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Carbon Emission and Engine Cleaning Forecast

K. Sabitha, S. Lavanya

Abstract


Owing to the emerging technologies, innovations are introduced in the context of car maintenance and environmental preservation. These novel systems provide increased access to vehicle diagnostics, and support precise and instantaneous measurement of engine efficiency as well as carbon emissions. Environmental well-being and atmospheric purity are concerns of considerable seriousness that should be addressed with the highest precautions and preventive protocols. Excess carbon emission by engines has been a huge issue in recent years, calling for predictive emission monitoring and pre-emptive engine maintenance. For the development of consumer confidence and eco-friendly transport, there is greater need for focus on the issue of emission prediction and engine cleaning. Carbon deposit inside engines, if undetected, results in excess fuel consumption and emissions. For its solution, a better method using a Modified Convolutional Neural Network (CNN) algorithm is presented. Through the application of the new approach, carbon emission patterns can be correctly predicted and engine cleaning can be scheduled accordingly, in turn providing improved engine performance and reduced environment degradation.

 


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References


Jin, Y., Sharifi, A., Li, Z., Chen, S., Zeng, S. and Zhao, S., 2024. Carbon emission prediction models: A review. Science of The Total Environment, p.172319.

Yan, Z., Cui, Z., Zhao, M., Zhong, S. and Lin, L., 2023. The carbon emission and maintenance-cost guided optimization of aero-engine clearance schedule. The International Journal of Advanced Manufacturing Technology, pp.1-18.

Pawanr, S., Garg, G.K. and Routroy, S., 2023. Prediction of energy efficiency, power factor and associated carbon emissions of machine tools using soft computing techniques. International Journal on Interactive Design and Manufacturing (IJIDeM), 17(3), pp.1165-1183.

Cui, Z., Zhong, S. and Yan, Z., 2020. Fuel savings model after aero-engine washing based on convolutional neural network prediction. Measurement, 151, p.107180.

Li, G., Yang, Z. and Yang, H., 2023. A new hybrid short-term carbon emissions prediction model for aviation industry in China. Alexandria Engineering Journal, 68, pp.93-110.

Zhang, Y., Wen, M., Sun, Y., Chen, H. and Cai, Y., 2022. Black carbon emission prediction of diesel engine using stacked generalization. Atmosphere, 13(11), p.1855.

Lu, X., Ota, K., Dong, M., Yu, C. and Jin, H., 2017. Predicting transportation carbon emission with urban big data. IEEE Transactions on Sustainable Computing, 2(4), pp.333-344.

Ashok, K. and Rithishbrahma, P., 2024, August. Prediction of Vehicle Carbon Emission Using Machine Learning. In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1814-1818). IEEE.

Ashok, K. and Rithishbrahma, P., 2024, August. Prediction of Vehicle Carbon Emission Using Machine Learning. In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1814-1818). IEEE.

Sanjeevannavar, M.B., Banapurmath, N.R., Kumar, V.D., Sajjan, A.M., Badruddin, I.A., Vadlamudi, C., Krishnappa, S., Kamangar, S., Baig, R.U. and Khan, T.Y., 2023. Machine learning prediction and optimization of performance and emissions characteristics of IC engine. Sustainability, 15(18), p.13825.


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