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A Dynamic Comparison Sustainability Study of Standard Wastewater Treatment System in the Straw Pulp Papermaking Process and Printing & Dyeing Papermaking Process Based on the Hybrid Neural Network and Emergy Framework
Sustainability study of the standard wastewater treatment system is the pivotal procedure in the water protection field. In order to better study the sustainability of sewage treatment systems, wastewater treatment system of straw pulp papermaking (WTSPP) and wastewater treatment system of printing and dyeing and papermaking (WTPDP) have been selected to assess the sustainable level in China. Based on the hybrid neural network and emergy framework, WTSPP and WTPDP were considered and analyzed in this paper. Therein, three types of indicators were used to evaluate these two systems, including basic structure emergy indicators (BEI), functional emergy indicators (FEI), and eco-efficiency emergy indicators (EEI). Through the basic neural network model and detailed neural network model design, the iteration paths and algorithm operation diagram of WTSPP and WTPDP were designed and realized in this article. Primary contents include: (1) For WTSPP and WTPDP, nonrenewable resources emergy are both the primary contributor and account for roughly 62.5% and 53.7%, respectively. (2) As the important indicator group, the environmental loading ratio (ELR) is 176 in the WTSPP and 323 in the WTPDP, respectively. Emergy sustainability indicators (ESIs) in the WTSPP and WTPDP, are 0.015 and 0.014, respectively. (3) Depending on fluctuation degrees, WTSPP is better than WTPDP. The maximum fluctuation ranges of WTSPP and WTPDP are (3%, −27%) and (28%, 61%), respectively. (4) All neural network analysis results manifest that the emergy sustainability indicators (ESIs) of WTSPP and WTPDP are [0.0151, 0.011] and [0.0179, 0.0055] in view of a long-term predictive view, respectively.
A Dynamic Comparison Sustainability Study of Standard Wastewater Treatment System in the Straw Pulp Papermaking Process and Printing & Dyeing Papermaking Process Based on the Hybrid Neural Network and Emergy Framework
Sustainability study of the standard wastewater treatment system is the pivotal procedure in the water protection field. In order to better study the sustainability of sewage treatment systems, wastewater treatment system of straw pulp papermaking (WTSPP) and wastewater treatment system of printing and dyeing and papermaking (WTPDP) have been selected to assess the sustainable level in China. Based on the hybrid neural network and emergy framework, WTSPP and WTPDP were considered and analyzed in this paper. Therein, three types of indicators were used to evaluate these two systems, including basic structure emergy indicators (BEI), functional emergy indicators (FEI), and eco-efficiency emergy indicators (EEI). Through the basic neural network model and detailed neural network model design, the iteration paths and algorithm operation diagram of WTSPP and WTPDP were designed and realized in this article. Primary contents include: (1) For WTSPP and WTPDP, nonrenewable resources emergy are both the primary contributor and account for roughly 62.5% and 53.7%, respectively. (2) As the important indicator group, the environmental loading ratio (ELR) is 176 in the WTSPP and 323 in the WTPDP, respectively. Emergy sustainability indicators (ESIs) in the WTSPP and WTPDP, are 0.015 and 0.014, respectively. (3) Depending on fluctuation degrees, WTSPP is better than WTPDP. The maximum fluctuation ranges of WTSPP and WTPDP are (3%, −27%) and (28%, 61%), respectively. (4) All neural network analysis results manifest that the emergy sustainability indicators (ESIs) of WTSPP and WTPDP are [0.0151, 0.011] and [0.0179, 0.0055] in view of a long-term predictive view, respectively.
A Dynamic Comparison Sustainability Study of Standard Wastewater Treatment System in the Straw Pulp Papermaking Process and Printing & Dyeing Papermaking Process Based on the Hybrid Neural Network and Emergy Framework
Junxue Zhang (author) / Lin Ma (author) / Yanyan Yan (author)
2020
Article (Journal)
Electronic Resource
Unknown
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