Process Control Performance Research Articles

ТЕОРЕТИЧЕСКИЕ АСПЕКТЫ АВТОМАТИЗАЦИИ ПРОИЗВОДСТВЕННО-ХОЗЯЙСТВЕННОЙ ДЕЯТЕЛЬНОСТИ НА ПРЕДПРИЯТИЯХ ПОЛИГРАФИИ

Automation of production and economic activities at printing enterprises involves the use of advanced digital solutions or technological equipment with automatic configuration and process control functions. The key benefits of automation include speeding up order processing, increasing the accuracy of calculations, optimizing the utilization of process equipment, reducing operating costs, and increasing the efficiency of planning and managing production processes. Digital solutions enable monitoring of financial and operational flows, leading to increased productivity and profitability. The object of this research is production management systems, automated information systems, as well as methods and technologies used to optimize work processes at the stages of production of printed products. The purpose of the study is to summarize the theoretical foundations for improving the efficiency of production and economic activities in printing enterprises by using automation capabilities. Research methods: analysis of literature and scientific sources on existing theories and practices of automation in printing, graphic method, deduction and induction, systematization. The scientific novelty of the study lies in the generalization of modern approaches to automation of production, technological, economic and managerial aspects of the activities of printing enterprises, as well as in a brief overview of currently proposed digital solutions in this area.

Comparison of PI/PID/PIDD2 Controllers for Higher Order Processes

PI, PID and PIDD2 controllers are compared for higher order processes. Tuning rules with robustness constraints are first proposed in this paper for PI, PID and PIDD2 controllers. Then the tuned rules are applied to the benchmark higher order processes and a three-tank system. It is shown that higher order derivatives can improve the control performance for processes with small time delays, while it is of no significant performance improvement for processes with large delays.

THE IMPACT OF INNOVATION ON THE COSTS INCURRED BY TRANSPORT COMPANIES

This article analyses the impact of innovation on the costs of transport companies. In order to carry out the research, the types of innovation in the transport industry and the innovation strategies of transport companies are considered. The case method has been identified as the research method. The analysis concludes that innovation has an impact on both operational performance and shareholder value. Firstly, the value of companies is influenced by innovations related to the purchase of new equipment, the improvement of production indicators and the increase in the quality of services/goods offered.The main objectives of digital technologies are to improve the control of operational processes, to enhance customer interaction and to reduce costs. Together with the purchase of new equipment, this can be seen as one of the ways of increasing the value of the company.

Do sales control systems affect service–sales ambidexterity and salesperson performance? A job demands–resources perspective

This study investigated the moderating effect of leadership humility on the relationship between sales control systems and service-sales ambidexterity (SSA), and the mediating effect of SSA on the relationship between sales control systems and salesperson performance. A two-wave online survey was conducted among Japanese salespeople working in the finance and insurance industry. SSA was found to mediate the positive relationship between process control and salesperson performance, while leadership humility reinforced the positive relationship between process control and SSA. These results indicate that process control positively affects salesperson performance through SSA and works well when leadership humility is high.

The technical difficulties and surgical key points of laparoscopic radical resection for perhilar cholangiocarcinoma

With the continuous accumulation of laparoscopic radical resection for perihilar cholangiocarcinoma(PHC), the safety and feasibility have been confirmed, and some studies have shown that considering the comparable long-term prognosis and short-term outcomes of laparoscopic surgery and open surgery, laparoscopic surgery could be a technically feasible surgical method for PHC patients of all Bismuth-Corlette types. However, laparoscopic radical resection for PHC is still challenging and controversial due to the complex operation process, surgery-related complications and quality control in different centers. How to solve some key points and difficulties in the operation process, reduce surgical complications, improve the survival prognosis of patients, to make the operation widely popularized and applied are urgent problems for hepatobiliary surgeons. In this paper, some technical difficulties and key points of laparoscopic radical resection for PHC are discussed with the author's team surgical experience and related literature.

Integration of Vacuum Processing into an Automated Vertical Quenching Furnace

Abstract Integral quenching (IQ) is a widely adopted heat treatment technique known for its efficient and rapid quenching capabilities. Vertical vacuum IQ furnaces give heat treaters a larger working zone for efficient and rapid quenching. This article describes the elements of a vacuum IQ furnace line and describes its operational workflow and process control.

Model-Based Decision Support System for Electric Arc Furnace (EAF) Online Monitoring and Control

In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control.

