Machine learning approaches to predict coagulant dosage in water treatment plants

Zhang, Kejiang; Achari, Gopal; Li, Hua; Zargar, Amin; Sadiq, Rehan

doi:10.1007/s13198-013-0166-5

Cited by 25 publications

(14 citation statements)

References 21 publications

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“…To minimize misclassification, PNN uses probability density functions to define complex decision boundaries, which generally improves its accuracy (Bressane et al, 2018a). Zhang et al (2013) analyzed the performance of the SVM method applied to predict coagulant dosage in water treatment plants of distinct sizes and concluded that such a method performs better for large-and medium-sized water systems compared to small ones. Although it shares similarities with ANNs, SVM has better ability to deal with high dimensional data and is less prone to overfitting (Kalantar et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

A Novel Data-Driven Fuzzy for Accurate Coagulant Dosage in Drinking Water Treatment

Bressane¹,

Goulart²,

Gomes³

et al. 2022

Preprint

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Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine-learning (ML) have been evaluated as a computer-aided alternative. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a novel data-driven fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this novel data-driven FIS for such application. The novel FIS provided the smallest error (0.86), indicating a promising alternative tool for real-time and highly accurate coagulant dosing in drinking water treatment.

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Section: Methodsmentioning

confidence: 99%

A Novel Data-Driven Fuzzy for Accurate Coagulant Dosage in Drinking Water Treatment

Bressane¹,

Goulart²,

Gomes³

et al. 2022

Preprint

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“…Two types of corrective actions have been recommended: (1) machine learning‐based predictive models to determine the proper coagulant dosage with varying source water quality (Zhang et al . ), and (2) mechanistic models to predict the performance of the treatment units (coagulation/flocculation/sedimentation and filtration) (Zhang et al . ).…”

Section: Development Of Dssmentioning

confidence: 99%

“…Corresponding preventive and corrective actions can then be implemented to fix this failure. Two types of corrective actions have been recommended: (1) machine learning-based predictive models to determine the proper coagulant dosage with varying source water quality (Zhang et al 2013), and (2) mechanistic models to predict the performance of the treatment units (coagulation/flocculation/sedimentation and filtration) (Zhang et al 2012). These predictive models are components of the model-based system of the DSS.…”

Section: Development Of Dss Frameworkmentioning

confidence: 99%

Performance management of small water treatment plant operations: a decision support system

Stein

Achari

Langford

et al. 2017

Water & Environment J

Self Cite

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A decision support system (DSS) is developed to optimise the performance of different operations of small water treatment systems to improve day-to-day decisions. The support system includes a data management system, knowledge-based system, performance assessment of different unit processes, fault tree analyses, preventive and corrective actions and event tree analysis (ETA). Performance assessment identifies the critical events (failures) and fault tree analysis identifies the interrelationships between the critical events and the root causes. Fault trees are developed based on the information obtained from events of waterborne outbreaks, responses to questionnaires by the participating smaller utilities, state-ofthe-art literature review and personal communication with the operators. ETA is used to identify the potential health outcomes which are further integrated with the quantitative microbial risk assessment. The developed DSS is advanced to an automated user friendly program that can be used by treatment plant operators to assess system performance.

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“…Many factors or inputs were considered in this work including hardness, pH, color, silica, conductivity and turbidity. More recently [30], two types of Support Vector Machine (SVM) that employed different kernel functions were examined for use in predicting with K-Nearest Neighbors (KNN) the necessary coagulant dosage in water treatment plants for various levels of water turbidity. The input parameters in this study were the dosage, pH and temperature.…”

Section: Introductionmentioning

confidence: 99%

Coagulation: Determination of Key Operating Parameters by Multi-Response Surface Methodology Using Desirability Functions

Bobadilla

Lostado-Lorza

Escribano-García

et al. 2019

Water

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The clarification process removes colloidal particles that are suspended in waste water. The efficiency of this process is influenced by a series of inputs or parameters of the coagulation process, of which the most commonly used are initial turbidity, natural coagulant dosage, temperature, mixing speed and mixing time. The estimation of the natural coagulant dosage that is required to effectively remove these total suspended solids is usually determined by a jar test. This test seeks to achieve the highest efficiency of removal of the total suspended solids while reducing the final turbidity of waste water. This is often configured in iterative fashion, and requires significant experimentation and coagulant. This paper seeks to identify regression models that relate the clarification process parameters to the process outputs (final turbidity and total suspend solid) by the Response Surface Methodology (RSM) based on experiments of Central Composite Design (CCD) of experiments that involve three emerging natural coagulants. Several clarification process scenarios also were proposed and demonstrated using the Multi-Response Surface (MRS) with desirability functions. The experimental results were found to be in close agreement to what are provided by the regression models. This validates the use of the MRS-based methodology to achieve satisfactory predictions after minimal experimentation.

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Machine learning approaches to predict coagulant dosage in water treatment plants

Cited by 25 publications

References 21 publications

A Novel Data-Driven Fuzzy for Accurate Coagulant Dosage in Drinking Water Treatment

A Novel Data-Driven Fuzzy for Accurate Coagulant Dosage in Drinking Water Treatment

Performance management of small water treatment plant operations: a decision support system

Coagulation: Determination of Key Operating Parameters by Multi-Response Surface Methodology Using Desirability Functions

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