Industrial Process Control Systems Research Articles

A novel tuning formula for PID/PIDD2 controllers for higher-order process plants

The industrial process control systems are high-order sophisticated and complicated systems with characteristics such as inertia, uncertainty, and even oscillatory. The controller design issue has long been challenging for the industry process control systems. This study proposes a novel tuning formula for PID/PIDD2 controllers for high-order process plants. Specifically, for the underdamped second-order with time delays (SOPDT) process plant, a formula for the tuning of PID/PIDD2 controller is proposed based on the relative damping coefficient x; for the critical and over-damped SOPDT process plants, the idea of using zero-pole cancellation is skillfully applied. The tuning parameters are determined by considering both robustness and integrated time absolute error (ITAE). The proposed tuning formula applies to the automatic voltage regulator (AVR) system and extends to the load frequency control (LFC) system and a practical double-capacity water tank experiment. Simulation results demonstrate that the proposed tuning method shows better robustness and anti-interference.

Industrial Metaverse-Based Intelligent PID Optimal Tuning System for Complex Industrial Processes.

In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end-edge-cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

Parameter estimation methods for time‐invariant continuous‐time systems from dynamical discrete output responses based on the Laplace transforms

SummaryIn industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous‐time models. This article presents a stochastic gradient estimation algorithm and a recursive least squares estimation algorithm for identifying the parameters of continuous systems. It derives the parameter identification model of linear continuous‐time systems based on the Laplace transforms of the input and output of the systems. To prove that the techniques given here work, we have included a simulated example.

An interactive software tool to teach ratio control

In this paper we present a software tool, developed using Matlab, that serves as an instructional aid for learning the design of ratio control systems for industrial processes. The tool considers both the series and the parallel control structure and enables the user to tune the parameters of the two PID controllers, as well as subsequently evaluating the impacts of these alterations. The evaluation of the set-point and load disturbance step response, along with the filtering of measurement noise, can be conducted. It is thought that this software has the potential to be effectively utilized in process control courses, facilitating a deeper comprehension of the practical application of theoretical concepts given by the instructor. An illustrative example is given in this context.

Improved Smooth Watermarking Methods for Detecting Replay Attacks in Process Control Systems

Network attack detection is a crucial issue in cyber–physical systems. One classical type of network attack is the replay attack. Although the watermarking scheme has proven to be effective in detecting replay attacks, it may confront numerous challenges when applied to process control systems. The watermarking signal, which consists of independent and identically distributed Gaussian noise sequences, responds slowly to industrial process control systems. To address this issue, two novel smooth watermarking methods are proposed. The smooth watermarking signals exhibit low-frequency characteristics, enabling them to effectively handle large inertia processes. The proposed schemes are validated through two simulated examples. These examples indicate that the proposed methods can significantly enhance detection efficiency while minimizing additional control performance loss.

LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank System

Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model’s local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods.

Towards Real-Time Analysis of Gas-Liquid Pipe Flow: A Wire-Mesh Sensor for Industrial Applications.

Real-time monitoring of gas-liquid pipe flow is highly demanded in industrial processes in the chemical and power engineering sectors. Therefore, the present contribution describes the novel design of a robust wire-mesh sensor with an integrated data processing unit. The developed device features a sensor body for industrial conditions of up to 400 °C and 135 bar as well as real-time processing of measured data, including phase fraction calculation, temperature compensation and flow pattern identification. Furthermore, user interfaces are included via a display and 4…20 mA connectivity for the integration into industrial process control systems. In the second part of the contribution, we describe the experimental verification of the main functionalities of the developed system. Firstly, the calculation of cross-sectionally averaged phase fractions along with temperature compensation was tested. Considering temperature drifts of up to 55 K, an average deviation of 3.9% across the full range of the phase fraction was found by comparison against image references from camera recordings. Secondly, the automatic flow pattern identification was tested in an air-water two-phase flow loop. The results reveal reasonable agreement with well-established flow pattern maps for both horizontal and vertical pipe orientations. The present results indicate that all prerequisites for an application in industrial environments in the near future are fulfilled.

