Analytica Chimica Acta 642 (2009) 94–101
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Analytica Chimica Acta 642 (2009) 94–101 Contents lists available at ScienceDirect Analytica Chimica Acta journal homepage: www.elsevier.com/locate/aca Correlation between sludge settling ability and image analysis information using partial least squares D.P. Mesquita a, O. Dias a, A.M.A. Dias a, A.L. Amaral a,b, E.C. Ferreira a,∗ a IBB-Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal b Instituto Superior de Engenharia de Coimbra, Instituto Politécnico de Coimbra, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal article info abstract Article history: In the last years there has been an increase on the research of the activated sludge processes, and mainly Received 10 September 2008 on the solid–liquid separation stage, considered of critical importance, due to the different problems that Received in revised form 1 November 2008 may arise affecting the compaction and the settling of the sludge. Furthermore, image analysis procedures Accepted 19 March 2009 are, nowadays considered to be an adequate method to characterize both aggregated and filamentous bac- Available online 24 March 2009 teria, and increasingly used to monitor bulking events in pilot plants. As a result of that, in this work, image analysis routines were developed in Matlab environment, allowing the identification and characterization Keywords: of microbial aggregates and protruding filaments. Moreover, the large amount of activated sludge data Activated sludge Image analysis collected with the image analysis implementation can be subsequently treated by multivariate statistical Sludge volume index procedures such as PLS. In the current work the implementation of image analysis and PLS techniques Partial least squares has shown to provide important information for better understanding the behavior of activated sludge processes, and to predict, at some extent, the sludge volume index. As a matter of fact, the obtained results allowed explaining the strong relationships between the sludge settling properties and the free filamen- tous bacteria contents, aggregates size and aggregates morphology, establishing relevant relationships between macroscopic and microscopic properties of the biological system. © 2009 Elsevier B.V. All rights reserved. 1. Introduction exceeding the initial manual quantification of filamentous bacteria proposed by Jenkins et al. [5]. Bulking can be caused by both fila- In activated sludge systems, an adequate balance between the mentous and non-filamentous factors, affecting in different ways different types of bacteria is necessary to ensure an efficient pol- the sludge settling ability which can be detected by image analy- lution removal, good sludge settling abilities and low suspended sis methodologies [4,6–12]. The combination of settling properties solid levels in the final effluent. After the oxidation of the organic and the parameters obtained from image analysis may offer power- matter in the aerated tank, the flocculated biomass is separated ful information enabling immediate interventions on the biological from the treated effluent by means of their settling ability in the system. In fact, the study developed by Sezgin [13] established that settling tank. The settling phase is considered a critical stage of the the sludge volume index (SVI) is strongly influenced by floc size and process in which filamentous bulking and deflocculation processes filamentous bacteria contents. Other authors [14–16] used auto- are the most common problems, causing the decrease of the sludge mated image analysis to relate the microorganism’s morphology in settling ability and effluent quality deterioration [1]. Poor settling biological systems with the sludge settling properties. The settling biomass is normally attained by an improper aggregate’s formation ability can be subsequently related with the microscopic parame- and filamentous bacteria proliferation, resulting in lower clarifier ters using multivariable statistical technique, such as partial least efficiency. Usually, some malfunctions may occur within the acti- squares (PLS) regression and principal component analysis (PCA) vated sludge system such as pin-point flocs bulking, filamentous [4,6]. A close correlation between the filamentous bacteria per sus- bulking, dispersed growth and zoogleal growth [2]. pended solids ratio and the SVI was indeed achieved by Amaral and Image analysis procedures, based on microscopic observations, Ferreira [4] during filamentous bulking events. are nowadays considered to be a feasible method to characterize Encouraged by the success of image analysis procedures over the quantitatively aggregates and filamentous bacteria, and subse- last