The difference of whiteness, brightness and lightness was clearly explained in this paper through the use of a tinting dye and a fluorescent whitening agent which are commercially wide]y used to make paper look whiter. Other optical properties such as tint, color shade, and color difference were also discussed. It was suggested that in comparing two tinting dyes, lightness $(L^*)$ is the most important property to be compared, while whiteness data should be used in caution in order not to surpass its significant range, and $a^*,\;b^*$ values can also be used to see the change of color shades together with ${\Delta}E$ as color difference. In comparing two fluorescent whitening agents, whiteness or brightness values are most important to be compared, but lightness values are not suitable fur this purpose; $a^*,\;b^*$ and color difference ${\Delta}E$ can also be referred, but with less significance.

In this work the Dynamic Matrix Control method is applied to control the grade change operations in paper mills. Paper process trained by neural network regards a real-plant. The model obtained with operation data is used to achieve DMC. Results of simulations for DMC control of grade change operations are compared with plant operation data response. From the comparison, we can see that the proposed DMC method exhibits faster response for the grade change of paper and achieves stable steady-state.

Paper process is complex and multivariable system. Identification of a paper process model is imperative for the development of predictive control method. 13-level Pseudo-Random Sequence Signals were used to identify the plant model in which the neural network model was considered model as a real paper process. Results of simulations for identification using 13-level PRS signals and Prediction Error Method are compared with plant operation data. From the comparison, we can see that the dynamics of the model show good agreement with those of real plant.