Processing Of Experimental Data Research Articles

Investigation on nanofiltration membrane fouling behaviour of cation-induced apam in strontium-bearing mine water

This paper investigates the effect of sodium, calcium and strontium ions interacting with APAM on the fouling behavior of nanofiltration membranes during mine water treatment. The membrane fouling behavior was assessed qualitatively and quantitatively by processing experimental data, characterization of fouled membranes and analysis of interfacial thermodynamic energy changes. The results show that the membrane fouling composition is mainly dominated by SrSO4, and the LW and EL interfacial interaction energies between APAM and cations during fouling formation are characterized by attractive forces, while the AB interaction energies are of the opposite nature, and it is mainly the AB interaction energy that affects the total interaction of the fouling surface thermodynamics. When Sr2+ is present, the AB interaction energy decreases significantly and the composite foulant adheres to the fouling membrane more easily. The addition of Ca2+ increases the repulsive forces and energy barriers, alleviating the degree of membrane fouling.

ON OPTIMIZING MINISCOPE DATA ANALYSIS WITH SIMULATED DATA: A STUDY OF PARAMETER OPTIMIZATION IN THE MINIAN ANALYSIS PIPELINE

In vivo calcium imaging is widely used technique in neuroscience to evaluate the activity of neuronal networks. The miniscope, a single-photon miniature fluorescent microscope, has made it possible to conduct in vivo calcium imaging in freely moving animals. Various algorithms and software packages have been developed for the analysis of miniscope data. This study investigates the relationship between the sensitivity of neuron detection and the processing parameters utilized in the Minian analysis pipeline at different noise levels. To achieve this objective, we generated simulated data possessing certain attributes of an experimentally derived dataset. Simulated data was generated with various noise levels and processed through to the Minian analysis pipeline. Based on our findings, we provide recommendations for optimal values of Minian pipeline parameters depending on different noise levels. The results obtained in this study may serve as a preliminary guide for selecting appropriate parameter values during the processing of experimental data using the Minian analysis pipeline. The findings of this study are expected to be relevant to neuroscientists involved in the acquisition and processing of miniscope data.

A Novel Support Vector Regression Algorithm for Processing Experimental Data with Perturbation

Processing experimental data with uncertainty is a huge challenge for robust regression modeling and high-accuracy data forecasting. The objective of the present study is the development of a robust support vector regression model for processing observed data of independent variables containing polyhedral perturbation and achievement of high forecasting precision. Firstly, the conception of the data collection of polyhedral perturbation is given and analyzed. Secondly, a novel robust support vector regression model is constructed by replacing the original independent variables in a linear support vector regression model with the independent variables containing polyhedral perturbation. Thirdly, the novel robust linear support vector regression method is also expanded to the non-linear regression model. Both the two models are validated by the linear and nonlinear numerical regression experiments. Comparison of the experimental results show that the proposed method provide more accurate predictions than the traditional regression method for processing data with polyhedral perturbation.

A port and starboard fuzziness suppressed algorithm based on vector directivity zero

Conventional linear array based on acoustic pressure hydrophone can not distinguish the target signal from the port side, so it can not locate the target accurately. The vector hydrophone has natural directivity and can be resolved from starboard to starboard after being formed. However, in practical engineering, it is necessary to further improve the resolution of starboard to starboard because of noise or interference, etc. A new method of port and starboard suppression based on directivity of vector linear array is proposed. The simulation results show that the starboard suppression ratio of vector beam output is enhanced. Experimental data processing results show that this method can suppress the starboard ambiguity of vector beamforming and improve the starboard resolution of vector linear array. The processing results verify the feasibility of the algorithm, and the algorithm has certain application value.

