Numerical study of the hydrodynamics and mass transfer in the external loop airlift reactor

The objective of this study was to investigate the hydrodynamics and the gas-liquid mass transfer coefficient of an external-loop airlift reactor (ELAR). The ELAR was operated in three cases: different inlet velocities of fluids, different alcohols solutions (water, 0.5% methanol, 0.5% ethanol, 0.5% propanol and 0.5% butanol) and different concentration of methanol in solutions (0%, 0.5%, 1%, 2% and 5%). The influence of superficial gas velocity and various diluted alcohol solutions on hydrodynamics and the gas-liquid mass transfer coefficient of the ELAR was studied. Experimentally, the gas hold-up, liquid velocities and volumetric mass transfer coefficient values in the riser and the downcomer were obtained from the literature source. A computational fluid dynamics (CFD) model was developed, based on two-phase flow, investigating different liquids regarding surface tension, assuming the ideal gas flow, applying the finite volume method and Eulerian-Eulerian model. The volumetric mass transfer coefficient was determined using the CFD and artificial neural network model. The effects of liquid parameters and gas velocity on the characteristics of the gas-liquid mass transfer were simulated. These models were compared with the appropriate experimental results. The CFD model successfully simulates the influence of different alcohols regarding the number of C-atoms on hydrodynamics and mass transfer

Biological and chemical diversity of Angelica archangelica L. — case study of essential oil and its biological activity

Garden angelica (Angelica archangelica L.), native to the northern temperate region, is widespread in Europe and Asia. Since the middle ages, it has been used for healing and as a vegetable in traditional dishes. In the modern era, it has been proven that A. archangelica has a complex chemical composition. The main derivatives that contribute to the plant’s biological activities are essential oil and coumarins. In this review, the focus is on the cross-analysis of the taxonomy of A. archangelica, and its distribution in different regions, with the presentation of the richness of its biochemical composition, which overall contributes to the widespread use of the roots of this plant in folk medicine. It belongs to the plants that were introduced to the wider area of Central, Eastern, and Southern Europe; as a medicinal plant, it represents a significant part of the medical flora of many areas. Cluster analysis of pooled data indicates a clear differentiation of chemotypes

Application of Artificial Neural Networks in performance prediction of cement mortars with various mineral additives

Prediction of physico-mechanical and thermo-mechanical properties of cement mortars with different mineral additives based on materials’ starting compositions by means of machine learning models is an essential feature in contemporary civil engineering. In this study, the prediction of performances of seventeen mortar mixtures based on Portland cement (CEM I 42.5R) with mineral additives and subsequent comparison with properties of mortars in which various cement types were used as binders was conducted using artificial neural network (ANN) modeling. Analytical model comprised discrimination based on similarities and differences between composite mortars and mortars based on 6 different cement types (without additives). The employed cements were: ordinary Portland cement, moderate heat hydration cement, high early strength cement, low heath hydration cement, high sulphate resistant cement, calcium aluminate cement, and high alumina cement. The mineral additives used were: fly ash, bottom ash, zeolite, bentonite, perlite, vermiculite, pyrophyllite, micro silica, silica fume, spinel, chamotte, calcinated clay, kaoline clay, alumina, limestone, talc, and copper slag. This investigation designates the impacts of various process parameters, such as the concentration of SiO2 , Al 2 O3 , Fe 2 O3 , CaO, MgO, K2 O, Na 2 O, TiO2 , SO3 , and LoI, and their effects on the quality of mortars with additives. The characteristics of mortars were evaluated regarding the dependent parameters such as: pozzolanic activity, heath of hydration, setting time, compressive strength, split tensile strength, compressive and split tensile strength under various temperatures up to 1000 °C, refractoriness, and sulphate resistence. Cluster Analysis and Principal Component Analysis were used for estimating the effect of ascertained process parameters on the quality of cements and additives. Artificial neural network model was employed to foresee the quality of cement mortars with additives of discovered outputs and its results show the high suitability level of anticipation: 0.999 during the training period, which can be regarded appropriately enough to correctly predict the observed outputs in a wide range of processing parameters. The developed ANN model displayed high predictive accuracy and it can be used in civil engineering for prediction of properties of novel mineral additives if their chemical composition is known.IX Serbian Ceramic Society Conference - Advanced Ceramics and Application : new frontiers in multifunctional material science and processing : program and the book of abstracts; September 20-21, 2021; Belgrad

