Artificial immune system based wastewater parameter estimation

Abstract

The basis of a wastewater treatment system is to achieve the desired characteristics of the wastewatertreatment process. An estimation of the obtained wastewater treatment characteristics provides the information neededto set up the current process steps, and it is important to have an optimum treatment. In this study, an artificialimmune system (AIS) structure is developed to estimate important wastewater output parameters such as pH, DBO,DQO, and SS for the first time. The proposed AIS models are based on the clonal selection principle, and the dataset isprovided from the University of California Irvine (UCI) Machine Learning Library. The current dataset is analyzed byprincipal component analysis (PCA) to obtain maximum system performance. As a result of the simulation, the outputparameters are successfully predicted using the AIS model with real data. The classifier’s performance ratios are studiedseparately using the coefficient of determination (R2) and the mean squared error of prediction (MSEP), and their ratesare given in this study