Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review
H Guo, S Wu, Y Tian, J Zhang, H Liu - Bioresource technology, 2021 - Elsevier
Conventional treatment and recycling methods of organic solid waste contain inherent flaws,
such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past …
such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past …
[HTML][HTML] Machine learning and soil sciences: A review aided by machine learning tools
The application of machine learning (ML) techniques in various fields of science has
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …
Prediction of soil heavy metal immobilization by biochar using machine learning
Biochar application is a promising strategy for the remediation of contaminated soil, while
ensuring sustainable waste management. Biochar remediation of heavy metal (HM) …
ensuring sustainable waste management. Biochar remediation of heavy metal (HM) …
[HTML][HTML] Machine learning for biochemical engineering: A review
The field of machine learning is comprised of techniques, which have proven powerful
approaches to knowledge discovery and construction of 'digital twins' in the highly …
approaches to knowledge discovery and construction of 'digital twins' in the highly …
Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource
Hydrothermal carbonization (HTC) is a promising technology for valuable resources
recovery from high-moisture wastes without pre-drying, while optimization of operational …
recovery from high-moisture wastes without pre-drying, while optimization of operational …
Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning
Conversion of wet organic wastes into renewable energy is a promising way to substitute
fossil fuels and avoid environmental deterioration. Hydrothermal carbonization and pyrolysis …
fossil fuels and avoid environmental deterioration. Hydrothermal carbonization and pyrolysis …
Prediction heavy metals accumulation risk in rice using machine learning and mapping pollution risk
B Zhao, W Zhu, S Hao, M Hua, Q Liao, Y Jing… - Journal of Hazardous …, 2023 - Elsevier
Rapid and accurate prediction of metal bioaccumulation in crops are important for assessing
metal environmental risks. We aimed to incorporate machine learning modeling methods to …
metal environmental risks. We aimed to incorporate machine learning modeling methods to …
Artificial intelligence and machine learning approaches in composting process: a review
Studies on developing strategies to predict the stability and performance of the composting
process have increased in recent years. Machine learning (ML) has focused on process …
process have increased in recent years. Machine learning (ML) has focused on process …
Toxicity prediction based on artificial intelligence: A multidisciplinary overview
E Pérez Santín, R Rodríguez Solana… - Wiley …, 2021 - Wiley Online Library
The use and production of chemical compounds are subjected to strong legislative pressure.
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …
Artificial intelligence in biological sciences
Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the
quality of life of human beings. The fields of AI and biological research are becoming more …
quality of life of human beings. The fields of AI and biological research are becoming more …