Industrial Production Control Research Articles

A Novel Model for Train Operation Adjustment in High-Speed Railway based on Max-plus Algebra

The running time of high-speed train is generally not late, and it can run normally in most cases. However, when severe weather conditions, train components and equipment, accidents or emergencies occur, it will lead to train operation delay and traffic congestion. Therefore, when an accident occurs, we need to adjust the train time or route timely and accurately. As an important algebraic system, max-plus algebra is widely used in the field of industrial production control. In industrial production, the most production mode is the discrete system , but the characteristics and the ability of discrete systems depends on the periodic of system and the number of workpiece produced by the system in unit time, and the characteristics of the system are closely related to the properties of the matrix, especially, the eigenvalues and eigenvectors of the matrix in the sense of max-plus algebra. Therefore, this paper studies the max-plus algebra theory and the solution of eigenvalues and eigenvectors of matrices in the sense of max-plus algebra, establish the operation time matrix to optimize the train operation adjustment model of high-speed railway, and analyze the failure propagation model.

COMPARISON OF PARTICLE SHAPES OF CONVENTIONALLY GROUND BARITE, CALCITE AND TALC MINERALS BY DYNAMIC IMAGING TECHNIQUE: A REVIEW

Shape of particles made by grinding is one of the important measures for determining the utilizations of industrial minerals namely barite, calcite, and talc particles, particularly at production (like coating pigments, paints, rubber and paper) and processing stages (beneficiation by flotation). Therefore, measurement of particle characteristics is a critical issue in the development and control of industrial mineral products in most of the industries for some demanding applications. Ball and rod mills are commonly used as conventional grinding mills to produce a controlled grind size for the flotation circuit in the beneficiation of industrial minerals. Dynamic Image Analysis (DIA) offers reproducible results of a huge number of particles for some industrial minerals namely, barite [1], calcite [2] and talc [3] particles, whose shapes are crucial for some industries utilized as fillers. Thus, this review is about the comparison of shape values in terms of circularity (C) and bounding rectangle aspect ratio (BRAR) determined by the real time DIA. It was found that the shape results of the previous studies for the same samples by SEM measurement [4] were in good agreement with DIA results. It was concluded that the more rounded particles were encountered in the rod milled products for calcite and barite minerals. On the other hand, the more elongated particles were found in the ball milled products for talc mineral. It was attributed to the material type since the same mills were used for all tests. Hence, DIA can be used as a useful tool, which is easy, fast and highly accurate to control the particle shape distributions whether the required powder is fit for use

Seasonal variations, source apportionment, and health risk assessment of trace metals in PM2.5 in the typical industrial city of changzhi, China

Studying the source-specific health risks of trace metals in PM2.5 is important for controlling its emission sources that are hazardous to human health. In this study, PM2.5-bound trace metals were analyzed during winter, spring, summer, and autumn in 2018 in Changzhi, a typical industrial city in China. The results revealed that the annual concentration of total trace metals in PM2.5 was 7531.9 ng m−3 in the study area, and the concentration was maximum in spring (14,781.7 ng m−3), followed by autumn (7163.8 ng m−3), winter (5237.0 ng m−3), and summer (3226.1 ng m−3). Dry and continual windy days in spring often led to high concentrations of trace metals in Changzhi. Trace metals with high enrichment factor (>100), such as Cd, Sn, Pb, and Zn, were predominantly released from coal combustion. Four sources, such as coal combustion, traffic emissions, resuspended dust, and industry, were established by positive matrix factorization, with contributions to PM2.5-bound trace metals of 29.5%, 26.6%, 26.3%, and 17.6%, respectively. In terms of their contribution to cancer risk, the top contributor both for children and adults was industry, with 1.50×10−6 and 5.99×10−6, respectively, both of which were higher than the safe level (1 × 10−6); coal combustion ranked second (0.49 × 10−6 and 1.94 × 10−6 for children and adults, respectively). In addition to the advancement of control measures for pollutants and control of industrial production, more attention should also be given in limiting coal consumption through the use of clean energy (e.g., natural gas).

Unraveling the Composition of Insecticidal Crystal Proteins in Bacillus thuringiensis: a Proteomics Approach.

