Enhanced Coagulation of Low Turbid Water for Drinking Water Treatment: Dosing Approach on Floc Formation and Residuals Minimization

Lin, Jr-Lin; Ika, Aldeno Rachmad

doi:10.1089/ees.2018.0430

Cited by 22 publications

(9 citation statements)

References 24 publications

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“…Ref. [2] has been conducted the automated colony and cell counting by digital image analysis based on edge detection [13]. The counting method involve the use of ImageJ macro which valuable in counting cells/colonies, measuring the area, volume, morphology and intensity [14].…”

Section: Data Acqusition Data Process and Data Analysismentioning

confidence: 99%

“…From the various water quality indicators, generally, turbidity is the parameter that being the indicators for WTP performance. In additions, this parameter becomes the reference for the chemical (coagulant) dosage in a treatment plants [1], [2]. Turbidity level also can be related to the bacteriological contamination of surface water, because in general, it tend to be suspended in water [3], [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach

Wikaningrum¹,

Alvasa²,

Panelin³

et al. 2021

JSE

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Monitoring of pollutant concentrations in surface water becomes a concern, considering the utilization of surface water as the raw water for drinking water treatment plants (WTP). The fluctuation of pollutant concentrations in surface water can affect the performance of WTP. This research was conducted to assess the potential for turbidity level prediction based on the calculation of the number and surface area of suspended particles through a digital image processing approach. Measurements of the amount and surface area were carried out in the form of laboratory-scale experiments using the open source software ImageJ 1.46r. The algorithm in ImageJ can convert pixels into a number “value” and surface area through a series of digital image processing steps, henceforth compared with the existing measurement method. The results showed that there was a strong correlation between the number of particles and the concentration of formazine suspension (r = 0.9821), but does not apply to the surface area. Referring to the results of laboratory experiments, it can be concluded that the approach to measure the number of suspended particles can be the basis for predicting the turbidity level in the turbidity range 100-800 NTU, but does not apply to the turbidity range 0.02-20 NTU.

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Section: Data Acqusition Data Process and Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach

Wikaningrum¹,

Alvasa²,

Panelin³

et al. 2021

JSE

View full text Add to dashboard Cite

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“…A low drop in pH occurred since the supplied water had high alkalinity (Table 1), a high basicity variant of PACl was employed and coagulant doses for turbidity removal with minimal aluminum influx were administered. Lin & Ika (2019a) had discussed the role of pH in dictating the precipitation of various aluminum species and hence the concentration of residual aluminum, especially the dissolved fraction with PACls. Therefore, if the PACl is considered to be constituted of pre-hydrolyzed forms of monomeric aluminum (Al a ), polymeric aluminum (Al b ) and colloidal aluminum (Al c ), then the Al b form may be expected to remain relatively stable with varying pH while precipitation of Al c will be facilitated at near-neutral pH.…”

Section: Residual Aluminum and Phmentioning

confidence: 99%

“…The behavior of these forms is affected differently by factors like pH and consequently performance parameters like turbidity removal efficiency, floc properties, and particulate and dissolved fractions of residual aluminum also vary. Hence, much contemporary research is focused towards tweaking the basicity, speciation and other properties of PACls for performance improvement (Kimura et al 2013;Shu-xuana et al 2014;Lin & Ika 2019a, 2019bSolanki et al 2020). However, the physico-chemical significance of the role the reaction environment provided by a reactor may play on the efficiency of a given coagulant like PACl for the provided control set of influent conditions has not been studied in much detail, and therefore the same constitutes the core subject matter of this paper.…”

Section: Introductionmentioning

confidence: 99%

Characterization of particles and their relation with residual aluminum in water treated with pulsating floc blanket clarifiers and conventional clariflocculators using PACl

2021

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A pulsating floc blanket clarifier (PFBC) employing cyclic contractions and rarefactions to a bed of densely concentrated suspension of flocculated particles in fluidized state, was compared with conventional clariflocculator (CC) at pilot scale (8,000 L/day) in continuous mode of operation. For influent turbidity varied from 2 to 20 NTU, coagulation-flocculation behavior exhibited under the two fundamentally different treatment processes with PACl influenced inter-related performance parameters. The residual turbidity was found lower by 74%, flocs and fine colloids in suspension larger by 73 and 75% respectively, and the total and dissolved residual aluminum lower by 50 and 49% respectively on average for PFBC compared to CC. Particulate form comprised major fraction (≈72%) of total residual aluminum for both. PFBC abetted formation of a more consolidated floc structure which rendered the shape, size and morphology such that the settling velocity was 50% to 410% higher than that of the CC flocs. Reaction-limited aggregation (RLA) process and inter-particle bridging were dominant and the resulting floc structure and its formation mechanism have been presented.

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“…The TLF can reflect the size and the amount of flocs through a transmitted light fluctuation value (Gregory 1997), but it is difficult to maintain the stability and sensitivity of the value (Wang 2005). Image signal should be a promising technology that has received a lot of attention, and it can provide feedback on water quality through structure parameters of floc images, thereby determining the chemical dose (Lin and Ika 2019;Yu et al 2015). However, there are inherent challenges, such as floc collection.…”

Section: Introductionmentioning

confidence: 99%

A flocculation tensor to monitor water quality using a deep learning model

et al. 2022

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The increasing quantities of polluted waters are calling for advanced purification methods. Flocculation is an essential component of the water purification process, yet flocculation is commonly not optimal due to our poor understanding of the flocculation process. In particular, there is little knowledge on the mechanisms ruling the migration of pollutants during treatment. Here we have created the first tensor diagram, a mathematical framework for the flocculation process, analyzed its properties with a deep learning model, and developed a classification scheme for its relationship with pollutants. The tensor was constructed by combining pixel matrices from a variety of floc images, each with a particular flocculation period. Changing the factors used to make flocs images, such as coagulant dose and pH, resulted in tensors, which were used to generate matrices, that is the tensor diagram. Our deep learning algorithm employed a tensor diagram to identify pollution levels. Results show tensor map attributes with over 98% of sample images correctly classified. This approach offers potential to reduce the time delay of feedback from the flocculation process with deep learning categorization based on its clustering capabilities. The advantage of the tensor data from the flocculation process improves the efficiency and speed of response for commercial water treatment.

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Enhanced Coagulation of Low Turbid Water for Drinking Water Treatment: Dosing Approach on Floc Formation and Residuals Minimization

Cited by 22 publications

References 24 publications

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach

Characterization of particles and their relation with residual aluminum in water treated with pulsating floc blanket clarifiers and conventional clariflocculators using PACl

A flocculation tensor to monitor water quality using a deep learning model

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