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    CO and C3H8 oxidation activity of Pd/ZnO nanowires/cordierite catalyst
    (Pergamon-Elsevier Science Ltd, 2016) Sen, Mehmet; Emiroglu, A. Osman; Celik, M. Bahattin
    Using nanowires grown on monolith cordierite as catalyst wash-coat is a new concept. ZnO nanowires array has different pore-region diffusion of reactants in the catalyst media because it can have greater effective porosity and average diffusion length scale than traditional catalyst carrier. Also, thickness of ZnO nanowires array is less than conventional wash coat. ZnO nanowires were grown on monolith cordierite channels, and Pd was impregnated on the nanowires. The catalyst structure was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and atomic absorption spectrometry (AAS). The activity performance of Pd/ZnO nanowires catalyst for C3H8 and CO was examined under lean, stoichiometric and rich conditions. T50 was achieved under stoichiometric condition of gas mixture for C3H8 and CO at 400 degrees C and 235 degrees C, respectively. (C) 2016 Elsevier Ltd. All rights reserved.
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    Optimization of performance and emission of a diesel engine fueled with isopropyl alcohol Blends: A comparative ANN-GA and RSM-HCO application
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2024) Sen, Mehmet
    This study focuses on optimizing the operating parameters of a diesel engine fueled by diesel-isopropyl alcohol blends, with the aim of enhancing engine performance and minimizing emissions. Using data collected from 144 experimental runs, predictive models were developed utilizing artificial neural networks (ANN) and response surface methodology (RSM). The models exhibited impressive accuracy, with an average absolute percentage error below 2 % for each parameter and an R2 value exceeding 0.99. Subsequently, these models were optimized using genetic algorithm (GA) and hill climbing algorithm (HCO) to identify the optimal engine operating conditions. The results indicate that both ANN-GA and RSM-HCO models exhibit satisfactory accuracy in predicting engine parameters. The ANN-GA model demonstrated an average deviation of 2.9 % from experimental data, while the RSM-HCO model exhibited an average deviation of 5.4 %. Both optimization results indicated that, in an emissions-focused approach, desirability above 0.9 could be achieved with isopropyl alcohol-diesel blends and high engine speeds, such as 2600-2700 rpm, with a 3.5 bar engine load, resulting in low emission values and high engine performance.