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The aim of the study was to investigate the changes in ultimate, proximate analysis, and calorific properties of Miscanthus x giganteus with three types of planting materials (two rhizomes - R1 and R2 - and one seedling - S) and three ash fertiliser treatments (P0, P2, and P5) were included in the study. The research further examined their effects on crop yield, stem height and various chemical properties. The results showed that the maximum yield was obtained with the R1 x P2 plant type, while the minimum yield was recorded with the R2 x P2 plant type. In addition, the greatest average stem height (3.34 m) was recorded for the R2 x P5 plant type. Significant differences were also found in the chemical components between the plant types and treatments. For example, the highest ash content of 2.25% was found in plant type 'S' x P5, while the highest coke content of 14.48 % was found in plant type R1 x P5. The statistical analysis confirmed that planting material and ash fertilisation had significant influence on the physicochemical properties of Miscanthus x giganteus. This consequently affects the calorific value, with the average higher and lower heating value being 18.32 and 17.04 MJ/kg, respectively. The neural regression network models showed robust predictive performance for the higher heating value and lower heating value, with low chi-square values, X², and high coefficients of determination, R². [ABSTRACT FROM AUTHOR]

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R2 of 0.92. SVM (R2 = 0.81), RFR, and polynomial models (R2 = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure. [ABSTRACT FROM AUTHOR]

In this study freeze-drying and oven-drying (at 50 and 65 °C) were applied for the preparation of dried pumpkin waste. The effect of drying methods on physical properties (moisture content, water activity, hygroscopicity, water solubility, bulk and tapped density, flowability and colour), content of bioactive compounds (carotenoids and phenolics) and antioxidant activity were investigated. Also, the influence of one-year storage at -20 °C on the bioactive compounds content and the antioxidant activity of dried pumpkin waste was examined. Results indicated that drying method showed a significant impact on the investigated characteristics. Bioactive compounds content and antioxidant activity by DPPH test of freeze-dried were higher than of oven-dried pumpkin waste, while higher antioxidant activity in reducing power assay was determined by oven-dried pumpkin waste. An artificial neural network model was developed, for the anticipation of antioxidant activity according to bioactive compounds content (phenolics and carotenoids), in oven-dried (at 50 and 65 °C) and in freeze-dried pumpkin waste after one-year storage. These models showed good prediction properties (the r² value during training cycle for output variables was 0.999). It was demonstrated that pumpkin waste is potentially an important source of bioactive compounds, which can be used after extraction in suitable forms in the development of functional food products. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the potential of using structural analysis parameters for estimating the higher heating value (HHV) of biomass by obtaining information on the composition of cellulose, lignin, and hemicellulose. To achieve this goal, several nonlinear mathematical models were developed, including polynomials, support vector machines (SVMs), random forest regression (RFR) and artificial neural networks (ANN) for predicting HHV. The performed statistical analysis "goodness of fit" showed that the ANN model has the best performance in terms of coefficient of determination (R2 = 0.90) and the lowest level of model error for the parameters X2 (0.25), RMSE (0.50), and MPE (2.22). Thus, the ANN model was identified as the most appropriate model for determining the HHV of different biomasses based on the specified input parameters. In conclusion, the results of this study demonstrate the potential of using structural analysis parameters as input for HHV modeling, which is a promising approach for the field of biomass energy production. The development of the model ANN and the comparative analysis of the different models provide important insights for future research in this field. [ABSTRACT FROM AUTHOR]

Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type - An Artificial Neural Network Approach Keywords: aniseed; essential oil; growing year; locality; fertilization; artificial neural network EN aniseed essential oil growing year locality fertilization artificial neural network 1722 16 11/17/22 20221101 NES 221101 1. [Extracted from the article]

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R2 = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature. [ABSTRACT FROM AUTHOR]

Although regular intake of fruits and vegetables has an essential role in a healthy diet and well-being, a majority of consumers in Serbia have a suboptimal intake of these groceries. To understand the main determinants of this unsatisfactory situation, the study tested an extended model of the theory of planned behavior intending to suggest necessary steps for improving fruits and vegetables daily intake. This theory, extended for the role of knowledge, was tested using structural equation modeling. Fit indices confirmed the utility of this extended model of the theory of planned behavior in explaining consumers’ behavior as well as the mediating role of behavioral intentions. Serbia, as one of the central developing countries in the Balkans, was chosen to test the model with the possibility of applying it to other developing countries facing malnutrition. Data were collected in north Serbia, through an online survey (n=688). Despite consumers’ high awareness of fruits and vegetables' beneficial health effects, the influence of consumers’ knowledge only is not sufficient to trigger behavioral changes. Consumers’ intentions and behavior should be influenced indirectly, by changing their attitudes and subjective norms. All custom-made activities promoting a higher fruit and vegetable intake should consider the present findings to achieve a bigger effect on behavioral changes among consumers. [ABSTRACT FROM AUTHOR]

The microbiologically induced calcite precipitation (MICP) can be an emerging approach that could tap onto soil bacterial diversity and use as a bioremediation technique. Based on the concept that bacteria with biomineralization capacity could be effective CaCO 3 inductance agents, this study aimed to evaluate the simultaneous influence of 11 operational and environmental factors on the MICP process, for the first time. Therefore, Bacillus muralis , B. lentus , B. simplex , B. firmus, and B. licheniformis , isolated from alkaline soils, were used in the selection of the best performing bacterium compared with a well-known MICP bioagent Sporosarcina pasteurii DSM 33. Plackett-Burman's experimental design was labouring to screen all independent variables for their significances on five outputs (pH value, number of viable cells and spores, amount of urea and CaCO 3 precipitate). According to experimentally obtained data, an artificial neural network model based on the Broyden-Fletcher-Goldfarb-Shanno algorithm showed good prediction capabilities, while differences in the relative influences were observed at the bacterial strain level. B. licheniformis turn out to be the most potent bioagent, with a maximum amount of CaCO 3 precipitate of 3.14 g/100 mL in the optimal conditions. • Screening of best-performing bacteria with biomineralization capacity was showed. • Plackett-Burman design involves ten operation parameters and one dummy factor. • Five variables were elaborated in the experimental design. • Developed ANN model showed good prediction capabilities. • Bacillus licheniformis was the best bacterium using multi-objective optimization. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the antibacterial activity of raw donkey milk toward the clinical isolate of Klebsiella pneumoniae at 9, 15 and 38 °C as well as to clarify the role of lysozyme, lactoferrin and calcium content in the antibacterial activity of donkey’s milk. The effects of contamination level and incubation period on this antibacterial activity were also examined. Antibacterial assays were performed by determing the bacterial count in intentionally contaminated (102, 103, 104 cfu/mL) donkey milk samples during 8 and 96 hours. Lab-on-a-chip electrophoresis and atomic absorption spectrometry were used for the determination of lysozyme, lactoferrin and calcium contents in donkey milk, respectively. The donkey milk samples showed varying degrees of antibacterial activity against the tested strain K. pneumoniae. The antibacterial synergism of lysozyme and lactoferrin was proven for this clinical strain as the samples with higher lactoferrin amount showed a stronger antibacterial activity. The correlation between calcium content and antibacterial activity of donkey milk samples was not established. Donkey milk showed stronger antibacterial potential at 15 °C compared to 9 °C, but this was limited by a higher growth rate of K. pneumoniae at 15 °C. The higher level of contamination resulted in a faster consumption of the antibacterial capacity of donkey milk. The artificial neural network model for prediction of K. pneumoniae count gave acurate fit to experimental data, showing a reasonably good (overall r2 for donkey milk was 0.986, with training error 4.67∙10-4, while r2 for nutrient broth was 0.982 and training error was 0.002). [ABSTRACT FROM AUTHOR]

