ANN modeling and residual analysis on screening efficiency of coal in vibrating screen

Shanmugam, Bharath Kumar; Vardhan, Harsha; Raj, M. Govinda; Kaza, Marutiram; Sah, Rameshwar; Hanumanthappa, Harish

doi:10.1080/19392699.2021.1910505

Cited by 18 publications

(1 citation statement)

References 31 publications

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“…It was observed that the fit is very satisfactory according to the scatter plots and error distribution diagrams shown in Figure 9. The model errors exhibit a nearly normal symmetric behavior around 0, with no offsets in the estimation of each contaminant [37]. Moreover, they behave completely randomly, thus ruling out the inference of other variables not considered in the ANNs' training.…”

Section: Validation Of the Neural Networkmentioning

confidence: 93%

GPS Data and Machine Learning Tools, a Practical and Cost-Effective Combination for Estimating Light Vehicle Emissions

Rivera-Campoverde,

Arenas-Ramírez,

Muñoz Sanz

et al. 2024

Sensors

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This paper focuses on the emissions of the three most sold categories of light vehicles: sedans, SUVs, and pickups. The research is carried out through an innovative methodology based on GPS and machine learning in real driving conditions. For this purpose, driving data from the three best-selling vehicles in Ecuador are acquired using a data logger with GPS included, and emissions are measured using a PEMS in six RDE tests with two standardized routes for each vehicle. The data obtained on Route 1 are used to estimate the gears used during driving using the K-means algorithm and classification trees. Then, the relative importance of driving variables is estimated using random forest techniques, followed by the training of ANNs to estimate CO2, CO, NOX, and HC. The data generated on Route 2 are used to validate the obtained ANNs. These models are fed with a dataset generated from 324, 300, and 316 km of random driving for each type of vehicle. The results of the model were compared with the IVE model and an OBD-based model, showing similar results without the need to mount the PEMS on the vehicles for long test drives. The generated model is robust to different traffic conditions as a result of its training and validation using a large amount of data obtained under completely random driving conditions.

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Section: Validation Of the Neural Networkmentioning

confidence: 93%