Research article Special Issues

Cradle-to-gate life cycle assessment of the dry etching step in the manufacturing of photovoltaic cells

  • Received: 25 August 2014 Accepted: 06 November 2014 Published: 12 November 2014
  • A new photovoltaic silicon crystalline solar cell dry chemical etching process (DCEP) is developed. It is an alternative to the current State-of-the-Art (SoA) wet chemical etching process (WCEP), associated with relatively large environmental loadings in the form of high water consumption and emissions of greenhouse gases with high Global Warming Potential (GWP). In order to compare the environmental impacts of DCEP to the corresponding impacts from WCEP, a comparative attributional life cycle assessment (LCA) is conducted. From the LCA it can be concluded that the DCEP will lead to 86% reduction in water consumption compared to WCEP (acidic), and 89% reduction compared to WCEP (alkaline). The emissions of greenhouse gases, as expressed by the GWP100 indicator of the etching step, are also reduced with 63% and 20% respectively, when compared with current SoA acidic and alkaline WCEP. The toxicity impacts are also assessed to be lower for the DCEP compared to WCEP technologies, although the uncertainty is relatively high for the applied toxicity indicators. All in all, DCEP can reduce the CO2eq emissions of solar photovoltaic systems production by 5-10%.

    Citation: Otto Andersen, Geoffrey Gilpin, Anders S.G. Andrae. Cradle-to-gate life cycle assessment of the dry etching step in the manufacturing of photovoltaic cells[J]. AIMS Energy, 2014, 2(4): 410-423. doi: 10.3934/energy.2014.4.410

    Related Papers:

    [1] Amira Bouhali, Walid Ben Aribi, Slimane Ben Miled, Amira Kebir . Impact of immunity loss on the optimal vaccination strategy for an age-structured epidemiological model. Mathematical Biosciences and Engineering, 2024, 21(6): 6372-6392. doi: 10.3934/mbe.2024278
    [2] Qiuyi Su, Jianhong Wu . Impact of variability of reproductive ageing and rate on childhood infectious disease prevention and control: insights from stage-structured population models. Mathematical Biosciences and Engineering, 2020, 17(6): 7671-7691. doi: 10.3934/mbe.2020390
    [3] Zhisheng Shuai, P. van den Driessche . Impact of heterogeneity on the dynamics of an SEIR epidemic model. Mathematical Biosciences and Engineering, 2012, 9(2): 393-411. doi: 10.3934/mbe.2012.9.393
    [4] Xinyu Bai, Shaojuan Ma . Stochastic dynamical behavior of COVID-19 model based on secondary vaccination. Mathematical Biosciences and Engineering, 2023, 20(2): 2980-2997. doi: 10.3934/mbe.2023141
    [5] Mostafa Adimy, Abdennasser Chekroun, Claudia Pio Ferreira . Global dynamics of a differential-difference system: a case of Kermack-McKendrick SIR model with age-structured protection phase. Mathematical Biosciences and Engineering, 2020, 17(2): 1329-1354. doi: 10.3934/mbe.2020067
    [6] Ying He, Junlong Tao, Bo Bi . Stationary distribution for a three-dimensional stochastic viral infection model with general distributed delay. Mathematical Biosciences and Engineering, 2023, 20(10): 18018-18029. doi: 10.3934/mbe.2023800
    [7] Tong Guo, Jing Han, Cancan Zhou, Jianping Zhou . Multi-leader-follower group consensus of stochastic time-delay multi-agent systems subject to Markov switching topology. Mathematical Biosciences and Engineering, 2022, 19(8): 7504-7520. doi: 10.3934/mbe.2022353
    [8] Pengyan Liu, Hong-Xu Li . Global behavior of a multi-group SEIR epidemic model with age structure and spatial diffusion. Mathematical Biosciences and Engineering, 2020, 17(6): 7248-7273. doi: 10.3934/mbe.2020372
    [9] Jinliang Wang, Hongying Shu . Global analysis on a class of multi-group SEIR model with latency and relapse. Mathematical Biosciences and Engineering, 2016, 13(1): 209-225. doi: 10.3934/mbe.2016.13.209
    [10] Shuang-Hong Ma, Hai-Feng Huo . Global dynamics for a multi-group alcoholism model with public health education and alcoholism age. Mathematical Biosciences and Engineering, 2019, 16(3): 1683-1708. doi: 10.3934/mbe.2019080
  • A new photovoltaic silicon crystalline solar cell dry chemical etching process (DCEP) is developed. It is an alternative to the current State-of-the-Art (SoA) wet chemical etching process (WCEP), associated with relatively large environmental loadings in the form of high water consumption and emissions of greenhouse gases with high Global Warming Potential (GWP). In order to compare the environmental impacts of DCEP to the corresponding impacts from WCEP, a comparative attributional life cycle assessment (LCA) is conducted. From the LCA it can be concluded that the DCEP will lead to 86% reduction in water consumption compared to WCEP (acidic), and 89% reduction compared to WCEP (alkaline). The emissions of greenhouse gases, as expressed by the GWP100 indicator of the etching step, are also reduced with 63% and 20% respectively, when compared with current SoA acidic and alkaline WCEP. The toxicity impacts are also assessed to be lower for the DCEP compared to WCEP technologies, although the uncertainty is relatively high for the applied toxicity indicators. All in all, DCEP can reduce the CO2eq emissions of solar photovoltaic systems production by 5-10%.


