Processes, Volume 10, Issue 12 (December 2022) – 282 articles

Convective cereal grain drying is an energy-intensive process. Mathematical models are applied to analyze and optimize grain drying processes in different types of dryers and in different stages of drying to improve final grain quality and reduce energy consumption. The aim of the present study was to develop a generalized mathematical model of the grain drying process that accounts for all drying stages, including loading and unloading of unprocessed grain, drying, and cooling of dry grain. The developed mathematical model is a system of algebraic equations, where the calculated coefficients are determined by the thermophysical and diffusive properties of dried grain. The model was validated for batch drying of wheat, canola, and corn grain, as well as continuous flow drying of wheat grain. The results were compared with published findings. The relationships between energy consumption during drying and drying time vs. air temperature at the dryer inlet and air stream volume were determined. Dryer capacity and drying conditions specified by the manufacturers, as well as loading and unloading capacity, were considered during batch drying. Continuous flow drying simulations were conducted in counter-flow, parallel-flow, and cross-flow mode. Simulation results indicate that the proposed models correctly depicted process flow in both batch and continuous flow dryers. Full article

Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Unlike conventional approaches, this paper proposes an end-to-end approach that uses deep reinforcement learning for autonomous mobile robot navigation in an unknown environment. Two types of deep Q-learning agents, such as deep Q-network and double deep Q-network agents are proposed to enable the mobile robot to autonomously learn about collision avoidance and navigation capabilities in an unknown environment. For autonomous mobile robot navigation in an unknown environment, the process of detecting the target object is first carried out using a deep neural network model, and then the process of navigation to the target object is followed using the deep Q-network or double deep Q-network algorithm. The simulation results show that the mobile robot can autonomously navigate, recognize, and reach the target object location in an unknown environment without colliding with static and dynamic obstacles. Similar results are obtained in real-world experiments, but only with static obstacles. The DDQN agent outperforms the DQN agent in reaching the target object location in the test simulation by 5.06%. Full article

In the tobacco redrying process, process parameter settings are greatly influenced by ambient temperature and humidity, and the moisture content of the tobacco leaf. In the face of complex and variable tobacco leaf characteristics, it is difficult to accurately adapt the process parameters to fluctuations in the incoming material characteristics by manual experience alone. Therefore, an improved optimization method combining an improved particle swarm optimization algorithm (IPSO) and an adaptive penalty function is proposed, which can adaptively recommend the best combination of process parameters according to the dynamic incoming characteristics of the tobacco leaf, to reduce the deviation in the outlet moisture and temperature of the roaster under different processing standards of the tobacco leaf. Firstly, the Radial Basis Function (RBF) Neural Network is used to fit the relationship between process parameters and roaster exit moisture content and temperature. Then, taking the standard tobacco leaf redrying export quality as the optimization goal, the optimization algorithm is used to search for the optimal solution. From the high-dimensional nature of the process operating conditions, the difficulty of this study lies in searching for the optimal solution under complex nonlinear constraints of multiple processes. To improve the convergence speed and accuracy of the searching algorithm, the position update method of the particle swarm optimization algorithm is improved, and the adaptive penalty function is combined to search for the optimal global solution to the optimization problem. Redrying experiments are conducted using the method proposed in this paper. Compared with the manual regulation of outlet moisture and temperature, the fluctuation range values are reduced by 7.5% and 11.8%, respectively, which has good application prospects and promotion value. Full article

As a key technical aspect contributing to shale gas development, nanoconfined methane flow behavior has received tremendous research interest, which remains challenging to understand clearly. The majority of previous contributions put emphasis on the mechanism model for methane confined in a single nanopore; at the same time, the other part focusing on an upscaling approach fails to capture the spatial pore-network characteristics as well as the way to assign pressure conditions to methane flow behavior. In light of the current knowledge gap, pore-network modeling is performed, in which a pore coordination number, indicating the maximum pores a specified pore can connect, gas flow regimes classified by Knudsen numbers, as well as different assigned pressure conditions, are incorporated. Notably, the pore-network modeling is completely self-coded, which is more flexible in adjusting the spatial features of a constructed pore network than a traditional one. In this paper, the nanoconfined methane flow behavior is elaborated first, then the pore network modeling method based on the mass conservation principle is introduced for upscaling, and in-depth analysis is implemented after that. Results show that (a) as for porous media with pore sizes ranging from 5~80 nm, dramatic advancement on apparent gas permeability takes place while pressure is less than 1 MPa; (b) apparent gas permeability evaluated at a specified pressure shall be underestimated by as much as 31.1% on average compared with that under the pressure-difference condition; (c) both a large pore size and a high coordination number are beneficial for strong gas flow capacity through nanoscale porous media, and the rising ratio can reach about 6 times by altering the coordination number from 3 to 7, which is quantified and presented for the first time. Full article

