Measuring Vapor and Liquid Concentrations for Binary and Ternary Systems in a Microbubble Distillation Unit via Gas Sensors

Abdulrazzaq, Nada N.; Al-Sabbagh, Baseem H.; Rees, Julia M.; Zimmerman, William B.

doi:10.3390/chemosensors6030031

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…It enables the deconvolution of the collected sensor data sets and minimizes the impact of low selectivity of sensors in the matrix. It should be noted that other methods of data analysis, such as Multiple Linear Regression (MLR) [ 28 ], Partial Least Squares Regression (PLSR) [ 29 ], or Artificial Neural Network (ANN) [ 30 ], can also be successfully used for this type of application.…”

Section: Methodsmentioning

confidence: 99%

Development of Gas Sensor Array for Methane Reforming Process Monitoring

Dobrzyniewski

Szulczyński

Dymerski

et al. 2021

Sensors

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The article presents a new method of monitoring and assessing the course of the dry methane reforming process with the use of a gas sensor array. Nine commercially available TGS chemical gas sensors were used to construct the array (seven metal oxide sensors and two electrochemical ones). Principal Component Regression (PCR) was used as a calibration method. The developed PCR models were used to determine the quantitative parameters of the methane reforming process: Inlet Molar Ratio (IMR) in the range 0.6–1.5, Outlet Molar Ratio (OMR) in the range 0.6–1.0, and Methane Conversion Level (MCL) in the range 80–95%. The tests were performed on model gas mixtures. The mean error in determining the IMR is 0.096 for the range of molar ratios 0.6–1.5. However, in the case of the process range (0.9–1.1), this error is 0.065, which is about 6.5% of the measured value. For the OMR, an average error of 0.008 was obtained (which gives about 0.8% of the measured value), while for the MCL, the average error was 0.8%. Obtained results are very promising. They show that the use of an array of non-selective chemical sensors together with an appropriately selected mathematical model can be used in the monitoring of commonly used industrial processes.

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Section: Methodsmentioning

confidence: 99%

Development of Gas Sensor Array for Methane Reforming Process Monitoring

Dobrzyniewski

Szulczyński

Dymerski

et al. 2021

Sensors

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Environmental Odour Quantification by IOMS: Parametric vs. Non-Parametric Prediction Techniques

Zarra¹,

Galang²,

Belgıorno³

et al. 2021

Chemosensors

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Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms of accuracy in deciphering information, especially in the implementation of the mathematical model, are still being researched, especially in environmental odour monitoring applications. This research presents and discusses the implementation of traditional and innovative parametric and non-parametric prediction techniques for the elaboration of an effective odour quantification monitoring model (OQMM), with the aim of optimizing the accuracy of the measurements. Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour concentrations using an advanced IOMS. Experimental analyses are carried out by using real environmental odour samples collected from a municipal solid waste treatment plant. Results highlight the strengths and weaknesses of the analysed models and their accuracy in terms of environmental odour concentration prediction. The ANN application allows us to obtain the most accurate results among the investigated techniques. This paper provides useful information to select the appropriate computational tool to process the signals from sensors, in order to improve the reliability and stability of the measurements and create a robust prediction model.

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Measuring Vapor and Liquid Concentrations for Binary and Ternary Systems in a Microbubble Distillation Unit via Gas Sensors

Cited by 2 publications

References 26 publications

Development of Gas Sensor Array for Methane Reforming Process Monitoring

Development of Gas Sensor Array for Methane Reforming Process Monitoring

Environmental Odour Quantification by IOMS: Parametric vs. Non-Parametric Prediction Techniques

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