Mining is crucial for advancing technology and science by providing essential resources and materials. Quarry blasting, a necessary process in mining, releases these resources and chemicals, driving research and engineering improvements. However, NOx (nitrogen oxides) is an undesired contaminant produced during quarry blasts, posing a significant negative impact on mining activities. Therefore, detecting and quantifying NOx emissions is essential for designing effective blasts, analyzing their dynamics, and reducing contaminant production.
Image processing and machine learning, including deep learning methods, have been applied to solve smoke plume detection and segmentation tasks. Most research has focused on smoke from campfires or forest fires, which are important for early fire and smoke detection. However, there is limited research on quarry blast smoke using machine learning methods, which presents challenges for recognizing the smoke plume from complex background scenarios and objects, including collapsed rocks, scattered soil dust and dirt, and the dynamic movement of the scene due to blast vibrations. Detecting NOx within the smoke plumes is further challenging because NOx must be distinguished from the overall smoke plume, as they share many common spatial features.
To resolve this issue, we propose an ensemble method for NOx detection that combines smoke plume segmentation with NOx fume detection. First, we apply a smoke plume segmentation model to identify the smoke plume regions. Then, we use these masked-out smoke plume regions to detect NOx based on its unique color features. The first stage of the ensemble method helps block out complicated confusers in the background, such as reddish and brownish dirt. Next, we specifically design a color classifier for NOx to further improve its sensitivity.
We evaluate the overall video-wise performance using accuracy, false alarm, and miss rates based on sampled image frames from videos. Additionally, we assess the mean Intersection over Union (mIoU) for smoke plume segmentation and the mean Average Precision (mAP) for image-wise performance from an algorithmic perspective. In summary, this paper represents the first attempt to apply machine learning approaches to NOx detection in a memory-efficient and time-efficient manner.
This paper presents a methodology for finite element (FE) modelling to analyse the total deformation of rockmass under blast-load, with a focus on predicting backbreak that may occur during surface blasting operations. Backbreak can result from improper use of explosive energy, leading to damage beyond the excavation zone and jeopardizing operational safety and productivity. The paper highlights the impacts of backbreak on bench stability, excavator operation, drilling operation, flyrock generation, and improper fragmentation and boulder formation in surface blasting. However, prior estimation of extent of rock breakage zone can provide an idea to formulate the blast design to control the backbreak. While vibration-based prediction models are commonly used to estimate backbreak, they require physical blasting experimentation. To overcome this limitation, the paper describes the development of a FE model using ANSYS Explicit dynamics, which simulates rock blasting and calculates the total deformation of the rockmass. The rockmass deformation is modelled using Drucker-Prager (D-P) strength model with the Jones-Wilkins-Lee (JWL) equation of state for the explosives. The accuracy of the FE model is evaluated through physical experiments, and the results are compared with the vibration models. The FE model demonstrates close proximity to physical observations as well as vibration model result in predicting backbreak, thereby offering a promising alternative to vibration-based prediction models. The primary objective of the study is to predict the response of the insitu rockmass to explosive blast loading and determine the effective rock breakage zone.
The Lafarge granite quarry in Cumming, Georgia is using Principal Component Analysis (PCA) and Biplot charting to identify important variables and control air blast and vibration. Every blasting situation is unique and PCA and Biplot charting help to clarify the major variables determining air blast and vibration.
NOx Detection in Video Data Based on Ensemble Machine Learning