Process Control Methods Research Articles

A comparative study of model approximation methods applied to economicMPC

AbstractEconomic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for next‐generation smart manufacturing. It has the potential to not only improve economic performance but also significantly increase computational complexity. Model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation methods applied to EMPC, including model reduction based on available first‐principle models (e.g., proper orthogonal decomposition), system identification based on input–output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input–output data. A representative algorithm from each model approximation method is considered. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely, an alkylation process and a wastewater treatment plant. The strengths and drawbacks of each method are summarized according to the simulation results, with future research direction regarding control‐oriented model approximation proposed at the end.

Application of near infrared spectroscopy and real time release testing combined with statistical process control charts for on-line quality control of industrial concentrating process of traditional Chinese medicine “Jinyinhua”

Concentrating process is an important unit operation during the manufacture of Lonicerae Japonicae Flos (LJF, “Jinyinhua” in Chinese) products. Near infrared (NIR) spectroscopy and real time release testing (RTRT) combined with statistical process control (SPC) methods were presented for on-line monitoring of concentrating and developing quality control strategy for the final LJF concentrates. An automatic NIR detection (AND) device was designed and has proven effective to eliminate the influence of bubble, solid impurity and flow of the concentrates on NIR spectra. Partial least squares (PLS) models were constructed for on-line determination of neochlorogenic acid (NCA), chlorogenic acid (CA), cryptochlorogenic acid (CCA), density and water content, which provided real-time data and exhibited satisfactory predictive abilities. SPC tools, including univariate Shewhart, multivariate Hotelling T2 and squared prediction error (SPE), were compared and applied for RTRT. The results of RTRT showed that the releasing criteria for final LJF concentrates were as follows: 1.09 mg/g < NCA < 1.53 mg/g, 10.00 mg/g < CA < 13.44 mg/g, 1.71 mg/g < CCA < 2.43 mg/g, 1.10 g/ml < density < 1.14 g/ml, 64.07% < water content < 72.99% and SPE result < 0.04. Shewhart and SPE charts have proven to be useful in detecting abnormalities and releasing normal batches. This work demonstrated the effectiveness of NIR and RTRT combined with SPC charts in on-line quality control of industrial concentrating process of traditional Chinese medicine.

Геоинформационный мониторинг для решения экологических задач горнопромышленных территорий Среднего Урала

Geographic information monitoring is shown as the leading method of process control in old industrial territories. The main stages of geoinformation monitoring are outlined. The role of geographic information systems in the study of pollution components, systematization and accumulation of spatially distributed data, analysis and development of predictive solutions is considered. The solution of the problem of the conceptual organization of the geoinformation system of mining territories has been substantiated. The structure of software products and geographic information systems selection criteria are considered. The QGIS software product was chosen as the desktop geographic information systems. The functionality of JavaScript-libraries Leaflet and Highcharts, the possibility of their use as a web-based geographic information systems are described. The structure of the base layers of the geographic information systems, created for the organization of geoinformation monitoring of the old industrial territory of the Levikhinsky mine, is described. An algorithm for organizing spatially distributed data of the monitoring object, a fundamental algorithm for data processing are proposed. Considerable attention is paid to the typification and subsequent differentiation of the types of initial information, the storage of geographic information systems data in exchange formats. The author's algorithm of interaction between desktop and web-based geographic information systems is described. The provisions and technical solutions set out in the article allow using geographic information systems for operational monitoring, forecasting and comprehensive assessment, and management decisions.

Optimal control to handle variations in moisture content and reactor in-feed rate

The variations in feedstock characteristics, such as moisture and particle size distribution, lead to an inconsistent flow of feedstock from the biomass pre-processing system to the reactor in-feed system. These inconsistencies result in low on-stream times at the reactor in-feed equipment. This research develops an optimal process control method for a biomass pre-processing system comprised of milling and densification operations to provide the consistent flow of feedstock to a reactor's throat. This method uses a mixed-integer optimization model to identify optimal bale sequencing, equipment in-feed rate, and buffer location and size in the biomass pre-processing system. This method, referred to as the hybrid process control (HPC), aims to maximize throughput over time. We compare HPC with a baseline feed forward process control. Our case study based on switchgrass finds that HPC reduces the variation of a reactor's feeding rate by up to 100% without increasing the operating cost of the biomass pre-processing system for biomass with moisture ranging from 10 to 25%. Additionally, HPC reduces the cost of processing biomass by 0.36%–2.22%, and reduces processing time by 0.35%–2.24%. A biorefinery can adapt HPC to achieve its design capacity.

