• Energy Research
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Abstract In Canada, space heating accounts for the largest proportion of energy consumption in residential buildings. Therefore, accurately predicting the heating demand and interior temperature of a residential building plays a vital role in estimating the building’s total energy consumption with the consideration of thermal comfort. The prediction results obtained through different models could be used to develop predictive controllers to achieve peak shifting as well as to provide utility providers with valuable information for electric power distribution. Common methods to predict heating demand mainly include physical models and statistical methods. This study used two physical models (i.e. TRNSYS model and TRNSYS-CONTAM model) and one statistical model using supervised machine learning algorithm to predict the heating demand as well as the indoor temperature of a residential building, located in Quebec, Canada. Results show that TRNSYS-CONTAM model has higher accuracy than TRNSYS model no matter in terms of interior air temperature or heating demand prediction, while the statistical model shows better interior air temperature prediction result than physical models.

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the “high hazardous” category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.

Abstract Communication between vehicles and road infrastructure can enable more efficient use of the road network and hence reduce congestion in urban areas. This improvement can be enhanced by distributed control due to its lighter computational load and higher reliability. Despite favourable impacts on traffic, little is known about the effects of such systems on near-road air quality. In this study, an End-To-End (E2E) dynamic distributed routing algorithm in Connected and Automated Vehicles (CAVs) was applied in downtown Toronto, to identify whether benefits to network throughput were associated with lower near-road NO2 concentrations. We observe significant reductions in the emissions of Greenhouse Gases (GHGs) with increased penetration of CAVs. Nonetheless, at times, the emissions of nitrogen oxides (NOx) increased with higher CAVs. Besides, a higher frequency and severity of NO2 hot-spots were observed under a 100% CAV scenario. Impacts of the proposed system on electric energy consumption in a full electric vehicle network were also investigated, indicating that the addition of CAVs that are electric did not contribute to high energy savings. We propose that such new transformative technologies in transportation should be designed with air pollution and public health goals.

Abstract Conversion of carbon contained in the solid residues (tars + biochar) derived from urban biomass gasification named herein TC would allow enhancing the yield of carbon species (CO/CO2) in synthetic gas. For this purpose, three low cost materials have been tested as possible catalysts: iron species (reduced Fe), bone meal (BM), and ashes (ash) recovered from biochar complete oxidation. The parametric study used the following as variables: air GHSV, onset of reaction temperature, reaction time to optimize CO/CO2 molar ratio and tar content in the produced gas. Results showed an autocatalytic effect of biochar leading to the catalytic conversion of approximately 78% of tars by the native metals contained in TC. The catalytic effect was further enhanced by adding Fe, BM, and extra ash. Addition of Fe catalyst resulted in significant heat generation (temperature increase of ca. 500 °C) and a twofold decrease in reaction time to consume all the carbon. Use of ash and BM as catalysts exhibit heat generation comparable to Fe, along with an improved reaction time, complete tars conversion and a CO/CO2 molar ratio to above 1.3 in the produced gas.

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method.

AbstractCellulose and lignin nanoparticles are high‐value‐added products obtained from lignocellulosic biomasses through several steps of cellulose purification and lignin extraction. These steps drastically reduce the potential feedstock revenue when carried out as stand‐alone methodologies. To increase biomass yields, we describe here a strategy to design a biorefinery focused on producing cellulose and lignin nanoparticles as main products, but also aim to recover and benefit from other biomass components using only water‐based processes. Sequential pressurized liquid extractions and diluted acid and alkaline treatments were carried out to fractionate elephant grass biomass, yielding (for every 100 g of biomass): 30 g of cellulose pulp (converted to 9 g of cellulose nanocrystals and 9 g of cellulose nanofibers); 10 g of lignin (used to produce 8.5 g of stable colloidal lignin nanoparticles by probe‐sonication in water); 7.5 g of extractives (e.g. sterols and phenolics) and 23 g of xylose (converted to 4.1 g of furfural). Alternatively, to allow for the flexible use of the cellulose fraction in the proposed biorefinery, 22 g of glucose could be produced by enzymatic hydrolysis. The results demonstrate that water‐based processes are suitable for a holistic use of biomass, providing a comprehensive set of high‐value‐added co‐products that are renewable and cost‐effective chemical, cosmetic, food, polymer and pharmaceutical solutions.

A special version of the NOAA HYSPLIT_4 model has been developed and used to estimate the atmospheric fate and transport of mercury in a North American modeling domain. Spatial and chemical interpolation procedures were used to expand the modeling results and provide estimates of the contribution of each source in a 1996 anthropogenic US/Canadian emissions inventory to atmospheric mercury deposition to the Great Lakes. While there are uncertainties in the emissions inventories and ambient data suitable for model evaluation are scarce, model results were found to be reasonably consistent with wet deposition measurements in the Great Lakes region and with independent measurement-based estimates of deposition to Lake Michigan. Sources up to 2000 km from the Great Lakes contributed significant amounts of mercury through atmospheric transport and deposition. While there were significant contributions from incineration and metallurgical sources, coal combustion was generally found to be the largest contributor to atmospheric mercury deposition to the Great Lakes.

The transportation impact on pollution and global climate change, has forced the automotive sector to search for more ecological solutions. Owing to the different properties of Fuel Cell (FC), real potential for reducing vehicles’ emissions has been witnessed. The optimization of FC integration within Electric Vehicles (EVs) is one of the original solutions. This paper presents an innovating solution of multi-stack Fuel Cell Electrical Vehicle (FCEV) in terms of efficiency, durability and ecological impact on environment. The main objective is to illustrate the interest of using the multi-stack FC system on the global autonomy, cycling, and efficiency enhancement, besides optimizing its operation performance.