Effects of temperature and immersion time on rehydration of osmotically dehydrated pork meat

The aim of this work was to study the changes in osmotically dehydrated (OD) pork meat during rehydration. Meat samples (lxlxlcm cubes) were osmotically treated in two solutions: (1) solution with 350g of NaCI and 1200g of sucrose diluted in I I of distilled water and (2) sugar beet molasses (80 °Brix) solution at 23±2°C for 1, 2, 3 and 4 hours. In both cases, the solution to sample mass ratio was 10:1 to avoid significant dilution of the medium by water removal. After being osmotically dehydrated, meat samples were rehydrated by immersing meat cubes in water bath at constant temperature (20, 40 and 60 °C). The samples were removed after different immersion periods (15, 30, 45 and 60 min) and examined for mass and volume gain and rehydration percentage was calculated. After relatively short time (15 min), significant weight and volume gains were observed for both treatments. Process temperature was the most significant variable affecting final dry matter content and rehydration kinetics. At the end of rehydration process, conducted at 20 °C and 40 °C, a significant recovery in mass was observed, although the values were lower than for fresh meat. Ruptured and shrunken meat tissue produced as the result of OD had reduced its ability to absorb water. Rehydration percentage at 20 °C for molasses solution was 24.11%, and for sucrose-salt solution was 26.19%. However, rehydration at 40° C brings higher mass gain in case of molasses as a solution (11.33%) compared with sucrose-salt solution (7.88%). Results obtained at 60 °C were negative which means that rehydration didn't take place. The best conditions for meat rehydration were obtained using a temperature of 20 °C and time of 60 min. Volume of samples increased almost linearly with weight increment

Granular flow in static mixers by coupled DEM/CFD approach

The mixing process greatly influences the mixing efficiency, as well as the quality and the price of the intermediate and/or the final product. Static mixer is used for premixing action before the main mixing process, for significant reduction of mixing time and energy consumption. This type of premixing action is not investigated in detail in the open literature. In this article, the novel numerical approach called Discrete Element Method is used for modelling of granular flow in multiple static mixer applications (1-3 Komax or Ross mixing elements were utilized), while the Computational Fluid Dynamic Method was chosen for fluid flow modelling, using the Eulerian multiphase model. The main aim of this article is to predict the behavior of granules being gravitationally transported in different mixer configuration and to choose the best configuration of the mixer taking into account the total particle path, the number of mixing elements and the quality of the obtained mixture. The results of the numerical simulations in the static mixers were compared to experimental results, the mixing quality is examined by RSD (relative standard deviation) criterion, and the effects on the mixer type and the number of mixing elements on mixing process were studied. The effects of the mixer type and the number of mixing elements on mixing process were studied using analysis of variance (ANOVA). Mathematical modelling is used for optimization of number of Ross and Komax segments in mixer in order to gain desirable mixing results

Comparative study of white cabbage, traditional variety and hybrid intended for biological fermentation

Traditional Serbian variety of white cabbage, cultivar “Futoski” (33 samples) and hybrid “Bravo” (10 samples) were investigated in this study for their applicability to biological fermentation. Different chemical, physical, texture and sensory characteristics of raw cabbage heads were investigated. Obtained experimental results were analyzed using descriptive statistics, while correlation analysis showed the relations between different assays. Also, Principal Component Analysis (PCA) has been applied to classify and discriminate between different cultivars cabbage heads. Furthermore, standard score has been introduced to enable more comprehensive comparison between the investigated samples, in order to find the optimum sample, regarding observed chemical, physical, texture and sensory properties. PCA analysis showed that the best sample for cabbage cultivar “Futoski” was sample 9, while sample 34 was the best for hybrid “Bravo”, regarding their chemical, morphological and sensory characteristics.

