Prediction system and method for heat efficiency of circulating fluidized bed boiler
A circulating fluidized bed and boiler thermal efficiency technology, applied in general control systems, control/regulation systems, instruments, etc., can solve problems such as low degree of automation, difficulty in fully exploiting boiler energy-saving potential, and lack of predictive systems and methods
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Embodiment 1
[0133] refer to figure 1 , figure 2 , a circulating fluidized bed boiler thermal efficiency prediction system, including a field intelligent instrument 2 connected to a circulating fluidized bed boiler 1, a data interface 3, a database 4, a control station 5 and a host computer 6, and the field intelligent instrument 2 is connected to a field bus , the data bus is connected with the data interface 3, and the data interface 3 is connected with the database 4, the control station 5 and the upper computer 6, and the upper computer 6 includes:
[0134] The standardization processing module 7 is used to collect historical records of operating condition variables and operating variables from the database, form a training sample matrix X of independent variables, and collect corresponding historical records of excess air coefficient, exhaust gas temperature difference, and carbon content of fly ash , form the dependent variable training sample matrix Y, and standardize the training...
Embodiment 2
[0192] refer to figure 1 , figure 2 , a circulating fluidized bed boiler thermal efficiency prediction method, the prediction method includes the following steps:
[0193] 1) Collect the historical records of operating condition variables and operating variables from the database to form the training sample matrix X of the independent variables, and collect the corresponding historical records of excess air coefficient, exhaust gas temperature difference, and carbon content in fly ash to form the dependent variable training Sample matrix Y, standardize the training sample matrix X, Y, so that the mean value of each variable is 0, the variance is 1, and the standardized independent variable training sample matrix X is obtained * (n×p), 3 normalized dependent variable training sample vectors (k=1,2,3), the following process is used to complete:
[0194] 1.1) Find the mean:
[0195] x ‾ j = ...
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