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<p>This investigation aims to estimate the performance and emissions’ characteristics of constant speed compression ignition (CI) engine using response surface methodology and artificial neural network. It focused on a ternary fuel combination consisting of catalytic co-pyrolysis oil derived from Bauhinia purpurea, medical plastic, and aluminium oxide with diesel. The test was carried out at 550 °C, 15 °C/min heating rate, 30% blend of waste medical plastics grained to 1&#xa0;mm in size. The engine used a blend of catalytic co-pyrolysis oil (0–30%) with diesel, Al<sub>2</sub>O<sub>3</sub> nanoparticles (0–100&#xa0;ppm), exhaust gas recirculation (0–20%), and varying engine loads. The study examines the engine’s performance, particularly its brake thermal efficiency and brake-specific fuel consumption (BSFC), as well as types of emissions it produces, including smoke, carbon monoxide (CO), nitrogen oxides (NO<sub><i>x</i></sub>), carbon dioxide (CO<sub>2</sub>), and hydrocarbon (HC). Data analysis using various evaluation criteria that the predictions of the response surface methodology (RSM) and artificial neural network (ANN) models were reliable and accurate, with accuracy greater than 99.7%. Pyrolysis oil adversely impacts engine performance and emissions; however, the use of Al<sub>2</sub>O<sub>3</sub> nanoparticles can potentially address these issues. The results of this study revealed that, at optimum conditions, CI engines make the best use of Bauhinia purpurea pyrolysis oil.</p>

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Performance and Emission Analysis of Catalytic Co-Pyrolysis of Bauhinia Purpurea With Waste Medical Plastic with Al2O3 Powered in CI Engine Using ANN and RSM

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Devan Budiki Arumugam,
  • Mohan Cigurupadi Ganesan

摘要

This investigation aims to estimate the performance and emissions’ characteristics of constant speed compression ignition (CI) engine using response surface methodology and artificial neural network. It focused on a ternary fuel combination consisting of catalytic co-pyrolysis oil derived from Bauhinia purpurea, medical plastic, and aluminium oxide with diesel. The test was carried out at 550 °C, 15 °C/min heating rate, 30% blend of waste medical plastics grained to 1 mm in size. The engine used a blend of catalytic co-pyrolysis oil (0–30%) with diesel, Al2O3 nanoparticles (0–100 ppm), exhaust gas recirculation (0–20%), and varying engine loads. The study examines the engine’s performance, particularly its brake thermal efficiency and brake-specific fuel consumption (BSFC), as well as types of emissions it produces, including smoke, carbon monoxide (CO), nitrogen oxides (NOx), carbon dioxide (CO2), and hydrocarbon (HC). Data analysis using various evaluation criteria that the predictions of the response surface methodology (RSM) and artificial neural network (ANN) models were reliable and accurate, with accuracy greater than 99.7%. Pyrolysis oil adversely impacts engine performance and emissions; however, the use of Al2O3 nanoparticles can potentially address these issues. The results of this study revealed that, at optimum conditions, CI engines make the best use of Bauhinia purpurea pyrolysis oil.