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1.
Experimental design of crystallization processes using Taguchi method
Miran Hvalec, Andreja Goršek, Peter Glavič, 2004, original scientific article

Abstract: Crystallization has become one of the most important unit operation in the chemical industries. The need to reduce the time from product discovery to market introduction is an inherent concern. In order to achieve the prescribedproduct quality characteristics, the process of engineering experimentation has to be optimized. Therefore, an experimental design method for crystallization processes is presented in this paper. Initially, the standardized Taguchi method was used to plan a minimum number of experiments. After identifying the working levels of the design factors and the main performance characteristics of the product under study, the method can be successfully applied to the crystallization processes. The simultaneous variations of the main crystallization parameters and their interactions were investigated using orthogonal array technique. A statistical analysis of 'signal-to-noise' ratio was followed by performing a variance analysis. After developing some special criteria, which depend on performance objectives, the optimal levels of the design factors were determined. Crystallization of $KNO_3$ with desirable particle size as a performance characteristic was used to illustrate the design procedure. The effects of rotational frequency of the stirrer, linear cooling rate and added admixture on final particle size were studied. In order to keep the selected parameters constant during the experiment and to ensure reproduction of entire experiment the automated reaction calorimeter RC1 was used.
Keywords: crystallization, particle size, Taguchi method
Published: 01.06.2012; Views: 1427; Downloads: 52
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2.
Sand as a medium for transmission of vibratory signals of prey in antlions Euroleon nostras (Neuroptera: Myrmeleontidae)
Dušan Devetak, Bojana Mencinger Vračko, Miha Devetak, Marko Marhl, Andreja Špernjak, 2007, original scientific article

Abstract: European pit-building antlions (Euroleon nostras/ Geoffroy in Fourcroy/) detect their prey by sensing the vibrations that prey generate during locomotory activity. The behavioural reactions and some of the physical properties of substrate vibrations in sand are measured to observe signal transmission through the substrate. The frequency range of the signals of four arthropod species (Tenebrio molitor, Pyrrhocoris apterus, Formica sp. and Trachelipus rathkei) is 0.1-4.5 kHz and acceleration values are in the range ▫$400 {mu}m s^{-2} to 1.5 mm s^{-2}$▫. Substrate particle size and the frequency of prey signals both influence the propagation properties of vibratory signals. The damping coefficient at a frequency 300 Hz varies from 0.26 to 2.61 dB ▫$cm^{-1}$▫ and is inversely proportional to the size of the sand particle. The damping coefficient is positively correlated with the frequency of the pulses. Vibrations in finer sand are attenuated more strongly than in coarser sand and, consequently, an antlion detects its prey only at a short distance. The reaction distance is defined as the distance of the prey from the centre of the pit when the antlion begins tossing sand as a reaction to the presence of prey. The mean reaction distance is 3.3 cm in the finest sand (particle size ▫$le 0.23 mm$▫) and 12.3 cm in coarser sand (particle size 1-1.54 mm). The most convenient sands for prey detection are considered to be medium particle-sized sands.
Keywords: biology, zoology, receptors, chordotonal organs, vibrations, vibratory signals, transmission of vibrations, reception of vibrations, electrophysiology, substrate vibration, antlions, Myrmeleontidae, sand, substrate vibration, particle size
Published: 07.06.2012; Views: 1313; Downloads: 62
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3.
Post insulator optimization based on dynamic population size
Peter Kitak, Arnel Glotić, Igor Tičar, 2012, published scientific conference contribution

Abstract: This paper suggests the use of dynamic population size throughout the optimization process which is applied on the numerical model of a medium voltage post insulator. The main objective of the dynamic population is reducing population size, to achieve faster convergence. Change of population size can be done in any iteration by proposed method. The multiobjective optimization process is based on the PSO algorithm, which is suitably modifiedin order to operate with the principle of the optimal Pareto front.
Keywords: dynamic population size, insulation elements, multi-objective optimization, particle swarm optimization
Published: 10.07.2015; Views: 600; Downloads: 13
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