Application of Machine Learning For Real-time Evaluation of Salinity (or TDS) in Drinking Water Using Photonic Sensor

2016 ◽  
Author(s):  
Anonymous
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Real Time ◽  
Photonic Sensor ◽  
Time Evaluation

scholarly journals Application of Machine Learning For Real-time Evaluation of Salinity (or TDS) in Drinking Water Using Photonic Sensor

2016 ◽  
Author(s):  
Sandip Kumar Roy ◽  
Preeta Sharan
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Real Time ◽  
Learning Algorithm ◽  
Training Data ◽  
Raspberry Pi ◽  
Photonic Sensor ◽  
Highly Sensitive ◽  
Sample Water ◽  
Time Evaluation

Abstract. World is facing unprecedented problem to safeguard 0.4 percent of potable water, which is eventually depleting day-by-day. From literature survey it has been observed that the refractive index (RI) of water changes with change in salinity or total dissolved solids (TDS). In this paper we have proposed an automatic system that can be used for real-time evaluation of salinity or TDS in drinking water. Photonic Crystal (PhC) based ring resonator sensor has been designed and simulated using MEEP tool and Finite Difference Time Domain (FDTD) algorithm. The modelled and designed sensor is highly sensitive to the changes in RI of water sample. This work includes a real-time based natural sequence follower which is a machine learning algorithm of Naïve Bayesian type. A sequence of statistical algorithm implemented in MATLAB with reference to training data to analyse the sample water. Further interfacing has been done using Raspberry Pi device to provide an easy display to show the result of water analysis. The main advantage of the designed sensor with interface is to check whether the salinity or TDS in drinking water is less than 1000 ppm or not.

scholarly journals Application of machine learning for real-time evaluation of salinity (or TDS) in drinking water using photonic sensors

2016 ◽  
Vol 9 (2) ◽  
pp. 37-45 ◽  
Author(s):  
Sandip Kumar Roy ◽  
Preeta Sharan
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Real Time ◽  
Learning Algorithm ◽  
Training Data ◽  
Raspberry Pi ◽  
Highly Sensitive ◽  
Sample Water ◽  
Photonic Sensors ◽  
Time Evaluation

Abstract. The world is facing an unprecedented problem in safeguarding 0.4 % of potable water, which is gradually depleting day-by-day. From a literature survey it has been observed that the refractive index (RI) of water changes with a change in salinity or total dissolved solids (TDS). In this paper we have proposed an automatic system that can be used for real-time evaluation of salinity or TDS in drinking water. A photonic crystal (PhC) based ring resonator sensor has been designed and simulated using the MEEP (MIT Electromagnetic Equation Propagation) tool and the finite difference time domain (FDTD) algorithm. The modelled and designed sensor is highly sensitive to the changes in the RI of a water sample. This work includes a real-time-based natural sequence follower, which is a machine learning algorithm of the naive Bayesian type, a sequence of statistical algorithms implemented in MATLAB with reference to training data to analyse the sample water. Further interfacing has been done using the Raspberry Pi device to provide an easy display to show the result of water analysis. The main advantage of the designed sensor with an interface is to check whether the salinity or TDS in drinking water is less than 1000 ppm or not. If it is greater than or equal to 2000 ppm, the display shows “High Salinity/TDS Observed”, and if ppm are less than or equal to 1000 ppm, then the display shows “Low salinity/TDS Observed”. The proposed sensor is highly sensitive and it can detect changes in TDS level because of the influence of any dissolved substance in water.

scholarly journals Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6671
Author(s):  
Sharif Hossain ◽  
Christopher W.K. Chow ◽  
Guna A. Hewa ◽  
David Cook ◽  
Martin Harris
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Real Time ◽  
Learning Algorithm ◽  
Measurement Data ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Kernel Functions ◽  
Support Vector ◽  
Organic Nitrate

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.

scholarly journals Online fluorescence spectroscopy for the real-time evaluation of the microbial quality of drinking water

2018 ◽  
Vol 137 ◽  
pp. 301-309 ◽  
Author(s):  
J.P.R. Sorensen ◽  
A. Vivanco ◽  
M.J. Ascott ◽  
D.C. Gooddy ◽  
D.J. Lapworth ◽  
...  
Keyword(s):  
Drinking Water ◽  
Fluorescence Spectroscopy ◽  
Real Time ◽  
Microbial Quality ◽  
The Real ◽  
Time Evaluation

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

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