CONSTRUCTION OF STATISTICAL MODELS OF CHEMICAL AND TECHNOLOGICAL OBJECTS ON THE BASIS OF A COMPLETE FACTOR EXPERIMENT

The article deals with the construction of a mathematical model of the process, which is created based on the results of a full factorial experiment. Often, only the results of experimental studies carried out at such objects are the information, based on the processing of which, according to the obtained mathematical model of the object, a high-quality control algorithm for this object can be implemented. At the same time, the construction of a mathematical model that describes the technological process with the required accuracy often involves a huge amount of work at the stage of experiments. The task of planning experiments is to establish the minimum required number of them and the conditions for their implementation, to choose methods for mathematical processing of the results, and to make decisions. Designing experiments significantly reduces the number of experiments required to obtain a process model. An active experiment involves a purposeful choice of factor values. Compared to a passive experiment, such an experiment turns out to be more efficient and economical, since the amount of experimental work is significantly reduced. In this article, in relation to chemical technology, the issues of organizing an experiment, mathematical processing of experimental data and mathematical planning of an experiment in the search for optimal conditions for conducting a technological process are described. Mathematical modeling and optimization of chemical technology objects is of great importance for obtaining higher professional education for students of the Educational and Scientific Institute of Chemical Technology and Engineering.

Developing a strategy for improving mayonnaise with different proportions of vegetable oils

The object of research is the process of forming a strategy for the improvement of mayonnaise. Based on the SWOT analysis, a strategy for improving mayonnaise with a different ratio of sunflower, linseed and hemp oils was formed in order to increase the content of polyunsaturated fatty acids, in particular the Omega-3 family, reduce its calorie content, and develop a strategy for bringing the product to the market. During the research, the following methods were used: economic-statistical, SWOT analysis method, expert method, analytical, program-targeted, as well as statistical methods of experimental data processing. The work solves the task of forming a strategy for improving the recipe composition of industrially produced mayonnaise and bringing it to the market. Today, the market offers a huge number of mayonnaises with different compositions and different quality, with low, medium and high fat content, with the addition of various additives that are undesirable for the human body. Based on the broad possibilities of using mayonnaise as an additional food product, the task arose to make changes in its composition, improving the biological and nutritional value of mayonnaise with an increased content of polyunsaturated fatty acids, in particular of the Omega-3 family. Such a modified food product can be considered functional because, on the one hand, polyunsaturated fatty acids (PUFAs) of the Omega-3 family are in short supply in the diet of the average Ukrainian, and on the other hand, they play an important role in the prevention of diseases of the cardiovascular, nervous, and immune systems of the body. The conducted analysis of economic, social and technological factors characterizing the products offered on the mayonnaise market made it possible to formulate a strategy for improving mayonnaise with different proportions of sunflower, linseed and hemp oils, as well as bringing it to the market using marketing tools. The practical implementation of these proposals will make it possible to bring mayonnaise with reduced calorie content to the market, which will contain a significantly higher content of PUFAs of the Omega-3, Omega-6 and Omega-9 family, which are useful for the human body. Therefore, the use of linseed and hemp oils is recommended for supporters of healthy food, which makes it popular among the main groups of consumers.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk.

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

Development of the helicopter turboshafted engine regulator

The subject of research was the development of the RDC-450M-117V – the TV3-117VMA-SBM1V digital engine regulator – based on the RDC-450M previously created at the enterprise for the AI-450M engine, considering differences in engine characteristics and exploitation conditions. The work purpose was to develop RDC-450M-117V based on information about aircraft engine characteristics, including refinement of its mathematical model based on experimental data, taking into account the exploitation condition difference. The tasks faced by the developers were to determine the optimal regulator structure in view of exploitation conditions, refine the mathematical model of the engine, and study the characteristics of the pump-regulator, to select the optimal processing form for the signal of rotation sensors for torque measurement. The methods that were applied were experimental engine and pump-regulator characteristics study on the stands of JSC "Element", JSC "Motor Sich", and on pump-regulator manufacturer’s stands; processing of experimental data using numerical simulation with subsequent statistical analysis. The rotation sensors signals experimental data (waveforms) were studied using a quantitative analysis method. The results. Design and software documentation was developed considering the requirements of international standards DO-254 and DO-178C, and a prototype of the regulator new modification was made. The engine mathematical model was refined, on the basis of which the stand-imitator of aircraft engines was finalized for a new type of engine. An adaptive algorithm for controlling the pump-regulator was developed, which made it possible to ensure stable maintenance of fuel consumption, despite the unstable operation of the pump-regulator. Using a quantitative analysis of the torque sensor signal waveforms, characteristic points were found that ensure the choice of the optimal level of comparison (measurement). The pilot regulator prototype passed tests including electromagnetic compatibility and lightning resistance in specialized laboratories in Ukraine. The regulator was tested as part of the engine at the Motor Sich JSC stands and as part of a helicopter at the foreign customer site, including the first demonstration flight. The scientific novelty of the obtained results are: the clarified engine TV3-117VMA-SBM1V mathematical model was formed and implemented in a stand-imitator; the adaptive algorithm for controlling the pump-regulator was formed and allowed to provide stable fuel consumption maintenance, despite the pump-regulator unstable operation; and the optimal processing form for the signal of rotation sensors for measuring torque was selected. Practical significance. A new engine regulator was developed and tested, and it provided the first helicopter demonstration flight.