The quality of protein is based on their amino acid composition, especially on the content and availability of essential amino acids. Cereals are important sources of protein for human nutrition, but are limited in the amounts of essential amino acids, notably lysine. The aim of this study was to analyze the chemical composition and amino acid profiles of different cereals that are important for nutritional purposes in human diet. The content of protein, moisture and crude fat in cereals varied significantly from 7.83 to 13.22%, 11.45 to 13.80%, and from 1.67 to 6.35%, respectively. The obtained results showed that oat had the highest contents of crude protein (13.22%), crude fat (6.35%) and crude cellulose (9.42%) compared to other cereals. Significant (p < 0.05) variation existed in the content of essential and nonessential amino acids among samples with the highest level in oat and wheat. Essential amino acids accounted for one-third of the total amino acids in the tested cereals. Glutamic acid was found to be the most abundant amino acid. It could be concluded that the amino acid composition of oat is the most favorable among cereals due to its high protein content and the content of lysine which can be found in limited amounts in most of the cereals. [ABSTRACT FROM AUTHOR]

Fresh fruit and vegetables contribute to almost 50% of food wasted by households in the EU. To understand the main reasons for wasteful behaviour among the consumers in Vojvodina (Serbia) cross-sectional data were collected through a self-administrated online survey of 711 correspondents. The questions presented in the study focused on the method of preserving the fruits and vegetables in domestic conditions before their consumption. Self-estimation of wasteful behaviour in the household and socio-demographic factors, including the respondents' selfestimated health status, were analysed using the correspondence analysis. The results showed that the first two dimensions explained for 95.06% of the total per cent of inertia (statistically significant at p<0.001 level) indicating the strong relationship among variables. According to the survey, the fruit and vegetables are most likely used fresh (54.43% and 48.95%, respectively), whereas the use of fruits and vegetables after storage in a refrigerator (17.16% and 29.96%, respectively) or in a deep freezer (2.81% or 5.20%, respectively) is significantly lower. Most of the survey correspondents claimed that they regularly discard the fruits and vegetables to waste (53.02% and 39.66%, respectively). The results pointed out that the storage and the amounts of discarded waste are affected by gender, income, and health conditions of the respondents'. Also, the study emphasized the growing need for educating and better planning of fresh fruit and vegetable management to reduce waste production. [ABSTRACT FROM AUTHOR]

This article aimed to identify sources and transport pathways of macro- and microelements in large urbanized rivers under significant anthropogenic pressure. To achieve the objective of this study, the geochemical characteristics of the sediments of the Sava and Danube Rivers and the soils flooded by the Kolubara River were determined. The high content of Mg indicates the dominant origin of the material from loess deposits. The significantly higher contents of Cr and Ni in sediment and soil samples from the Obrenovački Zabran (OZ) than in the samples from the Veliko Ratno Ostrvo (VRO) are the result of increased anthropogenic pressure. Sediments from the VRO show increased Sn and Sb contents. The increase in the content of chalcophile elements is the highest in the sediments from the OZ and ranges from 1.8 to 2.7 times, compared to their concentrations in the Earth's crust. The soils from OZ and the sediments from the VRO show a decrease in the content of chalcophile elements compared to the sediments from the OZ so the increase is 1.2–1.5, and 0.9–2.3 times, respectively compared to the Earth's crust. The obtained results revealed that the yield of sedimentary material that has higher concentrations of microelements decreases already at a distance of several km. [ABSTRACT FROM AUTHOR]

Using a Taguchi experimental design, this research focuses on utilizing indigenous bacteria from the Danube River to enhance the self-healing capabilities and structural integrity of cementitious materials. Bacillus licheniformis and Bacillus muralis were used as individual bacterium or in co-culture, with a concentration of 8 logs CFU, while the humidity variation involved testing wet and wet–dry conditions. Additionally, artificial neural network (ANN) modeling of the compressive strength of cement samples results in improvements in compressive strength, particularly under wet–dry conditions. By inducing targeted bacterial activity, the formation of calcium carbonate precipitates was initiated, which effectively sealed formed cracks, thus restoring and even enhancing the material's strength. In addition to short-term improvements, this study also evaluates long-term improvements, with compressive strength measured over periods extending to 180 days. The results demonstrate sustained self-healing capabilities and strength improvements under varied environmental conditions, emphasizing the potential for long-term application in real-world infrastructure. This study also explores the role of environmental conditions, such as wet and wet–dry cycles, in optimizing the self-healing process, revealing that cyclic exposure conditions further improve the efficiency of strength recovery. The findings suggest that autochthonous bacterial co-cultures can be a viable solution for enhancing the durability and lifespan of concrete structures. This research provides a foundation for further exploration into bio-based self-healing mechanisms and their practical applications in the concrete industry. [ABSTRACT FROM AUTHOR]

Mechanically pressed oils and the remaining semi-defatted cake flours obtained from fruit, vegetables and some of less utilized oilseeds have increasing trend on global oil market due to their health benefits and cost-effectiveness. Cold-pressed oils obtained from various plant seeds were evaluated in terms of physico-chemical properties, fatty acid composition and functional quality properties. Low oil yield was obtained for berry, red grape and sour cherry oils while moderate or high yield was obtained for other plant materials. Oil samples contained high amounts of unsaturated fatty acids, with linoleic and oleic acids as the major acids. Among all oils, flaxseed, raspberry and blackberry seed oils contained significant amounts of α-linolenic acid. In terms of functional quality indices, berry seed oils exhibited the highest quality of all obtained cold-pressed oils. Residual semi-defatted cake flours were examined in terms of basic proximal analysis. Poppy, sesame, flaxseed and both pumpkin flours had moderate or high contents of crude proteins and low content of fibres. Sour cherry, blackberry, raspberry and red grape flours had high fibre contents and relatively low content of proteins. A comprehensive study of oils and their residual cakes obtained from by-products of fruit and vegetable processing industry supports the concept of zero waste technology. [ABSTRACT FROM AUTHOR]

