Parameters Of Processes Research Articles

The effects of joint process parameters of two-step manufacturing processes on the mechanical performance of biocomposites using Taguchi and multiple regression techniques

This study evaluates the influence of joint parameters of extrusion (extrusion profile temperature) and injection molding (barrel profile temperature and mold temperature) manufacturing processes on the mechanical performance of biocomposite materials via the Taguchi and multiple regression techniques. Statistical analysis of variance (ANOVA) was performed to determine the most influential controllable factors for the individual responses. The results of the study revealed that the mechanical properties of the biocomposites are affected by the joint parameters of the two-step manufacturing techniques. This study will help in the process design of biocomposites with improved mechanical performance.

Sulfur powder utilization and denitrification efficiency in an elemental sulfur-based membrane bioreactor with coagulant addition

The integration of elemental sulfur-based autotrophic denitrification with membrane bioreactor (MBR) technology offers a cost-effective solution for nitrate removal; however, stable operation demands efficient sulfur utilization and phosphorus management. This study explores sulfur consumption dynamics and the impacts of coagulant injection on denitrification efficiency. Sulfur consumption was closely correlated with nitrate removal rates, highlighting the critical role of stoichiometric sulfur availability for sustained denitrification. While coagulant addition enhanced phosphorus removal, excessive dosing impaired elemental sulfur-based microbial activity, reducing nitrate removal efficiency and increasing nitrite accumulation. Notably, microbial community analysis revealed a decline in the abundance of key sulfur-oxidizing bacteria, such as Sulfurimonas, under high coagulant concentrations. These findings emphasize the need for optimized sulfur and coagulant dosing strategies to balance phosphorus and nitrate removal while preserving microbial diversity and reactor stability. This study provides practical insights into operational parameters for efficient and sustainable ESAD-MBR processes.

Intelligent control system for technological complexes of aluminum industry enterprises

Relevance. Aluminum production is one of the most important industries all over the world. It has a high environmental load, resource and energy intensity. To reduce the negative impact of aluminum production, efforts are underway to improve its technological processes, aimed at increasing aluminum recovery rates, reducing waste volumes, and lowering energy consumption. The reduction of energy consumption can be achieved, among others, through implementing control systems that provide energy management. Due to the complexity and multiplicity of technological processes in aluminum production, the use of traditional linear approaches for such control systems is ineffective, and adequate mathematical models are required. At the same time, for the production level, the use of mathematical models based on a phenomenological description of the ongoing physical processes overly complicates the control task. As a result, the use of intelligent and adaptive approaches to both the mathematical description of technological processes and the optimal energy consumption management is relevant. Objects. Technological complexes of aluminum production, which have the properties of inertia, nonlinearity, and closedness; and the control system based on artificial intelligence methods. Alumina production is chosen as an illustration. Aim. To develop mathematical models capable of adequately describing the interrelated processes in the technological complexes under consideration, as well as the control system that allows optimal energy consumption management. Methods. For the mathematical model based on balance equations under uncertainty, the fuzzy-set theory was used along with the gradient descent method to identify the model parameters; for the optimization task, the genetic algorithm method was used. Results. The mass balance model and the process conditions changing model have been developed to determine the energy consumption for the technological complexes of aluminum production with continuous inertial nonlinear closed production. Based on these models, the dynamic characteristics of energy consumption and the parameters of technological processes were determined depending on the main controlled parameters, allowing us to predict emergencies. Considering technological parameters and cost factors, the optimization task for energy consumption management was solved.

Analysis of Production Process Parameters Using Computer Simulation at the Planning Stage

Computer simulation is one of the tools that is often used to support activities related to the management of the production process. Computer simulation allows for the analysis of the parameters of production or logistics processes, among others. The study presents a comparative analysis of analytical methods and computer simulation (FlexSim 22.2.1 software) in relation to the subject matter. The use of computer simulation allowed for the verification of the number of production resources, determined by analytical methods, and for the proposal of improvements that would allow for shortening the duration of the production process and reducing its costs. The proposed improvements at the stage of planning the production process in terms of the number of production resources used to implement the assumed production plan allowed for the reduction of the duration of the production process and its costs. Therefore, it is justified to use computer simulation in solving problems related to various aspects of the functioning of an enterprise.