Using optimization method for determining lactic acid bacteria counts in white cheese with different salt concentrations

Total lactic acid bacteria, Lactococcus spp., yeast, and mold counts in white cheese were determined during 55 days of storage in brine solutions with 4%, 6%, 8%, 10%, 12%, 14%, and 16% (w/v) salt concentrations at 4°C. In addition to salt content, dry matter, acidity, protein, fat contents, and sensory evaluations of cheese were also determined. Lactococcus spp. numbers in cheese salted with 4% and 14% brines (w/v salt concentrations) were 9.32 and 8.48 log cfu/g at the 55 days of storage, respectively. The lowest total lactic acid number was 7.24 for cheese in brine with 16% (w/v) salt concentration after 55 days of storage. Yeast numbers were 7.01 and 6.71 log cfu/g for cheese in brines with 4% and 16% (w/v) salt concentrations. Cheese samples salted in 12% and 14% (w/v) concentrations of brines have the highest sensory scores. Analysis results were interpreted mathematically by “fuzzy soft set” modeling. Novelty impact statement This research is a novel study detailing mathematical modeling related to optimizing the salt concentration to preserve the viability of lactic acid starter culture bacteria in the white cheese. Also, it covers the first optimizing method for process control in the cheese industry with the application of “fuzzy soft set theory.”

Perspective Study of COVID-19 - A Case from Indian Subcontinent

The novel coronavirus disease (COVID - 19) now has a large global impact. The relationship between variables such as confirmed number of positive cases and number of deceased people is investigated in this paper. The purpose of a time-varying correlational study is to determine how one variable influences the other. The most vulnerable states on the variables studied were identified using statistical process control methods. To discover the association shift between the variables, the Exponential Moving Average Method covariance was performed. Time series models such as Autoregressive Integrated Moving Average (ARIMA) and GARCH models are examined in an attempt to estimate on future proven cases models. The performance measures of Mean Absolute per Error and Akaike Information Criteria are used to compare the predicted time series models.

A distribution-free control charting technique based on change-point analysis for detection of epidemics

Worldwide, the detection of epidemics has been recognized as a continuing problem of crucial importance to public health surveillance. Various approaches for detecting and quantifying epidemics of infectious diseases in the recent literature are directly influenced by methods of Statistical Process Control (SPC). However, implementing SPC quality tools directly to the general health care monitoring problem, in a similar manner as in industrial quality control, is not feasible since many assumptions such as stationarity, known asymptotic distribution etc. are not met. Toward this end, in this paper, some of the open statistical research issues involved in this field are discussed, and a distribution-free control charting technique based on change-point analysis is applied and evaluated for detection of epidemics. The main tool in this methodology is the detection of unusual trends, in the sense that the beginning of an unusual trend marks a switch from a control state to an epidemic state. The in-control and out-of-control performance of the adapted control scheme from SPC is thoroughly investigated using Monte Carlo simulations, and the applied scheme is found to outperform its parametric and nonparametric competitors in many cases. Moreover, the empirical comparative study provides evidence that the adapted change-point detection scheme has several appealing properties compared to the current practice for detection of epidemics.

A robust latent CUSUM chart for monitoring customer attrition

In competitive business, such as insurance and telecommunications, customers can easily replace one provider for another, which leads to customer attrition. Keeping customer attrition rate low is crucial for companies, since retaining a customer is more profitable than recruiting a new one. As a main statistical process control (SPC) method, the CUSUM scheme is able to detect small and persistent shifts in customer attrition. However, customer attrition summaries are typically available on an uneven time scale (e.g. 4-week and 5-week ‘business month’), which may not satisfy the assumptions of traditional CUSUM designs. This paper mainly develops a latent CUSUM chart based on an exponential model for monitoring ‘monthly’ customer attrition, under varying time scales. Both maximum likelihood and least squares methods are studied, where the former mostly performs better and the latter is advantageous for quite small shifts. We apply a Markov chain algorithm to obtain the average run length (ARL), make calibrations for different combinations of parameters, and present reference tables of cutoffs. Three more complicated models are considered to test the robustness of deviations from the initial model. Furthermore, a real example of monitoring monthly customer attrition from a Chinese insurance company is used to illustrate the scheme.

Pengendalian Kualitas Produk Unit Plastik Woven Bag Dalam Upaya Mengendalikan Tingkat Kecacatan Produk Dengan Bantuan Statistical Process Control (SPC): Studi Kasus PT. Dasaplast Nusantara

Quality is an achievement or assessment of an item or service from various factors based on the conformity of the specifications desired by the manufacturer. Good product quality can provide confidence for customers to order or cooperate in business so that it can improve the company's position against its competitors. PT. Dasaplast Nusantara is a company producing plastic woven bags or plastic sacks, the raw materials used in the manufacture of products have a high enough price, so the company must really maintain the quality of the product so that the company does not suffer a large enough loss. Thus, in this study, quality control was carried out using the Statistical Process Control (SPC) method which aims to control the level of product defects in the plastic woven bag unit. By using the SPC method, the company's defect rate is 12%, which means that the production capability is not good

Photoluminescence Imaging for the In-Line Quality Control of Thin-Film Solar Cells

Renewable energy sources such as photovoltaic (PV) technologies are considered to be key drivers towards climate neutrality. Thin-film PVs, and particularly copper indium gallium selenide (CIGS) technologies, will play a crucial role in the turnaround in energy policy due to their high efficiencies, high product flexibility, light weight, easy installation, lower labour-intensiveness, and lower carbon footprint when compared to silicon solar cells. Nonetheless, challenges regarding the CIGS fabrication process such as moderate reproducibility and process tolerance are still hindering a broad market penetration. Therefore, cost-efficient and easily implementable in-line process control methods are demanded that allow for identification and elimination of non-conformal cells at an early production step. As part of this work, a practical approach towards industrial in-line photoluminescence (PL) imaging as a contact-free quality inspection tool is presented. Performance parameters of 10 CIGS samples with 32 individually contacted cells each were correlated with results from PL imaging using green and red excitation light sources. The data analysis was fully automated using Python-based image processing, object detection, and non-linear regression modelling. Using the red excitation light source, the presented PL imaging and data processing approach allows for a quantitative assessment of the cell performance.

Discrepancy-based control of particle processes

The production of goods in particulate form is important in many industries such as the chemical, pharmaceutical and food industry. For the subsequent use, specific particle properties determine the quality of the product. In order to ensure high quality and furthermore a constant production rate, process control methods can be applied. Here we present discrepancy-based control for a generic agglomeration process with additional particle breakage. The advantage of this control approach is the direct and simple control design using the nonlinear and infinite-dimensional system description as well as guaranteed stability for the chosen process variables. Two practically relevant scenarios are investigated. First, it is shown that control laws for two moments of the particle size distribution ensure the production of agglomerates with a constant Sauter mean diameter. Then, control laws for the production rate are designed. The resulting closed-loop behavior is investigated by means of dynamic simulation. It is shown that the control laws improve systems convergence speed for both set point tracking and disturbance rejection and achieve zero steady state error even under the occurrence of non-vanishing disturbances.

