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The quality of polypropylene is one of the major components in the plastic industry. The quality of the polypropylene depends upon the melt flow index and the xylene solubility under the given condition such as hydrogen flow, donor flow, pressure, temperature, etc. This study investigates the use of artificial neural network (ANN) modeling for the prediction of the quality of polypropylene for petrochemical plants. This study proposes an ANN model to predict the 'melt flow index (MFI) and xylene solubility' as the quality of polypropylene depends upon these two factors. Hydrogen ([H.sub.2]) flow, pressure, temperature, and donor flow are the controlling parameters for the MFI and xylene solubility. The study proposes a new approach for the selection of the best topology using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) multicriterion decision-making (MCDM) process for the ANN technique. Experimental data are trained with three training algorithms each with four combinations of training functions and each with three combinations of numbers of neurons. In this study, the best model for ANN is found by the Levenberg-Marquardt backpropagation training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer functions. The best topology for the ANN model for prediction is selected by using the TOPSIS method. Some sensitivity analyses are provided graphically to show the physical nature of the problem. KEYWORDS artificial neural network, multicriteria decision-making, polypropylene, TOPSIS, 1 | INTRODUCTION Polypropylene (PP) is a thermoplastic polymer, which is also called polypropene, and utilized in broad categories such as textiles (e.g., carpets, ropes, and thermal underwear), packaging and [...]

The ever-increasing demand for fossil fuels and environmental issues have been the major concerns over the past few decades to search for viable alternative fuels where hydrogen find its suitability to be a viable and promising alternative fuel option on existing IC engine platforms in bridging the contemporary gap to the long term fuel cell based power train roadmap. It's clean burning capability helps to meet the stringent emission norms. Complete substitution of diesel with hydrogen may not be expedient for the time being but the potential use of hydrogen in a diesel engine in dual fuel mode is possible. The study also investigates the use of Artificial Neural Network modeling for prediction of performance and emission characteristics such as BSEC, BTE, NOx, Soot (FSN), UHC, CO 2 of the existing single cylinder four-stroke diesel engine with hydrogen in dual fuel mode. Levenberg–Marquardt back propagation training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer function have resulted in the best model for prediction of performance and emissions characteristics which has been well supported by the trade-off analysis between NOx–Soot (FSN)–BSEC. Fuzzy based analysis has been incorporated into existing ANN model for optimal parameter design which suggests the modesty of the employed transfer function of the existing ANN model. [ABSTRACT FROM AUTHOR]