years in a broad range of different areas, the present work uses quently used to monitor bulking events in pilot and full plants [3,4], an automated image analysis method to characterize the activated sludge structure, focusing on the prediction ability of sludge settling properties, in both good settling and filamentous bulking periods. ∗ Corresponding author. Tel.: +351 253 604 407; fax: +351 253 678 986. In this sense, the collected images were treated in order to char- E-mail address: [email protected] (E.C. Ferreira). acterize the aggregated and filamentous bacteria, thus originating 0003-2670/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2009.03.023 D.P. Mesquita et al. / Analytica Chimica Acta 642 (2009) 94–101 95 the different morphological parameters, further used as indepen- Translation, Marlboro) frame grabber using the commercial software dent X variables in the PLS regression for the sludge volume index package Image Pro Plus (Media Cybernetics, Silver Spring). In order (SVI) model determination. to compare both acquisition methodologies, calibration from pix- els to the metric unit dimensions was performed by means of a 2. Materials and methods micrometer slide. 2.1. Sampling survey 2.3. Image processing The activated sludge samples analyzed in this work were col- The image processing and analysis program for aggregated and lected during several months from the aeration basins of eight filamentous bacteria characterization was developed in Matlab 7.3 municipal wastewater treatment plants (WWTP) treating domestic (The Mathworks, Inc., Natick) language, adapting a previous ver- effluents located in the North of Portugal (Frossos1,Frossos2, Cunha, sion developed by Amaral [18]. Primarily, the image processing Sobreposta, Tadim, Pousa, Ucha, Oliveira). A total of 142 samples step established the binary images from the aggregated biomass (representing different days in different WWTP) were collected (17 and the protruding and freely dispersed filamentous bacteria and from Frossos1, 33 from Frossos2, 14 from Cunha, 23 from Sobre- thereafter, morphological parameters were determined. In total, posta, 21 from Tadim, 11 from Pousa, 12 from Ucha and 11 from and during the current work, a universe around 400,000 aggre- Oliveira) for a period between 4 and 7 months and time spans vary- gates, from the overall 142 samples dataset of the 8 WWTPs, was ing from 3 until 7 days between each sample. From each sample the acquired and individually characterized. total suspend solids (TSS) was measured by weight [17] and further Fig. 1 shows a schematic representation of the main steps of the used to calculate the sludge volume index (SVI) in a 10 L cylindri- program, comprising the image pre-treatment, segmentation, and cal column (15 cm diameter) for 30 min. This physical property was debris elimination whereas the image analysis program was ori- determined as follows: ented to the aggregated and filamentous bacteria characterization H and contents determination. SVI = 30 (1) H0 × TSS 2.4. Image analysis parameters where H0 and H30 are the height in the time 0 and 30 min, respec- tively. The SVI is highly used for sludge settling ability evaluation, Following the image processing step, the recognized aggregated varying inversely with the sludge ability to settle, making it an and filamentous bacteria from the collected images, were treated in interesting parameter within the system efficiency assessment. order to individually characterize each aggregate and filamentous Microscopic observations were performed for all samples in bacteria, in terms of the most relevant morphological parameters. order to determine the morphological parameters of microbial Therefore, and based on the previous study of Amaral and Ferreira aggregates and filament contents using digital image analysis. The [4], 36 morphological parameters were determined (in addition maximum period of time between sample collection and image to the 2 sludge physical properties, TSS and SVI), either directly acquisition did not exceed 2 h with not more than 30 min without from the image analysis program, either in association with the aeration. sludge physical properties. The overall dataset of 38 parameters is presented in Table 1. 2.2. Image acquisition Total aggregates number per sludge volume (Nb/Vol), total aggregates area per sludge volume (TA/Vol), aggregates individual For image acquisition, a volume of 25 L from each sample was area (Area) and total filaments length per sludge volume (TL/Vol), distributed on a slide and covered with a 20 mm × 20 mm cover described in [18], were determined for all the samples collected slip for visualization and image acquisition. The volume deposi- alongside the filaments individual length (FL), aggregates