Version 1
: Received: 26 April 2024 / Approved: 27 April 2024 / Online: 28 April 2024 (08:00:38 CEST)
How to cite:
Rosik, J.; Stegenta-Dąbrowska, S. Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach. Preprints2024, 2024041828. https://doi.org/10.20944/preprints202404.1828.v1
Rosik, J.; Stegenta-Dąbrowska, S. Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach. Preprints 2024, 2024041828. https://doi.org/10.20944/preprints202404.1828.v1
Rosik, J.; Stegenta-Dąbrowska, S. Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach. Preprints2024, 2024041828. https://doi.org/10.20944/preprints202404.1828.v1
APA Style
Rosik, J., & Stegenta-Dąbrowska, S. (2024). Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach. Preprints. https://doi.org/10.20944/preprints202404.1828.v1
Chicago/Turabian Style
Rosik, J. and Sylwia Stegenta-Dąbrowska. 2024 "Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach" Preprints. https://doi.org/10.20944/preprints202404.1828.v1
Abstract
Although composting has many advantages in the treatment of organic waste, there are still many problems and challenges associated with emissions, like NH3, VOCs, and H2S, as well as greenhouse gases such as CO2, CH4, and N2O. One promising approach to enhancing composting conditions is used of novel analytical methods bad on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during composting process kinetics thought mathematical models (MM) and machine learning (ML) models were utilized. Data about everyday emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% d.m.) were used for MM and ML models selections and training. MM has not been very effective in predicting emissions, (R2 0.1 - 0.9), while 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:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For the first time the ML models to predict CO and H2S during composting were demonstrated. Further research in a semi-scale and field study composting with biochar is needed to improve the accuracy of developments models.
Environmental and Earth Sciences, Waste Management and Disposal
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