As one of the main non-covalent relations in microbiological-based systems, cell surface hydrophobicity (CSH) can be observed as a relevant parameter for biodegradation capability and suggested bacterial behaviour and biofilm formation during a bioremediation process. On the other hand, the role of ureolytic bacteria in bioremediation has subsequently led to the examination of this bacterial type in different engineering fields. In order to optimize the operating parameters of microbial adhesion to hydrocarbons test (MATH) for ureolytic bacteria, Box-Behnken experimental design was conducted for five ureolytic bacteria isolated from soils, as well as for the reference strain Sporosarcina pasteurii DSM 33. The optimization was completed with and without the essential substrate for the targeted metabolic reaction, with the aim to compare differences in bacterial hydrophobicity. A vortex time of 2 min, a hydrocarbon volume of 0.5 mL, and a phase separation time of 15 min are recommended as MATH operating parameters for all tested ureolytic bacteria. Although all bacteria are hydrophobic, lower CSH values in the presence of urea were observed for the same bacterium, which could be explained by the interaction of urea with the organic phase of the separation system, as well as a rapid ureolysis process that also occurs during the application of ureolytic bacteria in biotechnology systems. [ABSTRACT FROM AUTHOR]

Osmotic drying of celery leaves was studied in two osmotic solutions (ternary aqueous solution and sugar beet molasses), at three temperatures (20, 35, and 50 °C), and diverse immersion periods (1, 3, and 5 hours). The aim was to examine the influence of the used hypertonic agent, temperature and immersion time on antioxidant capacity (AOC) and color characteristics of the samples. The AOC of celery leaves was assessed by the spectrophotometric assays (ABTS, FRAP, and DPPH), as well as two direct current polarographic assays, hydroxo perhydroxo mercury (II) complex assay based on the decrease of anodic current of hydroxo perhydroxo mercury (II) complex and mercury reduction antioxidant power assay based on the decrease of a cathodic current of Hg (II) reduction. Total phenolic content was determined by Folin-Ciocalteu assay. The relative antioxidant capacity index, calculated by assigning equal weight to all applied assays was used to achieve a more comprehensive comparison between analyzed samples, as well as applied assays. The obtained results indicated decreases in the AOC of celery leaves during the osmotic treatment in ternary solution, while the AOC was increased in sugar beet molasses solution. According to relative antioxidant capacity index evaluation the most convenient process parameters were temperature of 35 °C and immersion time of 5 hours. [ABSTRACT FROM AUTHOR]

The aim of this study was the prediction model of retention indices of compounds from the aboveground parts of Achillea clypeolata Sibth. & Sm. essential oil, obtained by hydrodistillation and analysed by GC-MS. The quantitative structure-retention relationship analysis was applied in order to anticipate the retention time of the obtained compounds. The selection of the seven molecular descriptors was done by a genetic algorithm. The chosen descriptors were uncorrelated and were used to construct an artificial neural network. A total of 40 experimentally obtained retention indices was used to build this prediction model. The coefficient of determination for the training, testing and validation cycles were: 0.950, 0.825 and 1.000, respectively, indicating that this model could be used for prediction of retention indices for A. clypeolata, essential oil compounds. [ABSTRACT FROM AUTHOR]

• Spreading chticsaracteris of the two fluid spray nozzle were investigated • The viscosities of the liquids were determined experimentally • Volume of Fluid computational model was used for computational analysis • Experiments for 5 liquids at 3 temperatures and 3 nozzle diameters were performed • The results of numerical simulation are compared with appropriate experimental data Spray nozzles are widely used in processing industry for spreading evenly large amount of fluids. The expansion of fluid depends on the nozzle type, the parameters of the nozzle and the characteristics of the working fluids. The experiments were performed for five fluid types used in food/pharma/animal feed application (glycerol, soybean oil, molasses, hydroxypropyl methylcellulose, tara gum), three diameters of the nozzle (1 mm, 3 mm, 5 mm) and three fluid temperatures (40 °C, 50 °C and 60 °C). An experimentally validated numerical model was developed, based on laminar two-phase flow, investigating different liquids, assuming the ideal gas flow, applying the finite volume method and volume of the fluid model with interface tracking. The effects of liquid parameters, nozzle diameter and liquid temperature on the characteristics of the jet were also analysed by artificial neural network model. The nozzle diameter strongly influenced the spreading characteristics of the jet, while the temperature affected the liquid viscosity. The increase of the temperature also led to the augment of the spreading angle of the fluid passing from the nozzle and also the enhancement of the spaying liquid expansion. [ABSTRACT FROM AUTHOR]

The mixing quality of the pigeon diet mix was studied after the mixing in the Ross-type static mixer (pre-mixer) and the vertical feed mixer (final mixer in the line). Three different experiments were performed: firstly, the possibilities of static mixer were investigated, secondly the blending performance of the vertical feed mixer, and the combination of these approaches in the third experiment. The Discrete Element Method was used in numerical modelling of granular flow in the pre-mixer and the vertical feed mixer, to predict the inter-particle mixing quality, and the results of the mixing. The results of the model were compared with appropriate experiments. The main goal was to foresee the behaviour of mixture in two type of mixers and to evaluate the combined mixing action of these two mixers, in order to explore the possibilities to increase the mixing quality, shorten the mixing time and to reduce the energy consumption. Unlabelled Image • Mixing of pigeon diet mix in Ross static mixer and rotating drum was investigated. • DEM was used for modelling of granular flow in these mixing devices. • The possibilities to shorten the mixing time in the main mixer were explored. • The results of numerical simulation are compared with appropriate experimental data. [ABSTRACT FROM AUTHOR]

A total of 26 experimentally obtained retention indices on a logarithmic scale (log RI) from Thymus serpyllum essential oil were used to build a robust predictive model. The selected descriptors were used as inputs of an artificial neural network model to build a predictive quantitative structure-retention time relationship model. The coefficient of determination for the training cycle was 0.977, indicating that this model could be used for the prediction of retention indices for T. serpyllum essential oil compounds. These 26 compounds comprise about 99.8 % of the total oil, but among them only 6 compounds had the average relative concentration over 1 percent: geraniol (63.4 %), nerol (or cis-geraniol) (18.9 %), geranyl acetate (4.7 %), trans- caryophyllene (4.6 %), β-bisabolene (2.0 %) and geranial (1.2 %). According to these results, it can be concluded that T. serpyllum from village Sesalac (Serbia) belongs to geraniol chemotype, in total 82.3 % in both, trans and cis forms (63.4 % and 18.9 %, respectively). [ABSTRACT FROM AUTHOR]

Concern over the harmful impacts of heavy metal pollution in soil has increased dramatically on a global scale. For the sake of environmental preservation, accurate estimates of the heavy metal concentrations in soil are essential. This study provides valuable data regarding heavy metal concentrations in soil collected from field crops production area in Central and Western Serbia. Five wider localities in the zones of Central and Western Serbia were selected for the collection of soil samples. Based on our research, focused on determining the total contents of heavy metals in the soil and the degree of pollution in the environment caused by their behavior, distribution, and origin, it can be concluded that there is pronounced variability in relation to localities. Heavy metal contents were mostly within the same ranges as those in similar soils from Europe and around the world. Any pollution control system must include heavy metal monitoring, including the methodical collection of data on the concentrations of heavy metals in a particular environment. Before environmental degradation occurs, it is crucial to set pollution limits and implement efficient monitoring procedures. [ABSTRACT FROM AUTHOR]

