Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel
This study aims to examine the energy use efficiency and conduct an environmental life cycle assessment (LCA) of the production of cumin and fennel using artificial intelligence (AI). The energy consumption for fennel and cumin is 34814.81 MJ ha−1 and 26214.17 MJ ha−1, respectively. The energy ratio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023006497 |
| _version_ | 1797385186281783296 |
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| author | Jahangir Mirzaei Mohammad Gholami Parashkoohi Davood Mohammad Zamani Hamed Afshari |
| author_facet | Jahangir Mirzaei Mohammad Gholami Parashkoohi Davood Mohammad Zamani Hamed Afshari |
| author_sort | Jahangir Mirzaei |
| collection | DOAJ |
| description | This study aims to examine the energy use efficiency and conduct an environmental life cycle assessment (LCA) of the production of cumin and fennel using artificial intelligence (AI). The energy consumption for fennel and cumin is 34814.81 MJ ha−1 and 26214.17 MJ ha−1, respectively. The energy ratio for cumin is 0.66, indicating a substantial energy consumption per unit of output energy. Specifically, it takes 22.36 MJ of energy to produce 1 kg of cumin. Both crops exhibit a negative net energy balance. The results of the LCA analysis revealed three key findings when comparing the environmental emissions of cumin and fennel. Firstly, in the context of cumin cultivation, the environmental emissions related to human health were found to be 0.15 DALY. Secondly, there were no significant variations in the environmental emissions of medicinal plants when considering ecosystems. Finally, fennel production exhibited 237.77 USD2013 environmental publications in the resource category. It is worth noting that a substantial portion of the emissions in all three categories of medicinal plants can be attributed to direct emissions from crop cultivation. Additionally, nitrogen plays a significant role in emissions after the direct emissions occur. In comparison to fennel, cultivating cumin is recommended as it helps decrease environmental emissions and minimizes harm to water, air, and soil. The ANN model achieved a determination coefficient greater than 0.93 for four factors related to cumin cultivation in test mode. Similarly, the determination coefficient for output energy in fennel cultivation was found to be 0.852. However, the results indicate that the ANFIS test model outperformed the ANN model in terms of performance and accuracy. The ANFIS model exhibited an average detection coefficient of 0.95 for cumin cultivation and 0.93 for fennel cultivation, highlighting its superior performance compared to the ANN model. The application of AI techniques provides valuable insights into the production of cumin and fennel, facilitating informed decision-making towards more sustainable practices in the spice industry. |
| first_indexed | 2024-03-08T21:50:30Z |
| format | Article |
| id | doaj.art-edbb6a21a8a64243b48414e2f95231a0 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-08T21:50:30Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-edbb6a21a8a64243b48414e2f95231a02023-12-20T07:36:04ZengElsevierResults in Engineering2590-12302023-12-0120101522Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennelJahangir Mirzaei0Mohammad Gholami Parashkoohi1Davood Mohammad Zamani2Hamed Afshari3Department of Biosystem Engineering, Takestan Branch, Islamic Azad University, Takestan, IranDepartment of Biosystem Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran; Corresponding author.Department of Biosystem Engineering, Takestan Branch, Islamic Azad University, Takestan, IranDepartment of Food Science and Engineering, Faculty of Civil and Earth Resources Engineering, Central Tehran Branch, Islamic Azad University, Tehran, IranThis study aims to examine the energy use efficiency and conduct an environmental life cycle assessment (LCA) of the production of cumin and fennel using artificial intelligence (AI). The energy consumption for fennel and cumin is 34814.81 MJ ha−1 and 26214.17 MJ ha−1, respectively. The energy ratio for cumin is 0.66, indicating a substantial energy consumption per unit of output energy. Specifically, it takes 22.36 MJ of energy to produce 1 kg of cumin. Both crops exhibit a negative net energy balance. The results of the LCA analysis revealed three key findings when comparing the environmental emissions of cumin and fennel. Firstly, in the context of cumin cultivation, the environmental emissions related to human health were found to be 0.15 DALY. Secondly, there were no significant variations in the environmental emissions of medicinal plants when considering ecosystems. Finally, fennel production exhibited 237.77 USD2013 environmental publications in the resource category. It is worth noting that a substantial portion of the emissions in all three categories of medicinal plants can be attributed to direct emissions from crop cultivation. Additionally, nitrogen plays a significant role in emissions after the direct emissions occur. In comparison to fennel, cultivating cumin is recommended as it helps decrease environmental emissions and minimizes harm to water, air, and soil. The ANN model achieved a determination coefficient greater than 0.93 for four factors related to cumin cultivation in test mode. Similarly, the determination coefficient for output energy in fennel cultivation was found to be 0.852. However, the results indicate that the ANFIS test model outperformed the ANN model in terms of performance and accuracy. The ANFIS model exhibited an average detection coefficient of 0.95 for cumin cultivation and 0.93 for fennel cultivation, highlighting its superior performance compared to the ANN model. The application of AI techniques provides valuable insights into the production of cumin and fennel, facilitating informed decision-making towards more sustainable practices in the spice industry.http://www.sciencedirect.com/science/article/pii/S2590123023006497EnergyLife cycle assessmentArtificial neural networksAdaptive neuro-fuzzy inference |
| spellingShingle | Jahangir Mirzaei Mohammad Gholami Parashkoohi Davood Mohammad Zamani Hamed Afshari Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel Results in Engineering Energy Life cycle assessment Artificial neural networks Adaptive neuro-fuzzy inference |
| title | Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel |
| title_full | Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel |
| title_fullStr | Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel |
| title_full_unstemmed | Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel |
| title_short | Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel |
| title_sort | examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence a case study on the production of cumin and fennel |
| topic | Energy Life cycle assessment Artificial neural networks Adaptive neuro-fuzzy inference |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023006497 |
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