Optimization of Sewage Treatment Plant Using Statistical Techniques

In recent years, there has been a remarkable surge in the volume of data generated and stored within wastewater treatment plants since 2010. Remarkably, 90% of global data has been generated within the past two years, signaling the advent of the big data era. This study analyzed the efficiency of sewage treatment by selecting a sewage treatment plant in Y-gun, which employs De-nitrogen and phosphorus (Denipho), Pumyang sequencing batch reactor (PSBR), and KS-membrane bioreactor processes. Statistical techniques based on the operation data of the sewage treatment, including correlation analysis using Pearson product-moment correlation coefficients, were applied. The total organic carbon (TOC), suspended solids (SS), total nitrogen (TN), and total phosphorus (TP) values of the influent were not statistically significant (p-value: >0.05) when compared with temperature values. However, the removal of sewage components was affected by temperature, mixed liquor-suspended solids, and dissolved oxygen. The correlation analysis revealed significant statistical relationships between operational conditions and water quality factors such as TOC, SS, TN, and TP. While the SS__sewage treated water (S) and all river water (R) were unaffected, the TP_S was affected by TOC_R, SS_R, and TP_R factors.