Theoretical and experimental understanding on ethanol steam reforming for H-2 production

H2 is considered to be a potential alternative fuel due to its high energy density by weight and working with pollution free. Currently, ethanol conversion to hydrogen has drawn much attention because it provides a viable way for H2 production from renewable resources. In this work, we combined theoretical and experimental efforts to study the reaction mechanism of ethanol steam reforming on Rh catalysts. The results suggest that acetaldehyde (CH3CHO) is an important reaction intermediate in the reaction on nano-sized Rh catalyst. Our theoretical work suggests that the H-bond effect significantly modifies the ethanol decomposition pathway. The possible reaction pathway on Rh (211) surface is suggested as: CH3CH2OH → CH3CH2O → CH3CHO → CH3CO → CH3+CO → CH2+CO → CH+CO → C+CO, followed by the water gas shift reaction to yield H2 and CO2. It was found that that the water gas shift reaction is paramount in the ethanol steam reforming process.

ALEA: Fine-Grain Energy Profiling with Basic Block Sampling

Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand how workloads affect it. Significant recent research effort has targeted direct power measurements on production computing systems using on-board sensors or external instruments. These direct methods have in turn guided studies of software techniques to reduce energy consumption via workload allocation and scaling. Unfortunately, direct energy measurements are hampered by the low power sampling frequency of power sensors. The coarse granularity of power sensing limits our understanding of how power is allocated in systems and our ability to optimize energy efficiency via workload allocation.
We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.