Optimizing the early-stage of composting process emissions – artificial intelligence primary tests
Abstract Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH3, CO and H2S, as well as greenhouse gases such as CO2. One promising approach to enhancing composting conditions is using novel analytical methods bas...
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| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79010-0 |
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| author | Joanna Rosik Maciej Karczewski Sylwia Stegenta-Dąbrowska |
| author_facet | Joanna Rosik Maciej Karczewski Sylwia Stegenta-Dąbrowska |
| author_sort | Joanna Rosik |
| collection | DOAJ |
| description | Abstract Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH3, CO and H2S, as well as greenhouse gases such as CO2. One promising approach to enhancing composting conditions is using novel analytical methods based on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during the early-stage of composting process machine learning (ML) models were utilized. Data about emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% dry mass) were used for ML models selections and training. ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0.71, CO2:0.81, NH3:0.95, H2S:0.72) and decision tree (DT, RPART; R2 accuracy CO:0.69, CO2:0.80, NH3:0.93, H2S:0.65) have demonstrated satisfactory results. The ML models to predict CO and H2S during composting were demonstrated for the first time. Utilizing emission data to predict other noxious gases presents a cost-effective and expeditious alternative to the empirical analysis of compost properties. |
| format | Article |
| id | doaj-art-02e28631fc554b43b8ec883c8e4a131e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-02e28631fc554b43b8ec883c8e4a131e2025-08-20T02:37:57ZengNature PortfolioScientific Reports2045-23222024-11-0114111210.1038/s41598-024-79010-0Optimizing the early-stage of composting process emissions – artificial intelligence primary testsJoanna Rosik0Maciej Karczewski1Sylwia Stegenta-Dąbrowska2Institute of Environmental Engineering, The Faculty of Environmental Engineering and Geodesy, Wrocław University of Environmental and Life SciencesDepartment of Applied Mathematics, Wrocław University of Environmental and Life SciencesDepartment of Applied Bioeconomy, Wrocław University of Environmental and Life SciencesAbstract Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH3, CO and H2S, as well as greenhouse gases such as CO2. One promising approach to enhancing composting conditions is using novel analytical methods based on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during the early-stage of composting process machine learning (ML) models were utilized. Data about emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% dry mass) were used for ML models selections and training. ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0.71, CO2:0.81, NH3:0.95, H2S:0.72) and decision tree (DT, RPART; R2 accuracy CO:0.69, CO2:0.80, NH3:0.93, H2S:0.65) have demonstrated satisfactory results. The ML models to predict CO and H2S during composting were demonstrated for the first time. Utilizing emission data to predict other noxious gases presents a cost-effective and expeditious alternative to the empirical analysis of compost properties.https://doi.org/10.1038/s41598-024-79010-0Machine learning 1Biochar application 2Greenhouse gases 3Composting optimizing 4 |
| spellingShingle | Joanna Rosik Maciej Karczewski Sylwia Stegenta-Dąbrowska Optimizing the early-stage of composting process emissions – artificial intelligence primary tests Scientific Reports Machine learning 1 Biochar application 2 Greenhouse gases 3 Composting optimizing 4 |
| title | Optimizing the early-stage of composting process emissions – artificial intelligence primary tests |
| title_full | Optimizing the early-stage of composting process emissions – artificial intelligence primary tests |
| title_fullStr | Optimizing the early-stage of composting process emissions – artificial intelligence primary tests |
| title_full_unstemmed | Optimizing the early-stage of composting process emissions – artificial intelligence primary tests |
| title_short | Optimizing the early-stage of composting process emissions – artificial intelligence primary tests |
| title_sort | optimizing the early stage of composting process emissions artificial intelligence primary tests |
| topic | Machine learning 1 Biochar application 2 Greenhouse gases 3 Composting optimizing 4 |
| url | https://doi.org/10.1038/s41598-024-79010-0 |
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