Skip to main content

Open Access Separation index prediction of cyclonic static micro bubble flotation column based on BP neural network

BP neural network model was used to predict the cleaned coal ash and combustible recovery in different operational conditions.Taking the circulation pressureair flowdosage of collector and frother as input setthe BP neural networkas prediction model was proposed to estimate the cleaned coal ash and combustible recovery as outputs.It is shown that BP neural network model can estimate the separation index quite satisfactorily in which the relative errors between the predicted values and experimental values are both less than 5% for the cleaned coal ash and combustible recovery.

Keywords: BP neural network; cyclonic-static micro-bubble flotation column; operational parameter; separation index

Document Type: Research Article

Publication date: 15 April 2012

More about this publication?
  • The Journal publishes the peer-reviewed papers with original research, new developments and innovations, site measurements and case studies in coal science and mining industry. It aims to be a leading platform for the publication of high quality papers and an authoritative source of information for analysis, review and evaluation related to coal science and mining technologies.

    The Journal covers the following areas:

    - coalfield geology and exploration
    - coal resources/reserves estimation
    - mine planning and design, mine optimization
    - mine construction and development, mine operation
    - mine electrical and equipment, mine automation
    - mine health and safety
    - ventilation and control, methane drainage and capture
    - coal processing and utilization
    - low carbon technology
    - coal economics, mine management
    - coal mine environmental impact assessment and control
    - mine rehabilitation, mine closure, etc.

    The Journal is a unique comprehensive academic periodical completely dedicated to the coal science and mining industry in the world. The Journal stands out as an excellent source of scientific information in coal industry with nearly 400 papers published in 12 issues per annum.

    The Journal is published monthly by China Coal Society.
  • Editorial Board
  • Information for Authors
  • Submit a Paper
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content