Generalized classification modeling of activated sludge process based on microscopic image analysis. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Lo, Po Kim; Yap, Vooi Voon
• Is Part Of: Environmental technology. Volume 39:Issue 1(2018)
• Page Start: 24
• Page End: 34
• Publication Date: 2018-01-02
• Subjects: Wastewater -- flocculation -- activated sludge -- image processing -- modeling -- classification; Environmental engineering -- Periodicals; Environmental protection -- Periodicals; 628.05
• DOI: 10.1080/09593330.2017.1293166
• Languages: English
• ISSNs: 0959-3330

Abstract

ABSTRACT: The state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.