## 1 Introduction
In recent years, the miniaturization of cameras (with or without smartphones) has caused a revolution in the digital image acquisition systems since they can be coupled to tracking systems and they allow in situ monitoring for process control and decision-making with a minimum margin of error [38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. Some advantages over other types of spectroscopic techniques are that these allow the simultaneous evaluation of the entire surface and not just a part of it. Furthermore, they necessitate a minimal financial outlay, and the efficacy and rate of the cleaning operation can be gauged in situ and within the same facility [48]. This simplifies the determination of the end point of the cleaning process and makes it considerably more straightforward to track the progress of the cleaning process at any given instant, as long as the illumination conditions are constant throughout the entire process. For this purpose, methodologies can be used that were developed for surface analysis during chemical changes involving color or homogeneity changes [39; 44; 49; 50]. In each digital image, the color of any pixel corresponding to any part of the probe surface is a combination of three chromatic coordinates (red, green and blue or RGB) quantified by 256 intensity levels: \(I_{R}\), \(I_{G}\), and \(I_{B}\). After separating the acquired video into individual frames, each pixel window of the studied surface area is converted in three data arrays, one per each color coordinate. For example, for an image of 100 \(\times\) 100 pixels, there will be a matrix of 10,000 color intensity values for each coordinate at each instant. If we analyze one image per second, we will have at the end of it 10,000 \(\times\) 3 \(\times\) 3600 = 1.08 \(\times\) 10\({}^{8}\) data points. This means a large amount of information to be processed, which can be summarized with two statistical parameters. For this purpose, from each array, the mean color intensity (\(\overline{I}_{i}\)) and variance of color intensity (\(sd_{i}^{2}\)) are calculated, one for each chromatic coordinate, where \(i\) represents the R, G or B color array. \[\overline{I}_{i}=\sum_{j=1}^{n_{p}}\frac{I_{i}(j)}{n_{p}} \tag{1}\]\[sd_{i}^{2}=\frac{\sum_{j=1}^{n_{p}}(I_{i}(j)-\overline{I}_{i})^{2}}{n_{p}} \tag{2}\]  
where \(n_{p}=n_{v}\times n_{h}\) is the total number of pixels of the analyzed area and depends on the number of vertical pixels (\(n_{v}\)) and the number of horizontal pixels (\(n_{h}\)). Finally, there are three mean color intensities (Equation (1)) and three values for variance of color intensity (Equation (2)) for each digital image that is analyzed as part of the experimental course. On the one hand, the mean color intensity allows us to quantify the amount of surface that remains covered by a different color layer with a fine precision, since an 8-bit camera discerns 256 \(\times\) 256 \(\times\) 256 possible colors (256 color levels per coordinate). On the other hand, the variance of the color intensity is a measure of the color dispersion and can also be interpreted as a measure of the quality and homogeneity of the surface. Recent studies have demonstrated a relationship between the shape of the time-evolution variance curves during a surface color change process and the kinetic orders of the underlying chemical process [44,49]. Therefore, we can consider these two parameters excellent indicators to evaluate and to quantify the time needed (kinetics) and the homogeneity of the cleaning process of a metallic surface under different experimental conditions. In this work, we analyze two real case studies of cleaning processes by digital video through the time evolution of mean color intensity and color intensity variance. The aim was to characterize and quantify the kinetics of the cleaning process by digital video analysis. The first case was the cleaning of a part of a stainless-steel pipe with an unknown black residue. The cleaning process took place apparently by a process of dissolution of the dirt layer without mechanical stirring.