Predrag Kojić

The Discrete Element Method (DEM) has become a cornerstone for analysing granular flow and mixing phenomena in static mixers. This review provides a comprehensive synthesis that distinguishes it from previous studies by: (i) covering a broad range of static mixer geometries, including Kenics, SMX, and Sulzer designs; (ii) integrating experimental validation methods, such as particle tracking, high-speed imaging, Particle Image Velocimetry (PIV), and X-ray tomography, to assess DEM predictions; and (iii) systematically analyzing computational strategies, including advanced contact models, hybrid DEM-CFD/FEM frameworks, machine learning surrogates, and GPU-accelerated simulations. Recent advances in contact mechanics—such as improved cohesion, rolling resistance, and nonspherical particle modelling—have enhanced simulation realism, while adaptive time-stepping and coarse-graining improve computational efficiency. DEM studies have revealed several non-obvious relationships between mixer geometry and particle dynamics. Variations in blade pitch, helix angle, and element arrangement significantly affect local velocity fields, mixing uniformity, and energy dissipation. Alternating left–right element orientations promote cross-sectional particle exchange and reduce stagnant regions, whereas higher pitch angles enhance axial transport but can weaken radial mixing. Particle–wall friction and surface roughness strongly govern shear layer formation and segregation intensity, demonstrating the need for geometry-specific optimization. Comparative analyses elucidate how particle–wall interactions and channel structure influence segregation, residence time, and energy dissipation. The review also identifies current limitations, highlights validation and scale-up challenges, and outlines key directions for developing faster, more physically grounded DEM models, providing practical guidance for industrial mixer design and optimization.

Rapeseed proteins, due to their quality and wide availability, have great potential for application in human nutrition. However, their high content of antinutritional compounds poses significant economic and environmental challenges for food industry applications. To overcome these obstacles, various extraction and modification techniques, including enzymatic and ultrasound-assisted methods, were used to enhance protein functionality and improve both nutritional and sensory properties. In this study, the effects of dephenolization on the structural, physicochemical, and functional properties of rapeseed protein isolate obtained from defatted rapeseed cake were investigated through four different protocols. All obtained protein isolates (PIs) exhibited high protein purity (above 65%), with a notable difference in extraction yield. Furthermore, the extraction process was optimized using an artificial neural network (ANN) model, which demonstrated high predictive performance. The optimal extraction conditions for the dephenolization of rapeseed oil cake were 84% ethanol concentration, a solid-to-liquid ratio of 1/60 w/v, and 15 min of ultrasound treatment, resulting in an impressive protein purity of 90.68% with a yield of 29.17%. The obtained proteins were further characterized and compared in terms of protein profile (FTIR and SDS-PAGE), amino acid composition, solubility, and digestibility. The protein isolate (PI) obtained under optimized conditions displayed superior functional properties, underscoring the relevance and necessity of a data-driven, mathematical approach for scale-up and industrial implementation.

Entrained flow gasification is a well-established technology, however, the main obstacle in process design is the complex gasification mechanism, since numerous phenomena at extreme process conditions take place simultaneously. This study is focused on integrated thermodynamic and artificial neural network approach (ANN) for entrained flow gasification kinetics investigation. Data on 102 feedstock materials composition was used in the AspenPlus gasification simulation, where sensitivity analysis was performed for different equivalence ratios (0.1?0.7) and gasification temperature (1200?1500?C) values. For analyzed materials, an optimal equivalence ratio range exists (usually 0.3?0.4), maximizing gasification efficiency. The obtained results were used in ANN development for each output variable (syngas composition, efficiency, heating value, and carbon conversion). Matlab algorithm was used for the determination of the optimal number of neurons (1?20 range) in each ANN. High R2 values (>0.99) for all models suggested good agreement between simulated and predicted values. Genetic algorithm-based optimization studies for maximization of hydrogen content and cold gas efficiency result in mean ER values of 0.35 and 0.41, respectively, at a temperature of 1200 ?C. Yoon interpretation method was used for quantifying the relative impacts of each input variable on syngas content and gasification efficiency. The proposed approach represents a powerful tool that can facilitate the investigation of the entrained flow gasification and process design.

