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<p>This article deals with optimizing the pyrolytic oil (PO) yield and its quality improvement from extensive thermal and catalytic pyrolysis experiments under various process conditions with waste polypropylene (WPP). The design of experiments adopted the central composite design (CCD) to measure PO yield under different process conditions and to develop the controlling intricate nonlinear relationships between process variables. Response surface methodology (RSM) and artificial neural network–genetic algorithm (ANN-GA) have been employed to detect the best temperatures, heating rates, residence times, and percentage of catalyst. The corresponding maximum PO yield of 83.45% was observed during thermal pyrolysis (thermolysis). The superior quality PO was obtained on catalytic treatment at the expense of a slightly lower yield. The optimum dosing of 10% kaolin and 12% zeolite significantly improved the oil quality with the higher contribution of gasoline-range hydrocarbons (GHC) in PO. The GC–MS analysis quantified various hydrocarbons in PO. The improved PO quality was also quantified with reduced density, viscosity, and increased flash and pour points. The study further characterizes various gaseous and solid products, providing valuable insights into waste valorization for efficient plastic waste management. The research objectives mainly focus on efficient resource recovery and environmental sustainability to produce green fuel for automobiles.</p>

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Assessment of the Effects of Process Parameters on Pyrolytic Oil Production from Waste Polypropylene: Optimization, Catalyst Influence, and Product Characterization

  • 期刊论文

  • 发表日期：

    2025年1月9日

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  • Ravindra Kumar,
  • Anup Kumar Sadhukhan,
  • Biswajit Ruj

摘要

This article deals with optimizing the pyrolytic oil (PO) yield and its quality improvement from extensive thermal and catalytic pyrolysis experiments under various process conditions with waste polypropylene (WPP). The design of experiments adopted the central composite design (CCD) to measure PO yield under different process conditions and to develop the controlling intricate nonlinear relationships between process variables. Response surface methodology (RSM) and artificial neural network–genetic algorithm (ANN-GA) have been employed to detect the best temperatures, heating rates, residence times, and percentage of catalyst. The corresponding maximum PO yield of 83.45% was observed during thermal pyrolysis (thermolysis). The superior quality PO was obtained on catalytic treatment at the expense of a slightly lower yield. The optimum dosing of 10% kaolin and 12% zeolite significantly improved the oil quality with the higher contribution of gasoline-range hydrocarbons (GHC) in PO. The GC–MS analysis quantified various hydrocarbons in PO. The improved PO quality was also quantified with reduced density, viscosity, and increased flash and pour points. The study further characterizes various gaseous and solid products, providing valuable insights into waste valorization for efficient plastic waste management. The research objectives mainly focus on efficient resource recovery and environmental sustainability to produce green fuel for automobiles.