Harmonizing the use of optimization and feedback in process operations and control

This paper addresses how to combine the two main engineering design tools, optimization and feedback, to obtain high performance process control or process operation systems. We discuss the historical development of process control, where feedback dominated the early practical designs, and optimization was then incorporated much later through technologies like model predictive control. In chemical production scheduling, on the other hand, optimization had a strong early influence on the problem formulation, and feedback has only recently made an appearance. The recent developments in both process control and scheduling are illustrated with specific examples emerging from this series of FOCAPO/CPC meetings. The paper next presents recent theoretical developments in nominal and stochastic model predictive control. The closed-loop properties that arise from these different open-loop optimal control problems are then compared. The paper closes with some discussion of when the improvements of the closed-loop properties are worth the added complexity of the stochastic optimal control problem.

Adaptive optimal process control with actor-critic design for energy-efficient batch machining subject to time-varying tool wear

Batch machining systems are essential for improving productivity and quality, but they consume considerable amounts of energy due to the continuous interaction with machine tools, workpieces, and cutting tools. In contrast to single-piece machining that has a short production cycle, the tool wear impacts in batch machining systems on energy consumption cannot be underestimated. However, few studies have focused on adaptive process control subject to time-varying tool wear because process optimization has always been previously considered a static problem. As an alternative to metaheuristic algorithms, reinforcement learning (RL) offers an attractive means for solving such a dynamic, high-dimensional, and high-coupling problem. In the case of turning cylindrical parts, an energy-efficient decision model is developed for the process control of pass operations of batch machining. The decision variables are decoupled by reformulating the problem as the Markov decision process, wherein the tool wear experiences dynamic changes. To solve the problem, an actor-critic RL framework with multi-constraint and multi-objective design is developed. Based on the framework, a dynamic process control method is proposed where the RL agent observes workpiece features, machining requirements, and tool wear states (inputs) and adaptively selects the control parameters such as cutting speed, feed rate, and cutting rate (outputs), with the aim to conserve energy. Two application tests and comparisons against metaheuristic methods are performed. The results indicate that the method can further reduce energy by over 20% compared with energy-efficient optimization ignoring tool wear effects. The learning efficiency of RL is about three times faster than that of metaheuristics. The online sampling time is less than 0.1 millisecond, which facilitates real-time control of process parameters.

Data-driven set-point control for nonlinear nonaffine systems

Optimal set-point control is important in maintaining good control performance of practical industrial processes. Considering the challenges when modeling a complex process, this work presents a data-driven optimal set-point control (DDOSC) scheme for nonlinear nonaffine systems bypassing modeling steps. The outer loop of the proposed DDOSC adopts an ideal nonlinear set-point control function which is theoretically achievable to obtain a perfect tracking. Then, a dynamic linearization (DL) is used to transfer the ideal nonlinear set-point control law into a linear parametric one such that it is implementable through a parameter updating law. To address the nonlinear nonaffine system within a data-driven framework, the DL method is again used to obtain its linear data model which is further updated by a parameter-adaptive law. With a proportional feedback controller in the inner loop, the convergence of the DDOSC method is shown. In addition, the results are further extended by considering a proportional-integral-derivative feedback controller in the inner loop. The simulation results on a numerical example and a car suspension system verify the effectiveness of the proposed DDOSC in improving the performance of the local feedback controller.

Saving the environment by automation of crude oil distillation in a rectification column

The article shows the results of the development of an automated control system for the distillation of oil in a distillation column, which makes it possible to save the environment. In the theoretical part, the process of distillation of crude oil in a distillation column is described and its technological scheme is given for atmospheric distillation with a single evaporation of oil. In the calculation part, the heat and material balance of mass transfer in the rectification column has been analyzed. In the practical part, the problems of installing process control instruments, improving the emergency shutdown system reliability, expanding the automatic and automated monitoring and control functions, and improving the process control and facility performance analysis quality have been solved. The authors used the Trace Mode 6 software package to develop the SCADA of the distillation column control system. In conclusion, it was noted that the development and implementation of an automated control system for the oil distillation process provides an increase in plant productivity, as well as the quality and reliability of the process, which in turn has a positive effect on environmental conservation.

