Machine learning-based prediction of compost maturity and identification of key parameters during manure composting DOI
Shuai Shi,

Zhiheng Guo,

Jiaxin Bao

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 419, P. 132024 - 132024

Published: Dec. 26, 2024

Language: Английский

Comprehensive analysis of spatiotemporal heterogeneity reveals the effects of physicochemical and biological factors on temperature rise during the Moutai-flavor Baijiu stacking fermentation process DOI

Yuanbu Li,

Xing Qin,

Xianglian Zeng

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161049 - 161049

Published: Feb. 1, 2025

Language: Английский

Citations

0
A data fusion system based on attenuated total reflectance mid-infrared spectroscopy and colorimetry combined with chemometrics for monitoring the fermentation process of Candida utilis DOI
Lin Zhang,

Lantian Liu,

Yefeng Zhou

et al.

Talanta, Journal Year: 2025, Volume and Issue: unknown, P. 128163 - 128163

Published: April 1, 2025

Language: Английский

Citations

0
Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach DOI Open Access
Joanna Rosik, Sylwia Stegenta-Dąbrowska

Published: April 28, 2024

Although composting has many advantages in the treatment of organic waste, there are still problems and challenges associated with emissions, like NH3, VOCs, H2S, as well greenhouse gases such CO2, CH4, N2O. One promising approach to enhancing conditions is used novel analytical methods bad on artificial intelligence. To predict optimize emissions (CO, NH3) during process kinetics thought mathematical models (MM) machine learning (ML) were utilized. Data about everyday from laboratory compost’s biochar different incubation (50, 60, 70 °C) doses (0, 3, 6, 9, 12, 15% d.m.) for MM ML selections training. not been very effective predicting (R2 0.1 - 0.9), while acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0,71, CO2:0,81, NH3:0,95, H2S:0,72)) decision tree (DT, RPART; CO:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For first time CO H2S demonstrated. Further research a semi-scale field study needed improve developments models.

Language: Английский

Citations

2
Predicting ammonia emissions and global warming potential in composting by machine learning DOI
Bing Wang, Peng Zhang,

Xingyi Qi

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 411, P. 131335 - 131335

Published: Aug. 23, 2024

Language: Английский

Citations

2
Based on Machine Learning Models Predicting Gas Emission and Maturity During Composting: Key Factors Identifying DOI
Bing Wang, Peng Zhang,

Xingyi Qi

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Optimizing the early-stage of composting process emissions – artificial intelligence primary tests DOI Creative Commons
Joanna Rosik, Maciej Karczewski, Sylwia Stegenta-Dąbrowska

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 8, 2024

Although composting has many advantages in treating organic waste, problems and challenges are still associated with emissions, like NH

Language: Английский

Citations

0
A comprehensive review on the application of neural network model in microbial fermentation DOI
Jiacong Huang, Qi Guo, Xuhong Li

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 416, P. 131801 - 131801

Published: Nov. 10, 2024

Language: Английский

Citations

0
Machine learning-based prediction of compost maturity and identification of key parameters during manure composting DOI
Shuai Shi,

Zhiheng Guo,

Jiaxin Bao

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 419, P. 132024 - 132024

Published: Dec. 26, 2024

Language: Английский

Citations

0