AI/ML

Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review.


Related Articles

Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review.

Bioresour Technol. 2020 Sep 11;319:124114

Authors: Guo HN, Wu SB, Tian YJ, Zhang J, Liu HT

Abstract
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 decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.

PMID: 32942236 [PubMed – as supplied by publisher]

Source link

Related posts

Madaket Health Raises $10M for Automated Healthcare Provider Enrollment Platform

Newsemia

Medical devices pose cybersecurity and patient threat

Newsemia

A robot and software make it easier to create advanced materials

Newsemia

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy

COVID-19

COVID-19 (Coronavirus) is a new illness that is having a major effect on all businesses globally LIVE COVID-19 STATISTICS FOR World