Bacillus thuringiensis (Bt) is the most widely used active ingredient for biological insecticides. The composition of δ-endotoxins (Cry and Cyt proteins) in the parasporal crystal determines the toxicity profile of each Bt strain. However, a reliable method for their identification and quantification has not been available, due to the high sequence identity of the genes that encode the δ-endotoxins and the toxins themselves. Here, we have developed an accurate and reproducible mass spectrometry-based method (liquid chromatography-tandem mass spectrometry-multiple reaction monitoring [LC-MS/MS-MRM]) using isotopically labeled proteotypic peptides for each protein in a particular mixture to determine the relative proportion of each δ-endotoxin within the crystal. To validate the method, artificial mixtures containing Cry1Aa, Cry2Aa, and Cry6Aa were analyzed. Determination of the relative abundance of proteins (in molarity) with our method was in good agreement with the expected values. This method was then applied to the most common commercial Bt-based products, DiPel DF, XenTari GD, VectoBac 12S, and Novodor, in which between three and six δ-endotoxins were identified and quantified in each product. This novel approach is of great value for the characterization of Bt-based products, not only providing information on host range, but also for monitoring industrial crystal production and quality control and product registration for Bt-based insecticides.IMPORTANCEBacillus thuringiensis (Bt)-based biological insecticides are used extensively to control insect pests and vectors of human diseases. Bt-based products provide greater specificity and biosafety than broad-spectrum synthetic insecticides. The biological activity of this bacterium resides in spores and crystals comprising complex mixtures of toxic proteins. We developed and validated a fast, accurate, and reproducible method for quantitative determination of the crystal components of Bt-based products. This method will find clear applications in the improvement of various aspects of the industrial production process of Bt. An important aspect of the production of Bt-based insecticides is its quality control. By specifically quantifying the relative proportion of each of the toxins that make up the crystal, our method represents the most consistent and repeatable evaluation procedure in the quality control of different batches produced in successive fermentations. This method can also contribute to the design of specific culture media and fermentation conditions that optimize Bt crystal composition across a range of Bt strains that target different pestiferous insects. Quantitative information on crystal composition should also prove valuable to phytosanitary product registration authorities that oversee the safety and efficacy of crop protection products.

Individual and combined effects of frying load and deteriorated polar compounds on the foaming of edible oil

Foaming of frying oil has troubled the production but not yet systematically explained. In this research, individual and combined effects of 16 process, chemical and physical parameters of frying oil were tested. Prepared oil samples with different deterioration levels were further physically blended to avoid the interference of colinearity. A new integrated index to describe the Molecular Weight Disparity (MWD) was proposed and proved to be well correlated with both deterioration and foaming of oil (r = 0.845 ~ 0.997, p < 0.05). Effects of all indices except the peroxide value, anisidine value, total oxidation value and surface tension of oil were significant (p < 0.05) and their combined effect was presented by equations (p < 0.05). The effect of frying load was approximately 1.3 times higher than that of oil deterioration. Data will be helpful to seek efficient oil formulation and achieve intelligent control of industrial production.

A High-Accuracy Calibration Method Using Sensor Sensitivity Difference for Piezoelectric Dynamometer

Abstract High-accuracy measurement of force affects real-time control and process adjustment of industrial production. Piezoelectric sensor can be applied to measure force with the characteristics of high frequency and stiffness, great dynamic response and resistance to harsh conditions. As an important part of test, calibration determines sensitivity coefficient which directly affects test accuracy. To improve the calibration accuracy of offset loading force for multisensor piezoelectric dynamometer, a novel calibration method using sensor sensitivity difference is proposed in this paper. The calibration experiments of four sensors are performed to obtain the axial sensitivities and the force-to-electricity conversion coefficients. The multiple loading-point (MLP) calibration experiments of dynamometer are carried out, and the calibration results prove that the sensitivity difference of four sensors is the main reason affecting the calibration accuracy. The sensitivity difference calibration method (SDCM) is applied to the MLP calibration experiments, and the results show that the average resultant force deviations are reduced from 418.79 N (8.38% full scare (FS) to 24.16 N (0.61% FS) under a loading force of 5000 N, which proves high accuracy and reliability of SDCM. The calibration method will provide guidance for improving the calibration accuracy of dynamometer.

The approach to organization of the production multi-agent system

Multi-agent systems are usefully employed in banking sector, logistics and military commitments. In machine-building industry the trend line is implementation of digital productions and Internet of Things concepts. In our opinion combine of digital production and multi-agent systems gives the opportunity of production control improvement. In this paper we propose the approach to the organization of a production multi-agent system based on the service-oriented architecture. The agent’s technology is considered, services are separated and communication between them is defined. As the means of communication we propose the XML manifest that provides an opportunity to informational support of interaction between agents and services. The results of this work are relevant to building of a multi-agent system for production control in machine-building industry.

Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry

Digital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in petrochemical industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the petrochemical industry. This paper proposes a framework and approaches for constructing a digital twin based on the petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support petrochemical and other process manufacturing industries to dynamically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the petrochemical industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the petrochemical industry.

Monitoring Electrochemical Reactions in Situ with Low Field NMR: A Mini-Review

The number of applications of time domain NMR using low-field spectrometers in research and development has been steadily increasing in recent years with applications ranging from quality control of industrial products to the study of physical and chemical properties of a wide array of solid and liquid samples to, most recently, electrochemical studies. In this mini-review we summarize the progress that has been achieved in the coupling between time domain NMR (using low-field spectrometers) and electrochemistry and how the challenges that this coupling poses have been overcome over the years. We also highlight the effect that the static magnetic field of the NMR spectrometer has on the electrochemical systems.

Determination of fatty alkyl dimethyl tertiary amines by gas chromatography in the synthesis of cationic surfactants

A method to determine fatty alkyl dimethyl tertiary amines by gas chromatography (GC) was set up using HP-INNOWax capillary column, hydrogen flame ionization detector (FID) and temperature programming. The linearities were all excellent in the range of 0.005-1.0 g/L with the correlation coefficients being above 0.9996. The limits of detection (LODs, S/N=3) of the method were between 0.001 g/L and 0.002 g/L, and the limits of quantification (LOQs, S/N=10) were between 0.003 g/L and 0.005 g/L. The recoveries ranged between 90% and 130% with relative standard deviations of 1.3%-6.9% (n=6). The proposed method has the advantages of wide linear range, higher recovery, and selectivity, which was suitable for the quantitative analysis of fatty alkyl dimethyl tertiary amines and monitoring process control in industrial production. The method was faster and more accurate than titration, and also precluded the need for pre-column derivatization and determination by liquid chromatography-tandem mass spectrometry.

Modeling time series of RFID signal level in automated system for identification and control of industrial products

The article is devoted to the development of a RFID signal level model using time series for an automated system for the control and identification of industrial products. It describes the developed project for industrial products based on RFID technologies. It describes the developed RFID signal level model. Conducted experimental studies in the laboratory are described in the article.

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

MODERN REQUIREMENTS TO INDUSTRIAL PRODUCTION AND PHARMACEUTICAL MANUFACTURE OF INFUSION MEDICINAL PREPARATIONS IN THE RUSSIAN FEDERATION: SIMILARITIES AND DIFFERENCES

The quantitative reduction of the manufacturing pharmaceutical organizations significantly lowers public accessibility to medicinal preparations manufactured by pharmacies. The aim of this research has is the analysis of the normative legal standards regulating the industrial production and the pharmaceutical manufacture of infusion medicinal preparations in the territory of the Russian Federation. Materials and methods: The research has been carried out by the analysis of the current legislative and normative acts by means of documentary observation and content analysis. Results and discussion. Hereby the review has been done of the main acts along with normativelegal documentation regulating the industrial production and the pharmaceutical manufacture of infusion medicines such as federal laws, regulations of the Government of the Russian Federation, the State Pharmacopeia (Editions XI and XIII), orders of the Ministry of Health of the Russian Federation, etc. It has been established that in the Russian Federation there has been developed and currently functioning the system of mandatory requirements to industrial production and quality control of infusion medicinal preparations produced by manufacturing enterprises. At the same time, despite the restrictions on the pharmaceutical manufacture of the aseptic medicines registered in the Russian Federation, the pharmaceutical organizations implement manufacture of the medicines which are not produced industrially. That makes it possible to provide an individual dosage of ingredients and take into account patients’ individual characteristics. The normative legal regulation system for the pharmaceutical manufacture significantly differs from the similar system for the industrial production. The scientific discussion on the implementation of the international Rules of Good Manufacturing Practices (GMP) into pharmaceutical activities has not arrived at a unanimous organizational and technological opinion because of essentially different purposes, a diverse range of the undertaken tasks and dissimilar economic opportunities of pharmaceutical organizations and manufacturing enterprises. Conclusion: The currently existing normative legal regulation system for the industrial production and the pharmaceutical manufacture of infusion medicines in the Russian Federation is aimed at providing the national health services’ needs of necessary remedies for infusion therapy. At the same time, the pharmaceutical manufacture does not oppose the industrial production, but quite the contrary, expands public accessibility to such preparations for treatment in emergency or urgent cases and also during routine treatment of patients.

The balance of autonomous and centralized control in scheduling problems

The scheduling of processes in a network is a core logistic challenge with a multitude of applications in our complex industrialized world. Often, scheduling decisions are based on incomplete and unreliable information. Here, a simple rule of ’more information, better decisions’ may no longer hold and heuristics balancing global and local information, or centralized and autonomous control, may yield better performance. So far, only anecdotal evidence for the potential benefit of autonomous control in scheduling exists. Here, we explore this hypothesis within a minimal model derived from scheduling principles and the phenomenology of dynamical processes on graphs. In this model, centralized and autonomous control can be represented and quantitatively assessed, performance is well defined and problem complexity can be varied.Our model shows that a balance of centralized and autonomous control can enhance the performance in networks of decision-making entities. The mechanistic insight gained from the model also reveals the limitations of hybrid control setups: We find that communication at a high hierarchy level can give an advantage to centralized control. Counter-intuitively, it arises not from a higher degree of coordination and quicker convergence towards a common solution, but rather from an accelerated sampling of candidate choices leading to a measurable increase in information flow from higher to lower hierarchical levels. Our study allows us to formulate a new view of autonomous control in industrial production and derive a set of suggestions with the potential to enhance performance under realistic conditions of scheduling heuristics of jobs in a production process.

