In this day and age, the deficiency of resources for synthetic chemicals and massive challenges for waste carries the circular economy, including re-cycling waste, into focus. Consequently, it would provide waste a value that is one of the most essential incentives for all researchers to take better care and to avoid non-recyclable waste. In fact, the researchers established how computers equipped with wide synthetic knowledge (forward-synthesis with well-known reactions in chemical and related industries) can help to address the chemical waste challenge. In this context, Artificial Intelligence/Machine learning (AI/ML) can automatically learn from data and can perform tasks such as predictions and decision-making. Interdisciplinary studies combining AI/ML with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this summary, recent research work of AI/ML in sustainable and cycle chemistry will be introduced.
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On Artificial Intelligence in Sustainable and Circle Chemistry
Published:
10 March 2023
by MDPI
in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
congress BIOMODE.ECO-08: Biotech., Mol. Eng., Nat. Prod. Develop. and Ecology Congress, Paris, France-Ohio, USA, 2023.
https://doi.org/10.3390/mol2net-09-14143
(registering DOI)
Abstract:
Keywords: Circular Economy; Recycling; Synthesis Chemicals; Machine Learning; Predictions
Comments on this paper
Shan He
28 December 2023
How does the utilization of computers equipped with synthetic knowledge contribute to addressing the challenge of chemical waste in industries, and what are the primary mechanisms through which this technology enhances waste management processes?
Shan He
2 January 2024
Computers with synthetic knowledge aid in reducing chemical waste by predicting reactions, optimizing synthesis routes, and identifying eco-friendly alternatives. Through AI/ML, these systems prevent waste by early detection, saving resources and costs while promoting sustainable practices in the chemical industry.
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