Short-term stability of slopes in Ankara clay

Imperial Users onl

Cool Roofs Savings and Penalties in Cold Climates: the Effect of Snow Accumulation on roof

Utilizing a cool roof is an efficient way to reduce a building’s usage of cooling energy, although it may increase the usage of heating energy in winter. In cold climates, during the winter, the sun angle is low, days are short, the sky is often cloudy, and most heating occurs during early morning or evening hours when the solar intensity is low. In addition, the roof may be covered with snow for most of the heating season. All these factors lead to wintertime heating penalties for cool roofs that are lower than what is commonly thought. We used DOE-2.1E to simulate energy consumption in several prototype office and retail buildings in four cold-climate cities in North America: Anchorage (AK), Milwaukee (WI), Montreal (QC), and Toronto (ON). The effects of sun angle, clouds, daytime duration, and heating schedules can be modeled with the existing capabilities of DOE-2. Snow on the roof provides an additional layer of insulation and increases the solar reflectance of the roof. To simulate four different types of snow on the roof, we defined a function consisting of the U-value and absorptivity of the roof on a daily basis. We used an average based on six years of meteorological data from the National Oceanic and Atmospheric Administration (NOAA) and Environment Canada to estimate the snow thickness on the roof. In the very cold climate of Anchorage, AK, the simulated annual heating energy consumptions of the prototype old retail building with a dark (warm) versus a cool roof (without considering the snow) are 123548 and 125848 MJ/100 m2, respectively (showing a 2300 MJ/100 m2 penalty for the cool roof). These numbers reduce slightly to 123216 and 124409 MJ/100 m2, respectively (showing 1193 MJ/100 m2 penalty for the cool roof), when “late-winter packed” snow is considered. In this way, for an old retail building in Montreal, a cool roof can save up to $62/100 m2. For a new, medium-sized office building with electric cooling and natural gas as heating fuel, a cool roof would save$4/100 m2 in Montreal, $14/100 m2 in Milwaukee and Anchorage, and$10/100 m2 in Toronto. Cool roof also saves maximum of 37$/100 m2 for a retail store building in Toronto. Cool roofs for the simulated buildings resulted in annual energy expenditure savings in all cold climate regions. A cool roof also reduces the electricity peak demand of the building during the cooling season; this effect is considered to be a practical method to improve the reliability of grids and plants or to prevent unwanted electricity shutdown on hot summer days. Cool roofs can reduce the peak electric demand of the retail buildings up to 1.9 and 5.4 W/m2 in Toronto and Montreal, respectively. Most HVAC systems are designed based on the peak summer cooling load. A cool roof can reduce the summer cooling load, which would lead to downsizing of HVAC systems. A downsized HVAC system can operate more efficiently throughout the year, including during the heating season

Cooling and heating energy performance of a building with a variety of roof designs; the effects of future weather data in a cold climate

Building engineers commonly use the Typical Meteorological Year (TMY) weather data for simulation and design purposes. However, the nature of TMY in excluding weather extremes makes them less suitable to investigate the effect of potential climate change on building design as climate change likely increases the frequency and magnitude of those extreme conditions. The current practice of designing buildings has lacked a clear method to incorporate future climate change trends. An approach is used to compare present weather simulation results of a commercial building with varying roof reflectance and insulation thermal resistance parameters with future year-by-year results which are affected by potential climate change. Future weather data for year-by-year simulations is obtained by “morphing” historical weather data with a General Circulation Model (HadCM3). Mean energy consumption and optimal roof configurations are discussed with regards to climate change over the study period, and are compared to results obtained with TMY data. Results show that increased roof solar reflectance always lead to less mean and less variant cooling energy consumption. The study shows the importance of considering possible future climate scenarios and in building energy performance design

Synthesis of imprinted polymers for the detection of tamoxifen or its metabolites and evaluation of their potential as drug carriers.

PhDRecent advances in the area of nanotechnology have led to interesting applications of nanomaterials in medicine, especially in the areas of imaging and treatment. This thesis presents the development of two molecularly imprinted polymers (MIPs) based on the same fluorescent functional monomer. One MIP, prepared in the bulk format, is investigated for its ability to detect tamoxifen and its metabolites. The other MIP synthesised in the nanogel format, holds the potential to be used as pH-responsive drug delivery system. Four objectives were identified within this project. The first was the design and synthesis of fluorescent functional monomer. Two coumarin derivatives carrying a polymerisable unit, for covalent bonding within the polymer, and a carboxylic moiety, for interaction site with the template, were synthesised and characterised. However, only one of them (the VCC: 6-vynilcoumarin-4-carboxylic acid) showed high fluorescent yield and was selected as functional monomer. The second objective involved the development of a detection system based on bulk MIP containing the VCC fluorescent monomer. This system proved effective in generating a detectable signal upon binding the analytes. The signal was observed as a quenching of the polymer fluorescence and it was proportional to the amount of target molecules detected. The third objective was the preparation of tamoxifen-imprinted nanogels for potential application in the drug delivery field. The optimisation of the procedure gave a set of NIP/MIP with the desired solubility, particle size and fluorescence emission. These nanogels were then employed in the last objective, which involved the toxicity study and evaluation of the drug loading on of transgenic line of zebrafish. The nanogels were non-toxic at the tested concentrations and the presence of tamoxifen was confirmed