Conductivity mapping of graphene on polymeric films by terahertz time-domain spectroscopy.

Fast inline characterization of the electrical properties of graphene on polymeric substrates is an essential requirement for quality control in industrial graphene production. Here we show that it is possible to measure the sheet conductivity of graphene on polymer films by terahertz time-domain spectroscopy (THz-TDS) when all internally reflected echoes in the substrate are taken into consideration. The conductivity measured by THz-TDS is comparable to values obtained from four point probe measurements. THz-TDS maps of 25x30 cm2 area graphene films were recorded and the DC conductivity and carrier scattering time were extracted from the measurements. Additionally, the THz-TDS conductivity maps highlight tears and holes in the graphene film, which are not easily visible by optical inspection.

PLC Simulation Control of Assembly Line

This paper mainly introduces the application of PLC simulation control in industrial production, and requires the use of configuration software and PLC (SIMEINS S7-200) control system connection, the use of lower computer implementation, the host computer monitoring method to build complete assembly line simulation control system. Through the PLC simulation control and confi guration monitoring, this paper implements the assembly line control and monitoring.

Study on the plane induction heating process strengthened by magnetic flux concentrator based on response surface methodology

In the surface heat treatment process of metals, the induction heating strengthened by ferromagnetic metal powder bonded magnetic flux concentrator (MPB-MFC) possesses more advantages due to its high efficiency. However, the further research of strengthen mechanism of MPB-MFC is still needed. It is necessary to establish the analytical model for accurately control and optimization of the plane induction heating process with MPB-MFC. In this paper, the coupled electro-magnetic-thermal multi-physics FEM model of the MPBMFC assisted induction heating is established, which involves the consideration of the temperature dependent thermal and magnetic material properties. The comparison between the results of several middle-frequency experiments and the simulation proves that the precision of simulation model is reliable. In addition, the influence of combined input variables on the final temperature is studied based on the response surface methodology (RSM), which could provide a comprehensive understanding for the process control and optimization in industrial production.

Enhanced Single-Step Bioproduction of the Simvastatin Precursor Monacolin J in an Industrial Strain ofAspergillus terreusby Employing the Evolved Lovastatin Hydrolase

Biosynthesis of simvastatin, the active pharmaceutical ingredient of cholesterol-lowering drug Zocor, has drawn increasing global attention in recent years. Although single-step in vivo production of monacolin J, the intermediate biosynthetic precursor of simvastatin, has been realized by utilizing lovastatin hydrolase (PcEST) in our previous study, about 5% of residual lovastatin is still a problem for industrial production and quality control. In order to improve conversion efficiency and reduce lovastatin residues, modification of PcEST is carried out through directed evolution and a novel two-step high-throughput screening method. The mutant Q140L shows 18-fold improved whole-cell activity as compared to the wild-type, and one fold enhanced catalytic efficiency and 3 °C increased T5010 over the wild-type are observed by characterizing the purified protein. Finally, the engineered A. terreus strain overexpressing Q140L mutant exhibited the increased conversion efficiency and the reduced lovastatin residues by comparing with A. terreus strain overexpressing the wild-type PcEST, where almost 100% of the produced lovastatin is hydrolyzed to monacolin J. Therefore, this improved microbial cell factory can realize single-step bioproduction of monacolin J in a more efficient way, providing an attractive and eco-friendly substitute over the existing chemical synthetic routes of monacolin J and promoting complete bioproduction of simvastatin at industrial scale.

Видеотелеметрический контроль промышленных изделий

Видеотелеметрический контроль промышленных изделий

Temperature intelligent prediction model of coke oven flue based on CBR and RBFNN

The temperature of coke oven is an important process parameter, but it is difficult to obtain the temperature of the vertical flue in real-time. The establishment based on the case-based reasoning (CBR) and radial basis function neural network (RBFNN) of coke oven flue temperature intelligent prediction model, realise the real-time prediction of the temperature, and help to realise the coke oven production process of intelligent optimisation control. The real-time forecast under different conditions is realised by the selective intelligent forecasting model of the coke oven, and the forecasting performance of system model is simulated. The results show that the forecasting model is faster and more reliable than the traditional artificial forecast. Finally, combining with the actual data of a steel enterprise to verify, the results show that the model meet the actual working condition, it can provide relevant processing methods for the soft measurement of complex industrial production control process, and it has some practical significance for intelligent optimisation control.