Analysis of the possibility of using exploration and learning algorithms in the production of castings

The research presented in the article indicates the process of building models based on machine learning algorithms, linear regression, decision trees, ensemble learning, random forest, ensemble averaging, Boosting, stacking, and support vector regression (SVR) algorithms. The basis for building these models are experimental data collected during research conducted at the Łukasiewicz Research Network-Krakow Institute of Technology. An analysis of the initial state and the analysis of the state of correlation in the set were performed, which facilitated the development of models. To increase the amount of data, augmentation was performed using the Bootstrapping. For selected results, castings were made and tested in real conditions. The research results indicate the possibility of identifying the most appropriate input parameters for specific production processes of austempered ductile iron (ADI), the possibility of predicting the expected mechanical parameters based on the indicated parameters of the production process, chemical composition, specific parameters of the heat treatment process, and the thickness of the target product. A set of such models constitutes the basis of the system, enabling the end user to estimate the final parameters of the casting planned to be produced.

CFD predictive simulations of miniature bioreactor mixing dynamics coupled with photo-bioreaction kinetics in transitional flow regime

High-throughput systems using miniaturised stirred bioreactors accelerate bioprocess development due to their simplicity and low cost. However, fluctuating hydrodynamics pose numerical challenges for coupling (bio)reaction kinetics, critical for optimisation and scale-up/down in chemical and bioprocess industries. To address this, hydrodynamic convergence was achieved by time-averaging instantaneous RANS solutions of the transitional SST model over a sufficiently long period to achieve statistical significance in step one. Subsequently, photo-bioreaction transport models, accounting for the photobioreactor’s directional illumination and curvature, were solved based on these converged fields, overcoming two-step coupling challenges in an approach not previously reported. Applied to a 0.7 L Schott bottle photobioreactor mechanically mixed by a magnetic stirrer (100–500 rpm), the model accurately predicted swirly vortex fields at 500 rpm, with a 7 % error margin for simulated tracer diffusion, and aligned biomass growth profiles with literature data on Rhodopseudomonas palustris. However, parallel computing efficiency did not scale linearly with processor count, making time-averaging computationally expensive. Also, improved bioreactor mixing enhanced biomass productivity, but rpms over 300 required increased incident light intensity (>100 Wm−2) due to observed light limitation. Hence, this model facilitates optimising stirring speeds and refining operational parameters for scale-up and scale-down processes.

Temperature measurement method with line and continuum emissions in Ar-N2 DC arc

This study discusses a technique for accurate temperature measurement of thermal plasmas with a high-speed camera. The temperature of thermal plasmas is an important parameter for plasma processes. High-speed cameras are useful to measure plasma temperatures in two dimensions. Measurements of plasma emission with a high-speed camera and band-pass filter provide only the spectral intensity of the entire transmitted wavelength range. Temperature measurements with high-speed cameras by the Boltzmann plot method or Fowler⎯Milne method are less accurate. Theoretical consideration of the line and continuum emissions has improved the accuracy of plasma temperature measurement. The measurement target was a free-burning arc in an argon and nitrogen atmosphere. Temperature errors were estimated based on the deviation from the true emission coefficient. The calculation and measurement errors were discussed as the factors of the deviation. Error estimation provides important insight into the selection of the measurement wavelength.

Improving the quality of Xiaoqu Qingxiangxing Baijiu through optimization of key parameters in industrial fermentation processes driven by regulation models

After the transition of Xiaoqu Qingxiangxing Baijiu brewing from traditional to automated production, the process faced challenges such as low levels of key flavor substances and high levels of higher alcohols in Baijiu. In this study, the environmental factors and Jiupei microorganisms in traditional workshops (TW) and automated workshops (AW) while also optimizing the dosage of saccharification fermentation agents. The results demonstrated that Lactobacillus and Staphylococcus were the dominant microbial communities in the environment of both workshops, with the AW environment exhibiting higher microbial abundance than that of the TW. In the Jiupei, Lactobacillus and Weissella were the dominant microorganisms. However, microbial richness was higher in the TW than in the AW. This study also introduced a regulation model: when 6.1‰ ester-producing yeast, 8.3% Daqu, and 5.8‰ Xiaoqu were added, the total acid content reached 0.58 g/L, total ester content was 1.09 g/L, and higher alcohols levels were decreased to 1.85 g/L. These adjustments successfully achieved the goal of "reducing impurities and improving quality," leading to an improvement in the overall quality of Baijiu.

Elimination of torasemide from aqueous medium: influence of sorption and photocatalytic processes parameters supported by DFT analysis.

