Pragmatic analysis of wastewater treatment methods from a statistical perspective


 Wastewater treatment is an environmental issue of utmost importance. Various models and architectures for wastewater treatment exist, each having its unique characteristics. In this study, an application-specific review of various treatment models is performed. Extensions to existing treatment models are discussed for improvement in process performance. The treatment models are compared statistically based on their performance metrics, namely the quality of treated water (Q), sludge percentage at output (SL), complexity of treatment (C), time needed for treatment (T), and deployment cost (DC). A novel parameter, model rank, is proposed that combines all the performance metrics into a single number so that the treatment models can be analyzed effectively. Results show that advanced oxidation processes with ozone treatment (AOPO), Kernel principal components analysis-based one-class support vector machine (KPCA SVM), electrochemical processes (EPs), membrane and absorption (MA), Nondominated Sorting Genetic Algorithm-based Optimal Controller (NSGAOC), and wet-type nonthermal plasma reactor (WTNPR) models have a rank above 3.5. The AOPO model has the highest model rank of 3.85 and thus has better overall performance than others. This study might aid major stakeholders in waste treatment industry including researchers in selecting the appropriate wastewater treatment method according to their requirements.