it suitable for application in large sewer networks and its performance was validated in this study by predicting the wastewater temperature variation across the network. Since heat energy recovery in sewers may impact negatively on wastewater treatment processes, the viability of large scale heat recovery was assessed by examining the distribution of the wastewater temperatures throughout a 3000 pipe network, serving a population equivalent of 79500, and at the wastewater treatment plant inlet. Three scenarios; winter, spring and summer were modelled to reflect seasonal variations. The model was run on an hourly basis during dry weather. The modelling results indicated that potential heat energy recovery of around 116, 160 & 207 MWh/day may be obtained in January, March and May respectively, without causing wastewater temperature either in the network or at the inlet of the wastewater treatment plant to reach a level that was unacceptable to the water utility.

A predictive modelling technique was employed to estimate wastewater temperatures in sewer pipes. The simplicity of abductive predictive models attracts large numbers of users due to their minimal computation time and limited number of measurable input parameters. Data measured from five sewer pipes over a period of 12 months provide 33,900 training entries and 39,000 evaluation entries to support the models' development. Two simple predictive models for urban upstream combined sewers and large downstream collector sewers were developed. They delivered good correlation between measured and predicted wastewater temperatures proven by their R2 values of up to 0.98 and root mean square error (RMSE) of the temperature change along the sewer pipe ranging from 0.15 °C to 0.33 °C. Analysis of a number of potential input parameters indicated that upstream wastewater temperature and downstream in-sewer air temperature were the only input parameters that are needed in the developed models to deliver this level of accuracy.