Training future psychologists to solve research tasks using regression analysis

Mathematical statistics methods are used in many fields of psychology and pedagogy as a tool necessary for processing research results of various nature.
 The skills of research activity are necessary for future bachelors studying in the specialty 053 Psychology, both when writing coursework and qualification papers, and in the future in professional activities or further studies in master's and postgraduate studies.
 The task of identifying the quantitative dependence between characteristics is most often found in psychological and pedagogical research.
 However, the question of the correctness, correctness of the use of certain methods of mathematical statistics in the processing of experimental data of psychological and pedagogical research, due to the specificity of the data itself, remains relevant, since the wrong choice of methods and criteria will contribute to the application of erroneous statistical procedures and the formulation of incorrect research conclusions. Therefore, a detailed study of methods, analysis and solution of research tasks is a necessary part of the process of training a competent psychologist.
 The training of bachelors in the specialty 053 Psychology at the Kharkiv State Academy of Physical Culture to solve research problems using regression analysis is carried out as part of the study of the discipline «Computer processing of experimental research data».
 The topic «Regression analysis» is studied in lectures and practical classes.
 The concepts of regression, regression model, forecasting are considered at the lecture. General characteristics of regression analysis methods and problems. Estimating the parameters of paired and multiple linear regression using the method of least squares.
 The article provides materials on the application of regression analysis, which can be used in the statistical analysis of a research experiment when performing coursework, qualification works for bachelors and masters, and dissertation works. The development of skills and abilities to solve applied research problems using the methods of mathematical statistics in practical classes is an integral part of the process of training future psychologists.

Efficiency of growth regulators in corn crops of the Northern Steppe of Ukraine

The continuous increase in prices for mineral fertilisers for corn limits the use of fertilisers, which leads to the search for unconventional sources of food elements, and in particular, the use of biological natural and synthetic growth stimulators that are not harmful to the environment and allow wider use of the entire genetic potential of corn. The purpose of the study was to examine the influence of various plant growth regulators on the intensity of photosynthesis, development, growth and productivity of corn of various ripeness groups in the steppe of Ukraine. The field scientific study was conducted according to generally accepted methods of research, followed by the use of mathematical processing of experimental data using variance analysis. An increase in the chlorophyll content was identified when Avangard Grow Amino and Avangard Grow Humate preparations were applied compared to Vympel 2 and Alpha Nano Grow by 11.3-23.7%. The increase in grain from the use of growth stimulators on corn was in the early-maturing hybrid DN Pivikha – 0.13-0.37 t/ha (2.7-7.7%), mediumearly DN Khortytsia – 0.85-1.08 t/ha (16.6-18.5%), medium-maturing DN Julia – 0.20- 0.22 t/ha (3.20-3.4%), medium-late DN Olena – 0.05-0.53 t/ha (0.65-7.6%). Among the preparations used, Avangard Grow Amino and Avangard Grow Humate should be highlighted, which provided a tendency to increase the crude protein content to 6.42- 8.4%, or by 0.12-0.48 percentage points more compared to the control (3.53-4.71%), and the crude fat content to 3.73-5.52%, or by 0.20-0.81 percentage points. The use of growth stimulators in the conditions of the Northern Steppe of Ukraine on corn, especially Avangard Grow Amino and Avangard Grow Humate on early-maturing and medium-early corn hybrids, allows increasing the gross yield of high-quality grain by 7.6-18.4%

