Modeling and simulation of a fully air swept ball mill in a raw material grinding circuit

A raw material grinding circuit was modeled using plant data. Samples were collected from around the circuit and, following a crash stop, from inside the mill. The size distributions of the samples were determined down to a few microns. Using the data from inside the mill a modeling approach, based on perfect mixing, was developed. The modelling approach implicitly assumes that the mixture of feed materials broken is homogenous from the breakage point of view. The air classification around the circuit was modeled using the efficiency curve approach. In order to measure the success of the method the circuit performance was predicted by simulation studies while it was operating at different conditions. The results were then compared with the measured data. It is concluded that modeling gives a useful quantitative indication of what may occur in fully air swept mills.     

Relation between abrasion resistance and flexural strength of high volume fly ash concrete

In this paper, abrasion of high volume fly ash (HVFA) concretes made with 50% and 70% of cement replacement with fly ash was assessed in terms of its relation to flexural tensile strength. Comparisons were made between normal Portland cement (NPC) concrete and fly ash concrete. Comparisons were also made between fly ash concretes. Investigation results have shown that the abrasion resistance increased as flexural tensile strength increased. Analysis of the results showed that, for concrete with tensile strength of greater than 4–5 MPa, the abrasion resistance of HVFA concrete with 70% replacement with cement was found to be higher than that of counterpart control NPC concrete and concrete made with 50% fly ash. The comparison between the relation of abrasion to compressive strength and abrasion to flexural tensile strength made in terms of R² of the linear regression showed that a stronger relation existed between abrasion and flexural tensile strength than that of abrasion to compressive strength of the concrete studied.

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Résumé L'étude a pour but d'estimer la relation entre la résistance à l'usure avec la résistance à la traction en flexion des bétons contenant de 50% et 70% de cendres volantes. On a comparé le béton pur au béton contenant des cendres volantes. Des comparaisons ont été faites également entre les différents bétons contenant des cendres volantes. Les resultats de la recherche ont montré que la résistance à l'usure augmente à mesure que la résistance à la traction en flexion de 4∼5 MPa ont une résistance à l'usure plus grande s'ils contienneint 70% de cendres volantes que s'ils étaient purs ou contenaient 50% de cendres volantes. La comparaison entre les relation de la résistance à l'usure en compression et de la résistance à l'usure en traction a été établie en termes de R² de la régression linéaire. On a prouvé qu'une relation plus forte a été obtenue entre la résistance à l'usure et la résistance à la traction en flexion par rapport à la résistance à l'usure en compression du béton étudié.

     

An experimental study on the performance parameters of an experimental CI engine fueled with diesel-methanol-dodecanol blends

The effects of using diesel-methanol-dodecanol blends including methanol of various proportions on a CI engine performance are experimentally investigated. The methanol concentration in the blend has been changed from 2.5% to 15% with the increments of 2.5%, and 1% dodecanol was added into each blend to solve the phase separation problem. Experimental study has been conducted on a single-cylinder, water-cooled CI engine. The engine has been operated at different compression ratios (19, 21, 23 and 25) and the engine speed was varied from 1000 to 1600 rpm at each compression ratio. The performance parameters such as torque, effective power, specific fuel consumption and effective efficiency for each blend at various conditions are calculated depending on the experimental data. It was concluded that among the different blends, the blend including 10% methanol (DM10) is the most suited one for CI engines from the engine performance point of view. Improvements obtained up to 7% in performance parameters with this blend without any modification to engine design and fuel system are very promising.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

Performance Evaluation of Range Queries in Key Value Stores

Recently there has been a considerable increase in the number of different Key-Value stores, for supporting data storage and applications on the cloud environment. While all these solutions try to offer highly available and scalable services on the cloud, they are significantly different with each other in terms of the architecture and types of the applications, they try to support. Considering three widely-used such systems: Cassandra, HBase and Voldemort; in this paper we compare them in terms of their support for different types of query workloads. We are mainly focused on the range queries. Unlike HBase and Cassandra that have built-in support for range queries, Voldemort does not support this type of queries via its available API. For this matter, practical techniques are presented on top of Voldemort to support range queries. Our performance evaluation is based on mixed query workloads, in the sense that they contain a combination of short and long range queries, beside other types of typical queries on key-value stores such as lookup and update. We show that there are trade-offs in the performance of the selected system and scheme, and the types of the query workloads that can be processed efficiently.     

Varnish layer hardness of oriental beech (fagus orientalist L.) wood as affected by impregnation and bleaching

The impacts of impregnation and bleaching on the varnish layer hardness of Oriental beech (Fagus orientalist L.) wood were investigated. A number of bleaching combinations {[NaOH−H₂O₂], [NaOH−Ca(OH)₂−H₂O₂], [NaOH−MgSO₄−H₂O₂] [NaHSO₃−H₂C₂O₄], [NaSiO₃−H₂O₂], [KMnO₄+NaHSO₃+H₂O₃]} were applied at 18% concentration for bleaching to both impregnated and unimpregnated specimens of Oriental beech wood. Subsequently, water-based (WB) varnish was coated over the samples and the varnish layer hardness values were determined in accordance with ASTM D 4366-95. All of the chemicals used for bleaching reduced the surface hardness. However, after varnish coating, the hardness of most samples was similar to that of the varnish-coated natural (control) samples.     

Robust multilayer neural network based on median neuron model

Multilayer perceptron has been widely used in time series forecasting for last two decades. However, it is a well-known fact that the forecasting performance of multilayer perceptron is negatively affected when data have outliers and this is an important problem. In recent years, some alternative neuron models such as generalized-mean neuron, geometric mean neuron, and single multiplicative neuron have been also proposed in the literature. However, it is expected that forecasting performance of artificial neural network approaches based on these neuron models can be also negatively affected by outliers since the aggregation function employed in these models is based on mean value. In this study, a new multilayer feed forward neural network, which is called median neuron model multilayer feed forward (MNM-MFF) model, is proposed in order to deal with this problem caused by outliers and to reach high accuracy level. In the proposed model, unlike other models suggested in the literature, MNM which has median-based aggregation function is employed. MNM is also firstly defined in this study. MNM-MFF is a robust neural network method since aggregation functions in MNM-MFF are based on median, which is not affected much by outliers. In addition, to train MNM-MFF model, particle swarm optimization method was utilized. MNM-MFF was applied to two well-known time series in order to evaluate the performance of the proposed approach. As a result of the implementation, it was observed that the proposed MNM-MFF model has high forecasting accuracy and it is not affected by outlier as much as multilayer perceptron model. Proposed method brings improvement in 7 % for data without outlier, in 90 % for data with outlier, in 95 % for data with bigger outlier.     

Ring opening polymerization of 2,5-dihydro-2,5-dimethoxyfuran by electrochemical initiation

Summary 2,5-Dihydro-2,5-dimethoxyfuran (DHMF) was polymerized via constant current electrolysis (CCE), in CH₃CN-NaClO₄ solvent-electrolyte couple. Poly(DHMF) was obtained from the anolyte. The effect of current density, temperature, monomer and electrolyte concentrations on the polymer yield have been examined. The apparent activation energy for CCE of DHMF was found to be 37.2 kj/mol. The FTIR and ¹H-NMR analyses show that DHMF polymerizes by a ring opening. Molecular weight of poly(DHMF) was found by using cryoscopy.     

(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.