Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains
Biomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet increasing energy demands. Pyrolysis has been one of the most prevalent thermochemical processes for biomass conversion. In the pyrolysis process, the biomass decomposes into three byprod...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/10/4070 |
| _version_ | 1797600224014761984 |
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| author | Kolton Keith Krystel K. Castillo-Villar |
| author_facet | Kolton Keith Krystel K. Castillo-Villar |
| author_sort | Kolton Keith |
| collection | DOAJ |
| description | Biomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet increasing energy demands. Pyrolysis has been one of the most prevalent thermochemical processes for biomass conversion. In the pyrolysis process, the biomass decomposes into three byproducts: bio-oil (60–75%), biochar (15–25%), and syngas (10–20%), depending on the feedstock and its composition. The energy required to convert the biomass varies depending on the levels of cellulose, hemicellulose, and lignin. This work proposes a novel two-stage stochastic model that designs an efficient biomass supply chain mindful of the trade-offs between pyrolysis byproducts (bioethanol and biochar). Remarkably, the model integrates biomass quality-related costs associated with moisture and ash content such as the energy consumption of preprocessing equipment and boiler maintenance due to excess ash. Biomass quality directly affects the production yield as well as the total cost of production and distribution. The results from our case study indicate a shortage of biomass from the suppliers to fulfill the demand for biochar from the power plants and bioethanol from the cities. Furthermore, the bioethanol price has the most impact on the total supply chain according to our sensitivity analysis. |
| first_indexed | 2024-03-11T03:46:24Z |
| format | Article |
| id | doaj.art-1f01c022700344cdb0e3879cfba625ff |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:46:24Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1f01c022700344cdb0e3879cfba625ff2023-11-18T01:12:28ZengMDPI AGEnergies1996-10732023-05-011610407010.3390/en16104070Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply ChainsKolton Keith0Krystel K. Castillo-Villar1Mechanical Engineering Department and Texas Sustainable Energy Research Institute, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USAMechanical Engineering Department and Texas Sustainable Energy Research Institute, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USABiomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet increasing energy demands. Pyrolysis has been one of the most prevalent thermochemical processes for biomass conversion. In the pyrolysis process, the biomass decomposes into three byproducts: bio-oil (60–75%), biochar (15–25%), and syngas (10–20%), depending on the feedstock and its composition. The energy required to convert the biomass varies depending on the levels of cellulose, hemicellulose, and lignin. This work proposes a novel two-stage stochastic model that designs an efficient biomass supply chain mindful of the trade-offs between pyrolysis byproducts (bioethanol and biochar). Remarkably, the model integrates biomass quality-related costs associated with moisture and ash content such as the energy consumption of preprocessing equipment and boiler maintenance due to excess ash. Biomass quality directly affects the production yield as well as the total cost of production and distribution. The results from our case study indicate a shortage of biomass from the suppliers to fulfill the demand for biochar from the power plants and bioethanol from the cities. Furthermore, the bioethanol price has the most impact on the total supply chain according to our sensitivity analysis.https://www.mdpi.com/1996-1073/16/10/4070optimizationstochastic programmingsupply chainsbiofuelsbiomass |
| spellingShingle | Kolton Keith Krystel K. Castillo-Villar Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains Energies optimization stochastic programming supply chains biofuels biomass |
| title | Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains |
| title_full | Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains |
| title_fullStr | Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains |
| title_full_unstemmed | Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains |
| title_short | Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains |
| title_sort | stochastic programming model integrating pyrolysis byproducts in the design of bioenergy supply chains |
| topic | optimization stochastic programming supply chains biofuels biomass |
| url | https://www.mdpi.com/1996-1073/16/10/4070 |
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