Evaluating the thermodynamic efficiency of the cement grate clinker cooler process using artificial neural networks and ANFIS
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Abstract
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The energy recovery of the grate cooler is a significant part of reducing production costs and tackling the
environmental challenges of the cement industry. ASPEN Plus and neural networks predictive model
were used to model, simulate and predict the grate clinker cooler in this paper. First, the process flow
model and thermodynamic efficiency assessment were carried out. A predictive model of neural networks
was then initiated to evaluate the optimal thermodynamic efficiency using plant operating data,
which includes clinker cooling airflow, clinker mass flow, ambient and clinker temperature. The energy
efficiency was 86.04, 86.1, and 86.5% respectively using the Aspen Plus process model, artificial neural
network (ANN), and Adaptive neural inference systems (ANFIS). Therefore, based on the energy efficiency
achieved, bootstrap aggregated neural network (BANN) was used to search for optimal operating parameters
with the lowest mean square error (MSE) of the model in view. The MSE for the BANN training, testing,
and validation data sets were 2.0 � 10�4, 1.5 � 10�4, and 1.0 � 10�4. The final optimal clinker cooling
air, clinker mass flow, ambient air, and kiln clinker discharge temperature are chosen from the ANFIS
optimal solutions and validated on-site. When compared to actual operating data, the total clinker cooling
air decreases by 5%, the energetic efficiency increases by 0.5%, and the ex-clinker cooler discharge
temperature decreases to 120 �C, resulting in a significant reduction in energy consumption.
Keywords
T Technology (General), TP Chemical technology