The sorption and photocatalytic activity of pharmaceutical torasemide using an immobilized TiO2 photocatalyst were investigate. The experimental design included optimization of reaction conditions such as pH and initial pharmaceutical concentration in aqueous solution using the response surface modeling approach, scavenger tests to gain insight into the photocatalysis mechanism, and application of the process to more complex water matrices. TiO2 in the role of sorbent showed a low capacity for torasemide (12.23-29.83μg/g within 24h of contact), making this type of removal inefficient on its own. Investigating the sorption process influenced by different process parameters such as pH, temperature, ionic strength, and dosage of TiO2 applied, the low tendency to this kind of material was affirmed by low Kd values (0.70 to 6.78mL/g) obtained by linear isotherms. Photocatalysis proved to be the better choice for the removal of torasemide from water, with the best kinetics at pH 4 and concentration of 5mg/L with half-time for degradation of 34.83min. Computational DFT analysis identified the zwitterionic torasemide structure as predominant under neutral and acidic conditions. It also showed that negatively charged areas around nitrogen-containing fragments probably have the highest potential to promote the TiO2 sorption at low pH conditions, where it is highest, through electrostatic attractions and N-H∙∙∙∙∙OTiO2 hydrogen-bonding contacts.

Revolutionizing acridine synthesis: novel core-shell magnetic nanoparticles and Co-Zn zeolitic imidazolate framework with 1-aza-18-crown-6-ether-Ni catalysts

Nanoparticles have emerged as a critical catalyst substrate due to their exceptional features, such as catalytic efficiency, high stability, and easy recovery. In our research, we have developed an innovative and environmentally friendly magnetic mesoporous nanocatalyst. Using the co-precipitation method, we produced magnetic nanoparticles (Fe3O4) and coated them with Zeolitic imidazolate frameworks (ZIFs) to enhance their surface area and chemical stability. The resulting substrate was functionalized with 1-aza-18-crown-6-ether and nickel metal. Our prepared catalyst has been rigorously evaluated using advanced techniques, including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Brunauer-Emmet-Teller (BET), vibrating sample magnetometry (VSM), scanning electron microscopy and energy dispersive X-ray (SEM-EDS), inductively coupled plasma (ICP), elemental mapping analysis (EMA), thermogravimetric analysis (TGA), and transmission electron microscopy (TEM). By synthesizing acridine derivatives, we have demonstrated the exceptional efficiency of our catalyst in organic compound synthesis. Through optimization, we have established the ideal parameters for catalytic processes, including catalyst amount, temperature, time, and ultrasonic use. Our catalyst has been proven to exhibit remarkable physical and chemical properties, such as porosity, temperature resistance, and recyclability. Notably, our heterogeneous nanocatalyst has shown outstanding performance and can be recycled six times without any loss in efficiency, affirming its potential in acridine.

A comparative experimental study on abrasive flow machining vs flow peening GOV processes on Inconel 718

Inconel 718, a nickel-based super alloy, has a broad applications area from aerospace, medical and defence to traditional industries because of its high durability, corrosion resistance and elevated temperature performance. Unconventional surface treatment methods of abrasive flow machining (AFM) and shot peening (SP) are applied to improve the mechanical performance of Inconel 718. In this work, a new surface treatment process known as flow peening (GOV) already developed by combining the advantages of abrasive flow machining (AFM) and shot peening methods such as surface finishing and residual compressive stress generation respectively was applied experimentally for Inconel 718. The effects of process parameters for GOV and AFM processes on the surface quality of the specimens were experimentally investigated. White layer decrease, surface roughness decrease and material removal amount were discussed for both processes. The best surface quality with 0.56 μm Ra surface roughness value and material removal amount of 7.2 mg were obtained by GOV process while similar level of surface roughness value of 0.52 μm was obtained by removing 6648.03 mg material for AFM. When the AFM process was continued, the best values of 0.24 μm Ra was obtained removing excess material of 17,385.60 mg compared to the GOV process. Also, all these results were supported by SEM images.

