Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways
Stochastic techno-economic analysis (TEA) is pivotal in assessing the financial viability and risks inherent in biofuel production processes. In this method, the Monte Carlo approach entails the random sampling of input variables and multiple runs of the TEA model to create probability distributions...
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| Language: | English |
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Elsevier
2024-11-01
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| Series: | Chemical Engineering Journal Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266682112400067X |
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| author | Chao Wu Yuxi Wang Ling Tao |
| author_facet | Chao Wu Yuxi Wang Ling Tao |
| author_sort | Chao Wu |
| collection | DOAJ |
| description | Stochastic techno-economic analysis (TEA) is pivotal in assessing the financial viability and risks inherent in biofuel production processes. In this method, the Monte Carlo approach entails the random sampling of input variables and multiple runs of the TEA model to create probability distributions of economic metrics. However, traditional Monte Carlo TEA, reliant on iterative calls to process simulation, is resource-intensive and time-consuming, hindering widespread adoption. To address these challenges, we present an accessible framework that harnesses machine learning methods to estimate techno-economic uncertainty in biofuel production pathways. Our approach streamlines the conventional simulation process by automating dataset generation and machine learning model training. These trained models enable rapid predictions of minimum fuel selling prices at any scale, accommodating randomized input variables based on their defined distributions. We illustrate the efficacy of our framework through examples from sustainable aviation fuel production pathways. Our research entails identifying the primary factors influencing uncertainties in minimum selling prices, exploring the synergistic effects of pathway inputs, and assessing how price variability is impacted by financial, technical, and supply chain factors. These examples underscore the framework's effectiveness in addressing breakeven price uncertainties in biofuel production across diverse input scenarios. |
| format | Article |
| id | doaj-art-d0326026cd234d06894ab85c9530e7d4 |
| institution | OA Journals |
| issn | 2666-8211 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chemical Engineering Journal Advances |
| spelling | doaj-art-d0326026cd234d06894ab85c9530e7d42025-08-20T02:30:23ZengElsevierChemical Engineering Journal Advances2666-82112024-11-012010065010.1016/j.ceja.2024.100650Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathwaysChao Wu0Yuxi Wang1Ling Tao2Biosciences Center, National Renewable Energy Laboratory, Golden, CO 80401, USA; Corresponding author.Catalytic Carbon Transformation and Scale-up Center, National Renewable Energy Laboratory, Golden, CO 80401, USACatalytic Carbon Transformation and Scale-up Center, National Renewable Energy Laboratory, Golden, CO 80401, USA; Corresponding author.Stochastic techno-economic analysis (TEA) is pivotal in assessing the financial viability and risks inherent in biofuel production processes. In this method, the Monte Carlo approach entails the random sampling of input variables and multiple runs of the TEA model to create probability distributions of economic metrics. However, traditional Monte Carlo TEA, reliant on iterative calls to process simulation, is resource-intensive and time-consuming, hindering widespread adoption. To address these challenges, we present an accessible framework that harnesses machine learning methods to estimate techno-economic uncertainty in biofuel production pathways. Our approach streamlines the conventional simulation process by automating dataset generation and machine learning model training. These trained models enable rapid predictions of minimum fuel selling prices at any scale, accommodating randomized input variables based on their defined distributions. We illustrate the efficacy of our framework through examples from sustainable aviation fuel production pathways. Our research entails identifying the primary factors influencing uncertainties in minimum selling prices, exploring the synergistic effects of pathway inputs, and assessing how price variability is impacted by financial, technical, and supply chain factors. These examples underscore the framework's effectiveness in addressing breakeven price uncertainties in biofuel production across diverse input scenarios.http://www.sciencedirect.com/science/article/pii/S266682112400067XMachine learningTechno-economic analysis (TEA)Uncertainty analysisMonte Carlo methodMinimum fuel selling price (MFSP) |
| spellingShingle | Chao Wu Yuxi Wang Ling Tao Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways Chemical Engineering Journal Advances Machine learning Techno-economic analysis (TEA) Uncertainty analysis Monte Carlo method Minimum fuel selling price (MFSP) |
| title | Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways |
| title_full | Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways |
| title_fullStr | Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways |
| title_full_unstemmed | Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways |
| title_short | Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways |
| title_sort | machine learning enabled techno economic uncertainty analysis of sustainable aviation fuel production pathways |
| topic | Machine learning Techno-economic analysis (TEA) Uncertainty analysis Monte Carlo method Minimum fuel selling price (MFSP) |
| url | http://www.sciencedirect.com/science/article/pii/S266682112400067X |
| work_keys_str_mv | AT chaowu machinelearningenabledtechnoeconomicuncertaintyanalysisofsustainableaviationfuelproductionpathways AT yuxiwang machinelearningenabledtechnoeconomicuncertaintyanalysisofsustainableaviationfuelproductionpathways AT lingtao machinelearningenabledtechnoeconomicuncertaintyanalysisofsustainableaviationfuelproductionpathways |