AIMC Topic: Pyrolysis

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Organic geochemical evidence for life in Archean rocks identified by pyrolysis-GC-MS and supervised machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Throughout Earth's history, organic molecules from both abiogenic and biogenic sources have been buried in sedimentary rocks. Most of these organic molecules have been significantly altered by geologic processes through deep time. Nonetheless, the na...

The potential of decision tree application in threshold analysis of hazardous volatile organic compound release from biochar: Implications for environmental risk assessment.

The Science of the total environment
The release of hazardous volatile organic compounds (HVOCs) from biochar poses a potential threat to both human health and the environment. This study investigates how low pyrolysis temperature (HTT) and the chemical characteristics of lignocellulosi...

Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics.

Waste management (New York, N.Y.)
A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR)...

Co-pyrolysis kinetics and enhanced synergy for furfural residues and polyethylene using artificial neural network and fast heating.

Waste management (New York, N.Y.)
The efficient co-utilization of biomass and waste plastics is a key method to address the widely concerned environmental problem and replace traditional energy. Co-pyrolysis behaviors and synergistic effects of furfural residues (FR) and polyethylene...

Determination of quality differences and origin tracing of green tea from different latitudes based on TG-FTIR and machine learning.

Food research international (Ottawa, Ont.)
Latitude differences can significantly affect the quality of tea, while in-depth research in this field is lacking. This study investigates green teas from different latitudes in China using thermogravimetric analysis coupled with infrared spectrosco...

Photonic platform coupled with machine learning algorithms to detect pyrolysis products of crack cocaine in saliva: A proof-of-concept animal study.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The non-invasive detection of crack/cocaine and other bioactive compounds from its pyrolysis in saliva can provide an alternative for drug analysis in forensic toxicology. Therefore, a highly sensitive, fast, reagent-free, and sustainable approach wi...

Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions.

Chemosphere
Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS a...

A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste.

Journal of environmental management
The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process para...

Machine learning constructs the microstructure and mechanical properties that accelerate the development of CFRP pyrolysis for carbon-fiber recycling.

Waste management (New York, N.Y.)
The increasing use of carbon-fiber-reinforced plastic (CFRP) has led to its post-end-of-life recycling becoming a research focus. Herein, we studied the macroscopic and microscopic characteristics of recycled carbon fiber (rCF) during CFRP pyrolysis ...

Multi-output neural network model for predicting biochar yield and composition.

The Science of the total environment
In biomass pyrolysis for biochar production, existing prediction models face computational challenges and limited accuracy. This study curated a comprehensive dataset, revealing pyrolysis parameters' dominance in biochar yield (54.8 % importance). Py...