The Concept of Using 3D Printing Technology in Ceramic Foundry Filter Manufacturing

The article presents the concept of using 3D printing technology in ceramic foundry filter manufacturing. They are a crucial component for obtaining an acceptable quality of nickel superalloys by carrying out the process using the precision casting method. Commonly used filters of this type have a number of disadvantages. They are characterized by irregularity of the filtering structure, high brittleness, lack of resistance to mechanical shocks, and impacts of a stream of liquid metal. All these factors create a risk of introducing the material from the damaged filter into the casting mold, which translates into contamination of the casting alloy, and thus the occurrence of casting defects such as nonmetallic inclusions. The hope for a change in this state of affairs is the use of additive manufacturing technology in their production, which by assumption will allow to obtain a product with a repeatable shape, high mechanical resistance, and a specially designed structure regulating the flow of liquid metal into the mold during casting. The suitability of robocasting and binder jetting technology for producing filtration structures was initially assessed. The results have presented the selection of ceramic powder, as well as the development of the composition of the ceramic paste. The parameters of paste preparation and 3D printing individual processes were described. Moreover, the representative microstructures and basic mechanical properties of samples obtained by both of the compared technologies were presented. Furthermore, the final effect of prototype casting filters with a repeatable shape, manufactured with the use of both technologies, was presented, which were transferred for further technological research in a foundry producing critical aircraft engine parts. The possibilities of using each technology in various applications were also discussed.

A Review of the Measurement and Control Technologies for the Critical Parameters of Microwave Drying Processes: Temperature and Humidity

A Review of the Measurement and Control Technologies for the Critical Parameters of Microwave Drying Processes: Temperature and Humidity

A bivariate dependent degradation model based on artificial neural network supported stochastic process and Copula function

AbstractIn order to use the high ability of the artificial neural network (ANN) in data fitting, this paper introduces an ANN in stochastic process to describe the mean function for degradation modeling. Due to the fact that the existing method cannot handle the bivariate dependent degradation conditions, a bivariate dependent degradation model based on Copula function and ANN‐supported stochastic processes is proposed. Considering the random effects caused by individual difference, it is assumed that the unknown parameters in the stochastic processes and Copula functions are randomly distributed. Based on the maximum likelihood and moment estimation methods, a related statistical inference method for ANN training and parameter estimation is developed to use the bivariate dependent degradation model. An actual fatigue crack dataset is used to demonstrate the validity of the proposed method. The obtained results show that the dependent relationship between two degradation indicators should not be neglected, and it can be efficiently handled by the proposed method. Furthermore, the proposed degradation model can provide reliability and degradation intervals with enough precision due to the fact that it considers the random effects caused by individual difference.

Morfológiai vizsgálatok Magyarország délnyugati határfolyóin = Morphological Studies on South-Western Border Rivers in Hungary

Nowadays, the preservation and sustainable use of our natural values are increasingly coming to the fore, and this has implications not only for economic and field development but also for ecological aspects. In recent years, attention has shifted towards river morphological studies, which were previously relegated to the background. Understanding long-term changes and assessing the current state is essential to estimate future processes. In this article, we present our morphological studies on the Hungarian–Croatian border sections of the Mura and the Drava rivers. Historical changes were analyzed through a comparative study of historical maps from earlier periods, while the evaluation of the current state was conducted based on a detailed terrain model from a recent survey. GIS evaluation was also compared with sediment transport and flow measurement results. This comparison suggests that the morphological status of the river section under study is consistent with the typical parameters of transport processes.

Correlations between Metabolic Parameters, Blood Cytokine Levels, and Behavioral Parameters in Male and Female Rats of Different Age after Intrauterine Stress.

We studied interrelationships between behavioral parameters, metabolic processes, and cytokine content in the blood of male and female rats at different stages of postnatal ontogeny after intrauterine stress (mothers were forced to swim in cold water from the 10th to the 16th day of pregnancy). Correlations between behavioral and metabolic parameters in prenatally stressed rats were revealed at an earlier age (day 21 of life) than in controls. In comparison with intact rats, in males exposed to intrauterine stress no relationships between these parameters were revealed at older age, while in females, their direction and character change on day 60 of life. In prenatally stressed males, correlations of the blood levels of IL-6 were revealed: inverse relationships with behavioral parameters on the days 21 and 30 and direct relationships with metabolic parameters on day 60 of life. In prenatally stressed females on day 30 of life, we observed negative correlations between IL-4 levels and metabolic parameters that were absent under normal conditions. Thus, intrauterine stress leads to reorganization of the relationships between the parameters of metabolic and immune processes, which are essential for the purposeful behavior of mammals. The effects of stress seem to depend on the sex of the offspring and stage of postnatal development.

