Real-time Process Control System Research Articles

ЧИСЛЕННАЯ МОДЕЛЬ КАК КОМПОНЕНТ СИСТЕМЫ УПРАВЛЕНИЯ ЭЛЕКТРОМАГНИТНОЙ ПЛАВКОЙ МЕТАЛЛОВ ВО ВЗВЕШЕННОМ СОСТОЯНИИ

The work presents the results of numerical simulation of the process of levitation melting of metal in an electromagnetic field. The developed model reflects the retention of the heated sample in a state of stable levitation. Based on the constructed model, a study of the levitation melting process was carried out, taking into account the technical characteristics of the laboratory equipment. During the experiments, the zone of stable levitation of the sample with the specified parameters in the alternating electromagnetic field was determined. The efficiency of the obtained numerical model and its suitability for use as part of a real-time technological process control system for melting were evaluated.

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review.

Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.

Trust But Verify: A Framework for the Trustworthiness of Distributed Systems

Many real-time process-control and industrial control systems, such as Supervisory Control and Data Acquisition (SCADA), use a distributed software architecture and rely on trusted message exchanges among software components. This article presents the Trust but Verify (TBV) middleware that promotes the idea that software components should not blindly trust each other. The TBV intercepts messages between a sender and a receiver to verify the consistency of the messages against rules associated with message types; this verification considers the system state. Based on the verification, a message is either delivered to the recipient or blocked. Even when components are mutually authenticated, it is possible that their counterparts are faulty or acting maliciously, persuading the receiver to take harmful actions. The contributions of this article are: (1) The design of the TBV middleware. (2) A proof-of-concept implementation of the TBV on a cyberphysical system—a water treatment facility. (3) An experimental validation of the TBV through several attack scenarios that allow compromised or faulty components to randomly send erroneous messages. These experiments measure the TBV’s detection rate as well as its overhead. (4) An evaluation of the TBV overhead and performance degradation.

A novel real-time monitoring and control system for waste-to-energy gasification process employing differential temperature profiling of a downdraft gasifier

A novel, cost-effective and real-time process monitoring and control system was developed to maintain stable operation of waste-to-energy gasification process. It comprised a feedback loop control that utilized the differential temperatures of the oxidation and reduction zones in the gasifier to determine the regional heat-flow (endothermic or exothermic), to assess the availability of oxidizing agent (for instance, air or O2) at the char bed and to calculate the fuel feeding rate. Based on the correlations developed, the air-to-fuel ratio or the equivalence air ratio (ER) for air gasification could be instantaneously adjusted to maintain stable operation of the gasifier. This study demonstrated a simplification of complex reaction dynamics in the gasification process to differential temperature profiling of the gasifier. The monitoring and control system was tested for more than 70 h of continuous operation in a downdraft fixed-bed gasifier with refuse-derived fuel (RDF) prepared from municipal solid wastes (MSW). With the system, fuel feeding rate could be adjusted accurately to stabilize the operating temperature and ER in the gasifier and generate syngas with consistent properties. Significant reductions in the fluctuations of temperature profiles at oxidation and reduction zones (from higher than 100 °C to lower than 50 °C), differential temperatures (from ±200 to ±50 °C) in gasifier and the flow rate (from 16 ± 6.5 to 12 ± 1.8 L/min), composition of main gas components, LHV (from 6.2 ± 3.1 to 5.7 ± 1.6 MJ/Nm3) and tar content (from 8.0 ± 9.7 to 7.5 ± 4.2 g/Nm3) of syngas were demonstrated. The developed gasifier monitoring and control system is adaptable to various types (updraft, downdraft, and fluidized-bed) and scales (lab, pilot, large scale) of gasifiers with different types of fuel.

Implementation of Real-Time Concentration Process Control System Design Based on PID and Fuzzy Logic Controller

Concentration process is commonly process in industries for chemicals and products that want to reach desired amount of matter in product, so concentration process control is important to reach the desired concentrate of product.Concentration in streams are affected by physical variables around it like pressure, temperature and amount of matter that solvent in streams, so the amplitude of concentration is randomly change with time that make control process not easy.In this project controller was designed for concentration process and was implemented in real-time system that constructed to verify the response of controller, in prototype concentrated solutions-juice and sugar-used to control in it separately with water then mixed to produce juice with desired property.The Mathematical model of concentrated process was evolved and Matlab used to analyze and design control loop for these model, control algorithms used as PID & Fuzzy logic controller to build controller that achieve the specification requirements of a system process.The Fuzzy PI-controller designed to control that for characteristic of nonlinearity of real-time system, and implement simulation of control loops in LABVIEW software with appropriate interface.The DAQ hardware with LABVIEW software are used to implement the control loop that designed for real-time prototype to produce the juice with desired concentrated.

