FCC coprocessing oil sands heavy gas oil and canola oil. 3. Some cracking characteristics

Coprocessing of bitumen-derived feeds and biomass through a fluid catalytic cracking(FCC) route has the potential to assist in the reduction of fuel and petroleum product carbon footprints while meeting government regulatory requirements on renewable transportation fuels. This approach is desirable because green house gas(GHG) emissions for producing renewable biofuels are significantly lower than those for fossil fuels, and coprocessing can be executed using existing refining infrastructure to save capital cost. The present study investigates the specific FCC performances of pure heavy gas oil(HGO) derived from oil sands synthetic crude, and a mixture of 15 v% canola oil in HGO using a commercial equilibrium catalyst under typical FCC conditions. Cracking experiments were performed using a bench-scale Advanced Cracking Evaluation(ACE) unit at fixed weight hourly space velocity(WHSV) of 8 h^(-1), 490–530℃, and catalyst/oil ratios of 4–12 g/g. This work focuses on some cracking phenomena resulting from the presence of oxygen in the blendda lower heat requirement for cracking due to the exothermic water formation, which also entails lower hydrogen yield at a given severity. The distribution of feed oxygen in gaseous and liquid products, the mitigation in GHG emissions, and the technological and economical advantages of the coprocessing option are also discussed.

Device-free human micro-activity recognition method using WiFi signals

Human activity tracking plays a vital role in human–computer interaction.Traditional human activity recognition(HAR)methods adopt special devices,such as cameras and sensors,to track both macro-and micro-activities.Recently,wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment.This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information(CSI)of wireless signals.Different from existed CSI-based microactivity recognition methods,the proposed method extracts both amplitude and phase information from CSI,thereby providing more information and increasing detection accuracy.The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity.We applied a machine learning algorithm to recognize the proposed micro-activities.The proposed method has been evaluated in both line of sight(LOS)and none line of sight(NLOS)scenarios,and the empirical results demonstrate the effectiveness of the proposed method with several users.