AIMC Topic: Gases

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Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.

Journal of hazardous materials May 10, 2023
Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many years by harming environmental tissue and living organisms. They can also cause mass deaths when use...
Machine Learning Algorithms Bayes Theorem Gases Amides United States
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Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm.

ACS sensors Jan 26, 2023
Real-time mixed gas detection has attracted significant interest for being a key factor for applications of the electronic nose (E-nose). However, mixed gas detection still faces the challenge of long detection time and a large amount of training dat...
Algorithms Electronic Nose Adsorption Gases Neural Networks, Computer
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Application of Neural Network in Predicting HS from an Acid Gas Removal Unit (AGRU) with Different Compositions of Solvents.

Sensors (Basel, Switzerland) Jan 16, 2023
The gas sweetening process removes hydrogen sulfide (HS) in an acid gas removal unit (AGRU) to meet the gas sales' specification, known as sweet gas. Monitoring the concentration of HS in sweet gas is crucial to avoid operational and environmental is...
Hydrogen Sulfide Solvents Gases Linear Models Neural Networks, Computer
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Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and Deep Learning.

ACS nano Dec 19, 2022
As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages o...
Electronic Nose Silver Gases Deep Learning Neural Networks, Computer
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Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships.

Bioresource technology Dec 9, 2022
Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimenta...
Models, Theoretical Gases Biomass Physics Machine Learning Neural Networks, Computer
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A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm.

Sensors (Basel, Switzerland) Nov 20, 2022
To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and ...
Algorithms Principal Component Analysis Support Vector Machine Neural Networks, Computer Gases
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Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor.

Sensors (Basel, Switzerland) Nov 19, 2022
Metal oxide semiconductor (MOS) gas sensors have many advantages, but the main obstacle to their widespread use is the cross-sensitivity observed when using this type of detector to analyze gas mixtures. Thermal modulation of the heater integrated wi...
Semiconductors Oxides Gases Neural Networks, Computer Algorithms
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Gas Recognition in E-Nose System: A Review.

IEEE transactions on biomedical circuits and systems May 19, 2022
Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as princ...
Electronic Nose Gases Algorithms Principal Component Analysis Neural Networks, Computer
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Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm.

Sensors (Basel, Switzerland) Apr 15, 2022
Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system ...
Ecosystem Gases Artificial Intelligence Neural Networks, Computer
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Modeling solubility of CO-N gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state.

Scientific reports Mar 7, 2022
Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO) and nitrogen (N) in water and brine is one of the most controversial challenges in the oil and chemical industries. Although many researches have been conducted on solubi...
Solubility Gases Water Carbon Dioxide Artificial Intelligence
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