ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

Stochastic techniques for the design of robust and efficient emission trading mechanisms

The assessment of greenhouse gases (GHGs) emitted to and removed from the atmosphere is highon both political and scientific agendas internationally. As increasing international concern and cooper- ation aim at policy-oriented solutions to the climate change problem, several issues have begun to arise regarding verification and compliance under both proposed and legislated schemes meant to reduce the human-induced global climate impact. The issues of concern are rooted in the level of confidence with which national emission assessments can be performed, as well as the management of uncertainty and its role in developing informed policy. The approaches to addressing uncertainty that was discussed at the 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories 1 attempt to improve national inventories or to provide a basis for the standardization of inventory estimates to enable comparison of emissions and emission changes across countries. Some authors use detailed uncertainty analyses to enforce the current structure of the emissions trading system while others attempt to internalize high levels of uncertainty by tailoring the emissions trading market rules. In all approaches, uncertainty analysis is regarded as a key component of national GHG inventory analyses. This presentation will provide an overview of the topics that are discussed among scientists at the aforementioned workshop to support robust decision making. These range from achieving and report- ing GHG emission inventories at global, national and sub-national scales; to accounting for uncertainty of emissions and emission changes across these scales; to bottom-up versus top-down emission analy- ses; to detecting and analyzing emission changes vis-a-vis their underlying uncertainties; to reconciling short-term emission commitments and long-term concentration targets; to dealing with verification, com- pliance and emissions trading; to communicating, negotiating and effectively using uncertainty

Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

FORECASTING CLIMATE AND LAND USE CHANGE IMPACTS ON ECOSYSTEM SERVICES IN HAWAIʻI THROUGH INTEGRATION OF HYDROLOGICAL AND PARTICIPATORY MODELS

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Assessing the current landscape of AI and sustainability literature:Identifying key trends, addressing gaps and challenges

The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.</p

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Modelling and optimisation of integrated urban energy systems for both heating and power

Taking into account the rapid increase of renewable energy power generation in the UK, the electrified heating represents an attractive solution for decarbonisation of heat in the long term. However, this will significantly increase the peak power demand in winter and bring further challenges to the grid. Therefore, this work aims to model and optimise a district-level multi-vector integrated energy system for both heating and power through technical and market analysis of using a variety of local renewable energy resources for electricity and heat. In this thesis, the integrated urban energy system is modelled and optimised in multi processes. As a target system, the heating and electricity demand of the University of Glasgow is used as a case study. In order to model the heating and electricity demand under different weather profiles, the heat demand of the buildings is modelled in an engineering model and a statistical model respectively to predict the hourly heat demand according to weather conditions; while the electricity demand is modelled considering both the building baseload and occupancy rate. In heat demand modelling, in order to distinguish the heat demand of each building from the data of whole campus provided by the Energy Center when the detailed building parameters are unknown, this work uses a bottom-up building energy model, which uses physical process of heat transfer to simulate the space heating of buildings, and proposes a Bayesian-based calibration method to calibrate the building parameters in the model. The results show that the Bayesian approach-based emulator performs better with fewer calibration times to find the optimal point, which is relable and efficient to calibrate the thermal parameters in building energy models. Due to the complexity of building a bottom-up building energy model, it is not easy to expand the model to larger areas or add more building samples in the model. Therefore, this work also builds a more general statistical model that can predict the heat demand of different types of buildings simply by giving weather conditions and building characteristics. This work uses artificial neural networks (ANN) technology to simulate the nonlinear relationship between weather conditions, building characteristics and heat demand. In order to improve the training efficiency of ANN, a new sensitivity analysis method is proposed to analyse the correlation between input variables and detect and remove the variables with low importance and the variables that have high importance but contain duplicated features. The result shows the proposed method can re duce training time by around 20% while achieving the same training and prediction performance compared with the traditional sensitivity analysis method. In the electricity demand modelling, the impact of randomness of occupants’ activity on power demand forecasting for buildings has been a difficult problem. In order to solve this problem, this work proposes two approaches for fitting and predicting the electricity demand of office buildings by splitting the time horizon for different occupancy rates. The first proposed approach splits the electricity demand data into fixed time periods and using linear regression approach to fit the building baseload and occupancy rate. The second proposed approach uses the ANN and fuzzy logic techniques to fit the building baseload, peak load, and occupancy rate with multi-variables of weather variables. The result shows that the proposed methods reduce the prediction error of electricity demand by 30% and 55% compared with the conventional ANN approach. To study the impact of electrified heating on buildings and the grid, an Integrated Energy Network (IEN) is established that includes the heat and electric demands of buildings, as well as the generation of local renewable resources and energy storage techniques. In order to rationally plan this new type of IEN based on electric heat pump (HP), this work studies and develops a particle swarm optimisation (PSO) algorithm-based optimisation size method to maximize the decarbonisation on building heating under limited investment cost. According to different source of electric driven, the IEN can be designed as a grid powered HP based heating system and a grid independent renewable heating system (RHS). For the grid powered IEN, this work formulates an operating scheme based on different electricity tariffs to reduce the operational cost of grid power. For the grid independent RHS, this work uses the PSO algorithm to optimise the size of local renewable resources, heat pumps and storage equipment based on the annual investment cost to minimise the total CO2 emission and reduce the operational cost of natural gas. This work provides a feasible solution for how to invest in RHS to replace the existing gas boiler/CHP based heating system. In summary, the significance of this study is to use of local renewable energy sources in electric heating taking into account the local weather conditions and the demand of heat and electricity to reduce carbon emissions in heating and electricity supply