Toward Resilient Building Design in Energy Performance under Climate Change

Building energy simulation is commonly used to evaluate the energy performance of buildings to support decisions made at the design stage or to quantify potential energy savings of various strategies for retrofitting existing buildings. However, in many cases, the anticipated performance through simulation output significantly deviates from actual measured data. A major reason for such discrepancy is due to uncertainty in the simulation inputs. One source of input uncertainty is weather data representing the climate condition. In order to predict the long-term performance of the buildings with energy simulation, modellers commonly use a single Typical Meteorological Year (TMY) weather data file which supposedly represents the climatic conditions. The single weather year file is composed of hourly resolution data from the most 12 representative calendar months of 30 years which are selected based on statistical similarity to long-term weather daily-averaged data. These weather files are synthetically constructed on historical weather data over a long period of time for an array of weather parameters, such as solar radiation, temperature, wind speed and others. The statistical procedure to construct the weather files depends on the weights assigned to these weather parameters. Under current practice, these weighting factors are universally assigned regardless of climatic locations nor the building application. This approach leads to energy performance predictions that deviate from the long-term averages. Nevertheless, the single weather file ignores the variation in building energy performance resulted from natural weather variation. This source of uncertainty becomes even more critical when the long-term superimposed effect driven by human and anthropogenic factors are added to natural variation. Historical weather data shows that compared to other regions, higher latitudes, including Canada, have been affected more by climate change, and it is expected that this change will be even more in the years to come. Uncertainty due to weather variation and climate change is one of the main reasons for unexpected actual energy performance. Under the changing climate, building's energy performance is expected to change significantly in the northern climates, including Canada. The current thesis mainly aims to address the two aforementioned issues with novel approaches: 1. Machine learning were deployed to extract the feature importance of the weather parameters in order to assign non-universal weighting factors straightly proportional to their impacts on energy performance of buildings. Weather files constructed with these systematically assigned weighting factors are climatic location and building type dependent. The newly constructed typical meteorological year weather files were applied to two different climatic locations to investigate the representativeness of these new weather files as compared to existing weather files and historical weather data of actual years. The representativeness was indicated in terms of the deviation in predicted energy performance of buildings between using the typical meteorological year weather file and actual historical weather data. The results indicated that typical meteorological year weather file based on the novel approach offers better prediction (with statistical significance) on energy performance for climatic locations with wider temperature range. As a result, the suggested method avoids potential under/oversizing of equipment and promotes energy conservation. 2. General circulation model (GCM) data considering various climate change scenarios based on socio-economic, population, land use, technology, and policies are used to provide information about future climatic condition. However, there are two primary challenges in application of data for building simulation: i. Bias in the models: considerable deviation can be found when the historical GCM data is compared to station observed weather data. ii. Inadequate resolution: GCM data has daily temporal resolution rather than the hourly resolution required in building energy simulation. In order to use this data for simulation purposes and better predict future building performance, further processing is conducted. A statistical bias-correction technique, known as the quantile-quantile method, is applied to remove the bias in the data in order to adapt GCMs to a specific location. The study then uses a hybrid classification-regression (K-Nearest Neighbour – Random Forest) machine learning algorithm to downscale the bias-corrected GCM data to generate future weather data at an hourly resolution for building energy simulation. In this case, the hybrid model is structured as a combined model, where a classification model serves as the main model together with an auxiliary regression model for cases when data is beyond the range of observed values. The proposed workflow uses observed weather data to determine similar weather patterns from historical data and uses it to generate future weather data, contrary to previous studies, which use artificially generated data. However, in cases where the future GCM data showed temperatures ranging outside of the observed data, the study applied a trained regression model to generate hourly weather data. The current study suggests a workflow that can be applied to global and regional models data to generate future weather files year by year for building simulation under various scenarios and, consequently, extreme weather characteristics are preserved for extreme or reliability analysis and design optimization. In addition, a novel method is introduced to find building design solutions under uncertainty of weather variation and climate change. The design options are architectural and envelop features at different levels. A full factorial design of experiment is used for large-scale simulations and training deep neural network surrogate models to assess energy performance of design alternatives under multiple future years under various climate change scenarios. The method with application of a novel performance indicator is applied to explore design space and find the design solutions that most probably contribute to meet building energy performance targets over the project's lifespan. This workflow takes into account the effect of weather variation under various climate change scenarios and suggests several design solutions that can be offered to stakeholders, architects, engineers, and third-parties including insurance companies. This way, design alternatives can be compared, and designs with a higher probability of success can be selected as a final solution. In addition, policy-makers can use the results and the suggested workflow to adopt and update national and provincial building energy codes such as National Energy Codes of Canada for Buildings (NECB) in line with the national policies following the Paris climate change agreement

