Experimental study on sawdust gasification in a spout–fluid bed reactor

This paper summarizes the experimental results of sawdust gasification in a spout–fluid bed reactor. Three scenarios were investigated in this study. In the base case scenario, a total of 15 experiments consisting of three different flow rates (55, 65 and 75 m³ h^{? 1}) of primary air of each of having five equivalence ratios (ER) (0.35, 0.3, 0.25, 0.2 and 0.15) were conducted. The influence of secondary air in the freeboard and the effect of the recirculation of carryover captured by the cyclone to the reactor's freeboard at an ER of 0.25 were investigated in two other scenarios. Higher heating values of 3.02 and 5.15 MJ Nm^{? 3} were obtained with the ER values of 0.35 and 0.15, respectively, in the base case. However, opposite trend was observed for the tar content in the producer gas. At ER of 0.35, a value of 2.35 g Nm^{? 3} was found compared with 8.4 g Nm^{? 3} at ER of 0.15. The tar content in the producer gas was reduced from 5.63 to 1.53 g Nm^{? 3} when secondary air was supplied in the freeboard due to an increase in temperature. The gasification efficiency was increased from 24.96% at the base case to 36.22% with the recirculation of carryover. Higher heating value of producer gas was found to be 4.2–4.4 MJ Nm^{? 3} in this case. The second law analysis of this process estimated the average exergy efficiency as 35.92% at ER of 0.35 and it increased with increasing ER. The recirculation of carryover not only increased the carbon conversion efficiency but also the exergy efficiency. Copyright © 2010 John Wiley & Sons, Ltd.     

A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images

Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of ‘mass’ that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover’s distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.     

Two-phase flow in a horizontal rectangular microchannel

Two-phase flow in a rectangular short horizontal channel 200 μm high was studied experimentally. The use of the fluorescent method made it possible to reveal flow of liquid in the channel and to determine its characteristics quantitatively. The existence of the regime of separate (stratified) flow is established. Based on analysis of previous investigations and newly obtained data, it is shown that a change in the height of the horizontal channel has a substantial effect on the boundaries between the regimes. The region of the churn regime increases with decreasing thickness of the channel.     

Why is a computational framework for motivational and metacognitive control needed?

This paper discusses, in the context of computational modelling and simulation of cognition, the relevance of deeper structures in the control of behaviour. Such deeper structures include motivational control of behaviour, which provides underlying causes for actions, and also metacognitive control, which provides higher-order processes for monitoring and regulation. It is argued that such deeper structures are important and thus cannot be ignored in computational cognitive architectures. A general framework based on the Clarion cognitive architecture is outlined that emphasises the interaction amongst action selection, motivation, and metacognition. The upshot is that it is necessary to incorporate all essential processes; short of that, the understanding of cognition can only be incomplete.     

A structured overview on the use of computational simulators for teaching statistical methods

Modern technology is affecting what is taught and how statistics is taught. Computer intensive methods like bootstrapping, generalized regression, coordinate exchange algorithms and computer experiment emulators are now part of the statistical toolkit. In parallel with these new methods and tools, a similar evolution appears in statistical education. Simulators offer opportunities for hands-on experience in the classroom, bridging the gap between theory and practice. In this article, we map a wide range of simulation based tools used in teaching statistical methods, putting them in the context of a quality ladder representing various maturity levels of organizations and course level sophistication. The mapping is designed to help educators assess and integrate options related to the incorporation of simulators as teaching aids in an educational and pedagogical roadmap. The mapping is applied to specific simulation products but provides a general framework that can be expanded and used in a generic way.