Life-cycle analysis of biohydrogen production via dark-photo fermentation from wheat straw.

This study presents a life-cycle analysis using energy conversion characteristics as an evaluation index to assess the feasibility of this production method. The results indicate that for a system processing 1000 kg/h of wheat straw, the addition of 12000 kg/h of 2 wt% H2SO4 and 120 kg/h of CH3COONa yields 340,000 L/h of H2 and 348.6 kW of electricity. The energy conversion efficiency from the feedstock to the product is 21.4 %, while the efficiency from the hydrolysate to the product is 62.2 %. The total CO2 emission is 27.1 kg/h. Variations in the hydrolysate have the most significant impact on energy conversion efficiency. This study explores the feasibility of industrial-scale biohydrogen production via dark-photo fermentation from wheat straw and analyzes the energy characteristic indices and the sensitivity of these indices to key parameters.

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach.

Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors' flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.