Industry is heavily dependent on the process of combustion and with a projected rapid increase for the demand of combustion-derived energy it is imperative to expose a new age of engineering professionals to the discipline of combustion engineering. One purpose of this study was to modify an existing scaled-down industrial furnace and to retrofit it with the ability to interchange burners for academic application and combustion testing. A number of available industrial burners are presented and their qualities and drawbacks discussed. The modification of an existing scaled-down industrial tunnel furnace is proposed in this work with the objective of providing users with exposure to the control and safe operating strategies associated with industrial combustion. The furnace system simulates a square-shaped tunnel geometry commonly found in industrial applications. A single nozzle mix burner is mounted along the furnace axis and operated with supporting equipment such as a burner control safeguard, a gas train, and an air supply. Details of the furnace are provided in this work. The concept of radiative heat transfer within a combustion enclosure is demonstrated through furnace simulation with Hottel’s Zone Method. / Thesis (Master, Chemical Engineering) -- Queen's University, 2010-07-19 09:50:22.797

Reservoir sedimentation is a major issue for water resource management around the world. It has serious economic, environmental, and social consequences, such as reduced water storage capacity, increased flooding risk, decreased hydropower generation, and deteriorated water quality. Increased rainfall intensity, higher temperatures, and more extreme weather events due to climate change are expected to exacerbate the problem of reservoir sedimentation. As a result, sedimentation must be managed to ensure the long-term viability of reservoirs and their associated infrastructure. Effective reservoir sedimentation management in the face of climate change necessitates an understanding of the sedimentation process and the factors that influence it, such as land use practices, erosion, and climate. Monitoring and modelling sedimentation rates are also useful tools for forecasting future impacts and making management decisions. The goal of this research is to create long-term reservoir management strategies in the face of climate change by simulating the effects of various reservoir-operating strategies on reservoir sedimentation and sediment delta movement at Mangla Reservoir in Pakistan (the second-largest dam in the country). In order to assess the impact of the Mangla Reservoir's sedimentation and reservoir life, a framework was developed. This framework incorporates both hydrological and morphodynamic models and various soft computing models. In addition to taking climate change uncertainty into consideration, the proposed framework also incorporates sediment source, sediment delivery, and reservoir morphology changes. Furthermore, the purpose of this study is to provide a practical methodology based on the limited data available. In the first phase of this study, it was investigated how to accurately quantify the missing suspended sediment load (SSL) data in rivers by utilizing various techniques, such as sediment rating curves (SRC) and soft computing models (SCMs), including local linear regression (LLR), artificial neural networks (ANN) and wavelet-cum-ANN (WANN). Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for SCMs development. Based on an evaluation of the outcomes of all leading models for SSL estimation, it can be concluded that SCMs are more effective than SRC approaches. Additionally, the results also indicated that the WANN model was the most accurate model for reconstructing the SSL time series because it is capable of identifying the salient characteristics in a data series. The second phase of this study examined the feasibility of using four satellite precipitation datasets (SPDs) which included GPM, PERSIANN_CDR, CHIRPS, and CMORPH to predict streamflow and sediment loads (SL) within a poorly gauged mountainous catchment, by employing the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANN), random forests (SWAT-RF), and support vector regression (SWAT-SVR). SCMs were developed using the outputs of un-calibrated SWAT hydrological models to improve the predictions. The results indicate that during the entire simulation, the GPM shows the best performance in both schemes, while PERSIAN_CDR and CHIRPS also perform well, whereas CMORPH predicts streamflow for the Upper Jhelum River Basin (UJRB) with relatively poor performance. Among the best GPM-based models, SWAT-RF offered the best performance to simulate the entire streamflow, while SWAT-ANN excelled at simulating the SL. Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating streamflow and SL, particularly in complex terrain where gauge network density is low or uneven. The third and last phase of this study investigated the impact of different reservoir operating strategies on Mangla reservoir sedimentation using a 1D sediment transport model. To improve the accuracy of the model, more accurate boundary conditions for flow and sediment load were incorporated into the numerical model (derived from the first and second phases of this study) so that the successive morphodynamic model could precisely predict bed level changes under given climate conditions. Further, in order to assess the long-term effect of a changing climate, a Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 for the 21st century is used. The long-term modelling results showed that a gradual increase in the reservoir minimum operating level (MOL) slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of sustainable reservoir use for societal benefit. In summary, this study provides comprehensive insights into reservoir sedimentation phenomena and recommends an operational strategy that is both feasible and sustainable over the long term under the impact of climate change, especially in cases where a lack of data exists. Basically, it is very important to improve the accuracy of sediment load estimates, which are essential in the design and operation of reservoir structures and operating plans in response to incoming sediment loads, ensuring accurate reservoir lifespan predictions. Furthermore, the production of highly accurate streamflow forecasts, particularly when on-site data is limited, is important and can be achieved by the use of satellite-based precipitation data in conjunction with hydrological and soft computing models. Ultimately, the use of soft computing methods produces significantly improved input data for sediment load and discharge, enabling the application of one-dimensional hydro-morphodynamic numerical models to evaluate sediment dynamics and reservoir useful life under the influence of climate change at various operating conditions in a way that is adequate for evaluating sediment dynamics.:Chapter 1: Introduction Chapter 2:Reconstruction of Sediment Load Data in Rivers Chapter 3:Assessment of The Hydrological and Coupled Soft Computing Models, Based on Different Satellite Precipitation Datasets, To Simulate Streamflow and Sediment Load in A Mountainous Catchment Chapter 4:Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan Chapter 5:Conclusions and Recommendations

