Parameters Of Processes Research Articles

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.

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.

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

AbstractThe 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.

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.

Effects of finishing processes on physical properties of knitted fabrics produced by recycled cotton fibers

This study examines conventional wet-finishing processes applicable to fabrics containing recycled cotton fibers. The knitted fabrics were produced using open-end and compact yarns, with up to 80% of the cotton fibers being obtained from mechanical recycling of pre-consumer textile wastes. The study evaluated the effects of these finishing processes on the physical properties of the fabrics based on the recycled fiber ratio, yarn type, and yarn count. After determining the parameters for enzymatic bio-polishing, scouring, and color removal processes, two sequential finishing routes were designed: using the fabrics as mélange without a dyeing process (F1) or coloring them with color stripping and subsequent dyeing (F2). The resulting fabrics were tested for bursting strength, pilling resistance, weight, thickness, air permeability, and abrasion resistance, and the results were evaluated statistically. The study demonstrated that compact yarns provide superior strength, particularly when the F2 process was employed. The F2 process resulted in high pilling resistance and increased breathability of recycled knitted fabrics. The study concluded that the quality of fabrics containing recycled cotton fibers could be optimized by the selection of the appropriate finishing processes.

Biophysically interpretable inference of cell types from multimodal sequencing data.

Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or 'clusters' in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for 'clusters' through the governing parameters of cellular processes.

Implementing artificial intelligence to measure meat quality parameters in local market traceability processes

SummaryThe application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri‐food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products' production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognise that this is an evolving field. This technology offers innovative solutions that enable efficient, cost‐effective, and consumer‐oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real‐time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human‐machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri‐food and agri‐industrial sector, highlighting and emphasising the importance of ongoing innovation in the field.

A study of tin electrodeposition from ethaline: The electrode material effect

AbstractThis article studies the influence of electrode material on tin (Sn) electrodeposition from deep eutectic solvent. The Sn electrodeposition from ethaline‐based electrolyte onto glassy carbon (GC) and Pt substrates has been studied using cyclic voltammetry and chronoamperometry. The patterns and parameters of Sn nucleation and growth processes have been determined by means of Scharifker and Hills and Scharifker–Mostany models. Results show that Sn nucleation onto GCE follows instantaneous 3D nucleation, while in the case of PtE, it is controlled by adsorption, instantaneous 3D nucleation, and residual water reduction. The growth mechanism is diffusion‐controlled for both electrodes. The parameters of Sn electrodeposition onto GCE and PtE such as diffusion coefficient (D), nucleation rate (A), and active site density of Sn nuclei (No) are evaluated. The results showed that A and No increase linearly as the deposition potential is displaced towards more electronegative values while D is almost unchanged, regardless of the involved working electrode. The morphology and the structure of the electrodeposited Sn are also discussed based on scanning electron microscopy, X‐ray diffraction, and energy‐dispersive X‐ray spectroscopy investigations.

Statistical inference for the first-order autoregressive process with the fractional Gaussian noise

While the statistical inference of first-order autoregressive processes driven by independent and identically distributed noises has a long history, the statistical analysis for first-order autoregressive processes driven by dependent noises is more recent. This paper considers the problem of estimating all the unknown parameters in first-order autoregressive processes driven by the fractional Gaussian noise, which is a self-similar stochastic process used to model persistent or anti-persistent dependency structures in observed time series. The estimation procedure is built upon the marriage of the log-periodogram regression and the method of moments. The usual asymptotic properties, including consistency and asymptotic normality, are established under some mild conditions. Monte Carlo simulations are performed to demonstrate the feasibility and effectiveness of the proposed method. Finally, the estimation methods are applied to model some realized volatility time series, where we find that the realized volatility is rough. Moreover, the proposed model is compared with some alternative models, including the first-order autoregressive process driven by the standard Gaussian noise, the heterogeneous autoregression model with lag structure (1,5,21) (HAR(1,5,21)), the autoregressive fractionally integrated moving average model (ARFIMA(1,d,0)) and the scaled fractional Brownian motion, in forecasting the logarithmic realized volatility.

Modular stochastic configuration network with attention mechanism for soft measurement of water quality parameters in wastewater treatment processes

Due to the complex and multi-distributed nature of wastewater treatment process data, stochastic configuration networks (SCNs) face difficulties such as large network structure and complex construction process when dealing with data modeling tasks. To address these challenges, this paper proposes a modular stochastic configuration network with attention mechanism, which integrates the advantages of modular partitioning, attention mechanism, and SCN. The model employs a fuzzy mean clustering strategy to simplify and decompose the complex multi-case and multi-distribution nonlinear modeling task, thereby reducing the modeling complexity of a single model. Furthermore, the model utilizes a stochastic configuration algorithm to learn the subtasks and construct corresponding sub-networks. The output weights are then assigned to each sub-network based on the attention mechanism to obtain the model’s final output. The validity of the proposed method is validated through two benchmark experiments. Then the model is applied to measure effluent ammonia nitrogen. The results indicate that compared with the classical randomized and modularized network models, the proposed model exhibits certain advantages in terms of learning accuracy and generalization performance.