Monitoring Of Production Processes Research Articles

Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4.0

With the rise of Industry 4.0-related technology in the plastic and composite industry, a new wealth of data from the production process is becoming available to manufacturers. The effective utilization of this data towards improving quality and output is therefore of critical importance but requires knowledge of the data that is truly useful and the application of that data to pre-developed models or trained algorithms. Accordingly, in this research, 12 different online data sources in the injection molding process are evaluated to determine their relative degree of importance in predicting variations on final part quality indices, namely part weight, thickness, and diameter. These data are obtained during each injection molding cycle using a data acquisition system connected to eight in-mold sensors and four machine data sources. Three distinct types of perturbations are introduced into the process to challenge the range of detection capacities of these various data sources: shot size variations, material disturbances, and shutdown of the mold cooling system. The resultant curves from these studies are then analyzed for critical values, and partial least square (PLS) regressions performed using the extracted values as predictors and the final part quality indices as responses. Using the standard coefficients from the PLS analysis, rankings of the correlations between the extracted values and final part quality indices are generated, indicating which data sources best detected variations in the final produced parts for each of the three perturbations.

A simple approach to multivariate monitoring of production processes with non-Gaussian data

Statistical monitoring of advanced production processes is becoming increasingly challenging due to the large number of key performance variables that characterize a process. These variables often are non-Gaussian, highly correlated and exhibit non-linear dependencies. Traditional multivariate monitoring methods handle non-Gaussian data through combining both independent component analysis (ICA) and support vector data description (SVDD). However, redundant independent components not only increase the modeling complexity of SVDD but also reduce the accuracy of the monitoring. In this article, we solve the above problems by introducing a Laplacian based weighting score to adjust the ICA-SVDD procedure. The key aspect of our model is that independent components are automictically selected using a Laplacian algorithm, which are inputted to a SVDD model to determine the control limits. The advantageous features of the proposed method are demonstrated through a numerical study as well as a case study which concerns an application to a hot rolling process for monitoring steel production processes.

Implementation of a UAV-sensory-system-based hazard source estimation in a chemical plant cluster

Since there has been growing concern about the damage that atmospheric leakage and dispersion accidents may have done to human beings, researchers are dedicated to study effective and feasible source estimation methods in chemical plant clusters. In this paper, the safety monitoring of chemical production process is conducted via an unmanned aerial vehicle (UAV) monitoring system. Based on the observed data from this system, a source estimation method incorporating Bayesian inference and Particle Swarm Optimization (PSO) is proposed. Furthermore, the method is extended to discuss the source tracing algorithm using game theory in the UAV system. Finally, a practical case study is carried out to verify the feasibility and credibility of the proposed method. Results show that the method and system are helpful for safety monitoring and risk assessment in a chemical plant cluster.

Sensitivity of a continuous hot-melt extrusion and strand pelletization line to control actions and composition variation

The purpose of this work was to develop a robust hot-melt extrusion and strand pelletization process for manufacturing pellets with an immediate release (IR) of a poorly water-soluble active pharmaceutical ingredient (API), nimodipine. The robustness of pharmaceutical continuous manufacturing processes and of its control strategy is vital for competitiveness to traditional batch-manufacturing. Therefore, first the sensitivity of product quality, process stability, and process monitoring tools to i) parameter changes due to control actions and ii) typical process deviations, i.e., feeding errors, was investigated in a design of experiments (DoE). Thereby, die melt pressure was found to be highly sensitive to composition deviations, i.e. a limiting factor for process stability. Especially critical were deviations to increased HPMC content, since it behaved as a filler in the melt. Pelletization, or pellet size and size distribution respectively, were found to be sensitive to an increased throughput, due to the resulting insufficient strand cooling before the pelletizer. API dissolution in contrast, was found to be robust across the entire investigated range of formulation and process settings. Second, a design space for the production of IR pellets for subsequent tableting was established, and finally, a technical control strategy was developed to ensure a robust process. Near-infrared (NIR) spectroscopy was applied to monitor API content and the sensitivity of the residence time distribution (RTD) was investigated by means of tracer measurements. NIR-based API content monitoring and RTD models for material tracking were found to be at risk after processing melt with high HPMC content, due to a lack of purging by less viscous formulation compositions.

Principal component analysis for process monitoring in distributed system environment

SummaryIn modern industrial production processes, process monitoring plays a major role in improving process efficiency and quality of product, and it is an important task in industrial process. The best way to implement process monitoring is to develop models that describe different phenomena in physics, chemistry, and processes. However, because the modeling of industrial processes is often very complex and has significant intrinsic nonlinearities, a reasonable theoretical modeling method are often impractical. With the adoption of distributed control systems, the application of multivariate statistical monitoring is becoming more and more popular in the distributed system environment. This paper uses the technology for multivariate monitoring of continuous production processes. Based on principal component analysis (PCA) and making full use of system statistics information, the method can well deal with the nonlinear and multimode distribution of industrial data, which is difficult to be dealt with by traditional methods. This method can be used not only in process detection but also in fault detection. Finally, the method is applied to the actual example and the Tennessee‐Eastman model; the simulation results prove the feasibility of the proposed method and achieve better results.

Analysis on Main Issues and Key Technologies of Real-time Monitoring of Veterinary Drug Production Process

Veterinary drug quality and safety is the key to the healthy development of animal husbandry. Veterinary drug quality is directly related to the safety of animal products and human health. In recent years, the Chinese government has promulgated regulations Good Manufacturing Practice of veterinary drugs and other regulations on the supervision of veterinary drug production process. However, the manufacture and sale of fake and inferior veterinary drugs and the illegal use of veterinary drugs are still prominent. It is urgent to further strengthen the quality and safety supervision of veterinary drugs. In the process of veterinary drug production, the identification and monitoring technology of HACCP key control points in veterinary drug production is studied, the dynamic real-time perception technology equipment of GMP key points in veterinary drug production is developed, the video analysis method of production operation specification is explored, and the traceability coding of minimum sales unit of veterinary drug is optimized. All these methods can realize the dynamic information monitoring on the production process of veterinary drugs. The research and implementation of the above-stated methods can strengthen the effective supervision of the source of veterinary drug production, ensure safe use of animal drugs, further improve the quality of livestock products, ensure food safety of people, and have significant economic and social benefits.

CEP ONLINE: A WEB-ORIENTED EXPERT SYSTEM FOR STATISTICAL PROCESS CONTROL

ABSTRACT In this paper, a new software for Statistical Process Control (SPC) is proposed. The system, the so-called CEP Online, was developed based on statistical computing resources of well-known free softwares, such as HTML, PHP, R and MySQL under an online server with operating system Linux Ubuntu. The main uni and multivariate SPC tools are available for monitoring and evaluation of manufacturing and non-manufacturing production processes over time. Some advantages of the new software are: (i) low operational cost, since it is cloud-based, only needing a computer connected to the Internet; (ii) easy to use with great interaction with the user; (iii) it does not require investment in any specific hardware or software; (iv) real time reports generation on process condition monitoring and process capability. Thus, the CEP Online offers for SPC practitioners fast, efficient and accurate SPC procedures. Therefore, CEP Online becomes an important resource for those who have no access to non-free softwares, such as SAS, SPSS, Minitab and STATISTICA. To the best of our knowledge, the CEP Online is unique with respect to its characteristics.

Network fault diagnosis and rectification Suggestions for 300MW unit under I/A Series system

With the progress of science and technology, DCS has been widely used in the field of power plant automation. It integrates modern computer technology, modern control technology, modern communication technology and CRT technology to realize the monitoring and control of power production process. DCS network adopts 10Mbps Ethernet, which is the data transmission channel of DCS system. Its stability is related to the safe operation of the whole power plant. Therefore, more and more power generation enterprises begin to attach importance to network security management of DCS. In this paper, the author puts forward his own views on the network failure phenomenon of Foxboro I/A Series system, and gives the solution, hoping to have some reference significance for the safety management work of other similar DCS network of the unit.

CONDITIONS FOR THE IMPLEMENTATION OF THE FOOD SAFETY MANAGEMENT SYSTEM (HACCP) IN UKRAINE: ADMINISTRATIVE AND LEGAL ASPECT

Purpose: to explore the regulatory bases for the implementation of the Hazard Analysis and Critical Control Points (HACCP) system in Ukraine as a factor in food safety. Methods: documentary analysis and synthesis, comparative analysis, objective truth, cognitive-analytical, etc. Results: generalized requirements for the development, implementation and application of permanent procedures based on the principles of the Food Safety Management System (HACCP). Discussion: clarified the provisions on the introduction of prerequisites by market operators, the principles of the HACCP, the development of permanent monitoring procedures, a simplified approach to the application of the HACCP system for certain market operators, etc. Requirements for safety and quality of food products were considered to be of particular relevance at all times, because non-compliance with these requirements could be detrimental to human life and health. Food safety is one of the main areas that determines the health of the population, preservation of the gene pool of the nation. At the same time, the decision of these issues is related not only to the quality control of the final product, but also to the monitoring of production processes throughout the production chain for the timely detection of hazardous products. This monitoring system was called Hazard Analysis and Critical Control Points (HACCP) - a system that identifies, assesses and controls the hazards that are critical to the safety of food products. The EU considers HACCP to be the most effective method of ensuring food safety and requires all food industry enterprises to implement, observe and implement procedures based on the principles of HACCP. The principles of the HACCP, the program-preconditions of the HACCP system, the sequence of the development of the HACCP system, are formalized in accordance with the Requirements for the development, implementation and application of permanent procedures based on the principles of the Food Safety Management System (HACCP) (from 01.10.2012 № 590). The analysis is of practical importance for the practical implementation of the procedures reviewed in order to ensure the safety of food products. The direction of further research is the details of the programs, the prerequisites for each of the predicted components, as well as the procedures that ensure the efficient functioning of the HACCP system.

Instruments for the effective organization of production processes and their improvement during the development of radio electronics products

Improvement of the quality of production processes organization, shift to a new engineering level and effective use of products are associated with the improvement of production and the development of organizational processes quality control system. The purpose of the paper is to study the methods and means that allow increasing the performance of the implementation and organization of production processes for the development of radio-electronic instrument making products, as well as their improvement. It is possible to increase production efficiency and quality of manufactured products, in particular, by ensuring the processes stability, reducing the dispersion of production parameters, timely detecting deviations of production processes and eliminating their causes. This can be achieved through the use of methods for organizing inter-operation statistical analysis, control and regulation of processes using cause-and-effect analysis of product parameters relationship, which allow establishing the statistical relationship of their parameters with the modes of the production process and patterns of product parameters distribution depending on processing factors. The analysis of converters production processes was carried out and the cause-and-effect diagram was drawn up taking into account the main factors affecting the quality and percentage of usable products yield. Structure of the analysis system for monitoring production processes and its algorithm are given. The paper shows the dependence of the filming process quality on films parameters in the form of a cause-and-effect graph, as well as products manufacture progress diagram, which demonstrates the reproducibility and stability of the converters production processes. An integrated approach to the implementation of production processes, taking into account special aspects of the technology, connection of instrument parameters with processing factors, organization processes quality control, allows reducing the dispersion of product parameters, improving process stability and overall production efficiency.

Ratiometric fluorescent monitoring of methanol in biodiesel by using an ESIPT-based flavonoid probe

Developing an analytical method for monitoring methanol content in biodiesel in a rapid and precise manner is of great importance for biodiesel production quality control, due to that an excessive amount of methanol in biodiesel will accelerate the engine aging thereby affect driving safety. In this study, we reported a novel flavonoid-based fluorescent probe HOF with the excited-state intramolecular proton transfer (ESIPT) character. This study demonstrated that the alcohols could form strong intermolecular hydrogen bonds with the HOF molecules and consequently block the ESIPT process, triggering a fast and specific fluorescence response. This alcohol-sensing method was proven to be rapid, sensitive, ratiometric, and visually discernible for detection of methanol with multi-channel signal output (fluorescent wavelength, intensity, and lifetime). Moreover, this method was successfully applied for determination of methanol content in commercial biodiesel, which may provide a practical tool for real-time monitoring of biodiesel production process.

Development and characterisation of a new fluorescence sensor for online monitoring of bioprocesses

Abstract. Fluorescence spectroscopy is a highly sensitive and non-invasive technique for the identification of characteristic process states and for the online monitoring of substrate and product concentrations. Nevertheless, fluorescence sensors are mainly used in academic studies and are not well implemented for monitoring of industrial production processes. In this work, we present a newly developed robust online fluorescence sensor that facilitates the analysis of fluorescence measurements. The set-up of the sensor was miniaturised and realised without any moveable part to be robust enough for application in technical environments. It was constructed to measure only the three most important biologic fluorophores (tryptophan, NADH and FAD/FMN), resulting in a significant data reduction compared to conventional a 2-D fluorescence spectrometer. The sensor performance was evaluated by calibration curves and selectivity tests. The measuring ranges were determined as 0.5–50 µmol L−1 for NADH and 0.0025–7.5 µmol L−1 for BSA and riboflavin. Online monitoring of batch cultivations of wild-type Escherichia coli K1 in a 10 L bioreactor scale were performed. The data sets were analysed using principal component analysis and partial least square regression. The recorded fluorescence data were successfully used to predict the biomass of an independent cultivation (RMSEP 4.6 %).

Surface-Enhanced Raman Scattering Detection of Pesticide Residues Using Transparent Adhesive Tapes and Coated Silver Nanorods.

The efficient extraction of analytes from complex and severe environments is significant for promoting the surface-enhanced Raman scattering (SERS) technique to actual applications. In this paper, a proof-of-concept strategy is proposed for the rapid detection of pesticide residues by utilizing the flexible, transparent, and adhesive properties of commercial tapes and SERS performance of Al2O3-coated silver nanorod (AgNR@Al2O3) arrays. The function of tapes is to rapidly transfer the analytes from the actual surface to the SERS substrate. The novel "tape-wrapped SERS (T-SERS)" approach was constructed by a simple "paste, peel off, and paste again" procedure. The easily obtained but clearly distinguished SERS signals allow us to quickly determine the constituents of complex surfaces, such as tetramethylthiuram disulfide and thiabendazole pesticides from fruits and vegetables, which may be practically applied to food safety, environmental monitoring, and industrial production process controlling.

Development of a methodology for event-based production control

Nowadays, industrial production environments are complex, volatile and driven by uncertainties. Manufacturing companies are striving for flexibility and adaptability to cope with these challenges and remain competitive. Decision-making and real-time reaction systems are promising concepts to cope with complex situations in industrial production environments. However, digitalization leads to an increasing amount of data describing the status of products and resources within an industrial production environment. In order to achieve near real-time monitoring and control of production and logistics processes, intelligent processing and analysis of the acquired data is necessary. As a result of this development, continuous data analysis plays a decisive role for analyzing extensive data streams in real-time. Patterns have to be recognized during the production process and actions have to be defined in order to realize an event-based production control. The main focus within this paper is the development of a methodology for an event-based production control. Furthermore, an exemplary implementation of the methodology will be presented by means of a prototypical implementation to optimize the exchange of material.

Multi-Species Hydrodynamic Electrochemical Modeling with Liquid Metal and Rotating Cylinder Electrodes in Molten Salt

The accurate prediction of a hydrodynamic electrochemical system with radioactive materials can reduce the economic costs and radiological risk of repeated experiments. Such a prediction requires the computationally rich coupling of hydrodynamic phenomena and electrochemical reactions at a multi-dimensional domain, especially for multiple species in heterogeneous phases. This study develops a finite element method model to simulate the transport phenomena and electrochemical reactions of U, Pu, and Nd simultaneously in a molten salt metallurgical electrorefining system. The results are validated against lab-scale experimental data conducted in high temperature LiCl-KCl molten salt. The experiment conducted in the Argonne National Laboratory has a unique electrochemical cell with a combination of liquid metal and rotating solid cylinder electrodes. The model successfully simulates the time-dependent multi-species behaviors of the system such as dissolution rates from the liquid metal anode and deposit composition on the rotating cylinder electrode. In particular, it successfully predicts the transition time of depositing element at the cathode from U to Pu, which is very important information for product quality and process monitoring. In addition, it can catch the change of thermodynamic behaviors of Pu by forming intermetallic compounds with liquid Cd metal.

Jump regression, image processing, and quality control

ABSTRACTImages have been widely used in manufacturing applications for monitoring production processes, partly because they are often convenient and economic to acquire by different types of imaging devices. Medical imaging techniques, such as CT, PET, X-ray, ultrasound, magnetic resonance imaging (MRI), and functional MRI, have become a basic medical diagnosis tool nowadays. Satellite images are also commonly used for monitoring the changes of the earth’s surface. In all these applications, image comparison and monitoring are the common and fundamentally important statistical problems that should be addressed properly. In computer science, applied mathematics, statistics and some other disciplines, there have been many image processing methods proposed. In this article, I will discuss (i) a powerful statistical tool, called jump regression analysis (JRA), for modeling and analyzing images and other types of data with jumps and other singularities involved, (ii) some image processing problems and methods that are potentially useful for image comparison and monitoring, and (iii) some of my personal perspectives about image comparison and monitoring.

Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods.

This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris. In a feasibility study, Partial Least Squares Regression (PLSR) models were created off-line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSEcvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on-line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSEPrel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed-forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.

Selecting subgrids from a spatial monitoring network: Proposal and application in semiconductor manufactoring process

AbstractThe monitoring of spatial production processes typically involves sampling network to gather information about the status of the process. Sampling costs are often not marginal, and once the process has been accurately calibrated, it might be appropriate to reduce the dimension of the sampling grid. This aim is often achieved through the allocation of a brand new network of less dimension. In some cases that is not possible and it might be necessary the selection of a subgrid extracted from the original network. Motivated by a real semiconductor problem, we propose a method to extract a monitoring subgrid from a given one, based upon grid representativeness, accuracy, and spatial coverage of the subgrid and, if available, by expert knowledge of the weights to be assigned to those areas where production may need greater precision. Discussion is mainly focused on circular spatial domain, since, in microelectronics, the basic production support, called wafer, is a circle. Straightforward generalizations to different spatial domains are possible. Furthermore, conditionally upon the availability of experimental data, we check the loss of accuracy by fitting a dual mean‐variance response surface on the reduced grid. Joining the latter information and the criteria used to select the subgrid, we provide additional guidelines on how to fine‐tune the subgrid selection. Real case studies are used to show the effectiveness of the proposal.

Методы анализа и способы минимизации технологических и технических рисков энергопредприятия

Objective: Identifying an accurate quantitative risk assessment. FEC (Fuel and Energy Complex) plants are a high-risk area as they may cause manmade disasters, various accidents, pose a threat to human life and environment. In addition, the Russian energy industry is noted for its high complexity and social responsibility. Its specific feature is that it is not always possible to make an accurate quantitative risk assessment reasonably in advance and its degree determination methods are not well enough developed. In view of the above, there are some difficulties in minimizing the risks and estimating risk management costs. There has been a recent trend in improved current legislation on industrial safety and Rostechnadzor (Federal Environmental, Industrial and Nuclear Supervision Service of Russia) oversight and supervision activity practice toward implementing a risk-based approach using the risk analysis methods. It allows optimizing the methods and frequency of inspections made by regulatory bodies depending on the risk level of facilities supervised. Methods: The (accident) risk analysis is performed as a certain scientific justification set forth using qualitative and quantitative analysis of a potential accident likelihood, consequences of its occurrence, and identification of the weakest points in the engineering system or complex. Using fault tree analysis, this article identifies hazards and assesses the high-pressure gas pipeline loss of containment risk, one of the events possible for an energy provider in operation. Results: Based on the risk analysis, there has been a proposal to replace gland seal valves with bellows seal valves noted for their optimum relationship between the unit reliability, cost and sophistication level. In case the facilities with gland seal valves remain in operation, improved production process monitoring is recommended using gas leak detectors and automatic interlocking devices. Practical importance: The measures proposed will allow minimizing the gas pipeline loss of containment risk.

Relational enablers of information sharing: evidence from Chinese food supply chains

PurposeFacilitating supply chain visibility and traceability through effective information sharing is vital to improve the quality and safety of food products. However, we know little about the enablers of information sharing in food supply chains. Drawing upon resource dependence theory and social exchange theory, the purpose of this paper is to explore how farmers’ dependence on companies influences their trust in and relationship commitment to those companies, and in turn facilitates effective information sharing with them.Design/methodology/approachData collected from 462 farmers in China were used to test the conceptual model and related hypotheses.FindingsThe results show that dependence has a significant positive effect on trust and relationship commitment, but it has no direct significant effect on information sharing. In addition, trust has a significantly positive influence on relationship commitment, and both trust and relationship commitment have significantly positive effects on information sharing. These findings show that strengthening farmers’ dependence on companies can cultivate farmers’ trust and relationship commitment, thereby encouraging them to share information with the companies.Originality/valueTheoretically, this study contributes to the supply chain information sharing and relationship management literature. Practically, it shows how companies can effectively monitor production processes and improve food quality and safety through information sharing and relationship management.