The study’s goal is to assess the flexural strength of concrete that includes waste marble powder using machine learning methods, i.e., ANFIS, Support vector machines, and Gaussian processes approaches. Flexural strength has also been studied by using the most reliable approach of sensitivity analysis in order to determine the influential independent variable to predict the dependent variable. The entire dataset consists of 202 observations, of which 120 were experimental and 82 were readings from previous research projects. The dataset was then arbitrarily split into two subsets, referred to as the training dataset and the testing dataset, each of which contained a weighted percentage of the total observations (70–30). Output was concrete mix flexural strength, whereas inputs comprised cement, fine and coarse aggregates, water, waste marble powder, and curing days. Using statistical criteria, an evaluation of the efficacy of the approaches was carried out. In comparison to other algorithms, the results demonstrate that the Gaussian process technique has a lower error bandwidth, which contributes to its superior performance. The Gaussian process is capable of producing more accurate predictions of the results of an experiment due to the fact that it has a higher coefficient of correlation (0.7476), a lower mean absolute error value (1.0884), and a smaller root mean square error value (1.5621). The number of curing days was identified as a significant predictor, in addition to a number of other factors, by sensitivity analysis. Full article

This article presents multidisciplinary research that involved design (i.e., textiles, tiles, pattern design), mathematics (i.e., symmetry and seven frieze groups) and a viewpoint on product design and development for business opportunities. This research comprised a design experiment and a survey. In the design experiment, two design approaches were created to translate the characteristics of traditional textile patterns into new pattern designs for floor tiles. These two design approaches were entitled: “partial replication”, and “combination and simplification”. The seven frieze groups were used as a transformation rule in both design approaches, resulting in two sets of frieze patterns. Although they were derived from the same origin, they looked different. A survey was conducted with 61 respondents to gain outsiders’ perspectives on these new pattern designs. The findings include: (i) positive responses to applying traditional textile patterns to other products, (ii) plausible products for pattern designs, (iii) preferences for design approaches and frieze patterns and (iv) opportunities for design research and education with other disciplines. This paper concludes with theoretical and practical implications for further research. Full article

In this study, the laser cutting characteristics were analyzed according to the shape of the back side of the specimen, and the laser cutting characteristics were compared according to the thickness of the edge (10 mm, 20 mm, and 30 mm). A Yb-YAG laser was used in this study, and the cutting target was STS304 with a thickness of 50 mm, and the cutting process was analyzed using a high-speed camera. In the experiment, it was found through image analysis that the cutting performance was excellent at 30 mm thickness of the edge. In order to analyze this reason, a thermal conduction analysis (numerical simulation) was performed, and it was confirmed that the thicker thickness of the edge caused a preheating effect during laser cutting due to a large amount of heat accumulation. This effect can be used as a reference for the initial processing state while cutting thick metals as it is a characteristic that has not been revealed before. Full article

The equilibrium potentials of iron in a LiF-BeF₂-FeF₂ melt were measured using the EMF method and were dependent upon the temperature and iron fluoride concentrations. The empirical equations for the isotherms and equilibrium polytherms of the iron fluoride concentration were obtained. The cathode polarization of iron fluoride in the molten mixture of lithium and beryllium fluoride was measured using the current switch off method from the stationary state. It was found that in the studied temperature and concentration ranges of iron fluoride in the LiF-BeF₂ electrolyte, the valence state of iron in the melt is mainly +2. According to the experimental values of the equilibrium potentials of the iron electrode in the LiF-BeF₂-FeF₂ melt, the conditional standard potentials of iron were calculated relative to the fluoride reference electrode in the molten mixture of lithium and beryllium fluoride. The conditional standard values of the Gibbs energy change were calculated at the formation of iron difluoride from the element in the form of dilute solutions, as were the thermodynamic values (enthalpy and entropy) when iron difluoride was mixed with LiF-BeF₂. Full article

