Application of a Small Unmanned Aerial System to Measure Ammonia Emissions from a Pilot Amine-CO2 Capture System

Schuyler, Travis J.; Irvin, Bradley; Abad, Keemia; Thompson, Jesse; Liu, Kunlei; Guzmán, Marcelo I.

doi:10.3390/s20236974

Cited by 1 publication

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have shown that a sensor’s response is linked to the flow velocity of dynamic airflow. Schuyler et al [ 41 , 42 ] proposed an algorithm to correct the impact of dynamic airflow velocity on sensors during ammonia detection.…”

Section: Resultsmentioning

confidence: 99%

“…The experimental substance of this work requires optimization. Utilizing space–time information to detect gas concentration values is beneficial for enhancing the accuracy of gas detection and analyzing gas diffusion [ 41 , 42 ]. Subsequently, it is necessary to arrange many electronic nose systems inside the same area in accordance with the surrounding conditions to ascertain the ammonia levels while considering both temporal and spatial factors.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation

Shi,

Niu

et al. 2024

Sensors

View full text Add to dashboard Cite

In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times and limited detection accuracy. In this study, we introduce a novel solution: the bionic chamber construction of the electronic nose is optimized, and the sensor response data in the chamber are analyzed using an intelligent algorithm. We analyze the structure of the biomimetic chamber and the surface airflow of the sensor array to determine the sensing units of the system. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals are processed, followed by the application of classification and regression models for data prediction. Our results suggest that the system, leveraging the biomimetic chamber, offers rapid gas detection response times. A high classification prediction accuracy, with a determination coefficient R2 value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions, is achieved by incorporating a backpropagation (BP) neural network algorithm. These outcomes demonstrate the effectiveness of the electronic nose, based on an optimized bionic chamber combined with a BP neural network algorithm, in accurately detecting ammonia emitted during livestock excreta fermentation, satisfying the ammonia detection requirements of breeding farms.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%