Coupling of adjoint-based Markov/CCMT predictive analytics with data assimilation for real-time risk scenario forecasting of industrial digital process control systems

Risk early detection and controls are critical to avoid unforeseen accidents or incidents across safety-critical industries especially in a digitalization context. Using the sequential Monte Carlo particle filter method, an adjoint-based Markov/Cell-to-Cell Mapping Technique (Markov/CCMT) predictive analytics coupling with data assimilation is proposed in the paper for dynamic risk and reliability scenario modeling of industrial digital process control systems. The particle filter-based data assimilation method is developed with a sequential importance sampling with resampling scheme for extended system state estimation and dynamic reshaping of system state-space model. Then the Markov/CCMT predictive analytics models are dynamically constructed using an equal-weight quadrature scheme for approximating the probabilistic cell-to-cell mapping relations that reflect the possible system state transitions on the discretized state-space model in real time. The adjoint-based Markov/CCMT modeling for risk scenario forecasting is demonstrated on a digital U-shaped Tube Steam Generator (UTSG) water level control system in nuclear power plants. The demonstration results show that super real-time and consistent predictions can be obtained within the adjoint-based Markov/CCMT predictive analytics framework. Multi-step time-series forecasting towards the identification of system-level degradation states and risk-significant scenarios evolution can also be envisioned when a blended state merging and pruning strategy is adopted in deep model search. The proposed Markov/CCMT predictive analytics is able to deliver proactive insights to intelligent operation and maintenance of complex safety-critical engineering systems.

An educational interactive software tool to learn cascade control design

In this paper we present an educational interactive software tool made with Matlab to learn how to design cascade control systems for industrial processes. The tool allows the user to modify the parameters of the two PID controllers in the secondary and primary loops and to verify the effects of such modifications. In particular, the set-point and the load disturbance step response can be evaluated as well as the filtering of measurement noise. In addition, a frequency response analysis can be performed. A comparison with the classic single-loop control can be also done. It is believed that this tool can be fruitfully exploited in process control courses, in order for the student to better understand the application of the theoretical concepts given by the teacher.

Intelligent energy-based product quality control in the injection molding process

Energy is a critical resource for powering modern society, supporting economic growth, and meeting basic human needs. In fact, energy efficiency is an important factor for companies looking to remain competitive in the market. This need is particularly more important in energy-intensive industries, such as plastics manufacturing by process injection molding, where energy costs can account for a significant portion of the overall operating costs. By investing in smart energy-efficient technologies based on artificial intelligence tools and on good practices manufacturing, companies can reduce their energy consumption, improve their environmental performance, and enhance their sustainability. In this paper, an artificial neural network, trained on experimental dataset, has been used for modelling the relationships between energy consumption, product quality, and process setting parameters. Then, an energy control system has been building in Matlab Simulink to simulate the behaviour of real production process of polypropylene product and to identify the optimal process settings that achieve the desired level of product quality while controlling energy consumption. The proposed system demonstrated its effectiveness in the case study adopted and then can be used in others similar plastics production. Moreover, its approach can be used to develop the smart control systems for others industrial processes.

Real-Time Memory Data Optimization Mechanism of Edge IoT Agent

With the full development of disk-resident databases (DRDB) in recent years, it is widely used in business and transactional applications. In long-term use, some problems of disk databases are gradually exposed. For applications with high real-time requirements, the performance of using disk database is not satisfactory. In the context of the booming development of the Internet of things, domestic real-time databases have also gradually developed. Still, most of them only support the storage, processing, and analysis of data values with fewer data types, which can not fully meet the current industrial process control system data types, complex sources, fast update speed, and other needs. Facing the business needs of efficient data collection and storage of the Internet of things, this paper optimizes the transaction processing efficiency and data storage performance of the memory database, constructs a lightweight real-time memory database transaction processing and data storage model, realizes a lightweight real-time memory database transaction processing and data storage model, and improves the reliability and efficiency of the database. Through simulation, we proved that the cache hit rate of the cache replacement algorithm proposed in this paper is higher than the traditional LRU (Least Recently Used) algorithm. Using the cache replacement algorithm proposed in this paper can improve the performance of the system cache.

