Geothermal Paving Systems for Urban Runoff Treatment and Renewable Energy Efficiency

Water and energy are two of the most precious and essential resources which are inseparably connected; vital for the survival and well-being of humanity. Sustainable water resources and energy management emphasizes the requirement for a holistic approach in meeting the needs of the present and future generations. In order to indentify the needs and obstacles relating to water reuse and renewable energy initiatives, Hanson Formpave in partnership with The University of Edinburgh implement a five-year pilot project between May 2005 and June 2010. The research project addressed the use of sustainable urban drainage systems (SUDS) such as permeable pavements systems (PPS) and integration of renewable energy tools such as geothermal heat pumps (GHPs). The research uses the novel and timely urban drainage system and focuses on water quality assessment when incorporated with GHPs. Twelve-tanked laboratory scaled experimental PPS were evaluated at The King’s Building campus (The University of Edinburgh, Scotland) using different compositions. Variations in designs included the presence of geotextiles layers and geothermal heating/cooling applications. The experimental rigs were examined for a two year period (March 2008 to April 2010). Two types of urban stormwater were used in the analysis; (i) gully pot liquor and (ii) gully pot liquor spiked with Canis lupus familiaris (dog) faeces. This urban wastewater represented the extreme worstcase scenario from a storm event, which can occur on a permeable pavement parking lot. The pavement systems operated in batch-flow to mimic weekly storm events and reduce pumping costs. Six PPS were located indoor in a controlled environment and six corresponding PPS were placed outdoors to allow for a direct comparison of controlled and uncontrolled environmental conditions. The outdoor rig simulated natural weather conditions whilst the indoor rig operated under controlled environmental conditions such as regulated temperature, humidity and light. The project assessed the performance of these pavement rigs with the integration of ground-source heating and cooling, standalone PPS and the abilities for water quality treatment from a physical, chemical and microbiological perspective. The performance efficiency of the GHP was measured by the energy efficiency ration (EER) for steady state cooling efficiency and the coefficient of performance (COP) for the heating cycle efficiency. Findings from the combined PPS and GHP system and standalone systems were able to significantly lower levels for all physiochemical and microbial water quality parameters in the range of (70-99.99%) respectively. Outflow concentrations for all pavement systems met the European Commission Environment Urban Wastewater Treatment Directive (91/271/EEC). The presence of geotextiles resulted in a significant reduction of contaminants when compared to PPS systems without (p <0.05). Photocatalytic disinfection with titanium dioxide (TIO2) was applied to the effluent from PPS for further treatment and polishing of the stormwater. After the photocatalytic disinfection, the water met the requirements for the United States Environmental Protection Agency (US EPA) water recycling guidelines and the World Health Organisation (WHO) guidelines for potable water consumption with regards to microbial contamination. An Energy and temperature balance was developed for two PPS using a 4th order Runge-Kutta numerical method to model the heat fluxes and energy balance within the pavement system. Machine learning techniques such as artificial neural networks (backpropagatioin feed forward neural networks) and self-organising maps (SOM) were applied and successfully predicted the effluent concentrations of nutrients, biochemical oxygen demand (BOD) and microbial pollutants. The overall outcome of this research is a significant contribution to the development of a new generable of sustainable and eco-friendly pavements. The research project proves scientifically that PPS is one of the most appropriate systems for GHP installation and does not affect its efficiency for water pollutant removal

Biological investigation and predictive modelling of foaming in anaerobic digester

Anaerobic digestion (AD) of waste has been identified as a leading technology for greener renewable energy generation as an alternative to fossil fuel. AD will reduce waste through biochemical processes, converting it to biogas which could be used as a source of renewable energy and the residue bio-solids utilised in enriching the soil. A problem with AD though is with its foaming and the associated biogas loss. Tackling this problem effectively requires identifying and effectively controlling factors that trigger and promote foaming. In this research, laboratory experiments were initially carried out to differentiate foaming causal and exacerbating factors. Then the impact of the identified causal factors (organic loading rate-OLR and volatile fatty acid-VFA) on foaming occurrence were monitored and recorded. Further analysis of foaming and nonfoaming sludge samples by metabolomics techniques confirmed that the OLR and VFA are the prime causes of foaming occurrence in AD. In addition, the metagenomics analysis showed that the phylum bacteroidetes and proteobacteria were found to be predominant with a higher relative abundance of 30% and 29% respectively while the phylum actinobacteria representing the most prominent filamentous foam causing bacteria such as Norcadia amarae and Microthrix Parvicella had a very low and consistent relative abundance of 0.9% indicating that the foaming occurrence in the AD studied was not triggered by the presence of filamentous bacteria. Consequently, data driven models to predict foam formation were developed based on experimental data with inputs (OLR and VFA in the feed) and output (foaming occurrence). The models were extensively validated and assessed based on the mean squared error (MSE), root mean squared error (RMSE), R2 and mean absolute error (MAE). Levenberg Marquadt neural network model proved to be the best model for foaming prediction in AD, with RMSE = 5.49, MSE = 30.19 and R2 = 0.9435. The significance of this study is the development of a parsimonious and effective modelling tool that enable AD operators to proactively avert foaming occurrence, as the two model input variables (OLR and VFA) can be easily adjustable through simple programmable logic controller

Is there chaos out there? : analysis of complex dynamics in plankton communities

Species often show irregular fluctuations in their population abundances. Traditionally, ecologists have thought that external processes (e.g., variability in weather conditions) are the main drivers of these ups and downs. However, recent theoretical work suggests that fluctuations in natural populations may also be driven by internal mechanisms (e.g., the interplay between species). In this thesis I use a combination of time series analysis and modeling to provide more insight into the question to which extent such internally generated chaos might drive the population dynamics of plankton communities under controlled as well as natural conditions. In short, this thesis demonstrates in theory and experiment that species in plankton communities may rise and fall forever in a chaotic way. This result challenges the traditional view that nature is at equilibrium and that only externally driven processes may disturb this equilibrium