Antioxidants in fruit and vegetable juices have become increasingly popular because of their potential health benefits. Nowadays, juice mixes made from berries present frequent consumer choices, due to their nutritive value and high content of bioactive compounds. Commercial fruit and vegetable juices available in Serbian markets (n = 32) were analyzed for the physicochemical properties, chemical composition, and antioxidant activity. Relative antioxidant capacity index was used for the ranking of the juices according to antioxidant capacity, while antioxidant effectiveness of phenolic compounds contained in juice samples was investigated depending on phenolic antioxidant coefficients. Principal component analysis was applied to study the data structure. In addition, a multi-layer perceptron model was used for modeling an artificial neural network model (ANN) for prediction antioxidant activity (DPPH, reducing power, and ABTS) based on total phenolic, total pigments, and vitamin C content. The obtained ANN showed good prediction capabilities (the r2 values during training cycle for output variables were 0.942). Phenolic, pigments, and vitamin C contents showed a positive correlation with the investigated antioxidant activity. The consumption of commercial berry fruit juices available in Serbian markets may deliver great health benefits through the supply of natural antioxidants. [ABSTRACT FROM AUTHOR]

Summary: The European Union's environmental legislation related to environmental protection, already implemented in the national legislation of the Republic of Croatia, aims to introduce a system of integrated and sustainable waste management. Within such a system, it is of utmost importance to have a better estimate of the amount of municipal waste generated, which directly influences future planning in the waste management sector. The aim of this research was to develop and optimize models for the estimation of generated municipal waste by application of methodology using neural network models, and taking into account the socio‐economic impact as well as the inputs regarding the actual waste management trends. In this paper, an artificial neural network models were used to predict the municipal waste generation in Zagreb, Croatia. The standardized socio‐economic and waste management variables were chosen to encompass 2013 to 2016 period. Moreover, the test prediction of the observed data was performed for 2017. Developed models sufficiently predicted the quantities of different municipal waste fractions and in that sense can contribute to better planning of upcoming waste management systems that will be sustainable and in order to meet the European Union commitments. [ABSTRACT FROM AUTHOR]

Within this work, the content of polyphenolic compounds, vitamin C and minerals in white cabbage (Brassica oleracea) was studied, as affected by the variety of cabbage (var. Futoški, hybrid Bravo) and osmotic dehydration in different osmotic solutions. Three osmotic solutions were used: 1) aqueous solution of NaCl and sucrose (S1); 2) mixture of S1 and sugar beet molasses in proportion 1:1 (S2); and 3) 100% beet molasses (S3). The osmotic dehydration of cabbage in molasses (S3) resulted in a substantial increase in the content of iron and potassium in the dehydrated cabbage by 80–100%. On the other hand, a loss of minerals (Fe, Mg and Ca) at 30–60% after cabbage dehydration in sucrose solution is presented. Also polyphenols content was significantly increased during osmotic dehydration in molasses particularly for kaempherol (6.63 mg/kg dm), p-cumaric acid (1.01 mg/kg dm), caffeic acid (20.35 mg/kg dm), catechin (153.81 mg/kg dm) and gallic acid (17.54 mg/kg dm). Additionally, osmotic dehydration process demonstrated a good retention of l -ascorbic acid in cabbage. • The osmotic dehydration of cabbage in sugar beet molasses was investigated. • The osmotic dehydration resulted in an increase in iron and potassium content. • Kaempherol, catechin and p-cumaric, caffeic, and galic acid content increased during osmotic dehydration. [ABSTRACT FROM AUTHOR]

Osmotic treatment (OT) of nettle leaves was assessed in various osmotic solutions (sugar beet molasses - SBM and ternary aqueous solution - TAS), at temperatures of 20, 35 and 50°C, under the atmospheric pressure. The influence of the kind of utilized osmotic solution, process temperature and osmotic time on the antioxidant activity (AOC), expressed with the spectrophotometric assays (ABTS, FRAP and DPPH), as well as two direct current polarographic assays, Hydroxo Perhydroxo Mercury (II) complex assay, based on the decrease of anodic current and assay based on the decrease of a cathodic current of Hg (II) reduction. For determination of the total phenolic content Folin-Ciocalteu assay was used. The Relative Antioxidant Capacity Index (RACI), obtained by setting equal weight for every involved assay was applied in order to get an extensive comparison among analyzed samples and between the used assays. Based on these results, after the OT of nettle leaves in TAS, the AOC decreased, while the OT in SBM increased AOC values. The phenolic antioxidant coefficients (PAC), calculated as the ratio between particular AO capacity and TPC, were used to achieve a more comprehensive comparison between analysed samples, as well as applied assays. The results of RACI evaluation revealed that the most favorable osmotic treatment is the one performed for 5h at 35°C. [ABSTRACT FROM AUTHOR]

Instrumental analyses accompanied by analytical modeling tools were employed to assess physico-chemical changes induced by variations in chemical composition of cementitious composites, i.e. mortar binders. Coal combustion ash was utilized as pozzolanic mineral additive. The binders’ mix-design was supplemented with sorptive clays to prevent migration of toxic elements from fly ash. The experiment was established on the premise of clay’s ion-exchanging ability. Ten binders comprising cement CEM I 42.5 and fly ash, zeolite and/or bentonite additions were prepared in accordance with chemometric experimental design rules. Chemical composition was determined via XRF method. The changes in mineral phases and crystallinity were traced by X-ray diffraction. Hydration mechanisms and thermal behavior were investigated via DTA/TGA. The chemical bonds were identified by FTIR. Morphology of hardened samples was detected by SEM. Mathematical tools employed data sets of instrumental analyses to form a clear differentiation between binders and to assess changes caused by adoption of mineral additives in the mix designs. Sorptive clays showed pozzolanic behavior, thereby causing no incapacitation to the cement hydration mechanism, and classifying as a possible economical resources which can be used in production technology of construction materials to redeem environmental pollution issues of building industry. [ABSTRACT FROM AUTHOR]

In this paper, five types of horizontal single-pitch screw conveyors with modified geometry, with three different lengths (400, 600 and 800 mm) were investigated for transport and auxiliary mixing action of two materials: natural zeolite and quartz aggregate (sand) with particle sizes 3, 4 and 5 mm. The geometry of the screw transporter is changed by welding three additional helices oriented in the same or the opposite direction from screw cutting edges, enabling the premixing of materials, during the transport. The proper mixing of the observed materials provides an adequate disposition of zeolite particles within the composite and prevents agglomeration and interference with cement hydration. Zeolite application as a binder in a building material is a possible solution to environmental pollution problems caused by cement production. The influences of screw length, particle diameter, the studied geometry variations of screw design, on the mixing performances of the screw conveyor-mixer during material transport were explored. All investigations were performed experimentally and numerically, by using Discrete Element Method (DEM). The experimental results and the results of the DEM investigation were used for the development of mathematical models for the prediction of mixing quality, which are presented in the form of second order polynomial and artificial neural network model. [ABSTRACT FROM AUTHOR]

