TDLAS Detection of Propane/Butane Gas Mixture by Using Reference Gas Absorption Cells and Partial Least Square Approach

Wang, Yin; Wei, Yubin; Liu, Tongyu; Sun, Tao; Grattan, K. T. V.

doi:10.1109/jsen.2018.2865508

Cited by 35 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PLS extracts the principal components iteratively by maximizing the covariance of the extracted principal components. PLS model development has two components, one is to develop inner models and the other one is to develop outer models [3], [19]. Outer models have a relationship with the inner model as:…”

Section: Pls-anfis Modeling Frameworkmentioning

confidence: 99%

Monitoring Distillation Column Systems Using Improved Nonlinear Partial Least Squares-Based Strategies

2019

View full text Add to dashboard Cite

Fault detection in industrial systems plays a core role in improving their safety, productivity and avoiding expensive maintenance. This paper proposed and veried datadriven anomaly detection schemes based on a nonlinear latent variable model and statistical monitoring algorithms. Integrating both the suitable characteristics of partial least squares (PLS) and adaptive neural network fuzzy inference systems (ANFIS) procedure, PLS-ANFIS model is employed to allow for flexible modeling of multivariable nonlinear processes. Furthermore, PLS-ANFIS modeling was connected with k-nearest neighbors (kNN)-based data mining schemes and employed for nonlinear process monitoring. Specifically, residuals generated from the PLS-ANFIS model are used as the input to the kNN-based mechanism to uncover anomalies in the data. Moreover, kNNbased exponentially smoothing with parametric and nonparametric thresholds is adopted to better anomaly detection. The effectiveness of the proposed approach is evaluated using real measurements from an actual bubble cap distillation column.

show abstract

Section: Pls-anfis Modeling Frameworkmentioning

confidence: 99%

Monitoring Distillation Column Systems Using Improved Nonlinear Partial Least Squares-Based Strategies

2019

View full text Add to dashboard Cite

show abstract

“…Multiple methods have been employed for correcting the interference of overlapping absorption lines [16,17]. Three reference multi-pass cells were utilized to obtain the absorption spectra of standard interferential gases by Yin Wang et al The interference of the overlap is effectively removed by fitting a linear superposition of the known absorption spectra with the absorption spectrum of the gas mixture detected, and the fitting coefficients were found to be higher than 0.96 [18]. A method of fourth harmonic was introduced by Dorota Stachowiak to correct the interference of overlapping absorption lines and achieve accurate measurements of hydrogen sulfide (H 2 S), methane (CH 4 ), carbon dioxide (CO 2 ), and ammonia (NH 3 ) [19].…”

Section: Introductionmentioning

confidence: 99%

A Method for Correcting the Interference of Overlapping Absorption Lines Using Second Harmonic Spectral Reconstruction

Yue

Huang

2021

Applied Sciences

View full text Add to dashboard Cite

The measurement accuracy of trace gas detection based on infrared absorption spectroscopy is influenced by the overlap of absorption lines. A method for correcting the interference of overlapping absorption lines using second harmonic spectral reconstruction (2f-SR) is proposed to improve the measurement accuracy. 2f-SR includes three parts: measurement of gas temperature and use of the differences in temperature characteristics of absorption lines to correct the temperature error, 2f signal restoration based on laser characteristics to eliminate the influence of waveform change on overlapping absorption lines, and fast multi-peak fitting for the separation of interference from overlapping absorption lines. The CH4 measurement accuracy based on overlapping absorption lines is better than 0.8% using 2f-SR. 2f-SR has a lower minimum detection limit (MDL) and a higher detection accuracy than the separation of overlapping absorption lines based on the direct absorption method. The MDL is reduced by two to three orders of magnitude and reaches the part per million by volume level. 2f-SR has clear advantages for correcting the interference of overlapping absorption lines in terms of both MDL and measurement accuracy.

show abstract

“…It has been widely used for detecting mineral samples [1], gas emission [2] and food volatiles [3]. Multivariate regression algorithms such as principle component regression [4] and partial least squares (PLS) [5] are fundamental and popular tools that have been successfully applied to spectroscopic analysis. Non-linear methods, such as support vector machine [6], genetic programming [7] and artificial neural networks (ANN) [1], are also adopted to increase prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis

Gan

Yuen

Lü

2019

MAKE

View full text Add to dashboard Cite

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multi gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance -partial least squares discriminant analysis when signal-to-noise ratio and training sample size are sufficient.

show abstract

TDLAS Detection of Propane/Butane Gas Mixture by Using Reference Gas Absorption Cells and Partial Least Square Approach

Cited by 35 publications

References 22 publications

Monitoring Distillation Column Systems Using Improved Nonlinear Partial Least Squares-Based Strategies

Monitoring Distillation Column Systems Using Improved Nonlinear Partial Least Squares-Based Strategies

A Method for Correcting the Interference of Overlapping Absorption Lines Using Second Harmonic Spectral Reconstruction

Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis

Contact Info

Product

Resources

About