Thermal process model parameter identification method adopting improved ant colony algorithm
A process model and parameter identification technology, applied in the field of thermal process model parameter identification, can solve problems such as poor versatility, dependence on calculation accuracy, and difficulty in application
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Embodiment 1
[0071] The thermal process model was identified on MATLAB, and compared with the parameter identification results using the basic ant colony algorithm. The structural thermal process model is as follows:
[0072] G ( s ) = 12.3 ( 1 + 54.3 s ) 5 e - 123 s - - - ( 7 )
[0073] Select m=30, α=1.1, ρ=0.1, the number of iterations is 50, use e(n) to record the minimum value of the identification error for each iteration, and the algorithm will stop executing when the number of iterations reaches 50 or e(n) is equal to 0. The selection range of model parameters is: K ∈ (-29.9, 29.9), ...
Embodiment 2
[0081] Figure 5 When the 300MW circulating fluidized bed boiler operates between 200MW-250MW load, keep other input volumes unchanged, and increase the primary air volume by 16km 3 / h, the effect curve on bed pressure. It can be seen that when the primary air volume increases, the bed pressure drops rapidly, and the entire drop process takes about 60s.
[0082] Set parameters m=30, α=1.1, ρ=0.1, and the number of iterations is 50. The selection range of model parameters is: K ∈ (-0.499, 0), T ∈ (0, 49.9), n ∈ (1, 5). The transfer function model identified by the basic ant colony algorithm is:
[0083] G ( s ) = - 0.096 ( 1 + 19 s ) 3 - - - ( 10 ) ...
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