In this study, the mixing performance of coupled mixing action of the Komax static mixer (which is used as a pre-mixer) and rotating drum (applied as the final mixer) was explored in the maize meal mixing operation. The main objective of this paper was to predict the behaviour of the previously grinded maize particles, during the mixing process in static mixer and drum mixer, and to explore the possibilities to shorten the mixing time in the main mixer (in order to reduce the energy consumption). Three different experiments were performed: in the first experiment, possibilities of static mixer were explored, second experiment showed the mixing performance of rotating drum, and the combination of these two mixing devices was investigated in the third experiment. Homogeneity of the obtained mixtures was determined experimentally, by the “Microtracers” method. The Discrete Element Method was used for modelling of granular flow in the pre-mixing and final mixing applications, and to predict the inter-particle mixing quality within a static mixer and the rotating drum mixer. The results of the numerical simulation are compared with appropriate experimental results. The possible industrial application of this model could be the optimization of parameters of mixing systems taking into account the quality and the duration of the mixing process. [ABSTRACT FROM AUTHOR]

For many years, linseed has been attracted a great attention in animal nutrition because of its exceptionally favourable fatty acid composition and high content of essential α-linolenic acid. However, the presence of antinutritive components, cyanogenic glycosides, limits its inclusion in the animal's diet. Several ways of linseed detoxification were observed in literature, emphasizing extrusion as one of the most effective processes. In the presented study, the application of Artificial Neural Network (ANN) has been observed, as a tool for prediction of process influence on the deterioration of cyanogenic glycosides during the extrusion process of linseed-sunflower meal co-extrudate. The content of hydrogen cyanide (HCN) was determined according to the AOAC method as an indicator of cyanogenic glycosides in the produced co-extrudate. Extrusion of the material was performed on a laboratory single screw extruder. The performance of ANN model was compared with experimental data in order to develop rapid and accurate method for prediction of HCN content in co-extrudate. According to the experimental results, the highest HCN content (126 mg/kg) was determined at the lowest moisture content (7%) and the lowest screw speed (240 rpm). With the increase of moisture content and temperature during extrusion, the content of HCN drastically decreased. The ANN model showed high prediction accuracy (r²> 0.999), which indicates that the model could be easily and reliably applied in practice. [ABSTRACT FROM AUTHOR]

Various cultivars of different cereal and pseudocereal species (9 wheat, 8 barley, 1 rye, 3 oat, 2 triticale, 3 spelt, 12 corn, 3 amaranth and 9 buckwheat cultivar samples) were milled into flour, extracted using n-hexane, derivatized with trimethylsulfonium hydroxide solution and subjected to GC- -MS analysis. Fatty acid methyl esters and non-saponifiable compounds (phytosterols, α-tocopherol and squalene) were identified by comparing mass spectra with the Wiley MS library. A binary system was applied in further data processing: the presence or the absence of a particular lipid component in each sample was coded with either (1) or (0). Major lipid components that were present in all analyzed flour samples were removed from further data analysis, leaving only those that represent a good pattern to differentiate the flour samples according to corresponding cereal/pseudocereal species. Pattern recognition tools (cluster analysis and principal component analysis) were applied to visualize groupings and separations among the samples. The presented approach enables the rapid differentiation of flour samples made from various cereal/pseudocereal species according to their botanical origin and gluten content, thereby, successfully avoiding exact quantitative determinations. [ABSTRACT FROM AUTHOR]

The main idea of this research was to evaluate rice and sunflower hulls, sawdust and their ashes, as additives in clay brick production using mathematical analysis. All available papers, containing the parameters of interest, were used in the study, which consisted of total 316 cases, obtained from the literature. The major oxide content of clays and mixtures, then weight percent addition and particle size ranges of secondary raw materials, and process parameters (firing temperature, soaking time and average heating rate) were selected as inputs to mathematical models. Shaping moist was the only parameter characterizing transition state of the products analyzed as an output parameter. The other parameters described the fired product quality: linear shrinkage, bulk density, water absorption, compressive and bending strength. The main goal was to find the relationships and the main influences between raw material properties, process parameters, and the quality of the obtained products and mixtures, by using mathematical tools. Statistical and mathematical analyses were applied for prediction of final product quality. Developed artificial neural network empirical models (ANNs) give a reasonable fit to experimental data and successfully predict the most of the observed output variables, showing the good prediction capabilities (coefficient of determination varied between 0.714 and 0.998). Sensitivity analysis showed that, among all the studied parameters concerning raw materials and process parameters, the dominant influence belonged to loss on ignition. [ABSTRACT FROM AUTHOR]

Various cultivars of different cereal and pseudocereal species (wheat – 9, barley– 8, rye– 1, oat– 3, triticale– 2, spelt– 3, corn – 12, amaranth– 3, buckwheat – 9cultivar samples) were milled into flour, extracted using nhexane, derivatized with trimethylsulfonium hydroxide solution, and subjected to GC-MS analysis. Fatty acid methyl esters and non-saponifiable compounds (phytosterols, alpha-tocopherol and squalene) were identifiedby comparing mass spectra with Wiley MS library. Binary system was applied in further data processing: the presence or the absence of a particular lipid component was coded with either (1) or (0), in each sample. Major lipid components, that were present in all analyzed flour samples, were removed from further data analysis, leaving only those that represent a good pattern to differentiate flour samples according to corresponding cereal/pseudocereal species. Pattern recognition tools (cluster analysis and principal component analysis) were applied to visualize groupings and separations among samples. The presented approach enables differentiation of flour samples made from various cereal/pseudocereal species according to their botanical origin and gluten content in a rapid way, successfully avoiding exact quantitative determinations. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the sensitivity of two analytical methods for the prediction of the shelf-life of unpacked and packed gluten-free rice–buckwheat cookies kept at ambient (23±1 °C) and elevated (40±1 °C) temperature during storage, namely the static headspace gas chromatographic method with flame ionisation detection (SHS-GC-FID) for volatile saturated aldehydes (propanal (C3), pentanal (C5), hexanal (C6), heptanal (C7) and octanal (C8)) and the HPLC method for malondialdehyde (MDA) determination. Both methods resulted in obtaining the same end-points of cookie shelf-life, i.e., 3 and 5 months for unpacked and packed cookies kept at elevated temperature, respectively, and 11 and 14 months for unpacked and packed cookies kept at ambient temperature, respectively. Two computational approaches, i.e., the second order polynomial (SOP) and artificial neural network (ANN) models, were used accordingly. The calculations of the contents of aldehydes and MDA could be predicted with an overall coefficient of determination of 0.722 using the ANN model compared to 0.312–0.773 for SOP models. According to sensitivity analysis, it might be suggested that the relevant parameter for the prediction of the end-point of cookie shelf-life is the MDA rather than the C3, C5, C6, C7 and C8 content. [ABSTRACT FROM AUTHOR]

