Prediction of Water Filter Bed Backwash Time for Water Treatment Plant using Machine Learning Algorithm *

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

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Real Time Water Treatment Plant Monitoring System using IOT and Machine Learning Approach

10.1109/icdi3c53598.2021.00064 ◽

2021 ◽

Author(s):

S J Sugumar ◽

Sahana R ◽

Saketh Phadke ◽

Sameer Prasad ◽

Srilakshmi G R

Keyword(s):

Machine Learning ◽

Water Treatment ◽

Real Time ◽

Monitoring System ◽

Treatment Plant ◽

Water Treatment Plant ◽

Learning Approach ◽

Machine Learning Approach ◽

Plant Monitoring

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Particle counter as a tool to control pre-hydrolyzed coagulant dosing and rapid filtration efficiency in a conventional treatment system

Water Science & Technology ◽

10.2166/wst.2014.506 ◽

2014 ◽

Vol 71 (4) ◽

pp. 615-621 ◽

Cited By ~ 2

Author(s):

Jolanta Gumińska ◽

Marcin Kłos

Keyword(s):

Water Quality ◽

Water Treatment ◽

Fine Particles ◽

High Surface ◽

Treatment Plant ◽

Water Treatment Plant ◽

Filtration Efficiency ◽

Treatment System ◽

Aluminum Speciation ◽

Filter Bed

Filtration efficiency in a conventional water treatment system was analyzed in the context of pre-hydrolyzed coagulant overdosing. Two commercial coagulants of different aluminum speciation were tested. A study was carried out at a water treatment plant supplied with raw water of variable quality. The lack of stability of water quality caused many problems with maintaining the optimal coagulant dose. The achieved results show that the type of coagulant had a very strong influence on the effectiveness of filtration resulting from the application of an improper coagulant dose. The overdosing of high basicity coagulant (PAC85) caused a significant increase of fine particles in the outflow from the sedimentation tanks, which could not be retained in the filter bed due to high surface charge and the small size of hydrolysis products. When using a coagulant of lower basicity (PAC70), it was much easier to control the dose of coagulant and to adjust it to the changing water quality.

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Modeling and Control of a Chemical Waste Water Treatment Plant

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)00171-3 ◽

1997 ◽

Vol 21 (1-2) ◽

pp. S947-S952 ◽

Cited By ~ 2

Author(s):

R Miller

Keyword(s):

Waste Water ◽

Water Treatment ◽

Waste Water Treatment ◽

Treatment Plant ◽

Water Treatment Plant ◽

Waste Water Treatment Plant ◽

Chemical Waste ◽

Modeling And Control ◽

And Control

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Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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