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Abstract: This paper discusses the opportunities and challenges of implementing District Heating (DH) in Canada, with focus on the network design and operation. We selected for case study an urban area in Ottawa. First, we proved that the medium-temperature district heating (MTDH) (70°C≤ T supply ≤90°C) had better energy delivery performance than high-temperature district heating (HTDH) (T supply >100°C), decreasing the heat loss by approximately 40%. The low-temperature networks (T supply <60°C) achieved even lower heat losses, but they required additional capital investment. The implementation of low-temperature district heating (LTDH) should be considered, thanks to the capability of including more renewable energy and excess industrial waste heat. Next, the simulations show that DH can be implemented to supply present heating loads with medium temperature DH, and operate in the future at low temperature, after energy saving measures have been implemented in the buildings. Areas having linear heat densities greater than 3MWh/(myr) could economically be supplied by DH. Areas with linear heat density below 1.5MWh/(myr) are considered not practically feasible with the current energy market situation in Canada. The paper discusses critical issues and quantifies the performance of design concepts for DH supply to low heat density areas. DH is a fundamental energy infrastructure and is part of the solution for sustainable energy planning in Canadian communities. [Copyright &y& Elsevier]

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]

Most process integration techniques and optimization procedures are based on the implicit assumption that the unit operations and equipment are working efficiently, which is not always the case. Equipment performance analysis by means of key performance indicators is a prerequisite step before undertaking any optimization or improvement measure. However, there are no key performance indicators specifically tailored for the Kraft process equipment, and that take into account both the governing operating conditions and the design parameters. In this paper, new key performance indicators (KPIs) have been developed based on an in depth characterization and dimensional analysis of the Kraft process equipment using the Buckingham Pi (Π) theorem. These KPIs were used to identify process inefficiencies, diagnose causes of inefficiencies and propose remedial actions through a systematic approach. Once adopted, the KPIs proposed could serve to monitor systems performances and allow a tighter process control as they incorporate the design characteristics of the unit operations and their influencing operating conditions. [ABSTRACT FROM AUTHOR]

Abstract: To extract information about the Earth''s surface from Earth Observation data, a key processing step is the separation of pixels representing clear-sky observations of land or water surfaces from observations substantially influenced by clouds. This paper presents an algorithm used for this purpose specifically for data from the AATSR sensor on ENVISAT. The algorithm is based on the structure of the SPARC cloud detection scheme developed at CCRS for AVHRR data, then modified, calibrated and validated for AATSR data. It uses a series of weighted tests to calculate per-pixel cloud presence probability, and also produces an estimate of cloud top height and a cloud shadow flag. Algorithm parameters have been optimized for daytime use in Canada, and evaluation shows good performance with a mean daytime kappa coefficient of 0.76 for the ‘cloud’/‘clear’ classification when compared to independent validation data. Performance is independent of season, and is a dramatic improvement over the existing AATSR L1B cloud flag for Canada. The algorithm will be used at CCRS for processing AATSR data, and will form the basis of similar processing for data from the SLSTR sensors on Sentinel-3. [Copyright &y& Elsevier]