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Geothermal systems installed at intermediate depths (∼1–2 km) in sedimentary basins represent an attractive option to provide low-carbon heat in cold countries, even where the geothermal gradient is low, using either closed- or open loop systems combined with heat pumps. However, the installation of such systems is costly and risky due to the uncertainty associated with the geological, hydrogeological and thermal properties of the targeted unit(s). Additionally, the performance of different types of systems is seldom compared in the literature for a given geological context. This paper presents a numerical approach to readily assess the maximum energy that could be produced by different geothermal systems (a deep borehole heat exchanger (DBHE) and three types of doublets) and compare their performance for preselection purposes. Since deep formations are often poorly characterized, the sensitivity of these systems to the most impactful site properties is evaluated. For each scenario of site properties, the systems were simulated for different operation flow rates. The maximum usable flow rate is determined from simulation results. The flow rate maximizing net energy production is used for the DBHE, while the maximum flow rate ensuring safe injection pressure has been selected for the doublets. The heating power produced per length drilled and system COP are calculated for each simulation and interpolated for the maximum usable flow rate to fairly compare the systems' performance. An illustrative case using the Bécancour area (eastern Canada) highlights that, even if the site stratigraphy and properties have been relatively well characterized previously, uncertainty regarding key properties significantly affects simulation results and, consequently, the choice of the geothermal system to install. Our numerical approach is intended as a decision-making aid in order to properly plan the installation of these expensive systems. [Display omitted] • New method for pre-selecting 1–2 km geothermal systems and assess heating potential. • Compares the DBHE and geothermal doublet performances for various designs. • Includes a sensitivity analysis to permeability, thermal conductivity and heat flux. • Determines the maximum usable flow rate for each system and scenario. • An application demonstrates the need to consider the uncertainty of rock properties. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is a fundamental sector of the economy of many countries. However, this sector requires real collaboration and initiatives from stakeholders to reduce its significant consumption of energy and emission of greenhouse gases. Heat exchangers are examples of equipment in pulp mills that are subjected to undesirable and complex phenomena such as evolution of fouling over time, which leads to inefficiency in terms of energy consumption and unplanned shutdowns, resulting in ineffective maintenance strategies and production costs. Therefore, there is a clear need to develop an accurate predictive maintenance tool that helps mill operators avoid such situations. It is necessary for that tool to effectively track the fouling evolution level and, based on it, deploy a reliable prognostics approach to estimate more accurately the time-to-clean of this equipment. This study presents a new hybrid prognostics approach for fouling prediction in heat exchangers. The proposed approach relies on the fusion of information of different prediction horizons to estimate the time-to-clean. Employing long short-term memory, it allows adaptation of long-term predictions by accurate short-term predictions using multiple non-linear auto-regressive exogenous models. This fusion not only captures the changes in degradation trend over time, but also ensures a good accuracy of prognostics results in both the short- and long-term horizons for planning maintenance actions. The effectiveness of the proposed approach was successfully proven on real industrial data collected from a pulp mill heat exchanger located in Canada. • New adaptive prognostic approach based on fusion of short and long-term predictors. • Selection of relevant physical parameters for monitoring of fouling evolution. • Accurate long-term fouling prediction by using short-term predictors. • High accuracy of fouling time-to-clean in a real heat exchanger plant. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions are currently at the crux of political, environmental, technological, and cultural discussions due to climate change. A drastic reduction of GHG emissions is needed in order to mitigate potentially catastrophic climate change impacts. Thus, thoroughly understanding emission sources is imperative. A disaggregated analysis of Canada’s future GHG emission projections has not yet been conducted. The objectives in this paper are to assess disaggregated GHG emissions in Canada for the years 2014, 2030, and 2050, and analyze the results through Sankey diagrams. Emissions are calculated using a bottom-up multi-regional accounting-based Long-range Energy Alternative Planning systems model. Each major economic sector in Canada is analyzed including the electricity generation, residential, commercial and institutional, industrial, transportation, and agriculture sectors. The emissions released in these sectors are traced to the resources and end-uses responsible. Results are presented for Canada and for provinces individually. GHG emissions contained in exported resources are evaluated. Results show that Canada’s business-as-usual GHG emissions will grow from 732 million tonnes in 2014 to 780 and 798 million tonnes in 2030 and 2050, respectively. Canada exports more emissions contained in resources than it emits. Per capita emissions intensity will fall by 14% between 2014 and 2050. Results are compared to climate targets and key areas of GHG mitigation potential are identified. Alberta’s oil and gas sector and Ontario’s transportation sector are the two single largest sectoral sources of emissions by 2050. This research can help policy makers, innovators, and the public better understand GHG emissions, which can lead to more effective GHG mitigation. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to explore deep decarbonization pathways for the Canadian energy sector that would allow Canada to participate in global mitigation efforts to keep global mean surface temperatures from increasing by more than 2 °C by 2100. Our approach consists in deriving minimum cost solutions for achieving progressive emission reductions up to 2050 using the North American TIMES Energy Model (NATEM), a detailed multi-regional and integrated optimization energy model. With this model, we analyze a baseline and two 60% reduction scenarios of combustion related emissions by 2050 from 1990 levels, with different assumptions regarding projected demands for energy services and availability of technology options for carbon mitigation. The first reduction scenario includes only well-known technologies while the second one considers additional disruptive technologies, which are known but are not fully developed commercially. Results show that three fundamental transformations need to occur simultaneously in order to achieve ambitious GHG emission reduction targets: electrification of end-use sectors, decarbonization of electricity generating supply, and efficiency improvements. In particular, our results show that electricity represents between 52% and 57% of final energy consumption by 2050, electricity generating supply achieves nearly complete decarbonization by 2025 and final energy consumption decreases by 20% relative to the baseline by 2050. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bibliometric analysis of heat and mass transfer and its management in PEMFC was carried out. • The number of annual publications has experienced a process of increasing, decreasing and increasing. • J Power Sources, J Electrochem Soc and Appl Energy are the authoritative journal in this field. • Water and heat management needs to be further optimized. Heat and mass transfer and its management is an important part of proton exchange membrane fuel cells (PEMFCs), which has a decisive impact on the performance and durability of PEMFCs. In order to comprehend the research progress and hotspots in heat and mass transfer and its management of PEMFC, 3977 literatures related to this field (up to 26th July 2022) were obtained from the Web of Science Core Collection database. Based on the mapping knowledge domain (MKD) and the VOSviewer visualization software, the number of publications, citation frequency, keyword co-occurrence, and clustering were counted and analyzed. The research progress, hotspots, and trends in the field of heat and mass transfer and its management of PEMFC were then summarized. The results show that since 2000, the annual quantity of publications in this field has experienced a period of increasing, decreasing, and then increasing, while in recent years, it is in a period of rapid growth. This field has received extensive attention in China, the United States and Canada, with many researchers, among which the quality of publications from Canada is relatively high. Related studies have been published in energy and electrochemistry journals such as the International Journal of Hydrogen Energy and the Journal of the Electrochemical Society. The Journal of Power Sources is the authoritative journal in this field with the largest total number of publications and total citation frequency. According to the keyword co-occurrence network map, there are four clusters in this field, and each cluster has a different research focus. Among them, cluster 4 has only one keyword, humidification. This field can be roughly divided into three research stages, namely, the exploration of gas–liquid two-phase flow characteristics in PEMFC, the development of PEMFC heat and mass transfer and its management, and the water and heat management and performance optimization of PEMFC. In order to achieve the development goals of high-power density, high reliability and long-life of PEMFC, the research of the third stage should be further deepened in future work. This paper will provide reference for scholars engaged in the field of PEMFC heat and mass transfer and its management. [ABSTRACT FROM AUTHOR]