Industrial symbiosis implementation by leveraging on process efficiency methodologies

Resource efficiency is a crucial step for manufacturing companies to improve their operations performance and to reduce waste generation. However, there is no guarantee of a zero waste scenario and companies need to look for new strategies to complement their resource efficiency vision. Therefore, it is important to enroll in an industrial symbiosis strategy as a means to maximize industrial value capturing through the exchange of resources (waste, energy, water and by-products) between different processes and companies. Within this, it is crucial to quantify and characterize the waste, e.g. to have clear understanding of the potential industrial symbiosis hot spots among the processes. For such characterization, it is proposed to use an innovative process efficiency assessment approach. This empowers a clear understanding and quantification of efficiency that identifies industrial symbiosis hot spots (donors) in low efficiency process steps, and enables a plausible definition of potential cold spots (receivers), in order to promote the symbiotic exchanges

A novel knowledge repository to support industrial symbiosis

The development of tools and methods supporting the identification of Industrial Symbiosis opportunities is of utmost importance to unlock its full potential. Knowledge repositories have proven to be powerful tools in this sense, but often fail mainly due to poor contextualization of information and lack of general applicability (out of the boundaries of specific areas or projects). In this work, a novel approach to the design of knowledge repositories for Industrial Symbiosis is presented, based on the inclusion and categorization of tacit knowledge as well as on the combination of mimicking and input-output matching approaches. The results of a first usability test of the proposed tool are also illustrated

Blended learning environments to foster self-directed learning

This book on blended learning environments to foster self-directed learning highlights the focus on research conducted in several teaching and learning contexts where blended learning had been implemented and focused on the fostering of self-directed learning. Several authors have contributed to the book, and each chapter provides a unique perspective on blended learning and self-directed learning research. From each chapter, it becomes evident that coherence on the topics mentioned is established. One of the main aspects drawn in this book, and addressed by several authors in the book, is the use of the Community of Inquiry (CoI) framework when implementing teaching and learning strategies in blended learning environments to foster self-directed learning. This notion of focusing on the CoI framework is particularly evident in both theoretical and empirical dissemination presented in this book. What makes this book unique is the fact that researchers and peers in varied fields would benefit from the findings presented by each chapter, albeit theoretical, methodological or empirical in nature – this, in turn, provides opportunities for future research endeavours to further the narrative of how blended learning environments can be used to foster self-directed learning

Material reutilization cycles across industries and production lines

The concept of Industrial Symbiosis aims at organizing industrial activity like a living ecosystem where the by-product outputs of one process are used as valuable raw material input for another process. A significant method for the systematic planning of Industrial Symbiosis is found in input–output matching, which is aimed at collecting material input and output data from companies, and using the results to establish links across industries. The collection and classification of data is crucial to the development of synergies in Industrial Symbiosis. Public and private institutions involved in the planning and development of Industrial Symbiosis rely however on manual interpretation of information in the course of creating synergies. Yet, the evaluation and analysis of these data sources on Industrial Symbiosis topics is a tall order. Within this chapter a method is presented which describes value creation activities according to the Value Creation Module (VCM). They are assessed before they are integrated in Value Creation Networks (VCNs), where alternative uses for by-products are proposed by means of iterative input-output matching of selected value creation factors

Blended learning environments to foster self-directed learning

This book on blended learning environments to foster self-directed learning highlights the focus on research conducted in several teaching and learning contexts where blended learning had been implemented and focused on the fostering of self-directed learning. Several authors have contributed to the book, and each chapter provides a unique perspective on blended learning and self-directed learning research. From each chapter, it becomes evident that coherence on the topics mentioned is established. One of the main aspects drawn in this book, and addressed by several authors in the book, is the use of the Community of Inquiry (CoI) framework when implementing teaching and learning strategies in blended learning environments to foster self-directed learning. This notion of focusing on the CoI framework is particularly evident in both theoretical and empirical dissemination presented in this book. What makes this book unique is the fact that researchers and peers in varied fields would benefit from the findings presented by each chapter, albeit theoretical, methodological or empirical in nature – this, in turn, provides opportunities for future research endeavours to further the narrative of how blended learning environments can be used to foster self-directed learning