Predicting Road Traffic Accidents—Artificial Neural Network Approach

Road traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence of traffic accidents, making it difficult to predict and prevent them on new road sections. Artificial neural networks (ANN) have demonstrated their effectiveness in predicting traffic accidents using limited data sets. This study presents two ANN models to predict traffic accidents on common roads in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors that can be easily determined, such as road length, terrain type, road width, average daily traffic volume, and speed limit. The models predict the number of traffic accidents, as well as the severity of their consequences, including fatalities, injuries and property damage. The developed optimal neural network models showed good generalization capabilities for the collected data foresee, and could be used to accurately predict the observed outputs, based on the input parameters. The highest values of r2 for developed models ANN1 and ANN2 were 0.986, 0.988, and 0.977, and 0.990, 0.969, and 0.990, accordingly, for training, testing and validation cycles. Identifying the most influential factors can assist in improving road safety and reducing the number of accidents. Overall, this research highlights the potential of ANN in predicting traffic accidents and supporting decision-making in transportation planning

Experimental and Discrete Element Model Investigation of Limestone Aggregate Blending Process in Vertical Static and/or Conveyor Mixer for Application in the Concrete Mixture

The numerical model of the granular flow within an aggregate mixture, conducted in the vertical static and/or the conveyor blender, was explored using the discrete element method (DEM) approach. The blending quality of limestone fine aggregate fractions binary mixture for application in self-compacting concrete was studied. The potential of augmenting the conveyor mixer working efficiency by joining its operation to a Komax-type vertical static mixer, to increase the blending conduct was investigated. In addition the impact of the feed height on the flow field in the cone-shaped conveyor mixer was examined using the DEM simulation. Applying the numerical approach enabled a deeper insight into the quality of blending actions, while the relative standard deviation criteria ranked the uniformity of the mixture. The primary objective of this investigation was to examine the behavior of mixture for two types of blenders and to estimate the combined blending action of these two mixers, to explore the potential to augment the homogeneity of the aggregate fractions binary mixture, i.e., mixing quality, reduce the blending time and to abbreviate the energy-consuming

Application of soybean oil and glycerol in animal feed production, ANN model

In the past few decades the diet preparation in feed production has evolved towards more complicated technological operations, which include different liquid addition. A wide scale of different liquids is used in contemporary animal feed production, from oils and glycerol to more expensive products in a liquid form, such as enzymes, flavourings, amino acids, vitamins and others. In the presented study the liquid addition in feed production was observed, with a specific goal to investigate the spraying systems in order to better understand the effects of fluids, such as soybean oil and glycerol, on feed production. The dispersion angles of spraying nozzle for glycerol and soybean oil were determined as an indicator of the uniform application of liquids during feed production. Dispersion of the material was accomplished using the two-fluid nozzle. The performance of Artificial Neural Network (ANN) was compared with experimental data in order to develop rapid and accurate method for prediction of dispersion angle. The ANN model showed high prediction accuracy (r2 = 0.945)

CaO&CaSO4 and CaO&Al2(SO4)3 as Pectin Precipitants–Model of Overlapping Diffuse Layers

This work is concerned with the theoretical basis of novel sugar beet juice purification method using binary systems CaO&CaSO4 and CaO&Al2(SO4)3. The Gouy–Chapman–Stern (GCS) model of overlapping of diffuse layers of EDLs on pectin surface and that on Ca2+ and Al3+ ions, theoretically explains this method. The change of the zeta potential was used to quantitatively indicate overlapping of diffuse layers. For the experiment two model solutions of pectin (0.1 % w/w) were prepared, while the concentrations of CaO&CaSO4 and CaO&Al2(SO4)3 in the range of 50–500 g dm-3 were used. The greater decrease in the absolute value of zeta potential indicated greater overlapping of diffuse layers between pectin particles and Ca2+ and Al3+ ions and faster coagulation of pectin. The overlapping degree increased with increased concentration of these binary systems. Pectin with a greater surface charge and multivalent Al3+ from CaO&Al2(SO4), exerted a greater impact on the zeta potential. Optimal quantities of the applied binary mixtures were as follows: 256–640 mg g-1 pectin. This is much lower than CaO commonly used in the conventional process of sugar beet juice purification (about 9 g g-1 pectin)