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

Research on Identification Algorithm of Cascade Control System

Aiming at the problem of object model identification of modern industrial process control systems, a new closed-loop moment parameter identification online method based on the data of normal operation of the running system is proposed. In this method, only one step response data of the system is required, and appropriate convergence factors are introduced into the Laplace formula, the trapezoidal integral method is used to calculate the values of two derivatives of the transfer function, then the four unknown parameters of the second-order model can be solved by fitting the data with the least square method, and the target model can be identified. Finally, the simulation results of building different objects through Matlab show that the identification method has general applicability and good robustness with high recognition, and it is not sensitive to noise signals.

Improved constrained min-max optimization for MPC tracking control in industrial networked process systems

Based on a modified min-max optimization strategy, an improved design of model predictive tracking control (MPC) is proposed to guarantee the performance of industrial process control systems when the networked system suffers from communication faults. Packet losses and uncertainties exist in the networked control system, which may deteriorate the performance of the systems and even cause a safety accident. To handle with this problem, an extended state space model is utilized in the proposed MPC design for enhancing control performance firstly. Then an improved min-max optimization approach is adopted for obtaining the optimal solution of the corresponding MPC method, where two separate optimization steps are proposed to enhance the control system operation. Different from MPC strategies that employ traditional state space models, the state variables can be tuned additionally for the modified MPC design because the tracking error information and the state variables are united in the improved model. As a consequence, extra degree of freedom is acquired for the relevant MPC scheme, and then better control performance is expected. Simulations on the temperature regulation process under model/plant mismatches and packet losses are simulated to evaluate the validity of the proposed MPC method finally.

Improved Faster R-CNN for the Detection Method of Industrial Control Logic Graph Recognition

In the process of developing the industrial control SAMA logic diagram commonly used in the industrial process control system, there are some problems, that is, the size of logic diagram elements is small, the shape is various, similar element recognition is easily confused, and the detection accuracy is low. In this study, the faster R-CNN network has been improved. The original VGG16 network has been replaced by the ResNet101 network, and the residual value module was introduced to ensure the detailed features of the deep network. Then the industrial control logic diagram dataset was analyzed to improve the anchor size ratio through the K-means clustering algorithm. The candidate box screening problem was optimized by improving the non-maximum suppression algorithm. The elements were distinguished via the combination of the candidate box location and the inherent text, which improved the recognition accuracy of similar elements. An experimental platform was built using the TensorFlow framework based on the Windows system, and the improved method was compared with the original one by the control variable. The results showed that the performance of similar element recognition has been greatly enhanced through an improved faster R-CNN network.

Parallel fuzzy relation matrix factorization towards algebraic formulation, universal approximation and interpretability of MIMO hierarchical fuzzy systems

Multi-input multi-output (MIMO) hierarchical fuzzy systems (HFSs) play a significant role in industrial process control and power systems. Using the max-min semi-tensor product of matrices, this paper proposes the algebraic formulation of MIMO HFSs, and discusses the corresponding interpretability. Firstly, the parallel fuzzy relation matrix (FRM) of MIMO fuzzy systems is proposed, based on which, an algebraic formulation of MIMO fuzzy systems is developed. Secondly, the factorization of parallel FRM is used to partition the MIMO fuzzy systems into fuzzy logic units (FLUs). Based on this operation, the algebraic formulations and construction algorithms of serial, parallel and hybrid MIMO HFSs are established. Thirdly, the algebraic formulations are used to study the universal approximation and interpretability of MIMO HFSs, and the optimal interpretability is obtained. Finally, the effectiveness of the obtained results is verified by the energy control systems in parallel hybrid vehicles.

Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex

This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes, and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognize the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for the tasks of identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for the construction of neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters is made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification on the basis of artificial neural networks is offered.

