Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation
Controlling electrical conductivity (EC), a key indicator of compost salinity, is essential to ensure the agronomic quality of compost produced from organic waste. This study assessed the effect of moisture content and bulk density on EC evolution across 72 composting batches using a mixture of biow...
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Sustainable Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19397038.2025.2538867 |
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| author | Tiago Santos Isabel Bentes Carlos Afonso Teixeira |
| author_facet | Tiago Santos Isabel Bentes Carlos Afonso Teixeira |
| author_sort | Tiago Santos |
| collection | DOAJ |
| description | Controlling electrical conductivity (EC), a key indicator of compost salinity, is essential to ensure the agronomic quality of compost produced from organic waste. This study assessed the effect of moisture content and bulk density on EC evolution across 72 composting batches using a mixture of biowaste and green waste (Bio:Green ratio). The Bio:Green ratio (the mass proportion of biosolids to lignocellulosic green waste) served as a calibration tool for optimising initial process conditions. A polynomial regression model (R2 = 0.656) demonstrated that maintaining moisture between 34.7% and 37.5%, bulk density between 363.3 and 461.3 g/L, and a Bio:Green ratio between 1.2 and 1.4 ensures EC remains within the optimal range (3.75–4.0 mS/cm). These conditions minimise the need for corrective aeration or irrigation, enhancing process efficiency. Future studies may incorporate variables such as the carbon-to-nitrogen (C/N) ratio, microbial activity, and germination index to expand the model’s robustness and applicability. These findings offer a practical data-driven approach to compost EC control in industrial composting operations. |
| format | Article |
| id | doaj-art-df375f399e5147ffab33b43f9be0eb38 |
| institution | Kabale University |
| issn | 1939-7038 1939-7046 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Engineering |
| spelling | doaj-art-df375f399e5147ffab33b43f9be0eb382025-08-20T03:55:49ZengTaylor & Francis GroupInternational Journal of Sustainable Engineering1939-70381939-70462025-12-0118110.1080/19397038.2025.2538867Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisationTiago Santos0Isabel Bentes1Carlos Afonso Teixeira2Department of Biology and Environment, School of Life and Environmental Sciences, University of Trás-os-Montes and Alto Douro, Vila Real, PortugalC-MADE—Centre of Materials and Building Technologies, University of Beira Interior, Covilhã, PortugalCentre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building, and Sustainability of Agri-Food Production (Inov4Agro), Universidade de Trás-os-Montes e Alto Douro (UTAD), Vila Real, PortugalControlling electrical conductivity (EC), a key indicator of compost salinity, is essential to ensure the agronomic quality of compost produced from organic waste. This study assessed the effect of moisture content and bulk density on EC evolution across 72 composting batches using a mixture of biowaste and green waste (Bio:Green ratio). The Bio:Green ratio (the mass proportion of biosolids to lignocellulosic green waste) served as a calibration tool for optimising initial process conditions. A polynomial regression model (R2 = 0.656) demonstrated that maintaining moisture between 34.7% and 37.5%, bulk density between 363.3 and 461.3 g/L, and a Bio:Green ratio between 1.2 and 1.4 ensures EC remains within the optimal range (3.75–4.0 mS/cm). These conditions minimise the need for corrective aeration or irrigation, enhancing process efficiency. Future studies may incorporate variables such as the carbon-to-nitrogen (C/N) ratio, microbial activity, and germination index to expand the model’s robustness and applicability. These findings offer a practical data-driven approach to compost EC control in industrial composting operations.https://www.tandfonline.com/doi/10.1080/19397038.2025.2538867Compostingorganic wasteelectrical conductivityprocess optimisationpredictive modellingpolynomial regression |
| spellingShingle | Tiago Santos Isabel Bentes Carlos Afonso Teixeira Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation International Journal of Sustainable Engineering Composting organic waste electrical conductivity process optimisation predictive modelling polynomial regression |
| title | Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation |
| title_full | Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation |
| title_fullStr | Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation |
| title_full_unstemmed | Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation |
| title_short | Tunnel composting Optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation |
| title_sort | tunnel composting optimisation using polynomial models with moisture and density control for electrical conductivity stabilisation |
| topic | Composting organic waste electrical conductivity process optimisation predictive modelling polynomial regression |
| url | https://www.tandfonline.com/doi/10.1080/19397038.2025.2538867 |
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