Case Studies and Best Practices in Cloud-Based Big Data Analytics for Process Control

In this research paper, case studies and exemplars and lessons learnt in cloud-based big data analytics for process control are reviewed. The paper presents big data, cloud computing and industrial process control system with prospects of enhancing effectiveness, increasing production rates, and effective decision making in the industries. The research in this paper involves a comprehensive literature review of the research topic, and an extension of the analysis to four specific business industries as well as a discussion of architectural elements for cloud-based big data solutions for process control business. It also presents various crucial issues such as data protection, adherence to legal requirements, and compatibility with other systems, giving solutions. In addition, the research compares the effectiveness of cloud-based solutions with on-premise ones and discuss other novelties, including edge computing and artificial intelligence as the tendencies potentially influencing process control. Consequently, the findings of this research can be helpful for both industry practitioners and researchers who aim to optimize process control and organization operation with the help of cloud-based big data analytics

Surgical workflow and infection prevention and control strategies for patients with COVID-19 during the epidemic in Children's Hospitals.

This study sought to investigate the surgical workflow and practical effects of infection prevention and control in specialist pediatric hospitals during the Corona Virus Disease 2019 (COVID-19) epidemic to provide a foundation for ensuring the safety of children and medical staff. The Guidelines for the Management of Surgical Procedures and Infection Prevention and Control of COVID-19 Pneumonia in Children were formulated according to the industry specifications and standards, the prevention and control work system for hospitals in China, and the experiences of the Chinese Nursing Association in infection prevention and control in the operating room. These guidelines focus on the characteristics of children, and provide management priorities in relation to personnel management, infection prevention and control during surgery, intraoperative safety, and the cooperation of medical staff teams. These operation management and prevention and control strategies were applied to children who were suspected or confirmed to have COVID-19. The operation process and prevention and control measures were effectively implemented. During the epidemic, a total of 219 surgeries which patients' COVID-19 nucleic test result are not out were completed. No medical staff or nosocomial infection occurred during the surgeries. As a special group, children are susceptible to COVID-19, and should receive special attention. As the only hospital designated to treat children with COVID-19 in Hubei Province, our hospital effectively implemented the infection prevention and control measures in surgery according to the characteristics of children. These measures ensured the safety of the surgeries and reduced the risk of infection in children and medical staff.

Integrating cybersecurity into the risk‐based process safety (RBPS) program

AbstractWith the industry's increasing reliance on computer systems for basic process control and safety functions, industrial processes have become more vulnerable to cybersecurity threats. The recently released CCPS book, Managing Cybersecurity in the Process Industries, A Risk‐based Approach, discusses the need for proactive and effective cybersecurity management in the interest of process safety. This paper will share some of the highlights from the book illustrating areas where cybersecurity considerations parallel and complement the key principles of Risk‐Based Process Safety (RBPS) elements. This paper will share some lessons from recent cybersecurity incidents impacting process safety and identify avenues where cyberattacks can gain access to the control or safety instrumented systems to cause harm. Finally, we will discuss a sampling of the RBPS elements in more detail to illustrate how cybersecurity can be integrated into the RBPS program.

Preclinical study of human umbilical cord mesenchymal stem cell sheets for the recovery of ischemic heart tissue

BackgroundHuman umbilical cord mesenchymal stem cells (hUC-MSCs) have been widely used due to their multipotency, a broad range of sources, painless collection, and compliance with standard amplification. Cell sheet technology is a tissue engineering methodology requiring scaffolds free, and it provides an effective method for cell transplantation. To improve the therapeutic efficacy, we combined hUC-MSCs with cell sheet technology to evaluate the safety and efficacy of hUC-MSC sheets in preclinical studies using appropriate animal models.MethodshUC-MSC sheets were fabricated by hUC-MSCs from a cell bank established by a standard operation process and quality control. Cytokine secretion, immunoregulation, and angiopoiesis were evaluated in vitro. Oncogenicity and cell diffusion assays of hUC-MSC sheets were conducted to verify the safety of hUC-MSCs sheet transplantation in mice. To provide more meaningful animal experimental data for clinical trials, porcine myocardial infarction (MI) models were established by constriction of the left circumflex, and hUC-MSC sheets were transplanted onto the ischemic area of the heart tissue. Cardiac function was evaluated and compared between the experimental and MI groups.ResultsThe in vitro results showed that hUC-MSC sheets could secrete multiple cellular factors, including VEGF, HGF, IL-6, and IL-8. Peripheral blood mononuclear cells had a lower proliferation rate and lower TNF-α secretion when co-cultured with hUC-MSC sheets. TH1 cells had a smaller proportion after activation. In vivo safety results showed that the hUC-MSCs sheet had no oncogenicity and was mainly distributed on the surface of the ischemic myocardial tissue. Echocardiography showed that hUC-MSC sheets effectively improved the left ventricular ejection fraction (LVEF), and the LVEF significantly changed (42.25 ± 1.23% vs. 66.9 ± 1.10%) in the hUC-MSC transplantation group compared with the MI group (42.52 ± 0.65% vs. 39.55 ± 1.97%) at 9 weeks. The infarct ratio of the hUC-MSCs sheet transplantation groups was also significantly reduced (14.2 ± 4.53% vs. 4.00 ± 2.00%) compared with that of the MI group.ConclusionAllogeneic source and cell bank established by the standard operation process and quality control make hUC-MSCs sheet possible to treat MI by off-the-shelf drug with universal quality instead of individualized medical technology.

