This work develops a consequential life cycle optimization (CLCO) framework that integrates the superstructure optimization, consequential life cycle assessment (CLCA) approach, market equilibrium models, and techno-e...
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This work develops a consequential life cycle optimization (CLCO) framework that integrates the superstructure optimization, consequential life cycle assessment (CLCA) approach, market equilibrium models, and techno-economic assessment methodology to determine the economically and environmentally optimal waste high- density polyethylene ( HDPE) chemical recycling technology pathway, which manufactures chemical and energy products that cause market dynamics. System expansion in CLCA can quantify the environmental consequences of an increment of feedstock suppliers' and downstream product consumers' processes as well as a decrement of feedstock consumers' and downstream product suppliers' processes. These market dynamics are ignored when performing attributional life cycle assessment (ALCA). The CLCO problem is formulated as a multi-objective mixed-integer nonlinear fractional programming problem and solved by an optimization algorithm that integrates the inexact parametric algorithm and branch-and-refine algorithm. The environmental and economic objectives are minimizing the unit consequential life cycle environmental impacts and maximizing the unit net present value, respectively. Market dynamics results show that consuming natural gas in the waste HDPE chemical recycling process increases the natural gas' market price and supply by 0.1%, while onsite manufacturing propylene decreases propylene market price by 5.46%, decreases propylene supply by 8.8%, and increases the propylene demand by 10.2%. Disparities and comprehensiveness are two effects of system expansion. Disparities are illustrated by a 14.22% decrease in greenhouse gas (GHG) emissions and a 60.37% reduction of photochemical oxidant formation compared to ALCA results. Comprehensiveness of system expansion is reflected by the diverse results of the environmental consequences of each consumer or marginal supplier's process of feedstocks and products. Specifically, the substitution of 1-butene supplier's pr
作者：
Li, QingYang, QinghaiQin, MengKwak, Kyung-SupXidian Univ
Sch Telecommun Engn Collaborat Innovat Ctr Informat Sensing & Underst State Key Lab ISN 2 Taibainan Lu Xian Shaanxi Peoples R China Inha Univ
Grad Sch Informat Technol & Telecommun 253 Yonghyun Dong Incheon South Korea
To enable sustainable wireless networks, though new technologies have been proposed to improve the system spectrum efficiency, the energy efficiency (EE) is also of vital importance due to the increasing users and dev...
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To enable sustainable wireless networks, though new technologies have been proposed to improve the system spectrum efficiency, the energy efficiency (EE) is also of vital importance due to the increasing users and devices. Active array system (AAS) and heterogeneous networks (HetNets) have been reckoned as an enormous enhancement in spectrum efficiency and EE. In this study, the energy efficient user association and resource allocation problem for preference-aware multicast service in AAS aided HetNets is investigated, formulated as a mixed-integer non-linear fractionalprogramming. By generalised fractionalprogramming theory and Lagrangian dual decomposition, an iterative algorithm is devised to determine user association and resource allocation. Further, an efficient solution is proposed to perform quality of service-guaranteed user association and resource allocation to maximise the EE. Simulation results demonstrate the convergence performance and potential gain of the proposed algorithms in terms of EE.
Recently, millimetre-wave (mmWave) wireless fronthauls have been regarded as an effective solution to deploy remote radio heads with higher flexibility and efficiency in cloud radio access networks (C-RANs). Different...
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Recently, millimetre-wave (mmWave) wireless fronthauls have been regarded as an effective solution to deploy remote radio heads with higher flexibility and efficiency in cloud radio access networks (C-RANs). Different from the traditional fibre fronthauls, in order to maximise the utilisation of the time-frequency resource, the mmWave wireless fronthauls are more expected to operate in a dynamic allocation manner. In this study, the energy efficient mmWave fronthaul and OFDMA resource optimisation in C-RANs is investigated. The TDMA-based fronthaul allocation mechanism is first presented and then the joint resource optimisation is formulated as an energy efficiency (EE) maximisation problem which is in the form of a mixed-integer non-linear fractionalprogramming (MINLFP) problem. By taking advantage of the Dinkelbach method, the MINLFP problem is transformed into a subtractive optimisation problem and solved by using the Lagrange dual decomposition theory. Moreover, a maximal weighted bipartite graph matching approach is proposed to determine the optimal resource block allocation. Finally, extensive simulation results are provided to evaluate the EE performance of the proposed algorithm by comparing with several benchmark schemes, and it shows that the proposed algorithm can achieve great EE performance gain over the benchmark schemes.
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