Predicting Glass Furnace Output Using Statistical and Neural Computing Methods

Details

• Personal Author:
  Bidanda B ; Carnahan BJ ; Colosimo Warner R ; LaScola Needy K
• Description:
  This paper describes the development of predictive models for glass production at a regional manufacturing company. The objectives of the models are to predict the actual batch tonnage produced per week from the glass furnace based on the planned production schedule. Four modelling methods were explored: (i) linear regression; (ii) nonlinear regression; (iii) artificial neural network using back-propagation; and (iv) radial basis function neural network. Using 175 cases of production schedule data and subsequent furnace output, the two neural network-based prediction models resulted in lower average absolute error and lower maximum absolute error than the linear or nonlinear regression models. Accurate neural network-based prediction models of furnace output will subsequently be used in the overall production planning system by utilizing estimates of furnace output to determine the necessary energy, raw material, repair and personnel requirements of the glass manufacturing facility. [Description provided by NIOSH]
• Subjects:
  Glass Manufacturing And Industrial Facilities Occupational Health Safety Software
• Keywords:
  Computer Models Glass Manufacturing Industry Manufacturing Models Statistical Analysis Work Schedules
• ISSN:
  0020-7543
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Alabama ; OSHA Region 3 ; OSHA Region 4 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  38
• Issue:
  6
• NIOSHTIC Number:
  nn:20057642
• Citation:
  Int J Prod Res 2000 Apr; 38(6):1255-1269
• Contact Point Address:
  Kim LaScola Needy, Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall, Pittsburgh, PA 15261, USA
• Email:
  kneedy@engrng.pitt.edu
• Federal Fiscal Year:
  2000
• Performing Organization:
  Deep South Center for Occupational Health and Safety, University of Alabama at Birmingham, Birmingham, AL
• Peer Reviewed:
  True
• Start Date:
  19980701
• Source Full Name:
  International Journal of Production Research
• End Date:
  20040630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:5559364517804dc62e1f721c58caec63e11a62320a8a2fce225657e8bfa9593149211116b4b14b127ab4ef09c014a88cb55f3da6cb761587e545041727777be3
• Download URL:
  https://stacks.cdc.gov/view/cdc/214914/cdc_214914_DS1.pdf
• File Type:
  [PDF - 292.60 KB ]