In terms of energy resources, Canada is an important player on the world scene. However, the energy systems of the Canadian provinces and territories are much diversified and a national energy strategy is missing in order to optimize the management of energy systems. The objective of this paper is twofold. First, we introduce TIMES-Canada, a new multi-regional energy model that has been developed using the most advanced TIMES optimization modeling framework, while keeping a very high level of details in the database (5000 specific technologies; 400 commodities) compared with other Canadian energy models. Second, we define and analyze possible futures for the Canadian integrated energy system on a 2050 horizon, under five different baselines: a Reference scenario as well as four alternate scenarios corresponding to different oil prices (Low and High) and socio-economic growth trends (Slow and Fast). In our Reference scenario, we show that the Canadian final energy consumption is expected to increase by 43% between 2007 and 2050. The Fast scenario leads to the maximum increase compared with the Reference scenario (21% in 2050). In all scenarios, oil products will continue to dominate on the long term, although in a decreasing proportion over time (from 43% in 2007 to 29% in 2050) in favor of electricity (31% of the additional demand in 2050) and biomass/biofuels. Regarding the corresponding optimal energy production paths, we illustrate two main trends: (1) a gradual replacement of onshore conventional oil & gas sources by unconventional and offshore sources (oil sands is expected to represent half of the production in 2050), and (2) a significant penetration of renewables in the electricity mix is shown after 2035 due to increases in oil import prices and decreases in renewable technology costs. The development and calibration of such a detailed technology-rich model represent an important contribution for Canada: TIMES-Canada is the only optimization model covering in details the large diversity of provincial energy systems on a long term horizon. [ABSTRACT FROM AUTHOR]

