Artificial Bee Colony Optimization of NOx Emission and Reheat Steam Temperature in a 1000 MW Boiler
Algorithm 1
rdCV-MO-NPLS.
Input: data and parameters ;
Output: optimal number of principal component and rdCV-MO-NPLS model.
for to do
Split dataset S equally into groups, i.e., ;
for to do
Select as Test set, and construct Calibration sets = ;
Split Calibration set equally into groups, ;
for to do
Select as Validation set and as Training set;
Fit MO-NPLS based on with, respectively, of principal components,
Apply the MO-NPLS models to and get predictive for ;
Calculate mean square error , with the number of objects in the used validation set and the output objects of validation set ;
Estimate optimum principal components , …, according to based on standard error method [15] (Here, more than one may be selected (i.e., ) because of different confidence interval);
Make MO-NPLS models based on Calibration set with ;
Test fitted models on and obtain a group of k predictions as well as k biases;
Find the smallest bias and determine the optimal principal component .
One can get with the number of after completing the outer loop.
Totally, after a complete rdCV run, we can get with the number of (). The final optimum of principal component is the one with highest frequency in .
Identify rdCV-MO-NPLS model with database S and to get model parameters [,p, c, q] based on (3)–(10).