Artificial intelligence for planning of energy and waste management

In the current era, waste management activities, including energy and material recycling, may create indirect environmental impacts beyond waste management systems. Energy waste is used to reproduce different products, such as electricity, heat, compost, and biofuels. Effective environmental protect...

Full description

Saved in:
Bibliographic Details
Published in:Sustainable energy technologies and assessments 2021-10, Vol.47, p.101426, Article 101426
Main Authors: Huang, Jueru, Koroteev, Dmitry D.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Machine learning
Planning of energy and waste management
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the current era, waste management activities, including energy and material recycling, may create indirect environmental impacts beyond waste management systems. Energy waste is used to reproduce different products, such as electricity, heat, compost, and biofuels. Effective environmental protection depends on the quality of the information available for a proper decision. Reliable data collection is essential to facilitate planning processes in the effective planning of waste management. In this paper, the Machine learning-driven Predictive Analytic framework (MLDPAF) has been proposed to prepare energy and waste management. Firstly, with a neural network, the amount of waste is predicted. An enhanced machine learning algorithm further improves waste collection on energy costs based on volatile sustainable energymarkets. Findings showed that proposed algorithms based on machine learning have been used successfully to generate efficient waste models. The simulation analysis shows that the analysis of waste quantity reduced by 90% using the proposed method, landfill analysis as 40%, and transportation reduced by 15%.
ISSN:2213-1388
DOI:10.1016/j.seta.2021.101426