Uncertainty and periodic behavior of process derived from online NIR

• Published in: Analytica chimica acta Vol. 642; no. 1; pp. 206 - 211
• Main Authors: Paakkunainen, Maaret, Kohonen, Jarno, Reinikainen, Satu-Pia
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 29.05.2009; Elsevier
• Subjects: Analytical chemistry; Autocorrelation; Chemistry; Exact sciences and technology; Fast Fourier transformation; NIR measurements; Periodic behavior; Spectrometric and optical methods; Uncertainty; Variographic analysis; Variographic analysis; Uncertainty; Periodic behavior; Fast Fourier transformation; Autocorrelation; NIR measurements; Fourier transformation; Process control; Mathematical method; Multivariate analysis; Optimization; Near infrared spectrometry; Sampling; Reliability; Monitoring; Principal component analysis
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2008.11.037

Past years have shown that near infra-red (NIR) can be successfully applied in online process control. The NIR measurements are commonly utilized because they are fast, versatile and relatively cost-effective. The online instruments produce an enormous amount of data, which need to be analyzed for, e.g., reliability, like any other online data. Instrumental data containing huge amount of simultaneously determined variables is multivariate in nature, and it has to be taken into account when the data is analyzed. The aim of this study was to show that variographic analysis gives a novel insight to online NIR data and the total uncertainty including variation arising from process itself can be estimated. It will be shown, that variographic analysis can be utilized in monitoring the process dynamics, as well as, in optimization of sampling interval. The periodic behavior was identified with autocorrelation and fast Fourier transformation (FFT) as well as with the variographic analysis. However, the variographic analysis gave a more detailed insight to the process dynamics and enabled estimation of uncertainty as a function of sampling interval. These approaches are illustrated with real industrial data originating from a petrochemical plant. Similar periodic behavior could be detected by applying any of the three mathematical methods to the online variable sets containing either NIR or other process control variables. The total uncertainty of the NIR data was estimated by applying variographic analysis with an assumption that the different principal components (PC) are individual “error sources” causing uncertainty.