Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System

We describe an Electronic Nose (ENose) system which is able to identify the type of analyte and to estimate its concentration. The system consists of seven sensors, five of them being gas sensors (supplied with different heater voltage values), the remainder being a temperature and a humidity sensor, respectively. To identify a new analyte sample and then to estimate its concentration, we use both some machine learning techniques and the least square regression principle. In fact, we apply two different training models; the first one is based on the Support Vector Machine (SVM) approach and is aimed at teaching the system how to discriminate among different gases, while the second one uses the least squares regression approach to predict the concentration of each type of analyte

E-nose Development for Safety Monitoring Applications in Refinery Environment

n/

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process

Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification

Classifying odor in real experiment presents some challenges, especially the uncertainty of the odor concentration and dispersion that can lead to a difficulty in obtaining an accurate datasets. In this study, to enhance the accuracy, datasets arrangement based on MOS sensors parameters using SVM approach for odor classification is proposed. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method to see the sensors' performance in real situation. These performances are considered as the base of choosing the position in collecting the datasets. The sensors in dynamic experiment have average of precision of 93.8-97.0%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the collected datasets can support the SVM in improving the intelligent sensing when conducting odor classification work

Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification

Classifying odor in real experiment presents some challenges, especially the uncertainty of the odor concentration and dispersion that can lead to a difficulty in obtaining an accurate datasets. In this study, to enhance the accuracy, datasets arrangement based on MOS sensors parameters using SVM approach for odor classification is proposed. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method to see the sensors' performance in real situation. These performances are considered as the base of choosing the position in collecting the datasets. The sensors in dynamic experiment have average of precision of 93.8-97.0%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the collected datasets can support the SVM in improving the intelligent sensing when conducting odor classification work

Application of Microbubbles Generated by Fluidic Oscillation in the Upgrading of Bio Fuels

With increasing energy demand and environmental concerns associated with the use of fossil-based fuels, the use of renewable sources of energy, such as biomass, has attracted considerable attention. Biofuels, such as bioethanol and bio-oil which are derived from the pyrolysis of biomass, are potential candidates to replace conventional fuels. However, the utilization of these fuels poses some challenges. In the case of bioethanol, it must have a composition higher than 98% to be used as an additive to gasoline in automobile engines. Pyrolysis oils, on the other hand, suffer from thermal instability, low heating values due to high water content and high acidity due to high acid content. In both cases, conventional distillation is not a feasible method for separation due to the azeotropic barrier, the high operating temperatures and the long residence times associated with its operation. The current work is a serious attempt to address these concerns by using a novel distillation technique mediated by hot microbubbles. The study suggests injecting a hot carrier gas in the form of microbubbles to remove the volatile components from the liquid phase and thus minimizing the sensible heat transfer to the liquid. Preliminary experiments were carried out with a 50 vol/vol ethanol-water mixture to evaluate the separation ability of microbubble mediated distillation. The experiments were planned based on a central composite rotatable design method, from which an empirical model was developed, giving an inference about the optimum operating conditions of the process. The results from the binary distillation experiments showed that upon decreasing the height of the liquid mixture in the bubble tank and increasing the temperature of air microbubbles, the separation efficiency of ethanol was improved significantly. Furthermore, it was demonstrated that separation can be achieved with only a small rise in the temperature of the liquid mixture, making this system suitable for treating thermally sensitive mixtures. Microbubble mediated distillation was successful for breaking the equilibrium barrier in separating liquid mixtures by traditional distillation. The enrichment of ethanol in the vapor phase was found to be higher than that predicted from equilibrium conditions for all liquid ethanol mole fractions considered, including the azeotrope, and within a very short contact time for the microbubbles in the liquid phase (i.e. thin liquid levels). Ethanol with a purity of 98.2% vol. was obtained using a thin liquid level of 3 mm in conjunction with a microbubble air temperature of 90C. Microbubble distillation was used to isolate the major problematic components, water and carboxylic acids, from a model bio-oil mixture. The model mixture was chosen to contain water, acetic acid and hydroxy propanone with concentrations close to those in real bio-oil mixtures. It was found that 84% of the water content and 75% of the corrosive acid content were removed from the model mixture after 150 min. These reductions, in turn, will increase the calorific value, reduce the corrosivity and improve the stability of the bio-oil mixture. This upgrading was accomplished with only a slight increase in the liquid temperature of about 5C under conditions of 3 mm liquid depth and 100C microbubble air temperature making this technique convenient for separating bio-oil mixtures without affecting their quality. A computational model of a single gas microbubble was developed using a Galerkin finite element method to complement the binary distillation experiments of ethanol-water mixtures. This model incorporates a novel rate law that evolves on a timescale related to the internal mixing of the microbubbles of 10-3 s. The model predictions were shown to be in very good agreement with the experimental data, demonstrating that the ratios of ethanol to water in the microbubble regime are higher than those predicted from equilibrium theory for all initial bubble temperatures and all liquid ethanol mole fractions considered. Furthermore, these ratios were achieved within very short contact times in the liquid mixture. The modelling data demonstrate that at shorter residence times, microbubbles are more efficient than fine bubbles in the separation process, however, as time passes the effect of bubble size diminishes. The modelling also showed improvements in the stripping efficiency of ethanol upon increasing the temperature of the air microbubbles, and an increase in the gas temperature with decreasing the residence time of the microbubbles. All of these results are consistent with experimental findings

Modern Approaches To Quality Control

Rapid advance have been made in the last decade in the quality control procedures and techniques, most of the existing books try to cover specific techniques with all of their details. The aim of this book is to demonstrate quality control processes in a variety of areas, ranging from pharmaceutical and medical fields to construction engineering and data quality. A wide range of techniques and procedures have been covered