Expression "Development conditions and perspectives" became important for numerous analyses in various industrial and social areas. Several strategic documents and studies in last two decades analysed projects with topics on perspectives of future development in Serbia. Various tools are used for development of such documents, based on recent scientific and numerical solutions, thus providing reliable assessment for strategic decision making. Almost all analyses tried to implement the theories and practical experiences through the prism of "sustainable development", which included establishment of most important sustainability parameters. Analysis and ranking presented in this paper considered the potential of the Stavalj coal deposit, near city of Sjenica in Serbia, and feasibility of construction project of new mine and thermal power plant. Basis for analysis was a hybrid assessment model which takes into account principles of sustainable development. The model incorporates quantified SWOT analysis, which applies to active underground mines in Serbia. Special attention was given to the parameters describing potential for development. [ABSTRACT FROM AUTHOR]

Osmotic treatment of celery root was studied in two osmotic solutions (sugar beet molasses and a ternary solution of water, sucrose and salt), at three temperatures (20, 35 and 50 °C), and three different immersion periods (1, 3 and 5 h), at atmospheric pressure. The aim was to examine the influence of the type of the used hypertonic agent, the temperature and the immersion time on the water loss, solid gain, water activity, dry matter content, antioxidant activity (expressed by DPPH) and colour attributes (described by CIELAB coordinates L*, a* and b*). During the experiments, the antioxidant activity of celery root was increased in sugar beet molasses, while the DPPH value tended to decrease in the ternary solution. The experimental data of osmotic dehydration was used for PCA modelling. The standard scores analysis revealed that the optimum process parameters were gained with an immersion time of 5 h and a temperature of 35 °C. [ABSTRACT FROM AUTHOR]

In this study, sunflower meal is ground by a hammer mill after which air zigzag gravitational air classifier is used for separating sunflower hulls from the kernels in order to obtain protein rich fractions. Three hammer mill sieves with sieve openings diameter of 3, 2 and 1 mm were used, while three air flows (5, 8.7 and 12.5 m 3 /h) and three feed rates (30%, 60% an 90% of bowl feeder oscillation maximum rate) were varied during air classification process. For describing the effects of the test variables on the observed responses Principal Component Analysis, Standard Score analysis and Response Surface Methodology were used. Beside experimental investigations, CFD model was used for numerical optimization of sunflower meal air classification process. Air classification of hammer milled sunflower meal resulted in coarse fractions enriched in protein content. The decrease in sieve openings diameter of the hammer mill sieve increased protein content in coarse fractions of sunflower meal obtained at same air flow, and at the same time decreased matching fraction yield. Increase in air flow lead to the increase in protein content along the same hammer mill sieve. Standard score analysis showed that optimum values for protein content and ratio of coarse and fine fractions have been obtained by using a sieve with 1 mm opening diameter, air flow of 12.5 m 3 /h and 60% of the maximum feeder rate. Fraction ratio and protein content were mostly affected by the linear term of air flow and the sieve openings diameter of the hammer mill sieve in the Second Order Polynomial model. The main focus of CFD analysis was on the particle simulation and the evaluation of the separation efficiency of the zigzag classifier. [ABSTRACT FROM AUTHOR]

Osmotic treatment of celery root was studied in two osmotic solutions (sugar beet molasses and a ternary solution of water, sucrose and salt), at three temperatures (20, 35 and 50 °C), and three different immersion periods (1, 3 and 5 h), at atmospheric pressure. The aim was to examine the influence of the type of the used hypertonic agent, the temperature and the immersion time on the water loss, solid gain, water activity, dry matter content, antioxidant activity (expressed by DPPH) and colour attributes (described by CIELAB coordinates L*, a* and b*). During the experiments, the antioxidant activity of celery root was increased in sugar beet molasses, while the DPPH value tended to decrease in the ternary solution. The experimental data of osmotic dehydration was used for PCA modelling. The standard scores analysis revealed that the optimum process parameters were gained with an immersion time of 5 h and a temperature of 35 °C. [ABSTRACT FROM AUTHOR]

The properties of seven montmorillonite-rich bentonites of different geological origin were investigated prior and subsequent to mechano-chemical processing in an ultra-centrifugal mill. The objective of the experiment was altering the bentonite types and activation parameters in order to determine the optimal milling conditions that produce material which is physico-mechanically and microstructurally applicable as a binder replacement and sorbent in the construction composites. The efficiency of bentonite activation was assessed by chemometrics and Artificial neural networks mathematical modeling. Principal component analysis and analysis of variance were used in the observation of the influence of input variables (bentonite chemical composition) and process parameters (milling duration, rotor velocity) on the product characteristics: density, specific surface area, grain size and distribution, cation exchange capacity, melting point, compressive strength, shrinkage and porosity. When the ANN models for the observed responses, related to predicted bentonite characteristics and quality, were compared to experimental results, they correctly predicted the responses. The processed data also adequately fitted to the regression second order polynomial models. The SOP models, which showed r 2 values from 0.357 to 0.948, and were able to predict the observed responses in a wide range of processing parameters, while ANN models performed high prediction accuracy (0.776–0.901) and can be considered as precise for response variables prediction. The combination of the conducted mathematical analyses showed that that increase/decrease in output values was stabilized after 30 min of activation. Mathematically attained interpretations were correlated with the results of the instrumental analyses (XRD, DTA/TG, SEM) to confirm the adoption of B6 bentonite as a preferable type and 30 min as an optimal milling time for acquiring quality of clay powder that will be used in structural and thermal applications. [ABSTRACT FROM AUTHOR]

Natural zeolites of clinoptilolite type from seven deposits were submitted to mechano-chemical activation in a Retsch ultra-centrifugal mill. The zeolite types and activation parameters were altered during the experiment with an aim to determine the optimal combination that would produce powder with adequate physico-chemical and microstructural properties for application as a binder replacement and an ion-exchanger in the construction composites. The effects of input variables (chemical composition of the samples) and process parameters (the rotor velocity and the activation period) on the efficiency of zeolite activation were investigated in terms of dependent parameters such as: specific surface area, grain size distribution, cation exchange capacity, melting point, compression strength, shrinking, water absorption and apparent porosity. Cluster analysis, Principal component analysis and Standard score analysis were applied in the assessment of the acquired product quality. Artificial neural networks (ANN) were developed in mathematical modeling of observed responses. Subsequently the ANN was compared to experimental results and the developed second order polynomial models. Developed models showed r 2 values in the 0.822–0.998 range, meaning that they were able to predict the observed responses in a wide range of processing parameters. ANN models performed high prediction accuracy (0.975–0.993) and can be considered as precise and very useful for response variables prediction. The combination of the conducted mathematical analyses isolated Z5 zeolite as a preferable type, and 20000 rpm and 30 min as an optimal activation set of parameters. Mathematically derived conclusions were confirmed by results of instrumental analyses (XRD, DTA/TG, SEM). [ABSTRACT FROM AUTHOR]

