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Physical parameter-based allocation for the energy consumption of pyro-metallurgical system—a case study on nickel production in China

  • LCI METHODOLOGY AND DATABASES
  • Published:
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Abstract

Purpose

The objectives of this study were to establish an allocation model for the energy consumption of pyro-metallurgical system based on the physical relationship between the input and output, apply the established model to nickel production in China and compare the difference between the allocation results derived from the established model and the traditional models.

Methods

The basic methodological idea of this study is that the energy input of a multi-output pyro-metallurgical process should be allocated according to how the energy input is consumed in the process. The energy consumption of pyro-metallurgical system was decomposed into different categories (e.g. energy absorption of chemical reaction and energy loss), and these categories were classified into two groups, i.e. the categories of energy consumption dependent on the product ratio (PRDE) and the categories of energy consumption independent on the product ratio (PRIE); then, the allocation basis for each category of energy consumption was determined through the physical relationship between the energy input and metal output. The allocation for the energy consumption of nickel production in China was carried out; the system boundary includes two sub-systems (i.e. Ni–Cu coproduction system and Ni refining system), and the energy consumption of the coproduction system should be allocated; the basic data required by the allocation model and the compilation of LCI were mainly obtained from literature.

Results and discussion

The energy consumption of the processes of mining, drying, converting and floating-grinding belongs to PRIE following the principle of mass-based allocation, and the corresponding allocation factors are 64, 64, 69 and 69 %, respectively. The categories of energy consumption involved in the smelting process (the key process for demonstrating the allocation model) include both PRIE and PRDE, and the aggregated allocation factor was calculated as 56 %. The allocation result derived from the established model is lower than the results derived from the mass-based model and the market value-based model, because that, through the desulphurization reactions that occurred in the smelting process, the chemical states of copper and nickel are transformed from copper pyrite (CuFeS2) and nicopyrite ((Fe,Ni)9S8) into cuprous sulphide (Cu2S) and nickel sulphide (Ni3S2), and copper compound releases more sulphur atoms than nickel compound. When analyzing a single pyro-metallurgical process, the result of allocation is sensitive to basis selection; however, when comparing two or more pyro-metallurgical processes, the comparison result of allocation is relatively insensitive to basis selection.

Conclusions

The energy input of pyro-metallurgical system is consumed in different physical-chemical ways; the decomposition of energy consumption is significant for choosing allocation bases; the basis of mass can only be used for the allocation of PRIE, and choosing the same allocation basis for different cases without a systematic analysis will be inappropriate. The requirement of some nontraditional extra inventory data is the major limitation of the established model. This study mainly focused on how nickel production system consumes the energy input, and the energy consumption patterns of other production systems should be taken into account in future studies.

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Acknowledgments

This work was financially supported by the National Basic Research Program of China (973 Program, No. 2012CB720405) and the Beijing Natural Science Foundation (No. 2164056). We express our sincere appreciation to the anonymous reviewers for their valuable comments and advices.

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Correspondence to Boxue Sun.

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Responsible editor: Adisa Azapagic

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Sun, B., Liu, Y., Nie, Z. et al. Physical parameter-based allocation for the energy consumption of pyro-metallurgical system—a case study on nickel production in China. Int J Life Cycle Assess 22, 199–212 (2017). https://doi.org/10.1007/s11367-016-1144-3

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  • DOI: https://doi.org/10.1007/s11367-016-1144-3

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