Study of Risks in Rural Water Supply Systems of Khorramshahr City, Iran, Based on Water Safety Plan

Background: Water safety planning is a comprehensive risk assessment and management approach encompassing all steps in a drinking-water supply chain, from catchment to consumer. A Water Safety Plan (WSP) ensures drinking water safety through this approach. In this study, risk factors are initially identified and evaluated. Then control and corrective measures are determined to reduce or eliminate health and environmental hazards of rural water supply systems in Khorramshahr City, Iran, according to the guidelines of the WSP provided by the World Health Organization. Methods: This research is a descriptive cross-sectional study in which rural water supply systems in Khorramshahr were studied using the Failure Mode and Effects Analysis (FMEA) risk systematic method. The Risk Priority Number (RPN) was calculated to determine the risk level after identifying 14 risk factors using experts’ opinions. Then, control and corrective measures were considered for medium, high, and very high-level risk factors. Results: The evaluation results of 14 risk factors identified in the distribution network and point of consumption indicated that 71.5% of them were at the medium risk level and 28.5% at the high (critical) risk level. After determining control and corrective measures, 92.9% of risk factors reached the medium (manageable) level and 7.1% the critical level. Conclusion: This result indicates that water safety guidelines can replace traditional methods of inspection and process control, and significant improvements can be achieved with the help of risk assessment by the FMEA method and step-by-step implementation of the WSP as an essential evolutionary solution for preventive measures and reducing the level of existing risks.

Production Process Control for Aero Engine Part Manufacturing

This paper presents a method for production process control for aero engine part manufacturing based on product quality problem identification and statistical process control. A framework is provided. The control flow of the method is developed. The proposed method applies single value moving range control chart to monitor the production process of aero engine parts. An application case is provided. The results of the case study indicate the quality improvement of the products produced. The research can provide a reference for companies in aviation industry in China to apply control charts to monitor and control production processes for product quality improvement.

An Improved Process Supervision and Control Method for Malware Detection

Most modern-day malware detection methods and algorithms are based on prior knowledge of malware specifications. Discovering new malwares by solely relying on computer based automatic solutions with no human intervention currently appears out of reach. Many malwares never decode harmful parts of their code until the triggering of a specific event. Others detect virtual machine or sandbox environments and hide their true nature. Detecting these kinds of malwares-specifically multi evented ones-are nearly impossible for fully automatic detection methods. Previous research found that about 75% of malwares studied did not react in a fully automatic environment without user intervention thus being undetectable. This paper introduces a near automated solution to detect malwares quickly by relying on a supervision and control method based on user level capabilities of the operating system. Improving on previous methods, this research can replace the need for debugging new malwares in almost all aspects. This solution forces malwares in automated environments to activate and be discoverable. Researcher intervention during malware code execution along with the malware’s intent over calling sensitive operating system functions and parameters aid this process. Since operating system functions are virtualized malwares are incapable of physically harming the system during execution. The solution reached 98% overall accuracy in conjunction with reducing code size by 80% in comparison with similar techniques, improving simplicity and reliability.

Modelling time-series process of an agricultural crop production process by EWMA quality control chart

Control charts are considered to be one of the main tools for analysing and controlling the steps of a product chain process. This research emphasises the control charts' versatility not only in manufacturing, but in taking enough actions for maintaining the quality of the crops with more flexibility. The time-series quality proceedings for crops that utilised the statistical process control (SPC) method serve as a case study in the EWMA control chart. One week of 168 hours of observation data is analysed with a sampling time of one hour. These measurements have been investigated over three models: AR, MA, and ARMA. The results of plotting EWMA for autoregressive (AR), moving average (MA), and ARMA models included eight points at which the process is out of control. It is found that a higher order of AR or MA model shows a lower out-of-control process. This satisfies the concluded point that higher order of the ARMA model means lower out-of-control process points.

STUDY OF THE METROLOGICAL CHARACTERISTICS OF CONDUCTIVITY SENSORS

Conductivity measurement is a universal method of process control. Measurement is fast and straightforward, and most modern sensors only require little maintenance. The measured conductivity value is applied to obtain different assumptions about what happens in the substance, so such measurements are relevant when controlling technological processes and products in various industries (e.g., food, pharmaceutical). The main metrological characteristics of sensors for measuring conductivity LDL100, LDL200 are analyzed in the article. Studies have been carried out for various objects: non-carbonated mineral water, fruit juice, and tap water.

