Effect of Process Parameters on Microstructure and Micro-hardness of AZ91/Al2O3 Surface Composite Produced by FSP

In this article, the effects of three different sizes of Al₂O₃ particles in the friction stir processing on grain size, cluster size, microstructure, and micro-hardness of as-cast magnesium alloy AZ91 were investigated. Moreover, the effects of two types of tool geometries and number of passes on the mentioned parameters were considered. Effect of mentioned parameters on microstructure, grain refinement, and micro-hardness profile in the friction stirred zone of the specimens was compared by as-cast received form and also friction stir processed (FSPed) specimens without particles. Microstructural characterization of the materials revealed reasonably uniform distribution of Al₂O₃ reinforcement and significant grain refinement. Hardness studies revealed that the incorporation of nano- and micro-size Al₂O₃ particulates in magnesium matrix led to a simultaneous increase in hardness.     

A fuzzy lot-sizing problem with two-stage composite human learning

Due to the repetitive nature of inventory planning over the planning horizon, the operator in charge has to perform planning tasks repetitively, and consequently s/he becomes more familiar with the tasks over time. Familiarity with the tasks suggests that learning takes place in inventory planning. Even though the operator’s learning over time might improve his/her efficiency, prior research on fuzzy lot-sizing problems mostly overlooked the effect of human learning in their models and its impact on the operator’s performance. To close the research gap in this area, this paper models the operator's learning in a fuzzy economic order quantity model with backorders. The paper models a situation where the operator applies the acquired knowledge over the cycles in setting the fuzzy parameters at the beginning of every planning cycle, where his/her learning ability includes the cognitive and motor capabilities of a human being. Subsequently, a mathematical model which takes account of a two-stage human learning over the planning cycles is developed, which is then analytically investigated using sample data-sets. The results indicate that both operator’s capabilities, cognitive and motor, affect the efficiency of the fuzzy lot-sizing inventory model, but the influence of the cognitive capability is more profound, which in turn suggests the importance of training programmes for the workforces. The results of the sensitivity analysis also draw some managerial insights for the case that some model parameters vary over the planning horizon.     

A review on biomass‐based hydrogen production for renewable energy supply

This article gives an overview of the state‐of‐the‐art biomass‐based hydrogen production technologies. Various biological and thermochemical processes of biomass are taken into consideration to find the most economical method of hydrogen production. Biohydrogen generated by biophotolysis method, photo‐fermentation and dark fermentation is studied with respect to various feedstocks in Malaysia. The fermentation approaches of biohydrogen production have shown great potential to be a future substitute of fossil fuels. Dark fermentation method is a simple biological hydrogen production method that uses a variety of substrate and does not require any light as a source of energy. A promising future for biohydrogen production is anticipated by this process both industrially and commercially. Feasibility of hydrogen production from pyrolysis and water gasification of various biomass feedstock confirm that supercritical water gasification (SCWG) of biomass is the most cost‐effective thermochemical process. Highly moisturized biomass could be employed directly in SCWG without any high‐cost drying process. Indeed, a small amount of energy is required to pressurize hydrogen in the storage tank because of highly pressurized SCWG process. The cost of hydrogen produced by SCWG of biomass is about US$3/GJ (US$0.35/kg), which is extremely lower than biomass pyrolysis method (in the range of US$8.86/GJ to US$15.52/GJ) and wind‐electrolysis systems and PV‐electrolysis systems (US$20.2/GJ and US$41.8/GJ, respectively). The best feedstock for biomass‐based hydrogen production is identified based on the availability, location of the sources, processes required for the preparation of the feedstock and the total cost of acquiring the feedstock. The cheapest and most abundantly available biomass source in Malaysia is the waste of palm industry. Hydrogen production from palm oil mill effluent and palm solid residue could play a crucial role in the energy mix of Malaysia. Malaysia has this great capability to supply about 40% of its annual energy demand by hydrogen production from SCWG of palm solid waste. Copyright © 2015 John Wiley & Sons, Ltd.