Predictions for Coal Spontaneous Combustion Stage Based on an Artificial Neural Network

Zhao, Gaochang

doi:10.1109/cis.2011.92

2011 Seventh International Conference on Computational Intelligence and Security 2011

DOI: 10.1109/cis.2011.92

|View full text |Cite

Predictions for Coal Spontaneous Combustion Stage Based on an Artificial Neural Network

Gaochang Zhao

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Coal Fire Detection and Prevention System Using IoT

Pandiyaraju,

Shunmuga Perumal,

Muthumanikandan

2023

Advances in Systems Analysis, Software Engineering, and High Performance Computing

View full text Add to dashboard Cite

Coal is one of the major natural sources of power production. It contributes to about 56% of the total production. Conventionally, coal is stored in stockpiles for production of electricity without any logs. Storage of coal for a longer time in the open type stockpile results in self-oxidation of coal, which leads to increased ignition temperature. Detection and prevention of spontaneous combustion and self-ignition stands to be major issues in stockpile. This chapter proposes an automated system using internet of things (IoT), which aids in detecting the fire at an earlier stage and to prevent it, thereby avoiding economic loss. Wireless sensor nodes are deployed to detect the parameters such as temperature, humidity, gases from the stockpile, and the data is transmitted to the server and to the ground station with the aid of GSM modules. Data is monitored in the ground station, and the critical values are evaluated to prevent fire by detecting it at its early stages. The system is implemented in an open rail coal stockpile, and the data is represented in the form of graph for evaluation.

show abstract

Coal Fire Detection and Prevention System Using IoT

Pandiyaraju,

Shunmuga Perumal,

Muthumanikandan

2023

Advances in Systems Analysis, Software Engineering, and High Performance Computing

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predictions for Coal Spontaneous Combustion Stage Based on an Artificial Neural Network

Cited by 1 publication

References 8 publications

Coal Fire Detection and Prevention System Using IoT

Coal Fire Detection and Prevention System Using IoT

Contact Info

Product

Resources

About