scholarly journals Impact energy model based research of nonlinear rub-impact assessment

2011 ◽  
Vol 60 (12) ◽  
pp. 124303
Author(s):  
Cong Fei-Yun ◽  
Chen Jin ◽  
Dong Guang-Ming
2018 ◽  
Vol 218 ◽  
pp. 70-78 ◽  
Author(s):  
Chen Chen ◽  
Weixing Zhu ◽  
Yizheng Guo ◽  
Changhua Ma ◽  
Weijia Huang ◽  
...  

Author(s):  
Megashnee Munsamy ◽  
Arnesh Telukdarie ◽  
Pavitra Dhamija

Logistics activities are significant energy consumers and known contributors to GHG emissions, hence optimisation of logistics energy demand is of critical importance. The onset of the fourth Industrial revolution delivers significant technological opportunities for logistics optimisation with additional benefits in logistics energy optimisation. This research propositions a business process centric logistics model based on Industry 4.0. A Logistics 4.0 architecture is developed comprising Industry 4.0 technologies and associated enablers. The Industry 4.0 architecture components are validated by conducting a Systematic Literature Review on Industry 4.0 and logistics. Applying the validated Logistics 4.0 architecture to a cyber physical logistics energy model, based on the digitalisation of business processes, a comprehensive simulation is developed identified as the Logistic 4.0 Energy Model. The model simulates the technological impact of Industry 4.0 on a logistics network. The model generates energy and CO2 emission values for “as-is” and “to-be” Industry 4.0 scenarios.


2013 ◽  
Vol 13 (7) ◽  
pp. 18951-18967 ◽  
Author(s):  
R. M. Bright ◽  
M. M. Kvalevåg

Abstract. Land use activities affect Earth's energy balance not only via biogeochemical emissions but also through perturbations in surface albedo, the latter of which is often excluded in impact assessment studies. In this short technical note, we present and compare a simple model for estimating shortwave radiative forcings at the top of Earth's atmosphere to a more sophisticated 8-stream radiative transfer model based on a discrete ordinate method. Outcomes from monthly albedo change simulations for ten globally distributed regions and a single year revealed that the simple model – based on a single exogenously supplied meteorological variable – performed quite well, having a sample correlation coefficient of 0.93 and a normalized root mean square error of 7.2%. Simple models like the one presented here can offer an attractive and efficient means for non-experts to begin including albedo change considerations in climate impact assessment studies enveloping land use activities.


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