The utilization of renewable energy resources such as wind and solar energy for electric power supply has received considerable attention in recent years due to adverse environmental impacts and fuel cost escalation associated with conventional generation. At the present time, wind and/or solar energy sources are utilized to generate electric power in many applications. Wind and solar energy will become important sources for power generation in the future because of their environmental, social and economic benefits, together with public support and government incentives. <p>The wind and sunlight are, however, unstable and variable energy sources, and behave far differently than conventional sources. Energy storage systems are, therefore, often required to smooth the fluctuating nature of the energy conversion system especially in small isolated applications. The research work presented in this thesis is focused on the development and application of reliability and economic benefits assessment associated with incorporating wind energy, solar energy and energy storage in power generating systems. A probabilistic approach using sequential Monte Carlo simulation was employed in this research and a number of analyses were conducted with regards to the adequacy and economic assessment of generation systems containing wind energy, solar energy and energy storage. The evaluation models and techniques incorporate risk index distributions and different operating strategies associated with diesel generation in small isolated systems. Deterministic and probabilistic techniques are combined in this thesis using a system well-being approach to provide useful adequacy indices for small isolated systems that include renewable energy and energy storage. The concepts presented and examples illustrated in this thesis will help power system planners and utility managers to assess the reliability and economic benefits of utilizing wind energy conversion systems, solar energy conversion systems and energy storage in electric power systems and provide useful input to the managerial decision process.

The utilization of renewable energy resources such as wind and solar energy for electric power supply has received considerable attention in recent years due to adverse environmental impacts and fuel cost escalation associated with conventional generation. At the present time, wind and/or solar energy sources are utilized to generate electric power in many applications. Wind and solar energy will become important sources for power generation in the future because of their environmental, social and economic benefits, together with public support and government incentives. <p>The wind and sunlight are, however, unstable and variable energy sources, and behave far differently than conventional sources. Energy storage systems are, therefore, often required to smooth the fluctuating nature of the energy conversion system especially in small isolated applications. The research work presented in this thesis is focused on the development and application of reliability and economic benefits assessment associated with incorporating wind energy, solar energy and energy storage in power generating systems. A probabilistic approach using sequential Monte Carlo simulation was employed in this research and a number of analyses were conducted with regards to the adequacy and economic assessment of generation systems containing wind energy, solar energy and energy storage. The evaluation models and techniques incorporate risk index distributions and different operating strategies associated with diesel generation in small isolated systems. Deterministic and probabilistic techniques are combined in this thesis using a system well-being approach to provide useful adequacy indices for small isolated systems that include renewable energy and energy storage. The concepts presented and examples illustrated in this thesis will help power system planners and utility managers to assess the reliability and economic benefits of utilizing wind energy conversion systems, solar energy conversion systems and energy storage in electric power systems and provide useful input to the managerial decision process.