    [1] Dominguez-Ramos A, Held M, Aldaco R, et al, (2010) Prospective CO2 emissions from energy supplying systems: Photovoltaic systems and conventional grid within Spanish frame conditions. Int J LCA 15(6): 557-566.
    [2] Andersen O (2013) Solar Cell Production. In: Unintended Consequences of Renewable Energy. Problems to be Solved. Springer London, London, 81-89.
    [3] Bianco C, Torrelli A, Squizzato V, et al.. Energy and carbon pay back times for renewable power supply systems for Italian RBS off-grid sites. Telecommunications Energy Conference (INTELEC), 2011 IEEE 33rd International; 2011 9-13 Oct.; Amsterdam, the Netherlands. IEEE. 1-6.
    [4] Andrae ASG, Dong H, Shudong L, et al.. Added value of life cycle assessment to a business case analysis of a photovoltaic/wind radio base site solution in South Africa. Telecommunications Energy Conference (INTELEC), 2012 IEEE 34th International; 2012 30 Sept.-4 Oct.; Scottsdale, AZ, USA. IEEE. 1-7.
    [5] Zehner O (2011) Unintended Consequences of Green Technologies. Green Technology. Sage, London, 427-432.
    [6] Duffy E (2012) SOLNOWAT—Development of a competitive zero Global Warming Potential (GWP) dry process to reduce the dramatic water consumption in the ever-expanding solar cell manufacturing industry. D7.3 Exploitation Plan (interim).
    [7] Agostinelli G, Dekkers HFW, De Wolf S, et al. (2004) Dry Etching and Texturing Processes for Crystalline Silicon Solar cells: Sustainability for Mass Production. Paris, 423-426.
    [8] Forster P, Ramaswamy V, Artaxo P, et al. (2007) Changes in Atmospheric Constituents and in Radiative Forcing. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
    [9] Piechulla P, Seiffe J, Hoffman M, et al.. Increased Ion Energies for Texturing in a High-Throughput Plasma Tool. European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC); 2011 5-9 Sept.; Hamburg, Germany. Fraunhofer-Publica. 2024-2027.
    [10] Alsema E (2000) Energy pay-back time and CO2 emissions of PV systems. Prog Photovoltaics: Res Appl 8(1): 17-25.
    [11] Alsema E, de Wild-Scholten M. Environmental Impact of Crystalline Silicon Photovoltaic Module Production. 13th CIRP Intern. Conf. on Life Cycle Engineering; 2006 31 May-2 Jun.; Leuven, Belgium. G3.3.
    [12] Fthenakis VM, Kim HC, Alsema E (2008) Emissions from Photovoltaic Life Cycles. Environ Sci Technol 42:2168-2174.
    [13] Fthenakis VM, Kim HC (2010) Photovoltaics: Life-cycle analyses. Sol Energ 85:1609-1628.
    [14] Pehnt M (2006) Dynamic life cycle assessment (LCA) of renewable energy technologies. Renew Energ 31:55-71.
    [15] Jungbluth N (2005) Life cycle assessment of crystalline photovoltaics in the Swiss ecoinvent database. Prog Photovoltaics: Res Appl 13:429-446.
    [16] Fthenakis VM, Kim HC (2007) Greenhouse-gas emissions from solar electric- and nuclear power: A life-cycle study. Energ Policy 35:2549-2557.
    [17] Bergesen J D, Heath GA, Gibon T, et al. (2014) Thin-film photovoltaic power generation offers decreasing greenhouse gas emissions and increasing environmental co-benefits in the long term. Env Sci Tec 48(16): 834-9843.
    [18] Kim, H. C., Fthenakis, V., Choi, J., & Turney, D. E. (2012). Life cycle greenhouse gas emissions of thin-film photovoltaic electricity generation: Systematic review and harmonization. J Ind Ecol 16(SUPPL.1): S110-S121.
    [19] Fthenakis V, Kim HC, Alsema E (2008). Emissions from photovoltaic life cycles. Env Sci Tec 42(6): 2168-2174.
    [20] European Commission (2003) External Costs. Research results on socio-environmental damages due to electricity and transport. European Commission, Directorate-General for Research Information and Communication Unit, Brussels.
