Application of data smoothing and principal component analysis to develop a parameter ranking system for the anaerobic digestion process. (July 2022) - Kim, Moonil; Chul, Park; Kim, Wan; Cui, Fenghao
Abstract: A parameter ranking system to enhance data interpretation of the anaerobic digestion process was developed using principal component analysis (PCA) and data smoothing. The experimental data collected from the start-up operation of a pilot-scale, two-stage anaerobic digester for food wastewater treatment was used to demonstrate the enhanced PCA procedures. The correlation of multiple parameters in the anaerobic digestion process could be identified by the ranked parameters based on statistically scored outcomes. According to the parameters ranked for their impact on biogas production and methane yield, the biochemical oxygen demand (BOD) in the food wastewater was shown to have the highest correlated ranks with a score of 0.246, and the pH and ammonium in the food wastewater were shown to have lower ranked scores of 0.834 and 1.019, respectively. Therefore, ammonia toxicity and pH shock might not have had significant influence, and the operating priority should be focused on the organic loads. This application of PCA to anaerobic digestion can be helpful for addressing stabilization issues by identifying the core parameters that primarily contribute to early detection of operating problems. Graphical abstract: All authors discussed the results and contributed to the final manuscript Image 1 Highlights: Conventional PCA was enhanced to adapt the datasets of anaerobic digestion. Unwanted disruption in the data was reduced using the data smoothing method. The PCA outcomes were interpreted simply by using a parameter ranking system. Clear correlations between biogas-related parameters were determined using PC ranking. Core parameters for the early detection of operating problems were identified.