SciELO - Scientific Electronic Library Online

 
vol.33 issue3Towards a systemic assessment of environmental impact (SAEI) regarding alternative hydrosedimentological management practice in the Canal del Dique, ColombiaAnalytical synthesis for four-bar mechanisms used in a pseudo-equatorial solar tracker author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Article

Indicators

Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google

Share


Ingeniería e Investigación

Print version ISSN 0120-5609

Abstract

GOMEZ SARDUY, J. R et al. Determining cement ball mill dosage by artificial intelligence tools aimed at reducing energy consumption and environmental impact. Ing. Investig. [online]. 2013, vol.33, n.3, pp.49-54. ISSN 0120-5609.

Energy management systems can be improved by using artificial intelligence techniques such as neural networks and genetic algorithms for modelling and optimising equipment and system energy consumption. This paper proposes modelling ball mill consumption as used in the cement industry from field variables. The regression model was based on artificial neural networks for predicting the electricity consumption of the mill's main drive and evaluating established consumption rate performance. This research showed the influence of the amount of pozzolanic ash, gypsum and clinker on a mill's power consumption; the dose determined according to the model ensured minimum energy consumption using a simple genetic algorithm. The estimated savings potential from the proposed dose was 36 600 kWh / year for mill number 1, representing $5,793.78 / year and a 33,708 kg CO2 / year reduction in the environmental impact of gas left to escape.

Keywords : energy management; energy; cement mill; artificial neural network (ANN); genetic algorithm.

        · abstract in Spanish     · text in English     · English ( pdf )