• Two new advanced configurations for CO 2 capture in pulp mill was investigated. • A rate-based model in ProMax® 4.0 was used for modeling the CO 2 capture process. • Techno-economic assessment was analyzed for the new advanced configurations. • The combined effect of carbon tax and CO 2 sales price on the NBSK price was analyzed. • CO 2 capture plant in pulp mill only led to a slight increase in the NBSK pulp price. This project study involves the techno-economic assessment of two new advanced configurations for amine-based carbon dioxide capture from a pulp mill. The newly proposed advanced configurations include advanced rich amine four split (ARA4S), and advanced rich amine three split with desorber inter-heating (ARA3S-DI) which is aimed at reducing carbon capture cost and carbon emissions. The rate-based model in ProMax® 4.0 was used for the simulation study while the flue gas was provided by a 365,000 air dry tonne of pulp (ADt Pulp) per annum pulp mill in British Columbia, Canada. Comparative analysis revealed that the Capture Cost (US$/tCO 2 and US$/ADt Pulp) of the 5 kmol/m3 MEA system was lowest for the conventional configuration, while for the 2 kmol/m3 AMP-3 kmol/m3 MEA blend it was lowest for the advanced rich amine 3-split with desorber inter-heating configuration. In addition, the Capture Costs of the MEA solution with a conventional configuration system is 8.7% higher than the AMP-MEA blend with advanced rich amine three split with desorber inter-heating configuration. Sensitivity analysis revealed that the combined effect of a carbon tax (US$ 40/tCO 2), CO 2 sales price (US$ 40/tCO 2) and advanced configuration only led to a slight increase (2.6% for MEA and 2.1% for AMP-MEA blend) in the price of the northern bleached softwood kraft pulp. With an even better-performing solvent system and process configuration, the price of NBSK pulp may not be affected given the indicated CO 2 tax and CO 2 price regime. [ABSTRACT FROM AUTHOR]

This paper presents the development, implementation, and commissioning of two different Energy Management Systems (EMSs) for the Canadian Renewable Energy Laboratory (CANREL), a microgrid testbed located in Guelph, ON, Canada, for the existing hardware, software, and communication infrastructure, which constrained the implementation options. A Rule-based EMS (RBEMS), which is typically found in microgrid controllers nowadays, and an implementation of an Optimization-based EMS (OBEMS), which is not usual in today's controllers, are proposed, tested, and demonstrated in the microgrid testbed. The RBEMS consists of a state machine that represents the commitment of different genset units in the system and the curtailment of load and renewable generation. The OBEMS is based on a unit commitment model for microgrids that minimizes the generation and curtailment costs, while operating the microgrid equipment according to technical limits. Both EMS systems are integrated into a Python application which integrates various open-source packages and solvers, making it affordable, flexible and easy to replicate and upgrade. The successful implementation and performance of the EMS is discussed, showing that the components of the microgrid follow the dispatch commands, with the OBEMS yielding better overall results than the RBEMS, as expected, using the existing communications links and maintaining the stability of the microgrid. • Two EMS algorithms were implemented and tested in a microgrid testbed. • The algorithms were coded using open-source programming languages and solvers. • The optimization-based EMS showed a better performance in terms of unitary energy costs. • The EMS tests exhibited typical issues in microgrid operation. [ABSTRACT FROM AUTHOR]

• Using deep temporal convolutional networks for building simulation surrogate models. • Deep network processes annual hourly weather time series data (≈ 150,000 inputs). • Accurate emulation of simulation outcomes for all locations in Canada. • 3% error in estimating annual heating demand for unseen locations and building designs. • Reasonable accuracy ( R 2 = 0.92) in estimating hourly demands given weather data. Surrogate models can emulate physics-based building energy simulation with a machine learning model trained on simulation input and output data. The trained model is extremely fast to run, allowing us to estimate simulation outcomes for thousands of different building designs in seconds. Recent studies have shown the diverse benefits for sustainable building design. Surrogates were applied to provide rapid feedback at the early design stage, to accelerate sensitivity analysis, uncertainty analysis and design optimization, or to improve building model calibration. However, the current process of surrogate modelling offers much room for improvement. In particular, a surrogate model is bound to the specific building design problem it has been trained for. This includes a specific site, requiring time-intensive retraining if the building performance at another location is to be analysed. In this paper, we develop a single surrogate model that spans arbitrarily many locations. For that purpose, we are among the first to use a deep temporal convolutional neural network to process annual multivariate weather data with hourly resolution (≈ 150,000 inputs). The network learns features relevant to estimate heating or cooling demand. We combine these location-specific weather features with building design parameters to serve as input to a single surrogate model (feed-forward neural network). In a case study with 569 weather files from locations in Canada, we show that the surrogate model deviates by less than 3% when predicting annual heating demand for new building designs at locations outside of the training data set. [ABSTRACT FROM AUTHOR]

