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AbstractConcrete is a valuable construction material with high mechanical strength and durability, used extensively in the construction industry. It is produced by mixing sand, stones, cement, and water in different proportions depending on the desired quality of the final product. Water reducers are additional chemical ingredients used in concrete to reduce the quantity of water required in the concrete mixture. When added to concrete, water reducers increase the workability and flowability of concrete in the freshly mixed state and improve the mechanical strength and durability of the final hardened product. This review paper describes the different types and applications of concrete water reducers used in the construction industry including their working mechanisms and fluidity effects on concrete properties. It discusses the production of synthetic and bio‐based concrete water reducers and reviews the present challenges involved in the preparation of bio‐based concrete water reducers from renewable resources. © 2023 Society of Industrial Chemistry and John Wiley & Sons Ltd.

Abstract Bitumen recovery by steam-solvent coinjection involves the coupled thermal/compositional mechanisms for reduction of bitumen viscosity. Reliable design of such processes requires reservoir flow simulation based on a proper phase-behavior model so that the oleic-phase viscosity near the steam-chamber edge can be modeled reliably. However, the effect of bitumen characterization (e.g., the number of pseudo components used) on steam-solvent coinjection simulation has not been studied in detail, and can be realized only after running multiple reservoir simulations, which is time consuming. There are two main objectives in this paper. One is to develop a reliable method for bitumen characterization by improving the fluid characterization method that was recently developed based on perturbation from n-alkanes (PnA). The other is to develop a novel analytical method for assessing the sensitivity of a particular coinjection simulation to bitumen characterization without having to perform reservoir simulations. A simulation case study is given to validate this analytical method. A proper number of pseudo components for bitumen characterization cannot be determined without considering the effect of phase behavior on the oleic-phase viscosity at chamber-edge conditions in steam-solvent coinjection simulation. Results show that the analytical method developed in this research can detect the sensitivity of recovery simulation to bitumen characterization without performing multiple flow simulations using different sets of fluid models. The PnA-based method developed for bitumen characterization gives reliable predictions of phase behavior for bitumen/solvent mixtures with a small amount of experimental data.

Abstract. Wake steering is a technique that optimizes the energy production of a wind farm by employing yaw control to misalign upstream turbines with the incoming wind direction. This work highlights the important dependence between wind direction variations and wake steering optimization. The problem is formalized over time as the succession of multiple steady-state yaw control problems interconnected by the rotational constraints of the turbines and the evolution of the wind. Then, this work proposes a reformulation of the yaw optimization problem of each time step by augmenting the objective function by a new heuristic based on a wind prediction. The heuristic acts as a penalization for the optimization, encouraging solutions that will guarantee future energy production. Finally, a synthetic sensitivity analysis of the wind direction variations and wake steering optimization is conducted. Because of the rotational constraints of the turbines, as the magnitude of the wind direction fluctuations increases, the importance of considering wind prediction in a steady-state optimization is empirically demonstrated. The heuristic proposed in this work greatly improves the performance of controllers and significantly reduces the complexity of the original sequential decision problem by decreasing the number of decision variables.

Summary Nearly all of Husky's SAGD projects in the Lloydminster, Saskatchewan area are in reservoirs with bottomwater. While operating several projects in this region, Husky observed that in some reservoir settings, the outer SAGD pair had significantly decreased performance compared to inner SAGD pairs. When the outer pair was shut in to improve overall pilot performance, the performance of the now exposed adjacent pair rapidly decreased. It was hypothesized that operating the edge pairs protected inner pairs from the detrimental effects of bottomwater. Numerical simulation runs have confirmed this behaviour. The conclusions reached are that in many bottomwater settings, poorer-performing edge SAGD pairs protect and preserve the performance of inner SAGD pairs; and this should be taken into consideration during facilities' design, development of operational strategy, and production forecasting.

A ring main unit (RMU) is used in the power distribution system to provide three-phase supply. This RMU needs proper grounding for reliable and smooth power supply to residential areas. In this paper, ground resistance of an RMU is measured using fall-of-potential method. The ground resistance is measured by Fluke 1625 Advanced earth tester equipment and the value is found to be 1.58 Ω. Soil resistivity is also measured near the RMU and calculated by CYME GRD software. The COMSOL Multiphysics software is used to simulate ground resistance, and the simulation results are found to be in good agreement while compared to the experimental results.

Fe-based catalysts for O 2 reduction have been prepared on four carbon supports (Vulcan, Black Pearl, Norit, and a developmental carbon, RC2, from Sid Richardson Carbon Corp.). Four preparation procedures have been used with each carbon support and the catalytic activities for the reduction of oxygen in H 2 SO 4 , pH 1, and in polymer electrolyte membrane fuel cell tests are compared for all the catalysts, which are nominally loaded with 0.2 wt % Fe, using iron II acetate as Fe precursor. The catalytic activity of these Fe-based catalysts greatly depends upon the chosen carbon support and also upon the preparation procedure used. The results are rationalized in terms of N content at the surface of the catalysts; the larger the N content, the better the catalytic activity. The best catalysts are obtained after refluxing either RC2 or Norit in HNO 3 before adsorbing iron acetate on the oxidized carbon supports and heat-treating the resulting materials at 900°C in an atmosphere containing NH 3 . The surface nitrogen content of these catalysts, measured by XPS, is 2.5 and 4.1 atom %, respectively. For the Fe-based catalyst prepared on Norit and tested in fuel cell, the mass activity at low current regime, expressed in A/mg Fe, is only slightly lower than the A/mg Pt recorded for a state-of-the-art, Pt-based membrane electrode assembly.

Methane hydrate was formed in water occupying the interstitial spaces of a cylindrical bed of silica sand particles. The sand particles have an average diameter equal to 329 μm. The amount of methane consumed during the experiment (methane gas uptake) was determined through pressure and temperature measurements and mass balance calculations. Three different sized beds of silica sand particles were used. Water conversion to hydrates in the range of 73−84% was achieved for all of the formation experiments. Hydrate formation was followed by decomposition at 4.0 °C driven by depressurization at 3.1 MPa (nine experiments) and 2.3 MPa (one experiment). Methane recovery measurement curves were determined for each experiment. The initial rate of recovery was found to be strongly dependent on the silica sand bed size. The rate of recovery was found to depend weakly on the size during the second stage, and after 1.25 h, it was constant. During decomposition at 2.3 MPa, the temperature in some locations inside the b...

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.

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.