The Random Vibrations of the Active Body of the Cultivators

The article continues the exposition of the results obtained in researching an agricultural machine for processing soil, designed for research with applications including exploitation. The MCLS (complex machine for soil tillage) was designed to research the working processes of the instruments intended for soil processing. The MCLS cultivator is a modulated machine (it can work for three working widths: 1, 2, and 4 m, with tractors of different powers) that is designed to use a wide range of working bodies. The experimental data obtained with the structure with a working width of 1 m and the results of their processing within the framework of the theory of random vibrations are presented in this article. The experimental results are analysed as random vibrations of the supports of the active working bodies. As a result, the main characteristics of random vibrations are exposed: the distribution function, the average value, the autocorrelation, and the frequency spectrum. These general results regarding random vibrations are used for several critical applications in the design, execution, and exploitation of some subassemblies and assemblies of agricultural machines of this type. The main applications include estimating the probability of the occurrence of dangerous load peaks, counting and selecting the load peaks that produce fatigue accumulation in the material of the supports of the working bodies, identifying some design deficiencies or defects in the work regime, and estimating the effects of vibrations on the quality of soil processing. All of the outcomes are composed of applications in MCLS research and exploitation. The applications pursue well-known objectives of modelling the working processes of agricultural machines: safety at work, increasing the quality of work, optimising energy consumption, and increasing productivity, all in a broad context to obtain a compromise situation. The material and the method are based on experimental data acquisition, processing, and interpretation.

Ionmob: a Python package for prediction of peptide collisional cross-section values.

Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing. We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task. The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.

Study on low cycle fatigue of ferritic-martensitic steel at high temperature

In 2011, a serious nuclear accident occurred in Fukushima, Japan, and one of the major causes was the destruction of the cladding material mainly made of Zr alloy due to a violent chemical reaction with high-temperature water vapor. Since then, scholars in various countries have been trying to find a better nuclear fuel cladding material. Because of its high temperature strength, ferritic/martensitic(F/M) steel is used in thermal power units. Over the past hundred years, the high temperature performance of ferritic/martensitic steel has developed simultaneously with the operating temperature of thermal power system. F/M steel is widely used in nuclear power system as well as thermal power, The fatigue life of nuclear fuel cladding material is an important parameter to study its service life, due to the special shape of the cladding material, especially for the pipe with a thickness of less than 0.5mm, it is difficult to directly measure its mechanical properties, this paper through special experimental materials and fixtures F/M pipe steel at room temperature and high temperature tensile test, as well as low cycle fatigue performance test, and its fracture port, surface crack analysis. Through the analysis and processing of experimental data, typical stress-cycle life curves, total strain amplitude-life curves, elastic strain amplitude-life curves, hysteresis curves with different strain amplitudes, and fatigue life prediction curves were obtained.

THE PARALLEL BETWEEN ARTIFICIAL INTELLIGENCE AND NEUROSURGERY

The prowess of neurosurgeons emerges from decades of holistic training centered on technical skills, phasic clinical data collection, decision making, care and recovery. Modern research has opened a discussion regarding a remarkable surge in the importance of artificial intelligence (AI) in neurosurgery and its foundational procedures. AI has proven itself to be an invaluable asset in neurosurgery by complementing the aforementioned expertise of neurosurgeons to provide exceptional patient care throughout the pre-operative, intra-operative and post-operative stages of neurosurgery by enhancing diagnostic and prognostic outcomes in clinical treatment and mitigating the pressures associated with manual decision making taken on by neurosurgeons to improve patient outcomes. AI also contributes to the production, processing and storage of clinical and experimental data and has been observed to decrease risks associated with neuroanaesthesiology. The implementation of AI in neurosurgery can globalize high-quality healthcare with central focus on developing nations and thereby reduce the costs associated with surgical procedures. Additionally, mutualism between AI and neurosurgery can be facilitated, where both fields lead neurosurgeons and AI engineers into further exploration and more complex procedures to possibly re-define traditional methods in AI and neurosurgery and ultimately solve healthcare-related global issues. The aim of this review is to explore the capacity of AI in aspects of neurosurgery and investigate the significance of its contributions with regards to the capabilities of human workers

Use of artificial neural networks to assess the impact of electromagnetic interference

Statement of a problem. In the process of testing electronic devices for the effects of electromagnetic interference, it is necessary to process large amounts of data. Such data includes information about the operation of the device under the influence of electromagnetic interference (EMI), configuration (EMI), polarization and many other parameters that affect the assessment of the quality of electromagnetic compatibility (EMC). Based on the fact that most modern devices operate at high and ultra-high frequencies, the amount of data coming from them can reach tens of gigabytes. Analytical processing of such data arrays can be difficult, and in some cases impossible at all. An alternative option for processing such arrays is the use of systems based on artificial intelligence and neural networks of various configurations. Purpose. Conducting an effective assessment of the behavior of the device when it is exposed to electromagnetic interference. Results. A method of using an artificial intelligence system for processing experimental data when testing electronic devices for EMF exposure has been developed. Practical importance. The developed method is aimed at improving the efficiency of the hardware and software complex for assessing the effect of electromagnetic interference on electronic means. Improving the quality of data processing will make it possible to more accurately predict the behavior of the device when working in a real operating environment, as well as to point out to the developer potentially problematic places in the design of the electronic means.

Algorithm for automation of the method of testing statistical hypotheses according to the Pearson criterion by the means of MATLAB

The purpose of the work is to develop and improve the methods of computer modeling and digitalization of procedures for testing statistical hypotheses according to consent criteria for use in high-tech software and measuring complexes of automated control systems. In most cases that are important for practice, the analysis of the statistical properties of distributions requires multiple checks performed using numerical methods, which require the tools of powerful software and computing environments. Examples of such cases are the procedures for finding information subsets of features in regression and statistical analysis, as well as identifying parameter deviations in automated control systems. It is noted that the tasks of processing experimental data do not always fit into the framework of the theory of normal processes and have exclusively such hypotheses. To test such hypotheses, modern mathematical statistics has developed criteria based on specially developed probabilistic rules that distinguish between zero and alternative hypotheses. Moreover, if for the testable (zero) hypothesis the distribution of statistics is known exactly or approximately, then for the alternative it has a greater degree of uncertainty and it (the alternative) itself is not a complete negation of the null hypothesis. As the zero and alternative hypotheses move away from each other, the statistics of the consent criterion begins to take a larger absolute value than with the null hypothesis, which allows us to build a critical area on the basis of estimating the set of this statistics values. However, the use of analytical methods for statistical analysis to determine the discrepancy between empirical and hypothetical statistics in practice is associated with the use, as a rule, of complex computational calculation algorithms and the implementation of high requirements for the assessment accuracy, which they cannot provide. To solve this problem, it is proposed to use numerical methods of computer modeling and integration of probability density of distribution according to the Simpson algorithm using the tools of built-in functions of the Statistics Toolbox of the MATLAB environment. The proposed method for automation of statistical analysis of zero and alternative hypotheses is implemented on the example of applying the Pearson criterion to verify compliance with the normal distribution of two random data samples, one of which meets the normal distribution law. The obtained graphic and numerical results of computer modeling confirm the correspondence of one of the testable distributions to a given normal distribution according to the null hypothesis.

Аналитическое представление зависимостей силы трения от температуры при фрикционном взаимодействии пространственно структурированных каучуков со стальной поверхностью

The analysis of the known friction-temperature laws is carried out in the article. A mathematical model of external friction of F.F. Ling and E. Seibel coefficient dependence on temperature, activation energies of formation and destruction of friction bonds and other factors, developed using the equations of absolute rates of chemical reactions, is analyzed. An approxima-tion of this model is implemented in temperature independence case for Bowden and Tabor shear strength. A mathematical model, describing sliding friction force-temperature relation under frictional interaction of spatially ordered rubbers hav-ing steel surface, is proposed. A distinctive feature of the proposed mathematical model is that it can simultaneously describe areas of constancy, friction force linear and nonlinear scaling under temperature changes. The testing and verification of the developed mathematical model is fulfilled through digitizing and processing experimental data, obtained by the fric-tional interaction of bars, made of spatially ordered natural rubber and spatially ordered rubber SCS-50 with a prism made of steel st.3. Analyzing the approximating dependencies, it is found, that for spatially ordered natural rubber, the maximum value of the friction force is 2,0 kgf under the temperature of approximately 37,6 ℃, and the average value of the friction coefficient is 0,987, for spatially ordered natural rubber, the maximum value of the friction force is 1.84 kgf under the tem-perature of approximately 31,4 ℃, while the average value of the coefficient of friction is 0,853. New tribotechnical charac-teristics have been introduced making possible to give a more detailed characterization of the frictional interaction in the rubber-steel system for the cases of temperature changes.

