Cost projections for microwave plasma CO production using renewable energy

Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt rec...
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Gespeichert in:

Autor*in:	Detz, Remko J. [verfasserIn] van der Zwaan, Bob [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	CO Synthetic fuels Renewable chemicals Learning curves Unit size

Übergeordnetes Werk:	Enthalten in: Journal of Energy Chemistry - Amsterdam [u.a.] : Elsevier, 2013, 71, Seite 507-513
Übergeordnetes Werk:	volume:71 ; pages:507-513

DOI / URN:	10.1016/j.jechem.2022.04.014

Katalog-ID:	ELV008228167

Internformat


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100	1		\|a Detz, Remko J. \|e verfasserin \|0 (orcid)0000-0003-1119-0113 \|4 aut
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520			\|a Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate.
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publishDate	2022
allfields	10.1016/j.jechem.2022.04.014 doi (DE-627)ELV008228167 (ELSEVIER)S2095-4956(22)00204-2 DE-627 ger DE-627 rda eng 540 DE-600 Detz, Remko J. verfasserin (orcid)0000-0003-1119-0113 aut Cost projections for microwave plasma CO production using renewable energy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate. CO Synthetic fuels Renewable chemicals Learning curves Unit size van der Zwaan, Bob verfasserin aut Enthalten in Journal of Energy Chemistry Amsterdam [u.a.] : Elsevier, 2013 71, Seite 507-513 Online-Ressource (DE-627)745616399 (DE-600)2714311-9 (DE-576)382032861 2096-885X nnns volume:71 pages:507-513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 71 507-513
spelling	10.1016/j.jechem.2022.04.014 doi (DE-627)ELV008228167 (ELSEVIER)S2095-4956(22)00204-2 DE-627 ger DE-627 rda eng 540 DE-600 Detz, Remko J. verfasserin (orcid)0000-0003-1119-0113 aut Cost projections for microwave plasma CO production using renewable energy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate. CO Synthetic fuels Renewable chemicals Learning curves Unit size van der Zwaan, Bob verfasserin aut Enthalten in Journal of Energy Chemistry Amsterdam [u.a.] : Elsevier, 2013 71, Seite 507-513 Online-Ressource (DE-627)745616399 (DE-600)2714311-9 (DE-576)382032861 2096-885X nnns volume:71 pages:507-513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 71 507-513
allfields_unstemmed	10.1016/j.jechem.2022.04.014 doi (DE-627)ELV008228167 (ELSEVIER)S2095-4956(22)00204-2 DE-627 ger DE-627 rda eng 540 DE-600 Detz, Remko J. verfasserin (orcid)0000-0003-1119-0113 aut Cost projections for microwave plasma CO production using renewable energy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate. CO Synthetic fuels Renewable chemicals Learning curves Unit size van der Zwaan, Bob verfasserin aut Enthalten in Journal of Energy Chemistry Amsterdam [u.a.] : Elsevier, 2013 71, Seite 507-513 Online-Ressource (DE-627)745616399 (DE-600)2714311-9 (DE-576)382032861 2096-885X nnns volume:71 pages:507-513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 71 507-513
allfieldsGer	10.1016/j.jechem.2022.04.014 doi (DE-627)ELV008228167 (ELSEVIER)S2095-4956(22)00204-2 DE-627 ger DE-627 rda eng 540 DE-600 Detz, Remko J. verfasserin (orcid)0000-0003-1119-0113 aut Cost projections for microwave plasma CO production using renewable energy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate. CO Synthetic fuels Renewable chemicals Learning curves Unit size van der Zwaan, Bob verfasserin aut Enthalten in Journal of Energy Chemistry Amsterdam [u.a.] : Elsevier, 2013 71, Seite 507-513 Online-Ressource (DE-627)745616399 (DE-600)2714311-9 (DE-576)382032861 2096-885X nnns volume:71 pages:507-513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 71 507-513
allfieldsSound	10.1016/j.jechem.2022.04.014 doi (DE-627)ELV008228167 (ELSEVIER)S2095-4956(22)00204-2 DE-627 ger DE-627 rda eng 540 DE-600 Detz, Remko J. verfasserin (orcid)0000-0003-1119-0113 aut Cost projections for microwave plasma CO production using renewable energy 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate. CO Synthetic fuels Renewable chemicals Learning curves Unit size van der Zwaan, Bob verfasserin aut Enthalten in Journal of Energy Chemistry Amsterdam [u.a.] : Elsevier, 2013 71, Seite 507-513 Online-Ressource (DE-627)745616399 (DE-600)2714311-9 (DE-576)382032861 2096-885X nnns volume:71 pages:507-513 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 71 507-513
language	English
source	Enthalten in Journal of Energy Chemistry 71, Seite 507-513 volume:71 pages:507-513
sourceStr	Enthalten in Journal of Energy Chemistry 71, Seite 507-513 volume:71 pages:507-513
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	CO Synthetic fuels Renewable chemicals Learning curves Unit size
dewey-raw	540
isfreeaccess_bool	false
container_title	Journal of Energy Chemistry
authorswithroles_txt_mv	Detz, Remko J. @@aut@@ van der Zwaan, Bob @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	745616399
dewey-sort	3540
id	ELV008228167
language_de	englisch
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author	Detz, Remko J.
spellingShingle	Detz, Remko J. ddc 540 misc CO misc Synthetic fuels misc Renewable chemicals misc Learning curves misc Unit size Cost projections for microwave plasma CO production using renewable energy
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topic_title	540 DE-600 Cost projections for microwave plasma CO production using renewable energy CO Synthetic fuels Renewable chemicals Learning curves Unit size
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title	Cost projections for microwave plasma CO production using renewable energy
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title_full	Cost projections for microwave plasma CO production using renewable energy
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title_sort	cost projections for microwave plasma co production using renewable energy
title_auth	Cost projections for microwave plasma CO production using renewable energy
abstract	Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate.
abstractGer	Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate.
abstract_unstemmed	Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes. To improve their performance, renewable fuel production technologies should follow a cost-reducing learning curve. In this article, we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO2 conversion in which the learning rate varies between 10%, 15%, and 20%. Our projections reveal that the total investments required to deploy this CO2 conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10% increase in the value of the learning rate. The CO production costs in 2050 amount to 247–346 €(2019)/tCO, in which the range is determined by the value of the learning rate. Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO production processes, except when a CO2 tax is applied of up to 150 €(2019)/tCO2. To optimally exploit effects of learning-by-doing, we recommend developing several CO production technologies simultaneously with multiple unit sizes, so as to improve the chance of ultimately selecting the process with the highest learning rate.
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title_short	Cost projections for microwave plasma CO production using renewable energy
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up_date	2024-07-06T18:58:54.283Z
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