In this study adsorption characteristics of lead, copper, cadmium, nickel and zinc ions onto the compost of Myriophyllum spicatum were examined. The effects of sorbent dose, duration of sorption and solution concentration on the sorption of heavy metals have been investigated. Scanning electron microscope (SEM) and thermogravimetric and differential thermal analysis (TG-DTA) were used for the characterization of this biosorbent. Low coefficients of variation have been obtained for each applied assay, which confirmed the high accuracy of measurements. Principal component analysis (PCA) was applied for differentiation of samples. Mathematical models (form of second order polynomials) were developed for prediction of adsorption. Score analysis is being useful for accessing the effect of process parameters and the tool for determination of sorption quality. On the basic of experimental results and model parameters, it can be concluded that compost has a high biosorption capacity can be utilized for the removal of selected metals from wastewater. [ABSTRACT FROM AUTHOR]

Two mechanical extraction techniques were used for the extraction of environmentaly interesting components of coal fly ash: shaking, during which the extraction process lasted from 6 up to 24 h, and sonication that lasted from 15 up to 60 min, using water as extractant. The concentration of anions in fly ash extracts was determined by ion chromatography, while atomic absorption spectrometry was used for determination of: As, Pb, Cd, Ni, Cr, Zn, Cu, Fe, Mn and Al. The ultrasonication yielded slightly higher amounts of extracted anions as well as Pb, Al, Mn and Fe cations, while shaking-assisted extraction was more efficient for the Cr, As, Zn and Ni ions. The changes in pH value, particle size distribution within colloid solution, zeta potential and conductivity during ultrasound-assisted extraction were measured in order to explain changes that occur on the surface of fly ash particles contacting water and different processes (adsorption, ion exchange and flocculation) that develop under natural conditions. Principal Component Analysis was used for assessing the effect of observed process parameters. It is essential to evaluate quantity of these elements leachable from coal fly ash into the surface waters in natural conditions in order to prevent contamination of the environment. [ABSTRACT FROM AUTHOR]

DEM/CFD (Discrete Element Method/Computational Fluid Dynamic) approach was used to develop a three dimensional model of fluid flow and mixing process of solid particles within the revolving static mixer, type Komax. Static mixers are widely used in process industry, food industry, pharmaceutical and chemical industry. Mixing process adds significant value to the final product and it can be regarded as a key process. The quality and the price of the product often depend on mixing efficiency. Both design and operation of the mixing unit itself have a strong influence on the quality produced. In this paper, DEM is used for modeling granular flow of zeolite spheres in the revolving Komax static mixer and CFD method was used for modeling fluid flow through the Eulerian multiphase model. Coupling of these two methods provides reliable analysis of particle motion and flow pattern of the working fluid. The objective of this paper is to predict the behavior of particles after several rotations of the revolving static mixer. This type of device 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. The results of the numerical simulation are compared with appropriate experimental results. The special design revolving static mixer, made of transparent Plexiglas was used for this experiment. Mixing quality was examined by RSD (relative standard deviation) criterion. Application of this model provides the optimization of the number of revolutions and geometrical parameters of mixing process taking into account the quality of the mixing process. [ABSTRACT FROM AUTHOR]

This paper promotes the biometric classification system of plant cultivars, unique characteristics, in terms of the uranium (U) uptake, primarily in the function of the application for phytoremediation. It is known that the degree of adoption of U depends on the plant species and its morphological and physiological properties, but it is less known what impact have plants cultivars, sorts, and hybrids. Therefore, we investigated the U adoption in four cultivars of three plant species (corn, sunflower and soy bean). “Vegetation experiments were carried out in a plastic-house filled with soil (0.66 mgU) and with tailing (15.3 mgU kg−1) from closed uranium mine Gabrovnica-Kalna southeast of Serbia”. Principal Component Analysis (PCA), Cluster Analysis (CA) and analysis of variance (ANOVA) were used for assessing the effect of different substrates cultivars, plant species and plant organs (root or shoot) on U uptake. Obtained results showed that a difference in U uptake by three investigated plant species depends not only of the type of substrate types and plant organs but also of their cultivars. Biometrics techniques provide a good opportunity for a better understanding the behavior of plants and obtaining much more useful information from the original data. [ABSTRACT FROM PUBLISHER]

The objective of this study was to investigate the clay drying as a unit operation in the refractory materials processing. Two clays that varied in chemical and mineralogical compositions were experimentally tested in a laboratory dryer. The results obtained on the green samples prior to drying indicated that clays have adequate plasticity and refractoriness for application in shaped refractories. The operating parameters of the dryer were regulated: temperature ranged from 40 to 60 °C, humidity increased in the interval 30–70%; and the airstream rate was 1.3 m/s. The correlation analysis between operating parameters and calculated and/or measured drying outputs was conducted for better comprehension of the clay׳s role as a refractory binder. Subsequently, a mathematical optimization of the drying regime was conveyed. The effect of the variables (operation parameters) on the drying parameters (critical moisture, equilibrium moisture, dryness degree, etc.) was compared and evaluated. The response surface method, standard score analysis, cluster method, and principal component analysis were used as a means of the drying regime optimization. Assessment of the drying regime impact on the dried samples quality highlighted optimal result for both clay types: SS=0.95, temperature 50 °C, and humidity 40%. Multiple comparison analyses pointed out that optimized combination of the drying operation parameters decreases the quantity of conducted tests. Furthermore, optimal combination of drying parameters reduces negative effects of clay binder inherent properties on the resulting product which in return improves energetic and economic sustainability of refractories production. [ABSTRACT FROM AUTHOR]

The impact of sunflower meal quality (i.e. crude protein content and crude fibre content) on the technical parameters of the pelleting process and the physical properties of produced pellets was investigated. Five model mixtures were prepared for pelleting, with different ratios of corn, sunflower meal (SFM) and soybean meal (SBM). Three grades of sunflower meal were used in the experiments with crude protein contents of approximately 37%, 40%, and 43%. Within each of the mixtures, granulation of material and retention time in the steam conditioner were varied. In order to describe the effects of the test variables on the observed responses response surface methodology, standard score analysis and principal component analysis (PCA) were used. The increase in the protein content and the decrease in the crude fibre content of sunflower meals caused an increase in the pelleting temperature, specific energy consumption, pellet hardness, and pellet durability. In addition, an increase in retention time increased specific energy consumption of pellet press, and produced fines. In terms of pellet durability values, a longer retention time was more beneficial for SFM mixtures than for SBM mixture. The type of mixture was found to be the most influential variable for second order polynomial model calculation. Standard score analysis showed that the optimum values for energy consumption, quantity of the fines, and pellet durability indices were obtained for the mixture with 40% protein SFM, with no retention after conditioning and with the finest granulation of the components (0.933). PCA showed that the first two principal components (91.10% of the total variability) enabled a neat separation of the five mixtures. [ABSTRACT FROM AUTHOR]