The influence of agricultural practices on photosynthetic activity and productivity of a new variety of cannabis in the Middle Volga region

Relevance. In 2023, a new variety of single-domed drug-free cannabis, Lyudmila, of the fibrous direction of use, passed the State Examination and was approved for accreditation for admis-sion to use on the territory of the Russian Federation. In this regard, research on the development of elements of cultivation technology in specific soil and climatic conditions is of scientific and practical interest.Methods. The scientific experiment is based on methodological guidelines for registration tests offungicides in agriculture and methodological guidelines for conducting field and vegetation tests with cannabis. Indicators of photosynthetic activity of plants in crops were determined by the method of A.A. Nichiporovich. Mathematical processing of experimental data was carried out by the method of dispersion analysis according to B.A. Dospekhov.Results. Foliar top dressing contributed to an increase in the assimilation surface of the leaves in the variants with the treatment of plants for vegetation with preparations: Izagri Phosphorus — from 111.0 to 155.7 thousand m2/ha, Izagri Vita — from 112.2 to 139.7 thousand m2/ha, Humate +7 — from 135.9 to 148.4 thousand m2/ha (with 101.1–114.3 thousand m2/ha under control). The high yield of stems was obtained when sowing with a seeding rate of 3 million germinating seeds per hectare — 15.67 t/ha, with a decrease in the seeding rate to 2.5 and 2.0 million germinating seeds per hectare, the yield of stems decreased to 14.48–12.98 t/ha. Foliar treatments are effective, the yield of stems on the treated variants is higher than the control by 0.36–0.9 t/ha. The highest seed yield was obtained when sowing with a seeding rate of 2.5 million germinating seeds per hectare, on average 0.98 t/ha.

Application of equivalent source intensity density interpolation in near-field acoustic holography

The spatial resolution of near-field acoustic holography based on the equivalent source method (ESM) is closely related to the number of measurement points, the higher the number of measurement points, the higher the resolution. However, the number of measurement points in the actual measurement cannot be increased infinitely. To solve the contradiction between the resolution and the number of measurement points, this paper proposes an equivalent source density interpolation method (ESDIM). First, the equivalent source intensity is obtained using the sound pressure measured by the array element and the Green function, and the equivalent source intensity density is obtained based on the equivalent source intensity and grid area. Second, the Hermite interpolation function was used to obtain the interpolated equivalent source intensity density. However, as the number of interpolated grids increased, the resolution, computation, and running time of ESDIM increased, and the number of subdivided grids per unit grid was 9–25 in this study. Finally, the sound field was reconstructed based on the obtained interpolated equivalent source intensity and Green transfer function, and the reconstruction accuracies of ESDIM and ESM were compared and analyzed. The simulation and experimental data processing results showed that the resolution of the equivalent source intensity density interpolation method was higher than that of the ESM.

Enhanced compressive strength of preheated limonite pellets with biomass-derived binders

Sodium lignosulfonate (SL) is a powder-like by-product of the pulp and paper industry derived from lignocellulosic biomass. SL can potentially be a low-cost and highly efficient binder for the pelletization of iron ore powder in the ironmaking industry because it is a natural polymer compound containing multiple functional groups, such as hydroxyl, carboxyl, and sulfonic acid. In this paper, the strong organic binder, namely SL, was used to enhance the compressive strength of the limonite pellet, which is required to reduce VOC emission during the iron-making process. The limonite pellets' pellet size variation and compressive strength prepared with SL-based binders were studied at a temperature range from 900 °C −1100 °C. Experimental results showed that the compressive strength of the preheated pellets was the highest at a preheating temperature of 1100 °C, reaching 342.55 N/P which is qualified for the subsequent iron-making process. These experimental results provide essential references for optimizing the preparation of limonite pellets with SL-based binders. Furthermore, this study employed artificial neural networks to process experimental data and generate predicted values for corresponding experimental conditions. The predicted results closely matched the observed values, further assisting enterprises in accelerating their digitization and automation processes.