• Data availability and cost can be prohibitive barriers for energy modeling. • We use a statistical method to predict power plant parameters using available data. • We apply two learning methods: linear regression and k-nearest-neighbors. • These approaches are applied in the context of U.S. and Canadian power plant data. • Results show reasonable predictions of heatrates and, to a lesser extent, costs. Energy systems models are used to perform energy and environmental policy analysis, inform company strategy, and understand implications of technological change. Although open-source models can promote transparency and reproducibility, data availability and cost can be prohibitive barriers for researchers and other stakeholders. This paper presents a novel application of a statistical approach to predict unknown power plant parameters in Canada using available data from the United States, which can be applied in other settings where critical model inputs are missing. We apply two statistical learning methods, linear regression and k -nearest-neighbors, and compare their performance on unseen portions of the United States data before applying the learned functions to unknown Canadian data. Results indicate that reasonable predictions of heatrates and, to a lesser extent, operation and maintenance costs are possible even with limited data about age, capacity, and power plant types. The nearest-neighbor approach generally outperforms linear regressions for the datasets and applications to power plant parameters investigated here. [ABSTRACT FROM AUTHOR]

• A Multi-Dimensional Hypothetical Fuzzy Risk Simulation model is developed. • Decision-makers' preference is reflected to develop the optimized policies. • Multi-dimensional risks in 2030 of various policy options are revealed. • A special case study of the Province of Saskatchewan, Canada, is conducted. • Hypothetical Extraction Method is used to estimate the comprehensive performances. Changing climate is one of the most challenging environment issues worldwide. The objective of this paper is to develop a Multi-Dimensional Hypothetical Fuzzy Risk Simulation Model to facilitate the Greenhouse Gases mitigation policy development and multi-dimensional risk simulation. In detail, the comprehensive performances of various industries are evaluated and analyzed through Hypothetical Extraction Method. The preferences of decision-makers are considered through Analytic Hierarchy Process and Fuzzy Technique for Order Preference by Similarities to Ideal Solution method to develop the optimized Greenhouse Gases mitigation policies. The multi-dimensional risks of optimized Greenhouse Gases mitigation policies are simulated through RAS method. A detailed case study of the Province of Saskatchewan, Canada, is conducted to illustrate the potential benefits of the proposed model and support the Greenhouse Gases mitigation policy development. It is found that Electric power generation, transmission and distribution sector is the key industry in Saskatchewan. The government supports are suggested to be allocated to the Electric power generation, transmission and distribution sector, since it will benefit the province from environmental, economic, and urban metabolic perspectives. [ABSTRACT FROM AUTHOR]

• A rock-pile waste heat storage is proposed, and its corresponding numerical model is developed. • Both fluid flow and heat transfer are validated against experimental data. • Local Thermal Equilibrium and Non-Equilibrium approaches are compared. • Parametric studies are performed on thermo-physical properties. • Payback time analysis is performed, and the system is economically justified. Remote communities in arctic climates are solely dependent on diesel generators for continuous power supply due to their detached loci from national power gridlines or natural gas pipelines. Moreover, to get along with the harsh, long winters, these communities directly or indirectly depend on fossil-fuel based heating systems. Due to the lower efficiency of the conventional diesel engines, these generators discard a significant amount of heat through the exhaust. Occasionally, during the wintertime, this waste heat from the exhaust is used directly as a heat source after recovery. However, during summer, when the heating demand is lower or nil, this heat is commonly discarded, and its energy potential wasted. A rock-pile based seasonal thermal energy storage is a viable solution that can sustainably resolve this issue. This paper presents a validated numerical study that focuses on the coupling of a waste heat recovery system to a diesel exhaust stream offering a performance assessment of the proposed rock-pile waste heat storage system in a remote, off-grid community located in northern Canada. It examines the impacts of local thermal equilibrium versus non-equilibrium approaches as well as temperature-dependent properties, variation of air mass flow rate, particle size and finally the thermo-physical properties on the proposed model. The presented results show that air should be treated as an ideal gas and that the local thermal non-equilibrium approach should be used for its considerably higher accuracy. Techno-economic assessment of the system resulted in a relatively short payback period of less than six years. [ABSTRACT FROM AUTHOR]