A real time process management system using RFID data mining

Recently, there have been numerous efforts to fuse the latest Radio Frequency Identification (RFID) technology with the Enterprise Information System (EIS). However, in most cases these attempts are centered mainly on the simultaneous multiple reading capability of RFID technology, and thus neglect the management of massive data generated from the RFID reader. As a result, it is difficult to obtain flow information for RFID data mining related to real time process control. In this paper, we propose an advanced process management method, called ‘Procedure Tree’ (PT), for RFID data mining. Using the suggested PT, we are able to manage massive RFID data effectively, and perform real time process management efficiently. Then we evaluate the efficiency of the proposed method, after applying it to a real time process control system connected to the RFID-based EIS. For the verification of the suggested system, we collect an enormous amount of data in the Enterprise Resource Planning (ERP) database, analyze characteristics of the collected data, and then compute the elapsed time on each stage in process control. The suggested system was able to perform what the traditional RFID-based process control systems failed to do, such as predicting and tracking of real time process and inventory control.

English

Supervisory control and data acquisition (SCADA) systems are real-time process control systems that monitor and control local or geographically remote devices. They are in wide use throughout a variety of critical infrastructure sectors, and are a critical component of operations. The SCADA system also provides a managementway for important plant performance information to be obtained for use by managers and engineers at a corporate level. SCADA historically is responsible for monitoring and controlling critical infrastructures and manufacturing processes in an isolated environment. But with the requirement of a timely access of information for making decisions, large and modern companies being in a geographically diverse location take advantage of the internet as an important communication channel to allow the exchange of data. However, with SCADA being in the internet raise the issue of security. As more components of control systems become interconnected with the outside world using IP-based standards, the probability and impact of a cyber attack heighten. Since, the reliable function of SCADA systems in our modern infrastructure may be crucial to public health and safety management. Attacks on these systems may directly or indirectly threaten public health and safety since SCADA control the sources of our daily necessities such as oil and gas, air traffic and railways, power generation and transmission, water and manufacturing. With the posted threats and listed vulnerabilities in this study, a retrofit for these threats through the crossed cipher scheme is presented. To get the best of both types of cipher symmetric using advanced encryption standard (AES) and the asymmetric elliptic curve cryptography (ECC) to address the confidentiality, authentication, integrity and non-repudiation issues in SCADA system management. Key words: Supervisory control and data acquisition (SCADA), security, threats, vulnerability, management.

Real-Time Process Control System of Dieless Tube Drawing with an Image Processing Approach

Dieless tube drawing is one of the advanced material forming technologies to reduce cross section area of a tube with the absence of dies, mandrel and lubricant. However, this promising technique has technological challenges to improve dimensional stability and accuracy. Machine vision based on image processing technique was selected to monitor deformation behavior on dieless drawing process. The present paper describes real-time monitoring using image processing approach in order to achieve real-time process control of dimensional profile and temperature distribution during dieless drawing process. The results show that machine vision is effective and efficient to monitor dieless drawing process.

Optical emission spectroscopy as a process control tool during plasma enhanced chemical vapor deposition of microcrystalline silicon thin films

The decisive criterion associated with the species emission intensity ratio (Hα/SiH*) which characterizes the crystallinity of microcrystalline silicon (μc-Si) film was found to display an unstable behavior resulting from species concentration variation during μc-Si film growth with optical emission spectroscopy (OES) tool. In this study, a real-time process control system i.e. closed-loop system was developed. It aims to control the species intensity ratio with OES device in a very high frequency (VHF) plasma enhanced chemical vapor deposition reactor, via modulating the VHF power and silane dilution to improve μc-Si film growth for high efficiency a-Si/μc-Si tandem solar cell. The experiment results show that the closed-loop system stabilized the Hα/SiH* intensity ratio within a variation of 5% during the μc-Si film deposition process. Higher growth rate of μc-Si film with the same crystallinity was obtained in the closed loop system which consumed less power and SiH4 gas than in the open loop system, i.e. without process control.

QoC elastic scheduling for real-time control systems

The elastic task model, a significant development in scheduling of real-time control tasks, provides a mechanism for flexible workload management in uncertain environments. It tells how to adjust the control periods to fulfill the workload constraints. However, it is not directly linked to the quality-of-control (QoC) management, the ultimate goal of a control system. As a result, it does not tell how to make the best use of the system resources to maximize the QoC improvement. To fill in this gap, a new feedback scheduling framework, which we refer to as QoC elastic scheduling, is developed in this paper for real-time process control systems. It addresses the QoC directly through embedding both the QoC management and workload adaptation into a constrained optimization problem. The resulting solution for period adjustment is in a closed-form expressed in QoC measurements, enabling closed-loop feedback of the QoC to the task scheduler. Whenever the QoC elastic scheduler is activated, it improves the QoC the most while still meeting the system constraints. Examples are given to demonstrate the effectiveness of the QoC elastic scheduling.