Особенности построения системы экологического менеджмента организации с учетом вновь введенных национальных стандартов ГОСТ Р ИСО 14006—2022 и ГОСТ Р МЭК 62430—2023

The information on legislative documents of the Russian Federation regulating the activities of companies in the sphere of environmental management, considering environmental standards in design, has been provided. The relevance and feasibility of application of the system-structural process approach at the stage of development and implementation of the environmental management system, considering the environmental standards in design based on the standards GOST R ISO 14006—2022 and GOST R IEC 62430—2023, have been substantiated. The use of the process approach to the environmental management system development considering environmental standards in design based on the systems approach using the well-known PDCA principle has been methodologically substantiated; the basic issues of its applicability have been considered. A matrix of processes and subprocesses is compiled in accordance with the sections of GOST R ISO 14006 and the process typology as provided by ISO/TC 176/SC 2/N 544R3 (company’s management processes; processes of measurement, analysis, and improvement; key processes of resource management); the processes are ranked by their level. A model of the environmental management system has been developed considering environmental standards in design based on the process approach, Structured Analysis and Design Technique (SADT), and IDEF0 functional modeling methodology. The main context diagram of the environmental management system considering environmental standards in design in IDEF0 notation is developed. The decomposition of processes and subprocesses of the second and third levels has been completed; all their functional connections are described. The provided process modeling methodology allows reaching positive effect as to minimization of costs and time of the development of the environmental management system model, considering environmental standards in design. It has been demonstrated that the process approach applied to design of the system model of environmental management system considering environmental standards in design enables structuring the composition of processes, precise determining of input and output parameters of processes, participants of processes, resources, and ensuring process control and adjustment.

UTILIZING GAUSSIAN PROCESS REGRESSION FOR NONLINEAR MAGNETIC SEPARATION PROCESS IDENTIFICATION

This paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes. It aims to address the complex and dynamic nature of these processes by employing advanced modeling methods. The effectiveness of GPR is demonstrated through its application to simulated signals representing real iron ore separation processes, highlighting its potential to enhance existing models and optimize processes. Conducted within the MATLAB, this research lays the groundwork for further advancement and practical implementation. The utilization of GPR in magnetic separation offers innovative modeling of nonlinear dynamic processes, promising improved efficiency and precision in industrial applications.

Investigation of synthesis of some ionic liquids from nitrogen compounds and fatty acids of waste oils

In order to reduce the environmental pollution and the dependence on fossil fuel resource which is depleting, the utilization of waste vegetable oils for preparation of valuable products are of concern. Four ionic liquids triethyl ammonium carboxylate ([TEA][WO]), octyl ammonium carboxylate ([OcA][WO]), trialkyl methyl ammonium carboxylate ([Aliquat][WO]) and n-octyl methyl imidazolium carboxylate ([OMIM][WO]) were synthesized from four different nitrogen compounds and fatty acids of waste vegetable oils. The influence of some parameters such as solvent, reaction temperature, reaction time and molar ratio of acid and nitrogen compound (NC) on ionic liquid synthesis yields was investigated. The results show that the structure of nitrogen compounds effects on the synthesis performance and governs the parameters of processes. This is due to the nitrogen compounds as starting materials control not only their activity in reaction with fatty acids but also the solubility and the melting points of corresponding ionic liquids. Certain suitable conditions were chosen for each ionic liquid.

New Lepage-type control charts for joint monitoring of location and scale

This paper introduces two new Shewhart Lepage-type control charts for jointly monitoring the location and scale parameters of continuous processes. The first is based on the van der Waerden (VW) and Ansari-Bradley (AB) tests while the other is on the VW and Mood tests. Statistical performances, in terms of average run length (ARL), of these charts are evaluated numerically and compared with that of their two competitor charts in the existing literature under four distributional models for the process output, namely, the normal, lognormal, Laplace, and logistic. The numerical results show that irrespective of the process distribution, one of the proposed charts (based on VW and Mood tests) has the best performance among all the four charts in the detection of simultaneous shifts in both the parameters of all sizes as well as in the detection of shifts in the scale that have not been accompanied by shifts in the location (only-scale shifts) of all sizes. Moreover, the other proposed chart (based on VW and AB tests) performs better than one of the competitor charts in the existing literature for detecting simultaneous shifts as well as only-scale shifts of all sizes. In addition, it performs better than the other competitor chart for detecting the simultaneous shifts consisting of a large location shift and a small scale shift, however, performs worse than that for detecting the simultaneous shifts consisting of a small location shift and a scale shift of any size, as well as for detecting the only-scale shifts of all sizes. All the four charts perform almost similarly in the detection of only-location shifts of all sizes. Regression models are provided for the proposed charts, which facilitate their designs in practice. Finally, the practical implementation of the proposed charts is illustrated through a real-data example.