Energy consumption optimization strategy for ball mill

 

Energy consumption optimization strategy and application for 
information are researched. The performance test experiments 
for the Ball Mill in a power plant are carried out. The optimal
 equipment parameters and work condition are obtained by the
 test. The results can be used as guidance for practical operation
 for ball mill. 

Aimed at the disadvantage of conventional optimization 
algorithm for ball mill, a new on-line optimization algorithm
 combined fill level is proposed. A new neural network called 
resource optimized networks (RON) is used to build the 
complex nonlinear relationship between the electric 
consumption and process parameters. Then the object
 function is determined based on the nonlinear model. The 
optimal work parameters of ball mill system are determined 
by genetic algorithm. The optimization results demonstrate 
that power consumption can be decreased obviously and 
the goal of energy consumption optimization is achieved.
 The optimization results of control variables as fill level, 
inlet negative press and outlet temperature are obtained. 

In order to overcome the uncertainty of model parameters
 and disturbance of system, robust control algorithm for 
ball mill system is researched. For the load control loop, 
based on the difference pressure control loop, the fill level 
control loop is imported. By choosing proper weighting 
function, a cascade robust control algorithm is proposed
 based on H, mixed sensitivity theory. For the coupling 
loop of inlet negative press and outlet temperature, the 
decoupling problem and mixed sensitivity problem design
 is combined. An H robust decoupling algorithm is proposed.


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