In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based on monitoring data. In the result analysis, the association rule algorithm demonstrates high computing power in the test, which can filter the data with strong association rules. In addition, the big data platform can allow data visualization, which has good data storage capacity and disaster early warning capacity. In the simulation test of the emergency management system, it was found that the system is feasible in theory. When it is applied to the actual disaster emergency management, it wasfound that, in the face of geological disasters, the processing speed of relevant departments increased by 59.4%, and the allocation of personnel and materials wasmore reasonable. The above results show that the big data platform monitoring data can improve the regional geological disasters emergency management capacity and ensure the safety of people’s lives and property. Full article

In recent years, the technology of storing and transporting natural gas in the form of hydrate has received a lot of attention. At present, the research on the synthesis of natural gas hydrate for the purpose of storage and transportation is still in the laboratory stage, and its synthesis process is in the design and conception stage. The influencing factors of natural gas hydrate synthesis under pilot-scale conditions are more complex. Moreover, pilot experiments are oriented to actual production, and its economic feasibility and operational convenience have higher requirements. This paper aimed to study the influencing factors of gas hydrate synthesis by spray method under pilot-scale conditions. Under specific conditions of surfactant and pressure, we carried out research on the effects of reaction temperature, different forms of atomizers, high-pressure pump flow, experimental water, and other factors. Experiments show that the optimal synthesis conditions were a temperature of −5 °C, a pressure of 5 MPa, a conical nozzle, a generated gas hydrate as the hydrate of type I structure, and a gas storage capacity of 1:123 (gas–water ratio). Full article

Genetically modified (GM) crops expressing insecticidal and herbicide-tolerant traits provide a new approach to agriculture production, but concerns about food safety were often raised by the public. The present research shows the findings of the nutritional assessment of ZDAB3 expressing insecticidal Cry proteins (Cry1Ab and Cry2Ab) and EPSPS protein (G10evo-EPSPS). The key nutrients and anti-nutrients of ZDAB3 maize were examined and contrasted with those of its non-transgenic control maize grown at the same locations during three planting seasons. The values for proximates, amino acids, fatty acids, minerals, vitamins, phytic acid, and trypsin inhibitor assessed for ZDAB3 were comparable to those of its non-transgenic control maize or within the range of values reported for other commercial lines. In addition, no adverse effects related to the G10evo-EPSPS protein in mammals were observed. These data indicated that the expression of Cry1Ab, Cry2Ab, and G10evo-EPSPS proteins in ZDAB3 maize does not affect the nutritional compositions, and ZDAB3 maize is equivalent to non-transgenic maize regarding those important compositions. Full article

For the purpose of studying compact strip production (CSP) funnel-shaped mold and flexible thin-slab rolling (FTSR) funnel-shaped mold, a three-dimensional (3D) multi-field coupling mathematical model was established to describe the electromagnetic braking (EMBr) continuous casting process. To investigate the metallurgical effect of EMBr in the CSP and FTSR funnel-shaped thin-slab molds, a Reynolds-averaged Navier–Stokes (RANS) turbulence model, together with an enthalpy–porosity approach, was established to numerically simulate the effect of ruler EMBr on the behaviors of melt flow, heat transfer, solidification, and inclusion movement in high-speed casting. The simulation results indicate that the application of ruler EMBr in the CSP and FTSR molds shows great potential to improve the surface temperature of molten steel and reduce the penetration depth of downward backflow. This contributes to the melting of the slag rim near the meniscus region and facilitates the floating removal of the inclusions in the molten pool. In addition, in comparison with the case of no EMBr, the parametric study shows that the braking effect of ruler EMBr with an electromagnetic parameter of 0.5 T can enhance the upward backflow in the two high-speed thin-slab molds. The enhanced upward backflow can successfully entrain the inclusions to the top of the mold and improve the activity of surface fluctuations to avoid the formation of the slag rim. For instance, for the ruler EMBr applied to the FTSR mold, the maximum amplitude of surface fluctuation and the floatation removal quantity of inclusions with a diameter of 100 μm are increased by 4.6 percent and 51 percent, respectively. Full article

