Assessment of the thermodynamics efficiency of a cement vertical raw mill using Aspen Plus and artificial intelligence models
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Globally, cement plants are striving to improve their energy efficiency. Therefore, it is critical for cement plant
operations to increase the monitoring and control of a vertical raw mill energy process. This technology has
attracted the interest of the cement industry with its proven benefits in cement grinding applications. A process
simulator was used to study an industrial-scale vertical raw mill (VRM) with 65.4% energy efficiency. The paper
proposes further a new model based on grid partitioning, sub-clustering, and fuzzy c-means, which incorporates
genetic algorithms (GAs) and particle swarm optimizations (PSOs). VRM data from a steady plant process
operation, such as raw material output, material moisture, kiln hot gas, mill fan flow, grinding pressure, and
separator speed, was used as input to the prediction model. ANFIS-based prediction models are compared with
process simulator predictions to determine the most accurate based on prediction performance criteria. Based
on the results, the ANFIS model with sub-clustering assimilated with PSO is the most accurate prediction model
for VRM energy efficiency. The coefficient of regression (R2) and root mean square error (RMSE) obtained by this
model are 0.945 and 1.3006. The results also showed that VRM's energy efficiency decreased from 65.4 to 64.2%
when the separator speed increased from 50 to 75 rpm; product particle size on P90μm decreased from 18.2–
10.8%. Finally, the proposed ANFIS based model can be considered to be an efficient technique for predicting the
energy efficiency of VRM production processes.
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TP Chemical technology