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

The coefficient diagram method (CDM) is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems (MRAS) are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.

Reducing security vulnerabilities for critical infrastructure

In this paper, we show the need for improved Process Control System (PCS) security, and describe some of the promising research areas in PCS security. One implementation of PCS in critical infrastructure and factory automation is a supervisory, control, and data acquisition (SCADA) system, a real-time industrial process control system which centrally monitors and controls remote and/or local processes utilizing plant, equipment, or devices (such as switches, valves, pumps, relays, etc.) while collecting and logging field data. Current SCADA systems are distributed, networked, and dependent on open protocols for the internet, which are exposed to remote cyber terrorism. They are particularly vulnerable to unauthorized access. We give some examples of SCADA processes with natural gas control systems in USA and the Ubiquitous Sensor Network (USN) in Korea. We also examine a representative vulnerability and corresponding measures for security, and present an example of concrete measures for the security of mass transportation as a critical infrastructure.

Monitoring, Analysis and Diagnosis of Distributed Processes with Agent-Based Systems

Multiagent systems provide a powerful framework for developing real-time process supervision and control systems for distributed and networked processes by automating adaptability and situation-dependent rearrangement of confidence to specific monitoring and diagnosis techniques. An agent-based framework for monitoring, analysis, diagnosis, and control with agent-based systems (MADCABS) is developed and tested by using detailed models of chemical reactor networks. MADCABS is composed of three main hierarchical layers, the physical communication layer, the supervision layer and the agent management layer. The supervision layer consists of agents and methods for data preprocessing, process monitoring, fault diagnosis, and control. The agent management layer conducts the assessment of agent performances to assign the priorities for selecting the most useful methods of process supervision for specific types of situations. The paper illustrates the operation of MADCABS for monitoring and fault detection.

Construction and operation of open-circuit methane chambers for small ruminants

A detailed description of the construction, calibration and operation of 4 open-circuit chambers designed to measure methane emissions from sheep is given. These chambers have accommodated sheep under ad libitum feeding and have been used in short-term experiments and over extended periods of time. A real-time base data acquisition and process control system provided 24 h operation of the methane chambers. The gas volume measurement system consisted of dry test meters and sensors for differential and absolute pressure, temperature and relative humidity. This enabled correction of methane chamber exhaust air volume to standard temperature and pressure. Temperatures and relative humidity during measurements ranged from 21.0 to 23.1°C and 53.8 to 78.9%, respectively. The gas chromatograms were calibrated 3 times a day using commercially available gas standards. Recovery tests were conducted on each chamber by bleeding a methane gas standard into the chamber at a rate similar to methane production by sheep, with 94.4–107.1% of the methane gas recovered. Measurements on 32 sheep gave methane emissions within predicted levels and identified several low methane-producing sheep.

Modeling a Full-Scale Primary Sedimentation Tank Using Artificial Neural Networks

Modeling the performance of full-scale primary sedimentation tanks has been commonly done using regression-based models, which are empirical relationships derived strictly from observed daily average influent and effluent data. Another approach to model a sedimentation tank is using a hydraulic efficiency model that utilizes tracer studies to characterize the performance of model sedimentation tanks based on eddy diffusion. However, the use of hydraulic efficiency models to predict the dynamic behavior of a full-scale sedimentation tank is very difficult as the development of such models has been done using controlled studies of model tanks. In this paper, another type of model, namely artificial neural network modeling approach, is used to predict the dynamic response of a full-scale primary sedimentation tank. The neural model consists of two separate networks, one uses flow and influent total suspended solids data in order to predict the effluent total suspended solids from the tank, and the other makes predictions of the effluent chemical oxygen demand using data of the flow and influent chemical oxygen demand as inputs. An extensive sampling program was conducted in order to collect a data set to be used in training and validating the networks. A systematic approach was used in the building process of the model which allowed the identification of a parsimonious neural model that is able to learn (and not memorize) from past data and generalize very well to unseen data that were used to validate the model. The results seem very promising. The potential of using the model as part of a real-time process control system is also discussed.