Rapeseed losses during storage can lead to undesirable difficulties in oil and biodiesel production. In this paper, three artificial neural networks were created to anticipate the main quality parameters of thirteen rapeseed varieties - cultivars and hybrids (Brassica napus L.) - during drying and storage. The varieties, drying temperature, air velocity and drying time were used as inputs to the artificial neural network model to predict the changes in seed weight and moisture during the drying process. The moisture diffusivity and activation energy of the investigated rapeseed varieties were determined under convective drying. For the experiment, an on-site drying system was used at 40, 60 and 80 °C drying air temperature. The effective diffusivity ranged from: 7.947.10−10 to 1.459.10−8 m2/s (first drying period) and 4.716.10−10 to 8.611.10−9 m2/s (second drying period). The predicted Arrhenius constant and activation energy ranged from 17.169 to 42.546 kJ/mol (first drying period) and from 31.261 to 50.474 kJ/mol (second drying period). Seed oil content, free fatty acids and thousand seed weight were determined after drying at different temperatures and after 12 months of storage under the three different storage conditions. To predict these parameters after storage time, a multilayer perceptron model with three layers (input, hidden and output) for three artificial neural networks (ANNs) was used for modelling using the implemented drying parameters (such as: variety, drying temperature, air velocity and drying time, along with initial oil and free fatty acid content and storage type) were used. The prediction of the developed model was accurate enough for the prediction of the output parameters. The coefficients of determination ranged from 0.965 to 0.998 when predicting the weight and moisture of the rapeseed during the drying process and the oil and free fatty acid content and thousand grain weights after the 12 months storage period. • 13 rapeseed hybrids quality parameters were anticipated during drying and storage. • prediction of drying and storage parameters was performed using neural network model. • rapeseed varieties, drying temperature, air velocity and drying time were ANN inputs. • optimization of drying parameters was achieved through multi-object optimization. [ABSTRACT FROM AUTHOR]

The impact of the mechanical processing parameters on the alumina grain-size distribution affiliated characteristics and on the γ to α phase transformation rate was investigated. The moderation in the alumina samples behavior has been correlated to the granulometric and mineralogical changes induced by activation via an ultra-centrifugal mill. The assessment of the activation process variables influence on the final quality of the product parameters was conveyed in order to optimize the mechanical treatment of the alumina, which otherwise could be regarded as either energetically or economically unsustainable procedure. The Response Surface Method, Standard Score Analysis and Principal Component Analysis were applied as means of the mechanical activation optimization. The r 2 values obtained by developed models were in range from 0.816 to 0.988. The established mathematical models were able to precisely predict the quality parameters in a broad range of processing parameters. The Standard Score Analysis emphasized that the optimal output sample was obtained using a sieve mesh of 120 μm set of processing parameters (SS=0.96). Diverse comparison analyses disclosed that the optimal set of activation process parameters could reduce the negative effect of γ-alumina samples immanent properties on the final score, and furthermore to enhance the rate of γ to α transition which would improve energetic and economic sustainability of the alumina phase transformation procedure. [ABSTRACT FROM AUTHOR]

Chemical composition and XRD qualitative analysis were used to calculate mineral contents of 139 brick clay raw materials using LPNORM. The second order polynomial models (SOP) for all the samples, which express the relation between mineral contents and the characteristics of fired laboratory products, did not fit to experimental data satisfactorily, due to low coefficients of determination (r 2 ). In order to improve the models, the samples are divided into four groups in factor space (four quadrants), according to their mineral content similarity, using principal component analysis (PCA). Predictive models of compressive strength ( CS ), water absorption ( WA ), firing shrinkage ( FS ), weight loss during firing ( WLF ) and volume mass of cubes ( VMC ) are obtained for each of the groups. Second order polynomial (SOP) models are developed, and the influence of certain minerals to brick clay bricks quality within the groups is discussed. Developed models were able to predict the final quality of products in a wide range of mineral content and temperature treatment data, showing coefficient of determination ( r 2 ) in range between 0.704–0.995. In order to estimate the adequacy of these models, the results were applied to the experimental data and compared according to additional statistical tests, so the next values are determined: coefficients of determination, reduced chi-square (χ 2 ), mean bias error ( MBE ), mean percent error ( MPE ) and root mean square error ( RMSE ). [ABSTRACT FROM AUTHOR]

Talc is broadly used in the ceramic materials industry, either as a basic raw material, or as filler, due to its chemical inertia, fragmentation proneness, thermal stability, and refractoriness. The mechanical activation is frequently employed in the direct enhancement of talc properties, and thereby in the design of talc based composites with advanced performances. The differences in the set of the process parameters measured before and after talc activation via ultra-centrifugal mill Retsch ZM-1, and their influence on the grain-size distribution related characteristics have been investigated. The mechanical treatments are energetically unsustainable procedures, therefore the talc activation was optimized on basis of assessment of the process variables (number of rotor revolutions, current intensity, activation period, circumferential rotor speed and mill capacity) effect on the final quality of product parameters (mesh sizes, cumulative oversizes, average grain size, level of micronization kinetics, mesh size appropriate to 95% micronized product cumulative undersize and specific surface area). The activated product parameters in all experimental sequences were obtained by the analytical procedure based on Rosin-Rammler-Sperling equation. Response surface method, standard score analysis and principal component analysis were used as a means of the optimization. Developed models showed r 2 values in the range of 0.714–0.908 and they were able to accurately predict quality parameters in a wide range of process parameters. Standard score analysis highlighted that the optimal sample was obtained using sieve mesh of 120 μm set of processing parameters (SS = 1.0). Multiple comparison tests revealed that the optimal variation in the processing parameters could reduce the negative effect of talc samples inherent properties on the final score and improve the activation procedure energetic and economic sustainability. [ABSTRACT FROM AUTHOR]

Screw conveyors are widely used in the food industry, building construction and mining companies, chemical, agricultural and processing industries, mostly for elevating and/or transporting bulk materials over short to medium distances. Despite their apparent simplicity, the improvement of the transport parameters is a very demanding task and designers usually have to rely on data obtained from empirical investigations. In this paper, fifteen horizontal single-pitch screw conveyors with modified geometry and the different lengths were investigated for premixing action, during the transport of materials. All investigations were performed experimentally and numerically, by using Discrete Element Method (DEM). The influences of screw length, observed geometry variations and different types of screw design, on the performances of the screw conveyor-mixer during material transport were explored. The auxiliary mixing action (used to improve the mixing process) was achieved during the transport of the material. The geometry of the screw conveyor is changed by adding three complementary helices oriented in the same or the opposite direction from screw blades. The particles of the material being transported tumble down from the top of the helix to the next free surface, and that segment of helix was used for additional mixing action. According to experiments and DEM analysis, the particle path length is increased, with the observed modification of screw conveyor, and the improved geometry could be determined for increasing the quality of mixing. [ABSTRACT FROM AUTHOR]