Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Biodegradation of cyano liquid crystal monomers by an aerobic enrichment culture: Key degraders and interspecies synergistic mechanisms.

Water research·2026

Same author

Development of nontarget method based on GC-QTOF-HRMS for analyzing organic pollutants in human serum.

Journal of environmental sciences (China)·2026

Same author

Spatiotemporal changes in inter-city sustainability impacts linked to emission challenges worldwide.

Nature communications·2026

Same author

Exploratory Study on Plasticiser Intake During Intermittent Fasting: Effects on Weight, Glycaemic Control and Vitamin D Levels in Type 2 Diabetes.

Toxics·2026

Same author

Resistance Mechanisms of Alectinib in ALK-Positive Non-Small Cell Lung Cancer and Therapeutic Strategies.

Current cancer drug targets·2026

Same author

Co-exposure profiles of PAHs and their derivatives in coking plant workers' serum and associations with liver function.

Environment international·2026

Same journal

Environmental benefits of reusing coastal expanded polystyrene waste as sand substitution in cement mortar.

Waste management (New York, N.Y.)·2026

Same journal

Impacts of concentrated leachate recirculation on methane production suppression, odorous gas emissions, and salinity accumulation in municipal solid waste.

Waste management (New York, N.Y.)·2026

Same journal

The role of aluminum in the synergistic enhancement of heavy metal immobilization via co-pyrolysis of sludge and phosphate tailings.

Waste management (New York, N.Y.)·2026

Same journal

Integrated neutral-hydrolysis recycling of mixed poly(ethylene terephthalate) and poly(butylene terephthalate) waste: System-level design, techno-economic, and environmental evaluation.

Waste management (New York, N.Y.)·2026

Same journal

Effect of microplastics from lithium-ion battery waste on lithium carbonate recovery and crystallization behavior.

Waste management (New York, N.Y.)·2026

Same journal

Decoupling salinity and carbon function during hydrothermal carbonization of anaerobic digestate.

Waste management (New York, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 18, 2025

Basic Research in Plasma Medicine - A Throughput Approach from Liquids to Cells

Basic Research in Plasma Medicine - A Throughput Approach from Liquids to Cells

Published on: November 17, 2017

Improved medical waste plasma gasification modelling based on implicit knowledge-guided interpretable machine

Jianzhao Zhou¹, Jingzheng Ren¹, Chang He²

¹Research Institute for Advanced Manufacturing, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.

Waste Management (New York, N.Y.)

|August 4, 2024

Summary

This summary is machine-generated.

This study introduces a knowledge-guided machine learning framework to enhance plasma gasification models. The approach improves prediction accuracy and interpretability for syngas quality, overcoming limitations of traditional methods.

Keywords:

Implicit knowledge-based error Interpretability Machine learning Medical waste Plasma gasification

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Related Experiment Videos

Last Updated: Jun 18, 2025

Basic Research in Plasma Medicine - A Throughput Approach from Liquids to Cells

Basic Research in Plasma Medicine - A Throughput Approach from Liquids to Cells

Published on: November 17, 2017

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

Area of Science:

Chemical Engineering
Machine Learning
Computational Chemistry

Background:

Interpretability of machine learning models in chemical engineering is hindered by data quality and inherent model limitations.
Reliable application of machine learning for complex processes like plasma gasification requires enhanced interpretability and accuracy.

Purpose of the Study:

To propose and validate a knowledge-guided machine learning framework for improving plasma gasification modeling.
To enhance the scientific interpretability and predictive accuracy of machine learning models in chemical engineering.

Main Methods:

Developed a framework integrating heuristic algorithms with pre-trained machine learning models.
Utilized Monte Carlo simulations to quantify implicit monotonic inconsistencies.
Applied the framework to artificial neural networks (ANN) and support vector machines (SVM) for plasma gasification modeling.

Main Results:

The knowledge-guided framework significantly improved model generalizability and interpretability for predicting syngas quality.
For ANN, root mean square error (RMSE) decreased by 36.44% and knowledge-based error (KE) by 83.22%.
For SVM, RMSE decreased by 2.58% and KE by 100%, successfully capturing monotonicity relationships.

Conclusions:

The proposed framework effectively addresses limitations in traditional machine learning for chemical engineering applications.
Implicit knowledge integration enhances the scientific interpretability and predictive performance of machine learning models.
This approach offers a robust method for reliable plasma gasification modeling and syngas quality prediction.