    [21] European Commission (1995) ExternE: Externalities of Energy. Prepared by ETSU and IER for DGXII: Science, Research & Development, Study EUR 16520-5 EN. Luxembourg.
    [22] Rentsch J, Jaus J, Roth K, Preu R. Economical and ecological aspects of plasma processing for industrial solar cell fabrication. Photovoltaic Specialists Conference, 2005. Conference Record of the Thirty-first IEEE; 2005 3-7 Jan.. IEEE, 931-934.
    [23] Agostinelli G, Choulat P, Dekkers HFW, et al.. Advanced dry processes for crystalline silicon solar cells. Photovoltaic Specialists Conference, 2005. Conference Record of the Thirty-first IEEE; 2005 3-7 Jan.. IEEE, 1149-1152.
    [24] Lopez E, Dani I, Hopfe V, et al.. Plasma enhanced chemical etching at atmospheric pressure for silicon wafer processing. European Photovoltaic Solar Energy Conference; 2006 4-8 Sept.; Dresden, Germany. Fraunhofer-Publica. 1161-1166.
    [25] Lopez E, Beese H, Mäder G, et al.. New developments in plasma enhanced chemical etching at atmospheric pressure for crystalline silicon wafer processing. The compiled state-of-the-art of PV solar technology and deployment. 22nd European Photovoltaic Solar Energy Conference, EU PVSEC 2007. Proceedings of the international conference; 2007 3-7 Sept.; Milan, Italy. Fraunhofer-Publica. 1484-1487.
    [26] Linaschke D, Leistner M, Mäder G, et al. Plasma Enhanced Chemical Etching at Atmospheric Pressure for Crystalline Silicon Wafer Processing and Process Control by In-Line FTIR Gas Spetroscopy. 21st European Photovoltaic Solar Energy Conference 2006. Proceedings; 2008 1-5 Sept.; Valencia, Spain. Fraunhofer-Publica.1907-1910.
    [27] Photovoltaics World, Champions of Photovoltaics: Cells and Modules. Photovoltaics World, 2011. Available from: http://www.renewableenergyworld.com/rea/news/article/2011/12/champions-of-photovoltaics-cells-and-modules.
    [28] Dresler B, Köhler D, Mäder G, et al. (2012) Novel Industrial single sided dry etching and texturing process for silicon solar cell improvement. 27th European Photovoltaic Solar Energy Conference and Exhibition 1825-1828.
    [29] International Standards Organisation (2006) Environmental management-life cycle assessment-principles and framework. International Organization for Standardization, Geneva, Switzerland.
    [30] International Standards Organisation (2006) Environmental management-life cycle assessment-Requirements and Guidelines. International Organization for Standardization, Geneva, Switzerland.
    [31] Andrae ASG (2009) Global life cycle impact assessments of material shifts: the example of a lead-free electronics industry. Springer.
    [32] Frischknecht R, Stucki M (2010) Scope-dependent modeling of electricity supply in life cycle assessments. Int J LCA 15:806-816.
    [33] European Commission Joint Research Centre Institute for Environment and Sustainability (2010) International Reference Life Cycle Data System (ILCD) Handbook—General guide for Life Cycle Assessment—Detailed guidance. First edition March 2010. EUR 24708 EN. Luxembourg. Publications Office of the European Union.
    [34] Institute of Environmental Sciences (CML) Faculty of Science, CML-IA Characterisation Factors. Leiden University Institute of Environmental Sciences (CML), 2013. Available from: http://cml.leiden.edu/software/data-cmlia.html.
    [35] Dominguez-Ramos A, Aldaco R, Irabien A (2007) Life cycle assessment as a tool for cleaner production: Application to aluminium trifluoride. Int J Chem Reactor Eng 5(1): 1542-6580.
    [36] Weidema B, Wesnaes M (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Cleaner Prod 4(3-4):167-174.
  • This article has been cited by:

    1. Jinhu Xu, Yan Geng, A nonstandard finite difference scheme for a multi-group epidemic model with time delay, 2017, 2017, 1687-1847, 10.1186/s13662-017-1415-8
    2. Zhijun Liu, Jing Hu, Lianwen Wang, Modelling and analysis of global resurgence of mumps: A multi-group epidemic model with asymptomatic infection, general vaccinated and exposed distributions, 2017, 37, 14681218, 137, 10.1016/j.nonrwa.2017.02.009
    3. Xue Ran, Lin Hu, Lin-Fei Nie, Zhidong Teng, Effects of stochastic perturbation and vaccinated age on a vector-borne epidemic model with saturation incidence rate, 2021, 394, 00963003, 125798, 10.1016/j.amc.2020.125798
    4. Yan Geng, Jinhu Xu, Stability preserving NSFD scheme for a multi-group SVIR epidemic model, 2017, 01704214, 10.1002/mma.4357
    5. Yan Liu, Pinrui Yu, Dianhui Chu, Huan Su, Stationary distribution of stochastic multi-group models with dispersal and telegraph noise, 2019, 33, 1751570X, 93, 10.1016/j.nahs.2019.01.007
    6. Xinyou Meng, Qingling Zhang, Complex Dynamics in a Singular Delayed Bioeconomic Model with and without Stochastic Fluctuation, 2015, 2015, 1026-0226, 1, 10.1155/2015/302494
    7. Suxia Zhang, Hongbin Guo, Global analysis of age-structured multi-stage epidemic models for infectious diseases, 2018, 337, 00963003, 214, 10.1016/j.amc.2018.05.020
    8. Junyuan Yang, Yuming Chen, Theoretical and numerical results for an age-structured SIVS model with a general nonlinear incidence rate, 2018, 12, 1751-3758, 789, 10.1080/17513758.2018.1528393
    9. Ying Guo, Wei Zhao, Xiaohua Ding, Input-to-state stability for stochastic multi-group models with multi-dispersal and time-varying delay, 2019, 343, 00963003, 114, 10.1016/j.amc.2018.07.058
    10. Lan Meng, Wei Zhu, Analysis of SEIR epidemic patch model with nonlinear incidence rate, vaccination and quarantine strategies, 2022, 200, 03784754, 489, 10.1016/j.matcom.2022.04.027
    11. Zhen Cao, Lin-Fei Nie, DYNAMICS OF A STOCHASTIC VECTOR-HOST EPIDEMIC MODEL WITH AGE-DEPENDENT OF VACCINATION AND DISEASE RELAPSE, 2023, 13, 2156-907X, 1274, 10.11948/20220099
    12. Han Ma, Yanyan Du, Zong Wang, Qimin Zhang, Positivity and Boundedness Preserving Numerical Scheme for a Stochastic Multigroup Susceptible-Infected-Recovering Epidemic Model with Age Structure, 2024, 1557-8666, 10.1089/cmb.2023.0443
  • Reader Comments
  • © 2014 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6599) PDF downloads(1199) Cited by(6)

Article outline

Figures and Tables

Figures(10)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog