Volume 14 Number 6
December 2017
Article Contents
Hanane Zermane and Hayet Mouss. Development of an Internet and Fuzzy Based Control System of Manufacturing Process. International Journal of Automation and Computing, vol. 14, no. 6, pp. 706-718, 2017. doi: 10.1007/s11633-016-1027-x
Cite as: Hanane Zermane and Hayet Mouss. Development of an Internet and Fuzzy Based Control System of Manufacturing Process. International Journal of Automation and Computing, vol. 14, no. 6, pp. 706-718, 2017. doi: 10.1007/s11633-016-1027-x

Development of an Internet and Fuzzy Based Control System of Manufacturing Process

Author Biography:
  • Hayet Mouss received the B. Sc. degree in electrical engineering from the National Polytechnic School of Algiers, Algeria in 1979, the M. Sc. degree in electrical and computer engineering from the ENSERB, France in 1982, and the Ph. D. degree in electrical and computer engineering, Bordeaux University, France in 1985. After graduation, she joined the University of Batna, Algeria, where she is an associate professor of electrical and computer engineering. She is a member of New York Science Academy. She is the head of Automatic and Computer Integrated Manufacturing Laboratory.
         Her research interests include industrial diagnosis of production system using the artificial intelligence techniques.
         E-mail:hayet mouss@yahoo.fr

  • Corresponding author: Hanane Zermane received the B. Sc. degree in computer science from University of Batna, Algeria in 2003 and 2011. She is currently a Ph. D. degree candidate in industrial engineering in University of Batna, Algeria. She has published journal and conference papers.
         Her research interests include manufacturing, simulation, manufacturing, automation, process control, fuzzy logic and expert systems.
         E-mail:hananezermane@yahoo.fr (Corresponding author)
         ORCID iD:0000-0003-4167-2578
  • Received: 2015-09-28
  • Accepted: 2016-03-18
  • Published Online: 2017-01-18
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Development of an Internet and Fuzzy Based Control System of Manufacturing Process

  • Corresponding author: Hanane Zermane received the B. Sc. degree in computer science from University of Batna, Algeria in 2003 and 2011. She is currently a Ph. D. degree candidate in industrial engineering in University of Batna, Algeria. She has published journal and conference papers.
         Her research interests include manufacturing, simulation, manufacturing, automation, process control, fuzzy logic and expert systems.
         E-mail:hananezermane@yahoo.fr (Corresponding author)
         ORCID iD:0000-0003-4167-2578

Abstract: The aim of this work is to develop an Internet and fuzzy based control and data acquisition system for an industrial process plant which can ensure remote running and fuzzy control of a cement factory. Cases studies of the proposed system application in three cement factories in Algeria, SCAEK (Setif), SCIMAT (Batna), and SCT (Tebessa), are discussed. The remote process control consists of alarms generated during running of the processes while maintaining and synchronizing different regulation loops thus ensuring automatic running of processes smoothly. In addition, fuzzy control of the kiln and the other two mills ensures that the system is operational at all times with minimal downtime. The process control system contains different operator station (OP), alarms table and a provision to monitor trends analysis. The operator can execute any operation according to his authorised access assigned by the system administrator using user administration tool. The Internet technology is used for human security by avoiding all times presence of operators at site for maintenance. Further, in case of a breakdown, the problem would be remotely diagnosed and resolved avoiding requirement of an expert on site thus eliminating traveling cost, security risks, visa formalities, etc. These trips are difficult to organize (costs, visas, risks). So the enterprise can reduce downtimes and travel costs. In order to realize a process control system guided by operators in the main control room or through Internet, the process control is based on programming in PCS 7 utilizing Cemat library and Fuzzy Control++ Siemens tools.

Hanane Zermane and Hayet Mouss. Development of an Internet and Fuzzy Based Control System of Manufacturing Process. International Journal of Automation and Computing, vol. 14, no. 6, pp. 706-718, 2017. doi: 10.1007/s11633-016-1027-x
Citation: Hanane Zermane and Hayet Mouss. Development of an Internet and Fuzzy Based Control System of Manufacturing Process. International Journal of Automation and Computing, vol. 14, no. 6, pp. 706-718, 2017. doi: 10.1007/s11633-016-1027-x
  • The growing pressure of competition forces cement factories to reduce costs, continually increase productivity and quality, reduce products marketing time and develop technologies with clean production processes while optimally using raw materials and energy. To achieve these aims, it is necessary to continuously optimize the processes to modernize and develop the automation systems. These automation system are used to avoid human errors and inaccuracies during manual data collection, waste of human resources, and to ensure performance and uniform documentation of all production facilities, generate evaluation reports, provide directions and keep the important data safe for historical analysis.Presently, in cement factories, developers need to integrate artificial intelligence techniques, such as fuzzy logic[1], expert systems, neural networks, etc.

    This work presents a new approach to control a manufacturing plant by applying Internet technologies with artificial intelligence techniques to optimize the process, minimize production time and ensure good quality of the products. The approach is based on using web-based remote and fuzzy control system in cement industry for process e-control, e-diagnostic and e-maintenance.

    This approach uses advanced technologies for high-level controller design, including programmable logic controller like Siemens programmable logic controller (PLC), SIMATIC PCS 7, Cemat library and Fuzzy Control++ tool, to ensure running of different workshops.

  • The cement sector plays an important role within the building materials industry. Productivity and product quality are internationally considered as the decisive factors. The cement industry, with its high level of energy and raw materials consumption, is particularly concerned with conserving natural resources and protecting the global climate. The cement sector must keep pace with scientific and technological developments in order to meet consumer expectations while remaining competitive with lower costs. This can be achieved by applying a strict management policy that enables the control of production.In the cement factory, the raw material comes from the quarry and is ground in a raw mill to get meal. This material passes through a process in the clinker furnace under ideal conditions. The ground clinker is then stored with addition of gypsum to mill, which gives final product, the cement. (see cement manufacturing plant in Fig. 1).

    Figure 1.  Cement manufacturing plant

  • Due to close relationship between cement consumption and per-capita income, higher cement consumption is reflective of growth and progress of the country. Regional development depends upon several factors, which include demand, raw material reserves, market access and economic conditions. As such, the global cement industry has undergone major changes in recent years. Emerging markets such as India and China now represent approximately 90% of the worldwide cement market. Economically advanced nations, such as Europe and the Americas account for most of the remainder despite on-going financial difficulties. Global cement magazine has compiled the top75 global cement producers, ranked according to installed production capacity, with data collected from the global cement directory 2013and individual company websites (where available). Two sets of data are presented in separate tables (Tables 1 and 2) due to differences between sources.

    Table 1.  Global cement companies 1-11 ranked by capacity

    Table 2.  Global cement companies 1-14 ranked by capacity

    For example, the Italcementi group claims a cement production capacity of 68Mt/yr on its website while data collected for publication of the global cement directory 2014 states a production capacity of 80 (Mt/yr)[2]. Table 3 presents the economical growth of cement production for 2014, 1-5 ranked by region.

    Table 3.  Economical growth by region (%)

    All those cement factories and groups cited previously need to have stable plants, optimize the production, manage and correct process disruptions and minimize wear on plant equipment, all to ensure optimum plant performance. By improving equipment availability and utilization, fuzzy logic helps reduce operational and maintenance costs. The optimization and fuzzy control of plants ensure that the initial recipe targets are adjusted according to the given conditions of the process such as raw material grinding or kiln process. As a case study, cement factories in Algeria are considered like global companies.

    Systems based on fuzzy logic improves process stability through continuous, accurate and consistent process evaluation and control actions. In addition, it ensures lower energy and maintenance costs due to rapid control and guaranteed suppression of process upsets, better and more uniform end-product quality. More of that, better utilization of human, capital and material resources. Also, it minimizes emissions and environmental effects besides relieving operators from routine control actions.

  • Amongst the different cement groups of the world, Algerian cement societies classified according to GICA group (groupe industriel des ciments algerian) have been considered as the case study. Each society has a rate of cement production and sales, presented in graphs showed in Fig. 2[3]. Different cement factories in Algeria did not benefit from advantages of fuzzy logic techniques to optimize the processes and reduce cost effects.For example, in SCIMAT (Batna), the problem is old equipment of cement factory and instability of different values and parameters.

    Figure 2.  Cement production and sales of the group GICA (groupe industriel des ciments algerian) 2014

  • The industrial systems become more complex in our days, the increasing complexity explains the need for an efficient monitoring system to ensure performance, safety and reliability.This need for security and reliability requires implementation of preferment solutions such as artificial intelligence techniques to control these industrial processes. One such technique is fuzzy logic.

