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Determining cement ball mill dosage by artificial intelligence tools aimed at reducing energy consumption and environmental impact
Dosificación en molinos de cemento con apoyo de herramientas inteligentes para reducción del consumo energético y el impacto ambiental
DOI:
https://doi.org/10.15446/ing.investig.v33n3.41043Keywords:
energy management, energy, cement mill, artificial neural network (ANN), genetic algorithm (en)gestión energética, consumo energético, molinos de cemento, Redes Neuronales Artificiales (RNA) y algoritmo genético. (es)
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.
Los sistemas de gestión energética pueden ser mejorados mediante la utilización de técnicas de inteligencia artificial, tales como, las redes neuronales y los algoritmos genéticos; con el propósito de modelar y optimizar el consumo energético de equipos y sistemas. Este trabajo, propone la modelación del consumo de los molinos y de las bolas que se emplean en la industria cementera, a partir de las variables disponibles en el campo. El modelo de regresión obtenido está basado en redes neuronales artificiales, permitiendo predecir el consumo de la electricidad en el accionamiento principal de los molinos, así mismo, permite evaluar el comportamiento de los índices de consumo establecidos. Además, se demuestra la influencia que ejerce la cantidad de puzolana, yeso y clinker en el consumo eléctrico del molino y se determina la dosificación que de acuerdo con el modelo, garantiza un mínimo consumo energético utilizando un algoritmo genético simple. El potencial de ahorro estimado a partir de la dosificación propuesta, es de 36 600 kWh/año para el molino 1; lo que representa 5 793,78 $/año y una reducción del impacto ambiental por gases sin emitir de 33 708 kg CO2/año.
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References
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Copyright (c) 2013 Julio R. Gómez Sarduy, José P. Monteagudo Yanes, Manuel E. Granado Rodríguez, Jorge L Quiñones Ferreira, Yudith Miranda Torres
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