Multivariate Optimization of Electrochemical Struvite Precipitation from Wastewater Using Principal Component Analysis

Electrocoagulation can enhance struvite production with proper optimization to make it a promising future technology for resource recovery from wastewater. However, optimization of all of the factors is noneconomical and practically impossible. Hence, the study aims to investigate the ability of principal component analysis (PCA) to reduce the key influential parameters (16 input variables) based on their correlation with each other and the output for electro-coagulated nutrient recovery (N & P) as struvite using a magnesium-based anode. The result was evaluated using principal components: selected based on the eigenvalue (≥1), variance (≥7), cumulative variability, and scree plot representing the contribution of the input variable on the data set. The selected first five and six PCs accounted for 81.87% and 83.05% cumulative variability for N & P recovery, respectively. Additionally, analyses derived from loading plot, score plot, and biplot indicate that several factors─namely, current density, interelectrode distance, electrolysis duration, pH, cathode material, and electrode surface area by volume ratio and nutrient-rich wastewater significantly influence the corrosion of anode. The study further reveals that the principal factors are intricately interconnected, resulting in enhanced yields. Overall, this research delineates specific condensed criteria that could facilitate upgrading of the technology, thereby optimizing efficiency and cost-effectiveness.