Several studies have already demonstrated that electrospinning is an excellent tool for forming nano/microfibers. However, the number of parameters affecting the formation of the structures has become a great challenge, including the polymeric solutions’ rheological properties, directly affecting the morphology of the fibers formed. The present work aimed to produce polymeric composites and determine their rheological properties, comparing them to the morphology of the fibers formed by electrospinning. Also, to evaluate their potential use as the carriers of natural pigments. To this end, a distinct combination of solutions containing Chitosan/Gelatin, Chitosan/poly(ethylene) oxide (PEO) and Zein/PEO was produced and submitted to electrospinning. The sample containing zein manufactured the structures smaller in diameter (201.3 ± 58.6 nm) among those studied. Besides, it was observed that adding PEO to the solutions impacts the increase in viscosity and shear thinning behavior, guaranteeing uniformity in the structures formed. Natural pigments were successfully incorporated into the chosen zein/PEO solution, and it was observed that adding these compounds led to changes in the rheological characteristics, as expected. Nevertheless, it was possible to produce uniform fibers with diameters ranging from 665.68 ± 249.56 to 2874.44 ± 1187.40 nm, opening the possibility of using these natural pigments in biotechnological processes. Full article

The preparation and application of ultra-clean coal is one of the important aspects of clean energy technology. However, the preparation of ultra-clean coal is mainly chemical methods, which are low in efficiency, high in energy consumption and expensive. It is urgent to find an effective method to prepare ultra-clean coal. In this paper, the combined method of grinding and the collector gasification flotation method was used to obtain ultra-clean coal. The effects of grinding time on the particle size composition, mineral dissociation, surface properties and flotation results of coal samples were studied. The grinding test results show that with the increase in grinding time, the particle size and the pore diameter of coal samples decreased gradually, while the specific surface area and pore volume of coal samples gradually increased. When the grinding time was 20 min, the D90 and D[4,3] of grinding products were 5.20 um and 4.23 um, respectively. The ash content of −1.3 g/cm³ was less than 1% when the grinding time was 20 min. Compared with the traditional flotation method, the collector gasification flotation method can obtain a higher concentrate yield and lower concentrate ash content. When the amount of collector was 2.0 kg/t, the yield of clean coal obtained by the collector gasification flotation method was 4.1% higher than that by the traditional flotation method, while the ash content of clean coal was 0.3% lower. Full article

Recent environmental concern has been raised about the development of biocomposites because of their low cost, eco-friendliness, and biodegradability. Machining of polymeric composite is inevitable during assembly of structural components. In view of creating holes in structural composites, drilling is necessary and it is essential to carry out research to find the optimal machining parameters. The experimental assessment and prediction of the thrust force and torque involved in drilling composites reinforced with sheep horn are presented in this work. The matrix and sheep horn particles were combined in the right proportions before being moulded and poured into a mould, then allowed to cure at room temperature. Investigated properties included ultimate tensile strength, flexural strength, and hardness. To evaluate the quality of the hole, micrographs of the drilled hole were employed. When the mixture was optimised based on the properties, it was found that a 70:30 ratio produced the best results. Thrust force and torque of 58 N and 4.8 N-mm, respectively, were observed for sheep horn filler laminates which were drilled using the combination of 6 mm diameter, 0.1 mm/rev feed rate, and 400 rpm speed. This is by far the best among the combinations used in the experiment. Additionally, the experimental outcomes indicate that the feed rate and spindle speed are the most significant factors affecting the thrust force. Since there were minimal errors in the comparison, the central composite design modelling is consummate. Overall, the extensive experimental effort offers several options to utilise this composite material in future applications across a wide range of fields. Full article

Mixed martial arts (MMA) has always been surrounded by controversy due to the unusual muscle development of its participants, so it is crucial to know the strategies that have been implemented to reduce doping cases. The main purpose of this paper is to describe the various cases of doping detected by USADA in UFC MMA participants. In addition, strategies that are being developed to reduce cases of positive doping are proposed. From the UFC USADA database, doping cases were extracted, obtaining the substance or substances involved; the formula, physiological effect and the athletes involved; the dates of the sampling; if it was out of competition or in-competition and the sanction time. The substances that were most involved were found to be Ostarine (22), Clomiphene (9), Diuretics (10) and Stanozolol (9). Some sanctions were diminished because they were treated with contamination of supplements (cases of Ostarine) and cases of contamination of meat (Clomiphene). When contaminated supplements were reported, they were added to the list of high-risk supplements maintained as part of USADA’s online dietary supplement safety education and awareness resource—Supplement 411. There were also cases in which positive doping could be avoided through the early report of therapeutic use exemptions. The methodology that the USADA has implemented allows us to register the athletes with positive doping, check the risk of the supplements before being bought and provide a teaching portal. These efforts are necessary to implement in all countries in which MMA is practiced, avoiding the participation of doped martial artists. Full article

