TY - JOUR
T1 - Artificial intelligence for waste management in smart cities
T2 - a review
AU - Fang, Bingbing
AU - Yu, Jiacheng
AU - Chen, Zhonghao
AU - Osman, Ahmed I.
AU - Farghali, Mohamed
AU - Ihara, Ikko
AU - Hamza, Essam H.
AU - Rooney, David W.
AU - Yap, Pow Seng
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/8
Y1 - 2023/8
N2 - The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.
AB - The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.
KW - Artificial intelligence
KW - Chemical analysis
KW - Cost efficiency
KW - Optimization
KW - Waste management
UR - http://www.scopus.com/inward/record.url?scp=85159023289&partnerID=8YFLogxK
U2 - 10.1007/s10311-023-01604-3
DO - 10.1007/s10311-023-01604-3
M3 - Review article
AN - SCOPUS:85159023289
SN - 1610-3653
VL - 21
SP - 1959
EP - 1989
JO - Environmental Chemistry Letters
JF - Environmental Chemistry Letters
IS - 4
ER -