Globally, the carbon footprint (CF) is constantly increasing, contrasting with the decreasing trend observed for decades in the European Union (EU) countries, where EU guidelines are responsibly followed and outlined in its strategic documents. Information and communication technology (ICT) carbon emissions have historically increased in parallel with global emissions, contributing to continuous increases in ICT's CF over time, even when excluding full life cycle emissions. This study examines the impact of ICT on household electricity consumption, aiming to quantify the potential reduction of greenhouse gas (GHG) emissions through improved household energy efficiency. The methodology includes the data collection on ICT device usage in households within the city of Novi Sad (Republic of Serbia), employing the survey method that queries respondents on device quantities and their usage patterns. This study provides results for decision-makers to recognize concrete benefits from the transition to a circular economy (CE) and low-carbon emissions, which are reflected as benefits for the local community and socio-economic environment.

In this comparative study, the bacterial inactivation efficiency of plasma-activated water (PAW) generated by two distinct plasma reactors, one utilizing a nitrogen plasma jet electrode and the other a hybrid argon plasma reactor, was explored. The present study involved the assessment of antimicrobial activity against suspensions of three Gram-positive and three Gram-negative bacterial strains in their planktonic cell state. Bacterial suspensions were introduced into PAW five days after generation. Subsequently, the viability of the bacteria was assessed at various time intervals, specifically at 0.5, 1, 3, 5, 10, and 24 h, in order to evaluate the effect of inactivation. Structural changes in bacteria after PAW treatment were assessed using a scanning electron microscope (SEM). The physicochemical properties of PAW, including pH, conductivity, and concentrations of H₂O₂, NO₂⁻, and NO₃⁻ during aging were measured. The present study demonstrated the effective inactivation of the tested bacterial strains by PAW. Gram-positive bacteria displayed greater resistance compared to Gram-negative species, with the lowest reductions in bacterial counts observed for B. cereus, and the highest for Escherichia coli O157:H7. Morphological damage was evident across all bacterial species examined. Physicochemical measurements showed slow decay of the reactive species in the aging process. This study illustrated the potential utility of PAW as an alternative disinfectant.

The purpose of the study was to identify and predict the optimized parameters for phosphoric acid production. This involved modeling the crystal reactor, UCEGO filter (as a detailed model of the filter is not available in Aspen Plus or other simulation software), and acid separator using Sci-Lab to develop Cape-Open models. The simulation was conducted using Aspen Plus and involved analyzing 10 different phosphates with varying qualities and fractions of P₂O₅ and other minerals. After a successful simulation, a sensitivity analysis was conducted by varying parameters such as capacity, filter speed, vacuum, particle size, water temperature for washing the filtration cake, flow of recycled acid and strong acid from the separator below the filter, flow of slurry to reactor 1, temperature in reactors, and flow of H₂SO₄, resulting in nearly one million combinations. To create an algorithm for predicting process parameters and the maximal extent of recovering H₃PO₄ from slurry, ANN models were developed with a determination coefficient of 99%. Multi-objective optimization was then performed using a genetic algorithm to find the most suitable parameters that would lead to a higher reaction degree (96–97%) and quantity of separated H₃PO₄ and lower losses of gypsum. The results indicated that it is possible to predict the influence of process parameters on the quality of produced acid and minimize losses during production. The developed model was confirmed to be viable when compared to results found in the literature.