The poly(ethylene) glycol (PEG) capped mancozeb nanoformulation was prepared by the ultrasonic method using a 1% mancozeb solution and 20% capping agent, PEG-4000. The synthesized nanoformulation was characterized using UV-visible, FTIR, SEM and TEM techniques. The photolytic and photo catalytic experiments were carried out in a Borosil glass bottle in the presence of sunlight, varying the pH proportions at a single fortification level (1.0 g/mL) in ground water, under sunlight. The optimal catalyst concentration for complete degradation was observed to be 0.1 percent. The mancozeb nanoformulation in water was determined using the HPLC-PDA method, and the rate constant and the 50% degradation (DT50) values were calculated based on the results. The photolytic results show that there is no significant loss of residues due to adsorption. Titanium dioxide (TiO₂) was discovered to be an excellent decontaminating catalyst in a variety of water samples. The compound survives for several days in the absence of a catalyst. Full article

The delays and disruptions during the pandemic have awakened interest in the sustainability and resilience of production systems to emergencies. In that context, the deployment of smart technologies has emerged as an almost mandatory development orientation to ensure the stability of manufacturing. The core value of smart technologies is to reduce the dependence on human labor in production systems. Thereby, the negative impacts caused by emergency situations are mitigated. However, the implementation of smart technologies in a specific production system that already exists requires a high degree of suitability. Motivated by this fact, this study proposes an integrated spherical fuzzy bounded rationality decision-making approach, which is composite of the spherical fuzzy decision-making trial and evaluation laboratory (SF DEMATEL) and the spherical fuzzy regret theory-based combined compromise solution (R-SF CoCoSo) method. The proposed approach reflects both the ambiguities and psychological behaviors of decision-makers in prioritization problems. It was applied to prioritize seven smart technologies for manufacturing in Vietnam. The results show that reliability, costs, and maturity are the most important criteria for choosing smart technology which is suitable for an existing production system in Vietnam. Our findings seem to suggest that the automatic inspection, remote machine operation, and robots are the most suitable smart technologies to stabilize and sustain production in Vietnam for emergency situations. Full article

Aiming at the problem where the current engineering wake model does not describe the wind speed distribution of the wake in the complex terrain wind farm completely, based on the three-dimensional full wake model (3DJGF wake model), this paper proposed a wake model that can predict the three-dimensional wind speed distribution of the entire wake region in the complex wind farm, taking into account the Coanda effect, wind shear effect, and wake subsidence under the Gaussian terrain. Two types of Doppler lidar were used to conduct wind field experiments, and the inflow wind profile and three-dimensional expansion of the wake downstream of the wind turbine on the Gaussian terrain were measured. The experimental results showed that the wake centerline and terrain curve showed similar variation characteristics, and the near wake profile was similar to a super-Gaussian shape (asymmetric super-Gaussian shape) under low-wind-speed conditions, while the near wake profile presented a bimodal shape (asymmetric bimodal shape) under high-wind-speed conditions. The predicted profiles of the Gaussian terrain wake model were compared with the experimental data and the three typical wake models. The comparison results showed that the newly proposed Gaussian terrain wake model fit well with the experimental data in both near wake and far wake regions, and it had better performance in predicting the wake speed of the Gaussian terrain wind farm than the other three wake models. It can effectively predict the three-dimensional velocity distribution in the whole wake region of complex terrain. Full article

The interest in the design and numerical modelling of adhesively-bonded components and structures for industrial application is increasing as a research topic. Although research on joint failure under pure mode is widespread, applied bonded joints are often subjected to a mixed mode loading at the crack tip, which is more complex than the pure mode and affects joint strength. Failure of these joints under loading is the objective of predictions through mathematical and numerical models, the latter based on the Finite Element Method (FEM), using Cohesive Zone Modelling (CZM). The Single leg bending (bending) testing is among those employed to study mixed mode loading. This work aims to validate the application of FEM-CZM to SLB joints. Thus, the geometries used for experimental testing were reproduced numerically and experimentally obtained properties were employed in these models. Upon the validation of the numerical technique, a parametric study involving the cohesive laws’ parameters is performed, identifying the parameters with the most influence on the joint behaviour. As a result, it was possible to numerically model SLB tests of adhesive joints and estimate the mixed-mode behaviour of different adhesives, which enables mixed-mode modelling and design of adhesive structures. Full article