On-line Recognition of Abnormal Patterns in Bivariate Autocorrelated Process Using Random Forest

It is not uncommon that two or more related process quality characteristics are needed to be monitored simultaneously in production process for most of time. Meanwhile, the observations obtained online are often serially autocorrelated due to high sampling frequency and process dynamics. This goes against the statistical I.I.D assumption in using the multivariate control charts, which may lead to the performance of multivariate control charts collapse soon. Meanwhile, the process control method based on pattern recognition as a non-statistical approach is not confined by this limitation, and further provide more useful information for quality practitioners to locate the assignable causes led to process abnormalities. This study proposed a pattern recognition model using Random Forest (RF) as pattern model to detect and identify the abnormalities in bivariate autocorrelated process. The simulation experiment results demonstrate that the model is superior on recognition accuracy (RA) (97.96%) to back propagation neural networks (BPNN) (95.69%), probability neural networks (PNN) (94.31%), and support vector machine (SVM) (97.16%). When experimenting with simulated dynamic process data flow, the model also achieved better average running length (ARL) and standard deviation of ARL (SRL) than those of the four comparative approaches in most cases of mean shift magnitude. Therefore, we get the conclusion that the RF model is a promising approach for detecting abnormalities in the bivariate autocorrelated process. Although bivariate autocorrelated process is focused in this study, the proposed model can be extended to multivariate autocorrelated process control.

Design of self-starting multivariate control chart for monitoring patients suspected to bone marrow metastasis

Nowadays, statistical process control methods are widely used in healthcare, especially for cancer patients, to help doctors in interpreting and diagnosing medical data. Since adequate data from patients with bone marrow metastasis are often unavailable, the researchers use a self-starting control chart. In this regard, using the self-starting multivariate control chart, we monitor the status of people suspected of bone marrow metastasis in the pelvic region. For this, using a two-dimensional discrete wavelet transformation, we extracted some features from the ADC and T1 magnetic resonance images of ten bone marrow metastasis samples. Out of these features, we selected six ones for final analysis. Then, using the self-starting SSMEWMA and SST2, we performed the simulation studies, as well as a numerical example, on the extracted features and evaluated the performance of the proposed methods in terms of average run length measure. The simulation results verified the appropriate performance of the proposed methods in diagnosing patients with bone marrow metastasis than non-metastasis ones.

Detection and identification of mean shift using independent component analysis in multivariate processes

Multivariate Statistical Process Control (MSPC) methods are used commonly to detect and identify shifts in multivariate industrial processes. However, these methods are limited by assumptions and complexities. In this study, a new two-stage MSPC approach based on independent component analysis is proposed. The proposed method aims to provide solutions to both the determination and the identification of the shift in the mean vector of a multivariate process. In the first step of this new method, independent components extracted by ICA were used as monitoring statistics in the MSPC chart to detect the shift in the process. The second step of the method started to deal with the problem of identifying the source of this shift by decomposing the monitoring statistics. The simulation results show the superiority of the new method over traditional methods in both determining and identifying the shift in the process mean vector.

Data-driven and model-based framework for smart water grid anomaly detection and localization

Abstract With increasing adoption of advanced meter infrastructure, smart sensors together with SCADA (Supervisory Control and Data Acquisition) systems, it is imperative to develop novel data analytics and couple the results with hydraulic modeling to improve the quality and efficiency of water services. One important task is to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. In this paper, a comprehensive solution framework has been developed for anomaly detection and localization by formulating and integrating data-driven analytics with hydraulic model calibration. Data analysis for anomaly detection proceeds in multiple steps including the following: (1) data pre-processing to eliminate and correct erroneous data records, (2) outlier detection by statistical process control methods and deep machine learning, and (3) system anomaly classification by correlation analysis of multiple sensor events. Classified system anomaly events are subsequently localized via hydraulic model calibration. The integrated solution framework is developed as a user-friendly and effective software tool, tested, and validated on the selected target areas in Singapore.