Jet cup attrition testing

Jet cup attrition testing is a common method for evaluating particle attrition in fixed fluidized beds and circulating fluidized beds. An attrition index, calculated from jet cup data, is used to compare with one or more reference materials. However, this method is far from perfect despite its popularity. Results obtained at Particulate Solid Research, Inc. (PSRI) in different-sized jet cups and a 29-cm (11.5-in.) diameter fluidized bed test unit did not provide the same ranking of catalyst with respect to particle attrition. To obtain a better understanding of attrition in a jet cup, both computational fluid dynamics (CFD) and cold flow studies were performed with a 2.5-cm (1-in.) diameter Davison-type jet cup and PSRI's cylindrical 7.6-cm (3-in.) diameter jet cup. Results showed that a significant amount of material in the Davison and PSRI jet cup remained stagnant. Based on these results and additional CFD modeling, PSRI designed a new jet cup, where most of the material was hydrodynamically active. The new jet cup showed a 25% increase in attrition compared to PSRI's cylindrical jet cup under similar conditions and run times. Results were also compared to cyclone attrition data for several materials at PSRI. The new jet cup provided data that correlated with attrition results from the 29-cm (11.5-in.) diameter fluidized bed unit.     

Attacks and Defenses for JTAG

Editor's note:JTAG is a well-known standard mechanism for in-field test. Although it provides high controllability and observability, it also poses great security challenges. This article analyzes various attacks and proposes protection schemes.—Mohammad Tehranipoor, University of Connecticut     

Selectively breaking data dependences to improve the utilization of idle cycles in algorithm level re-computing data paths

Although algorithm level re-computing techniques can trade-off the fault detection capability vs. time overhead of a Concurrent Error Detection (CED) scheme, they result in 100% time overhead when the strongest CED capability is achieved. Using the idle cycles in the data path to do the re-computation can reduce this time overhead. However, dependences between operations prevent the re-computation from fully utilizing the idle cycles. Deliberately breaking some of these data dependences can further reduce the time overhead associated with algorithm level re-computing. According to the experimental results the proposed technique, it brings time overhead down to 0-60% while the associated hardware overhead is from 12% to 50% depending on the design size.     

GRAPHICAL SOLUTION OF MULTISTAGE FLUIDIZED BED HEAT EXCHANGER

The design of multistage fluidized beds for heat exchange necessitates the solution of the mass and energy balance equations combined with the equilibrium relations for each stage. This paper presents a novel way of applying well known methodology to a different technology, one where it has not been widely applied.

In the present work a McCabe-Thiele type of graphical approach is presented for both counter-current and cross-current contacting multiple fluidized beds. The necessary equations for a multistage calciner are developed and the application of the proposed method is demonstrated. Generalized fluidized bed efficiency for counter-current and cross-current multiple fluidized bed is presented.     

Intrusive probes in riser applications

Many of the probes used to understand hydrodynamics in circulating fluidized bed risers intrude into the environment they are measuring, although assumptions are typically asserted that the intrusive probes do not affect the data collected. This could be a poor assumption in some cases and conditions. We found that intrusive fiber‐optic probe measurements consistently mis‐predicted the solids concentration compared to the nonintrusive pressure drop measurements outside the fully developed flow region of a riser containing fluid catalytic cracking catalyst or glass bead particles. The discrepancy was sensitive to superficial gas velocity, solid circulation rate, probe position, and flow direction. Barracuda VR? computational fluid dynamics simulations confirmed this, and indicated that particle momentum was lost at the leading edge of the probe and particles were spilling over to the probe tip. Accordingly, new probe designs were proposed to mitigate the intrusiveness of a fiber‐optic probe for more accurate characterization. © 2017 American Institute of Chemical Engineers AIChE J, 63: 5361–5374, 2017     

Physical solvent extraction of levulinic acid from dilute aqueous solution with 2-methyltetrahydrofuran

The results of the present investigation reveal that 2-methyltetrahydrofuran is a potential solvent for the extraction of levulinic acid from dilute aqueous solution. This conclusion is based on the relatively high values of distribution coefficient K_d (1.3-1.6) measured for the system of levulinic acid – water – 2-methyltetrahydrofuran at temperatures 298.2 K, 313.2 K, and 328.2 K, as well as encouraging performance of the continuous counter current Kühni column. The results give reason to believe, that 2-methyltetrahydrofuran can be considered for the extraction of other low molecular weight acids, such as formic or lactic acid, as well.     

Concurrent error detection of fault-based side-channel cryptanalysis of 128-bit RC6 block cipher

Fault-based side channel cryptanalysis is very effective against symmetric and asymmetric encryption algorithms. Although straightforward hardware and time redundancy based concurrent error detection (CED) architectures can be used to thwart such attacks, they entail significant overhead (either area or performance). In this paper we investigate two systematic approaches to low-cost, low-latency CED for symmetric encryption algorithm RC6. The proposed techniques have been validated on FPGA implementations of RC6, one of the advanced encryption standard finalists.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.