期刊
JOURNAL OF CLEANER PRODUCTION
卷 156, 期 -, 页码 451-458出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2017.04.084
关键词
Environmental impact; National level; Zinc ore mining; Energy; Heavy metal
资金
- National Natural Science Foundation of China [71671105]
- Institute of the Fundamental Research Funds of Shandong University [2015JC016]
- Institute of plateau meteorology, CMA, Chengdu, China [LPM2014002]
- China Energy Conservation and Emission Reduction Co. Ltd [GJN-14-07]
Life cycle assessment was carried out via ReCiPe H method to evaluate environmental impact of the hydrometallurgical zinc production chain in China. National statistical data and process-based life cycle inventory (LCI) were used to build a zinc production LCI at the macro level. To confirm the credibility of this study, an uncertainty analysis was conducted via Monte-Carlo simulation. The impacts of climate change, human toxicity, marine ecotoxicity, freshwater ecotoxicity, metal depletion, and fossil depletion categories on the overall environmental burden were examined. The overall environmental burden was dominated by key processes, such as zinc ore mining and energy (i.e., electricity and natural gas) consumption. In 2013, the amounts of carbon dioxide, sulfur dioxide, nitrogen oxide, particulate matter, zinc, and copper generated by hydrometallurgical zinc production were 2.60 x 10(7), 4.29 x 10(4), 6.77 x 10(4), 1.23 x 10(4),1.38 x 10(3), and 3.20 x 10(2) t. The first four substances accounted for 0.25%, 0.21%, 0.30%, and 0.10% of the overall national emission. Effective approaches to reduce the overall environmental impact of hydrometallurgical zinc production include improving the efficiencies of electricity, natural gas, and zinc ore consumption; substituting clean energy for coal-based electricity production; reducing the direct emission of zinc and copper; and increasing the national zinc recycle rate. (C) 2017 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
Article
Green & Sustainable Science & Technology
Relative evaluation of probabilistic methods for spatio-temporal wind forecasting
Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.
JOURNAL OF CLEANER PRODUCTION (2024)
Article
Green & Sustainable Science & Technology
Comparison of ethane recovery processes for lean gas based on a coupled model
Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang
Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.
JOURNAL OF CLEANER PRODUCTION (2024)
Article
Green & Sustainable Science & Technology
A novel deep-learning framework for short-term prediction of cooling load in public buildings
Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu
Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.
JOURNAL OF CLEANER PRODUCTION (2024)
Article
Green & Sustainable Science & Technology
The impact of social interaction and information acquisition on the adoption of soil and water conservation technology by farmers: Evidence from the Loess Plateau, China
Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang
Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.
JOURNAL OF CLEANER PRODUCTION (2024)
Article
Green & Sustainable Science & Technology
Study on synergistic heat transfer enhancement and adaptive control behavior of baffle under sudden change of inlet velocity in a micro combustor
Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He
Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.
JOURNAL OF CLEANER PRODUCTION (2024)
分享论文