An improved control strategy for an industrial copper solvent extraction process

ABSTRACT In this paper, to solve the constraints and upgrade the control systems of industrial copper solvent extraction processes, an improved two-layer (optimization and stabilization) control strategy was proposed. The proposed control strategy is equipped with an advanced–step multistage nonlinear model predictive controller. It was successfully tested against simulated disturbances, noise and set point changes. Moreover, the proposed strategy was applied for an industrial process and the obtained results were compared to the plant control strategy and the latest proposed control strategy for industrial copper solvent extraction in the literature. With the proposed control strategy and the latest proposed control strategy in the literature, the production of the copper solvent extraction process was increased by almost 6% and 3%, respectively, compared to the plant control strategy, and the process variation was decreased by 90–92% and 80–85%, respectively.

High-Tech Companies are an Important Structural and Innovative Aspect of the Development of the Regional Economy

This article is devoted to the development of high-tech companies in the modern economic market. The author presents the definition of a high-tech company, shows that with the development of the digital economy, high-tech companies are more and more efficient in their activities in the economic market and have a multilateral positive impact on the development of regions. The author analyzes the main functions of high-tech companies in the modern economic market. In addition, the article shows that high-tech companies, through their own scientific developments, the introduction of advanced solutions in the activities of industries and spheres of the regional economy, contribute to the development of the business environment, training of the personnel of economic objects, the quality of life of the population of the regions. The article notes that high-tech companies in their activities use a full range of modern innovations: technical, technological, marketing, organizational, process, etc. The article tells that in the modern period in the economic market of the Russian Federation there are many high-tech companies that specialize in the development and implementation of information and communication technologies and digital projects. For example, such as the development of data stores, web and mobile applications, enterprise resource planning systems, industrial process control systems, etc. As the main characteristics of high-tech companies, the article highlights such as the use of progressive achievements of science and technology in the main areas of the company's work, the presence of trade secrets, the production of goods and services, information security, profitability, instant rate of change, constant integration with scientific institutions, mobility and uniqueness of specialists.Purpose. The purpose of this article is to prove the provisions that high-tech companies are an effective tool in the development of the regional economy.Materials and methods. In the process of writing this article, a systematic and comparative analysis, methods of working with WEBsites, monographic studies, research materials of domestic and foreign authors in the field of regional and digital economy were used.Results. In this article, based on the study of domestic and foreign experience, the main directions of the functioning of high-tech companies in the economic market, their advantages and disadvantages are identified; the main provisions for further improving their activities and a positive impact on the growth of economic indicators in the development of regions of the country are stated.Conclusion. This paper shows that high-tech companies that are effectively developing in the modern period have many positive effects on the development of the regional economy. In particular, they contribute to the creation of an innovative ecosystem, the development of production and urban infrastructure based on advanced scientific discoveries, innovative practical developments and the attraction of qualified personnel.

Development of Resilient Control Systems for Technological Plants

The aims of the research are increasing resiliency of industrial process control systems, the de-velopment of methods of creating resilient industrial process control systems and creating a resil-ient control system of the distillation column for the production of food alcohol. The set of ob-jectives were achieved without the use of hardware backup, but using the developed algorithms which were based on the use of the model of dynamics of technological plants in the software of the control system and modern theory of multivariable automatic control systems. The opti-mal linear-quadratic controller with disturbances model and a state observer was used as a full controller. The most important result was improvement of the survivability of industrial process control systems using the developed algorithm. Also, the resilient distillation column control sys-tem was developed. For the further researches significant results are formulas which allow de-fining new mode values of controls in a distillation column at failure event of corresponding control output channels and formulas of logic algebra for a choice of optimum configuration of control system of a distillation column of food spirit industry. The basis of the developed tech-nique was reconfiguration of the control system with inclusion, instead of the failed, additional devices, which were not being used in a current configuration, but were carried out the similar function taking into account dynamics coupling of a technological plant. The significance of these results is that they can be applied to the development of cyber-production systems that do not require permanent maintenance by personnel.