A neural network model to predict the wastewater inflow incorporating rainfall events

Under steady-state conditions, a wastewater treatment plant usually has a satisfactory performance because these conditions are similar to design conditions. However, load variations constitute a large portion of the operating life of a treatment facility and most of the observed problems in complying with permit requirements occur during these load transients. During storm events upsets to the different physical and biological processes may take place in a wastewater treatment plant, and therefore, the ability to predict the hydraulic load to a treatment facility during such events is very beneficial for the optimization of the treatment process. Most of the hydrologic and hydraulic models describing sewage collection systems are deterministic. Such models require detailed knowledge of the system and usually rely on a large number of parameters, some of which are uncertain or difficult to determine. Presented in this paper, an artificial neural network (ANN) model that is used to make short-term predictions of wastewater inflow rate that enters the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest plant in the Edmonton area (Alberta, Canada). The neural model uses rainfall data, observed in the collection system discharging to the plant, as inputs. The building process of the model was conducted in a systematic way that allowed the identification of a parsimonious model that is able to learn (and not memorize) from past data and generalize very well to unseen data that was used to validate the model. The neural network model gave excellent results. The potential of using the model as part of a real-time process control system is also discussed.

A real-time information system for multivariate statistical process control

Statistical process control (SPC) is widely used in process industries to monitor variations in process attributes. Typically, automatic devices capture a multitude of measurements on process and product characteristics every few seconds. Operators and engineers commonly monitor only a small subset of these. Multivariate SPC has been proposed to fully utilize the available data, however, interpretation of multivariate information is often too complex for most line operators. This paper describes the design and implementation of a real-time multivariate process control system that features a graphical user interface (GUI) and provides useful information for both line operators and engineers. The information system described in this paper should provide large-scale manufacturers with better access to information for identifying opportunities in continuing to improve processes performance and business competitiveness.

Relational programs: An architecture for robust real-time safety-critical process-control systems

Software for safetydcritical systems, such as avionic, medical, defense, and manufacturing systems, must be highly reliable since failures can have catastrophic consequences. While existing methods, such as formal techniques, testing, and faultdtolerant software, can significantly enhance software reliability, they have some limitations in achieving ultrahigh reliability requirements. Formal methods are not able to cope with specification faults, testing is not able to provide high assurance, and faultdtolerant software based on diverse designs is susceptible to commondmode failures. We present a new approach that starts with a decomposition of the system requirements into a conjunction of subtasks (goals and constraints). The system state space is then projected onto a restricted space that is specialized for a subtask. The control problem corresponding to each subtask is solved and validated in its restricted “view” of the system state space. To allow the programs for the individual subtasks to be easily composed together, the model for each subtask is relational rather than functional, i.e., it represents a set of control trajectories for each input rather than just one trajectory. The overall system is obtained by composing the models for the subtasks using wellddefined set intersection and union operations. The relational approach has several significant advantages. With appropriate priority assignments, it provides strong guarantees that the safetydcritical components are immune to defects in other components of the system. Also, the system reliability can be rigorously derived from the component reliabilities. This significantly reduces the validation effort since the number of states and transitions in the decomposition is a fraction of those in the overall system. The system can be composed from its components either statically or dynamicallys the latter facilitates ondthedfly maintenance as well as incorporation of advanced adaptive and evolving control programs. The paper contains a detailed example to illustrate the relational approach.

The Performance of near Infrared Analysers Can Be Improved by Digital Filtering Techniques

On-line near infrared (NIR) analysers are used widely for quantitative composition measurements in real-time process control systems. The accuracy and repeatability of the measurements are amongst the most important factors when evaluating the total performance of these analysers, but the lower detection limit is often limited by noise in the measurement signal. There are two major alternatives for reducing noise in an optical analyser: prevention of noise contamination and post-processing of the signal by filtering. In the second alternative, the measurement signal can be post-processed by digital filtering techniques, for example, to enhance the desired signal component. Although digital signal processing (DSP) technology offers many advantages for on-line process measurements, the behaviour of the signal must be understood thoroughly before a successful application of this technology can be developed. A digital filtering technique called matched filter was used in an experimental set-up. The performance of this filter was compared to an analog filtering of a pulse shaped signal. Experimental data were collected and filtered with a novel digital spectrometer which consists of a modulated light source, a spectrograph, a linear array detector and the analog and digital signal processing electronics needed to control and filter the signal. In this case the matched filter gave a clear improvement of 2.2–4.6 dB in the signal-to-noise ratio (SNR) relative to an analog lock-in amplifier. Among the other advantages afforded by digital filters are that they are programmable, easy to design, test and implement on a PC and do not suffer from drift. Also digital filters are extremely stable with respect to both time and temperature and versatile in their ability to process signals in a variety of ways.

Software Design Based on Fundamental Subsystems: A Synthesis Action Applied to the Essential Model

This work presents a design method addressing the development of software for large, distributed, real-time process-control systems. This method, introduced and experimented as described in Sanchez (1996), uses standard concepts, tools and techniques based on information hiding, exchange of messages and configuration languages, aiming at the definition of Fundamental Subsystems which implement Classes derived through a synthesis action applied to the Essential Model for the control subsystem.