To solve the water pollution problem caused by methylene blue (MB), areca residue biochar (ARB) was prepared by pyrolysis at 600 °C, and modified areca residue biochar (M-ARB) was obtained by modifying ARB with 1.5 mol/L NaOH, and they were utilized to adsorb and eliminate MB from water. The structural characteristics of ARB and M-ARB were examined, and the main influencing factors and adsorption mechanism of MB adsorption process were investigated. The outcomes demonstrated an increase in M-ARB’s specific surface area and total pore volume of 66.67% and 79.61%, respectively, compared with ARB, and the pore structure was more abundant, and the content of oxygen element was also significantly increased. When the reaction temperature was 25 °C, starting pH of the mixture was 10, the initial MB concentration was 50 mg/L, the ARB and M-ARB dosages were 0.07 g/L and 0.04 g/L, respectively, the adsorption equilibrium was achieved at about 210 min, and the elimination rate for MB exceeded 94%. The adsorption behaviors of ARB and M-ARB on MB were more in line with the Langmuir isotherm model (R² > 0.95) and the quasi-secondary kinetic model (R² > 0.97), which was characterized by single-molecule layer chemisorption. The highest amount of MB that may theoretically be absorbed by M-ARB in water ranging from 136.81 to 152.72 mg/g was 74.99–76.59% higher than that of ARB. The adsorption process was a spontaneous heat absorption reaction driven by entropy increase, and the adsorption mechanism mainly involved electrostatic gravitational force, pore filling, hydrogen bonding, and π–π bonding, which was a complex process containing multiple mechanisms of action. NaOH modification can make the ARB have more perfect surface properties and more functional group structures that can participate in the adsorption reaction, which can be used as an advantageous adsorption material for MB removal in water. Full article

Nitrous oxide (N₂O) is a major radiative forcing and stratospheric ozone-depleting gas. Among natural sources, freshwater ecosystems are significant contributors to N₂O. Although temperature is a key factor determining the N₂O emissions, the respective effects of temperature on emitted and dissolved N₂O in the water column of freshwater ecosystems remain unclear. In this study, 48 h incubation experiments were performed at three different temperatures; 15 °C, 25 °C, and 35 °C. For each sample, N₂O emission, dissolved N₂O in the overlying water and denitrification rates were measured, and N₂O-related functional genes were quantified at regular intervals. The highest N₂O emission was observed at an incubation of 35 °C, which was 1.5 to 2.1 factors higher than samples incubated at 25 °C and 15 °C. However, the highest level of dissolved N₂O and estimated exchange flux of N₂O were both observed at 25 °C and were both approximately 2 factors higher than those at 35 °C and 15 °C. The denitrification rates increased significantly during the incubation period, and samples at 25 °C and 35 °C exhibited much greater rates than those at 15 °C, which is in agreement with the N₂O emission of the three incubation temperatures. The NO₃⁻ decreased in relation to the increase of N₂O emissions, which confirms the dominant role of denitrification in N₂O generation. Indeed, the nirK type denitrifier, which constitutes part of the denitrification process, dominated the nirS type involved in N₂O generation, and the nosZ II type N₂O reducer was more abundant than the nosZ I type. The results of the current study indicate that higher temperatures (35 °C) result in higher N₂O emissions, but incubation at moderate temperatures (25 °C) causes higher levels of dissolved N₂O, which represent a potential source of N₂O emissions from freshwater ecosystems. Full article

