Mars Atmosphere Resource Recovery System (MARRS)

Author(s):  
Christopher England
2003 ◽  
pp. 103-110 ◽  
Author(s):  
Sumio MASUDA ◽  
Isao FUCHIGAMI ◽  
Masahito YAMAUCHI ◽  
Yutaka DOTE ◽  
Toshiro MARUYAMA

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Changru Li

The research on the reverse resource network of e-waste at home and abroad is still in its infancy, and most of it is only based on traditional forward logistics. Reverse resources are the process of moving goods from their typical final destination for recycling value or proper disposal. With the intensification of market competition and the strengthening of environmental protection legislation by the government, reverse resources are no longer a neglected corner in the supply chain. The DLRNN model of the e-waste reverse resource recovery system constructed in this paper can provide an important theoretical and empirical basis for the rational utilization of waste electronic products and fully tap the potential value of waste electronic products, which is of great significance to the recycling of natural resources. In this paper, a hybrid network framework DLRNN based on deep learning (DL) and cyclic neural network (RNN) is designed for problem classification. Experimental results show that the classification accuracy of this framework is improved by 2.4% on TREC and 2.5% on MSQC without additional word vector conversion tools.


2017 ◽  
Vol 2017 (1) ◽  
pp. 850-861
Author(s):  
Joseph Hughes ◽  
Ing Andreas Duennebeil

Author(s):  
Nathiel G. Egosi ◽  
Mark E. Raabe ◽  
Robert Weidner ◽  
Gary A. Freel

The City of Ames, IA (City) processes 220 tons per day of municipal solid waste (MSW) at their Arnold O. Chantland Resource Recovery System (RRS). This facility is depicted in Figure 1. The objectives of this facility have both an economic and an environmental component: to reduce the amount of MSW that is otherwise disposed in their local landfill, thereby increasing the life of the landfill; and produce refuse-derived fuel (RDF) to reduce the amount of coal consumed at the City’s municipal electrical generating station. [Note: Approximately 70% of the MSW is converted into RDF, comprising 10 to 20% of the feedstock to the generating station.]


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