Pragmatic analysis of wastewater treatment methods from a statistical perspective. Issue 1 (5th December 2022)

• Record Type: Journal Article
• Authors: Pande, Pournima; Bhagat, Ashish
• Is Part Of: Water practice and technology. Volume 18:Issue 1(2023)
• Page Start: 1
• Page End: 15
• Publication Date: 2022-12-05
• Subjects: complexity -- oxidation -- ozone -- treatment -- wastewater; Sewerage; Sewerage -- Management; Water-supply; Water-supply engineering; Periodicals; 628.205
• DOI: 10.2166/wpt.2022.153
• Languages: English
• ISSNs: 1751-231X

Abstract

Abstract: Wastewater treatment is an environmental issue of the utmost importance. Pesticides, industrial waste, chemical fertilizers, and radioactive waste are some of the causes for water pollution. Several models exist for treating contaminated wastewater. In this study an application-specific review of various wastewater treatment models is performed. Extensions to existing treatment models are discussed to improve their performance. The treatment models are compared statistically based on performance metrics such as quality of treated water, sludge percentage at output, complexity of treatment, time needed for treatment, and deployment cost. The treatment models are ranked using a novel parameter called Model Rank, which combines all performance metrics into a single number. According to the results, six models, including Advanced Oxidation Processes with Ozone treatment (AOPO), Kernel Principal Components Analysis based one-class Support Vector Machine (KPCA SVM), and four others, have a rank greater than 3.5. The AOPO model has the highest model rank of 3.85 and performs better than all other models. This study might aid major stakeholders in the waste treatment industry, including researchers, in selecting the appropriate waste water treatment method per their requirements. HIGHLIGHTS: Application-specific pragmatic review of various wastewater treatment models. Assistance in identification of various advantages, limitations, and future research scope in these models. Statistical comparison of various performance metrics of different models. Using statistical analysis, identifying the best possible method or methods. Recommendations of various extensions to models for improving process performance Graphical Abstract