In this paper, a novel method is proposed for the incorporation of data-driven machine learning techniques into process optimization. Such integration improves the computational time required for calculations during optimization and benefits the online application of advanced control algorithms. The proposed method is illustrated via the chemical absorption-based postcombustion CO₂ capture process, which plays an important role in the reduction of CO₂ emissions to address climate challenges. These processes simulated in a software environment are typically based on first-principle models and calculate physical properties from basic physical quantities such as mass and temperature. Employing first-principle models usually requires a long computation time, making process optimization and control challenging. To overcome this challenge, in this study, machine learning algorithms are used to simulate the postcombustion CO₂ capture process. The extreme gradient boosting (XGBoost) and support vector regression (SVR) algorithms are employed to build models for prediction of carbon capture rate (CR) and specific reboiler duty (SRD). The R² (a statistical measure that represents the fitness) of these models is, on average, greater than 90% for all the cases. XGBoost and SVR take 0.022 and 0.317 s, respectively, to predict CR and SRD of 1318 cases, whereas the first-principal process simulation model needs 3.15 s to calculate one case. The models built by XGBoost are employed in the optimization methods, such as an agent-based approach represented by the particle swarm optimization and stochastic technique indicated by the simulated annealing, to find specific optimal operating conditions. The most economical case, in which the CR is 72.2% and SRD is 4.3 MJ/kg, is obtained during optimization. The results show that computations with the data-driven models incorporated in the optimization technique are faster than first-principle modeling approaches. Thus, the application of machine learning techniques in the optimization of carbon capture technologies is demonstrated successfully. Full article

Nitrous oxide is widely used as oxidizer or nitriding agent in numerous industrial activities such as production of adipic acid and caprolactam and even for production of some semiconductors. Further, it is used as an additive in order to increase the power output of engines, and as an oxidizer in propulsion systems of rockets, because it has a large heat of formation (+81.6 kJ mol⁻¹). N₂O is highly exothermic, and during its decomposition a supplementary heat amount is released, so it needs special handling conditions. The combustion of fuels in nitrous oxide atmosphere can lead to high unstable and turbulent deflagrations that speedily self-accelerate and therefore a deflagration can change to a detonation. The peak explosion pressure and the maximum rate of pressure rise of explosions in confined spaces are key safety parameters to evaluate the hazard of processes running in closed vessels and for design of enclosures able to withstand explosions or of their vents used as relief devices. The present study reports some major explosion parameters such as the maximum (peak) explosion pressures p_max, explosion times θ_max, maximum rates of pressure rise (dp/dt)_max and severity factors K_G for ethylene-nitrous oxide mixtures (lean and stoichiometric) diluted with various amounts of N₂, at various initial pressures (p₀ = 0.50–1.50 bar), in experiments performed in a spherical vessel centrally ignited by inductive-capacitive electric sparks. The influence of the initial pressure and composition on p_max, θ_max and (dp/dt)_max is discussed. The data are compared with similar values referring to ethylene-air mixtures measured in the same initial conditions. It was found that at identical C/O ratios with ethylene-air, ethylene-N₂O-N₂ mixtures develop higher explosion pressures and higher rates of pressure rise, due to the exothermic dissociation of N₂O under flame conditions. Full article

The CNC machine tool is the passive executor of machining code. It cannot predict the machining accuracy during machining. If the error is found to be out of tolerance after processing, it will not only scrap the parts, but also greatly affect the processing efficiency. This phenomenon is very prominent when machining sculptured surface parts with five-axis machine tools. Therefore, this paper proposes a Digital Twin (DT) modeling method of five-axis machine tools for predicting Continuous Trajectory Contour Error (CTCE) caused by tracking errors and geometric errors. The DT consists of three parts: the Setpoints Trajectory (ST) model, the Actual Trajectory (AT) model considering tracking errors and geometric errors and the CTCE model. For a specific machine tool, according to the basic geometric information of the machine tool (tool length, kinematic chain information, etc.) and 41 geometric errors, the DT can be established. Inputting the Setpoints Positions (SPs) and the Linear Encoder Detection Positions (LEDPs), the DT can be used to predict the Tool-Tip Position Trajectory (TTPT) contour error and the Tool Orientation Trajectory (TOT) contour error. In order to verify the proposed method experimentally, the KMC400S U five-axis machine tool is selected to establish its DT by which the contour error of the S-shaped trajectory are predicted offline. Then, the DMU50 five-axis machine tool is selected to establish its DT to predict the contour error of the circular trajectory in real time. Combined with the deep motion mechanism, this paper proposes a DT modeling method for the vertical application scene of parts machining accuracy prediction, which is of great significance to developing the DT application theory and ensuring the machining accuracy of parts. Full article

