Integrated multi-objective optimization, MCDM, and bias modelling of Al2O3/TiO2 nano-additives with HHO enrichment in diesel engine fuelled with waste tire pyrolysis oil

Waste tire was converted into pyrolysis oil; however, its elevated density, viscosity and aromatic content may adversely affect engine emissions and performance. Impact of TiO 2 and Al 2 O 3 nano-additives with oxyhydrogen (HHO) addition on performance and emissions of diesel engine operating on pyrolysis oil and diesel blends was demonstrated. Pyrolysis oil was blended with diesel fuel at 20% volume ratio to create T20 base blend. Al 2 O 3 and TiO 2 nanoparticles were individually scattered in an oil mixture at proportions of 25, 50, and 100 ppm. An alkaline dry electrolyser was used to produce HHO at a rate of 0.5 L/min. The diesel engine was run at 3000 rpm under various loads. Mixtures of T20A100+HHO and T20T100+HHO increased thermal efficiency by 23 and 29%, respectively. When 100 ppm of Al 2 O 3 and TiO 2 were added to the oil-oxyhydrogen mixture, CO decreased by 16% and 23%, respectively, compared to T20. The drops in smoke were 40% and 52% for T20A100 + HHO and T20T100 + HHO, and the HC decreases were 33% and 43%. When oxyhydrogen was added to an oil combination containing 100 ppm of titanium oxide and alumina, NOx was dropped by 29% and 39%, respectively. Mathematical models were built for the different responses using regression-based response modeling. Fuzzy multi-objective optimization and multi-criteria decision-making, including AHP and entropy, are used to obtain optimal operating conditions, which are experimentally validated. Sensitivity analysis was performed using a set of biased mathematical models to assess the relationship between the optimal solution and the decision-maker's bias. The Pareto front was plotted for the different predicted biased responses; in addition, TOPSIS was used to rank them. The quality of the predicted optimal operating conditions shows a strong relationship with decision-maker bias. Integration of Al 2 O 3 /TiO 2 nano-additives and HHO enrichment enhanced engine performance and emissions of waste-tire pyrolysis oil–diesel blend under the specified testing conditions.

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Publication Details

Journal
International Journal of Hydrogen Energy
Published
2026-10-16
DOI
https://doi.org/10.1016/j.ijhydene.2026.158032
Primary Topic
Advanced Combustion Engine Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrated multi-objective optimization, MCDM, and bias modelling of Al2O3/TiO2 nano-additives with HHO enrichment in diesel engine fuelled with waste tire pyrolysis oil

A.S. El-Shafay, M.S. Gad, El-Awady Attia
International Journal of Hydrogen Energy
Advanced Combustion Engine Technologies
article

Integrated multi-objective optimization, MCDM, and bias modelling of Al2O3/TiO2 nano-additives with HHO enrichment in diesel engine fuelled with waste tire pyrolysis oil

A.S. El-Shafay, M.S. Gad, El-Awady Attia
article en

Abstract

Waste tire was converted into pyrolysis oil; however, its elevated density, viscosity and aromatic content may adversely affect engine emissions and performance. Impact of TiO 2 and Al 2 O 3 nano-additives with oxyhydrogen (HHO) addition on performance and emissions of diesel engine operating on pyrolysis oil and diesel blends was demonstrated. Pyrolysis oil was blended with diesel fuel at 20% volume ratio to create T20 base blend. Al 2 O 3 and TiO 2 nanoparticles were individually scattered in an oil mixture at proportions of 25, 50, and 100 ppm. An alkaline dry electrolyser was used to produce HHO at a rate of 0.5 L/min. The diesel engine was run at 3000 rpm under various loads. Mixtures of T20A100+HHO and T20T100+HHO increased thermal efficiency by 23 and 29%, respectively. When 100 ppm of Al 2 O 3 and TiO 2 were added to the oil-oxyhydrogen mixture, CO decreased by 16% and 23%, respectively, compared to T20. The drops in smoke were 40% and 52% for T20A100 + HHO and T20T100 + HHO, and the HC decreases were 33% and 43%. When oxyhydrogen was added to an oil combination containing 100 ppm of titanium oxide and alumina, NOx was dropped by 29% and 39%, respectively. Mathematical models were built for the different responses using regression-based response modeling. Fuzzy multi-objective optimization and multi-criteria decision-making, including AHP and entropy, are used to obtain optimal operating conditions, which are experimentally validated. Sensitivity analysis was performed using a set of biased mathematical models to assess the relationship between the optimal solution and the decision-maker's bias. The Pareto front was plotted for the different predicted biased responses; in addition, TOPSIS was used to rank them. The quality of the predicted optimal operating conditions shows a strong relationship with decision-maker bias. Integration of Al 2 O 3 /TiO 2 nano-additives and HHO enrichment enhanced engine performance and emissions of waste-tire pyrolysis oil–diesel blend under the specified testing conditions.

International Journal of Hydrogen EnergyVol. 285
Prince Sattam Bin Abdulaziz University (SA), Fayoum University (EG)
Deanship of Scientific Research, Prince Sattam bin Abdulaziz University
Affordable and clean energy, Responsible consumption and production, Industry, innovation and infrastructure, Climate action
Openalex Percentile: Top 23%
Advanced Combustion Engine Technologies
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