Automatic control system for walking dragline excavator digging

This paper presents the results of the development of automatic control systems for walking dragline excavator digging process. The process enables operational productivity to be enhanced through optimizing digging process. This also prevents extreme loads on machinery and hoist cable deflection. The paper also describes mathematical models of the electric drives of the main excavator machinery which form the bucket motion and the model of cable length change. Further the study will analyse the tructure of the control system and the automatic digging algorithm. Computer modeling findings are also described to confirm the operability of the automatic digging algorithm. Computer simulation of the processes in electric drives of main machinery of a walking dragline in digging operations was performed by means of SimInTech software. The automatic control system optimizes digging trajectory with very fast penetration with permissible overregulation following digging at a constant cut depth. The integrated system of dragline operation process control is practically independent due to the following factors: the automatic digging control system in combination with automatic systems for transporting the loaded bucket to dump and the empty bucket to the face; the automatic main machinery overload protection systems; and the system of control over safe bucket movement in the dragline working space.

Development of an efficient cement production monitoring system based on the improved random forest algorithm

Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies’ value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97%, compared to the SVM model’s accuracy which is 94.18%. However, the K-NN model’s accuracy is about 93.83%. An accuracy of 80.25% is achieved by the logistic regression model. About 83.73% is obtained by the decision tree’s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.

Understanding motives for and barriers to implementing asset management system: an empirical study for engineered physical assets

This study contributes to the literature by exploring, from a multidisciplinary viewpoint, the emerging discipline of asset management, and by providing a better understanding of the motives for and barriers to asset-intensive organisations’ adoption of asset management systems (AMS). To this end, the authors conceptualise and explore the core themes of AMS, and link this novel body of knowledge to organisational motives for and barriers to the formal introduction of AMS. The study collects and analyses empirical data from 93 middle/senior managers in Slovenian organisations dealing with engineered assets. The results, obtained by applying partial least squares path modelling, show that internal motives, such as better control of operational processes and continuous improvement, are the main drivers of the introduction of AMS. The potential increase in documentation and bureaucracy, and lack of resources, were identified as the main barriers to AMS implementation. By assessing the factors that facilitate and inhibit formal AMS implementation, the study provides important insights into developing strategies to promote the novel and important discipline of asset management.

Fuzzy logic based plantwide control applied to ethanol production from potato starch

Industrial plants dedicated to ethanol production have a large number of interrelated processes and operations with nonlinear dynamics, requiring a process control design method considering plantwide aspects and nonlinear control techniques application, as fuzzy logic, to overcome disturbance effects on process control performance. In this work, disturbance in an ethanol process plant were investigated in order to propose a fuzzy system to improve the plantwide control and keep process productivity. The fuzzy system was set to adapt the bioreactor temperature setpoint in case of disturbances, avoiding process deregulation. The results indicated that the fuzzy system developed was able to improve plantwide process control performance and also increasing process productivity.

Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

In process control system, nonlinearity-induced unit-wide oscillations are a common fault, which degrades the control performance and threaten the stability. It is important to detect and diagnose the nonlinearity-induced unit-wide oscillations to improve the process control performance. In this paper, a novel method, termed as SSA-MVMD, is proposed by combining the sparrow search algorithm (SSA) and multivariate variational mode decomposition (MVMD) to detect and diagnose the nonlinearity-induced unit-wide oscillations. MVMD is an advanced signal decomposition and time-frequency method. However, its performance is affected by the mode number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and penalty coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> . SSA is adopted to optimize the parameters of MVMD. Then, a novel SSA-MVMD-based detector is presented to detect and diagnose the nonlinearity-induced unit-wide oscillations. The proposed method is model-free and data-driven thus requiring no prior knowledge about the process dynamics. Compared with the latest related works, the proposed method can better decompose the multivariate nonstationary signals and adaptively analyze the unit-wide oscillations. In the end, the effectiveness and advantages are demonstrated by simulations as well as industrial cases.