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test. Full article

Wind generators have attracted a lot of attention in the realm of renewable energy systems, but they are vulnerable to harsh environmental conditions and grid faults. The influence of the manta ray foraging optimizer (MRFO) on the dynamic performance of the two commonly used variable speed wind generators (VSWGs), called the permanent magnet synchronous generator (PMSG) and doubly-fed induction generator (DFIG), is investigated in this research article. The PMSG and DFIG were exposed to identical wind speed changes depending on their wind turbine characteristics, as well as a dangerous three-phase fault, to evaluate the durability of MRFO-based wind side controllers. To protect VSWGs from hazardous gusts and obtain the optimum power from incoming wind speeds, we utilized a pitch angle controller and optimal torque controller, respectively, in our study. During faults, the commonly utilized industrial approach (crowbar system) was exclusively employed to aid the studied VSWGs in achieving fault ride-through (FRT) capability and control of the DC link voltage. Furthermore, an MRFO-based PI controller was used to develop a crowbar system. The modeling of PMSG, DFIG, and MRFO was performed using the MATLAB/Simulink toolbox. We compared performances of PMSG and DFIG in reference tracking and resilience against changes in system parameters under regular and irregular circumstances. The effectiveness and reliability of the optimized controllers in mitigating the adverse impacts of faults and wind gusts were demonstrated by the simulation results. Without considering the exterior circuit of VSWGs or modifying the original architecture, MRFO-PI controllers in the presence of a crowbar system may help cost-effectively alleviate FRT concerns for both studied VSWGs. Full article

This study aimed at the preparation and characterization of co-amorphous loratadine–citric acid orally disintegrating dosage forms (ODx). A co-amorphous loratadine–citric acid was prepared by solvent evaporation method in three different molecular ratios. DSC, FTIR, and dissolution studies have been conducted for the binary system. The co-amorphous system was used to obtain oral lyophilizates and orally disintegrating tablets by direct compression. Diameter, thickness, hardness, disintegration time, uniformity of mass, and dissolution was determined for the dosage forms. DSC curves showed a lack of sharp endothermic peaks for the binary systems. FTIR spectra presented a hypsochromic modification of the characteristic peaks. Dissolution studies indicated a five-fold increase in the dissolved amount compared to pure loratadine in water. Disintegration times of direct compression ODx varied in the range of 34–41 s and for freeze-dried ODx in the range of 8–9 s. Friability was under 1% in all cases. The dissolution of loratadine in buffer solution at pH = 1 was almost complete. In conclusion binary systems of loratadine and citric acid enhance solubility and combined with the orally disintegrating pharmaceutical form also increase patient compliance. Full article

The drying industry has grown considerably due to the tremendous demand for non-perishable food. Convective drying is one of the most popular equipment in the drying industry (food, chemical, pharmaceutical, etc.). One of the drawbacks of this equipment, when used for convective drying, is the non-uniformity in the final product quality. This study presents the development of a numerical model through Computational Fluid Dynamics (CFD). The drying chamber of a heat pump dryer is assessed from the perspective of drying air velocity and temperature profiles. The model was developed by solving different transport phenomena-related equations. The established methodology was set up to evaluate how the drying air velocity and temperature distribution affect the drying chamber. These results will define if there is a need to redesign it. The air velocity and temperature profile results show a need to redesign the chamber. Only trays 2, 3, and 4 are the ones that would achieve the drying of the products. The proposed solution is to implement air distributors or modify the tray positioning to make the drying air and temperature distribution homogeneous. Full article

In recent years, more and more biochars have been employed to treat dye wastewater. In order to increase the utilization of moringa seed shell resources and enrich the removal method of methylene blue (MB) in solution, in the current study, the magnetic moringa seed shells biochar was prepared through ultrasonic-assisted impregnation and pyrolysis, while Fe₃O₄ was used to activate the material to obtain adsorption (Fe₃O₄-MOS). The prepared adsorbents were characterized by SEM-EDS, XRD, XPS, FTIR, N₂ adsorption and desorption and VSM. Under the suitable experimental conditions, the removal rate can be close to 100% and the maximum adsorption capacity of MB could be 219.60 mg/g. The Freundlich model provided a good match to the data presented by the adsorption isotherm, and the adsorption of MB on Fe₃O₄-MOS was a spontaneous and endothermic reaction. Study of the mechanism indicated that pore adsorption, electrostatic interaction, hydrogen bond, and π-π interaction were the major adsorption mechanisms. After five cycles, it was found that Fe₃O₄-MOS had a high removal rate for MB, which was close to 90%. This work provides a new idea for moringa seed shells and the results confirm that Fe₃O₄-MOS has substantial potential for dye wastewater treatment. Full article