Natural rubber composites were reinforced by the co-fillers ‘hydrochar’ (HC), obtained by hydrothermal carbonization of hardwood sawdust and commercial carbon black (CB). The content of the combined fillers was kept constant while their ratio was varied. The aim was to test the suitability of HC as a partial filler in natural rubber. Due to its larger particle size and hence smaller specific surface area, large amounts of HC reduced the crosslinking density in the composites. On the other hand, due to its unsaturated organic character, HC was found to display interesting chemical effects: if it was used as the exclusive filler component, it displayed a very strong anti-oxidizing effect, which greatly stabilized the rubber composite against oxidative crosslinking (and hence embrittlement). HC also affected the vulcanization kinetics in different ways, depending on the HC/CB ratio. Composites with HC/CB ratios 20/30 and 10/40 displayed interesting chemical stabilization in combination with fairly good mechanical properties. The performed analyses included vulcanization kinetics, tensile properties, determination of density of permanent and reversible crosslinking in dry and swollen states, chemical stability tests including TGA, thermo-oxidative aging tests in air at 180 °C, simulated weathering in real use conditions (‘Florida test’), and thermo-mechanical analyses of degraded samples. Generally, the results indicate that HC could be a promising filler material due to its specific reactivity.

The kinetic model, encompassing the curing and reversion phenomena of the NR/SBR rubber vulcanization process, was developed by means of the finite element method simultaneously with heat transfer equations, including heat generation due to curing reactions. The vulcanization simulation was conducted for three spheres of different diameters (1, 5 and 10 cm) and two rubber wheels, one of which was a commercial product of the rubber industry. The proposed advanced simulation model, based on products’ two-dimensional axisymmetry, includes cooling after vulcanization, during which the crosslinking reactions continue to take place as a result of the products’ heated interiors. As a criterion for removing the product from the mold, an average vulcanization degree of 0.9 was set, whereby, during cooling, the vulcanization degree increases, due to crosslinking reactions. Based on the minimal difference between the maximal and minimal vulcanization degrees, which did not exceed a value of 0.0142, the optimal process parameters for each product were determined, achieving homogeneity and obtaining high-quality rubber products, while simultaneously ensuring a more efficient vulcanization process and enhanced cost effectiveness for the rubber industry.

Chamomile is one of the most consumed medicinal plants worldwide. Various chamomile preparations are widely used in various branches of both traditional and modern pharmacy. However, in order to obtain an extract with a high content of the desired components, it is necessary to optimize key extraction parameters. In the present study, optimization of process parameters was performed using the artificial neural networks (ANN) model using a solid-to-solvent ratio, microwave power and time as inputs, while the outputs were the yield of the total phenolic compounds (TPC). Optimized extraction conditions were as follows: a solid-to-solvent ratio of 1:80, microwave power of 400 W, extraction time of 30 min. ANN predicted the content of the total phenolic compounds, which was later experimentally confirmed. The extract obtained under optimal conditions was characterized by rich composition and high biological activity. Additionally, chamomile extract showed promising properties as growth media for probiotics. The study could make a valuable scientific contribution to the application of modern statistical designs and modelling to improve extraction techniques.

A new generation biomass-based filler for natural rubber, ‘hydrochar’ (HC), was obtained by hydrothermal carbonization of hardwood waste (sawdust). It was intended as a potential partial replacement for the traditional carbon black (CB) filler. The HC particles were found (TEM) to be much larger (and less regular) than CB: 0.5–3 µm vs. 30–60 nm, but the specific surface areas were relatively close to each other (HC: 21.4 m²/g vs. CB: 77.8 m²/g), indicating a considerable porosity of HC. The carbon content of HC was 71%, up from 46% in sawdust feed. FTIR and ¹³C-NMR analyses indicated that HC preserved its organic character, but it strongly differs from both lignin and cellulose. Experimental rubber nanocomposites were prepared, in which the content of the combined fillers was set at 50 phr (31 wt.%), while the HC/CB ratios were varied between 40/10 and 0/50. Morphology investigations proved a fairly even distribution of HC and CB, as well as the disappearance of bubbles after vulcanization. Vulcanization rheology tests demonstrated that the HC filler does not hinder the process, but it significantly influences vulcanization chemistry, canceling scorch time on one hand and slowing down the reaction on the other. Generally, the results suggest that rubber composites in which 10–20 phr of CB are replaced by HC might be promising materials. The use of HC in the rubber industry would represent a high-tonnage application for hardwood waste.