    For its important value, fuzzy logic is developed in several works[4] and applied in different domains, such as quad-rotor[5]. In complex systems such as cement manufacturing plant, one of the proposed fuzzy models is Takagi-Sugeno (TS) fuzzy model. This is being used to design decentralized networked control system for control of continuous large-scale systems where the measures and control functions are distributed on calculating members that can be shared with other applications and connected to digital network communications[6]. Other works of Takagi-Sugeno presents fuzzy controllers which used to represent non-linear systems, fuzzy identification of systems and its applications to modelling and control a process[7]. FLSmidth automation has been a pioneer in high-level expert control systems for cement kiln applications. It was one of the initiating company that applied fuzzy logic technique in cement factories such as SCIMAT in 1987.Actually, FLS had a new system based on fuzzy logic technique for installation in the cement industry, called ECS/ ProcessExpert(ECS/PXP). However, it was a separate package, initiated after the stabilization of the system. The control system based on the famous industrial platform expert control & supervision (ECS) was specially developed for its features of remote monitoring, supervision and reporting. ECS/PXP (Fig. 3) is a solution for control and optimization of complex high-level process, such as the baking process. The control is optimized using advanced functions of ProcessExpert application, customized to meet the requirements of each user. Depending on the type of application techniques, advanced expert system, fuzzy logic, neural networks and a predictive model-based controller (MPC) are used in modules of ProcessExpert application to allow patterns of hybrid control for meeting the requirements of a given process control. These modules carry out regular assessments of complex process conditions and perform appropriate actions for a more frequent and reliable control than human [8].

    Figure 3.  ECS/ProcessExpert systems data flow

    The control strategies behind ECS/PXP-Kiln are based on two decades of experience in cement kiln control and optimization project[9].

  • Since development of Internet technology, several industries want to integrate it in different areas. The globalization of the Internet has succeeded faster than anyone could have imagined.Innovators will use the Internet as a starting point for their efforts creating new products and services specifically designed to take advantage of the network capabilities. In the business world, the use of networks to provide efficient and cost-effective employee training is increasing in acceptance. On-line learning opportunities can decrease time-consuming and costly travel while still ensuring that all employees are adequately trained to perform their jobs in a safe and productive manner.

    Initially, data networks were used by businesses to internally record and manage financial information, customer information, and employee payroll systems. The intelligent communications platform offers much more than basic connectivity and access to applications. However, it can be used to conduct the control system experiment remotely via Internet[10], for addressing design issues and implementation of internet-based process control systems[11]. In addition, other works use Wireless and Internet communications technologies for monitoring and control[12], ranging from modelling and control for wireless networked control system[13], for web-based SCADA display systems (WSDS) and access via Internet[14]. Some initial works even in laboratory experiments tried to apply web technologies to acquire information remotely[15] or for remote supervision of industrial processes, using self-organizing maps[16]. Another work represent a development of remote control and monitoring of web-based distributed OPC system[17].

    Actually, different works focussed on networked control systems as well as their progress and new technologies integrated in these networks[18]. Recent research focuses on how to use Internet technology in a large area. A web-based remote voice control of robotised cells was developed based on the use of quasi-natural language instead. The main result of this research was the architecture of industrially oriented remote voice control system[19], for development of remote monitoring and control system for mechatronics engineering practice in the case of flexible manufacturing system[20]. Another work represents a Web-based Supervisory Control System based on Raspberry Pi[21]. For web-based applications in cement industry, one of the new applications, based on web used for e-diagnostic, is called IzeeDiag. IzeeDiag is a web-based remote inspection platform that allows connecting a field technician with a distant expert[22].

  • Cement factories which want to face global competition and be competitive on the market, necessarily need a good management of the information and guaranteed advantages in terms of security, availability, integrity and cost.

    Implementation of the new network to control the raw mill workshop via Internet is divided on two parts. The first is to create the fuzzy controller using FuzzyControl++ Siemens tool. The second concerning the network is to ensure the access via Internet and monitoring the cement kiln, e-diagnosis of alarms occurrence, e-maintenance and e-fuzzy control. These innovations facilitate operators tasks, ensure increased production, consistent quality with a reduction in standard deviation and more stable operation.

    This work proposes a network architecture using several tools of PCS 7, e.g., Step 7, continuous function chart (CFC), Windows control center (WinCC), Graphics Designer, WebNavigator, DataMonitor, FuzzyControl++.

  • Modern chemical processing plants consist of process units arranged in a complex network. These large networks can lead to difficulties in plant-wide analysis and control design, as the resulting large-scale system may have complex nonlinear dynamics[23].

    In this work, the proposed industrial network architecture consists of two components, web clients operator station (OS) and web server operator station. In order to control and monitor the process using Intranet or Internet and to transmit information and production reports between operators or send them to managers, Table 4 presents some Internet protocol (IP) addresses of the network, including computers, PLC, printers and servers. The network contains two sub-nets.

    Table 4.  IP addresses of the industrial network

  • Siemens has developed a configuration tool based on fuzzy logic, called FuzzyControl++, but it has not been applied in cement industry yet. A few works have applied this tool in the cement factory SCIMAT for fuzzy control, diagnosis and maintenance[24, 25], and for neuro-fuzzy prognostic[26]. The philosophy behind the fuzzy control is the ability to break down complicated control problems into smaller parts that could be dealt with individually. Fuzzy control process, based on FuzzyControl++ Siemens tool, is applied on three workshops in the cement factory, raw mill, kiln and cement mill. FuzzyControl++offers solutions for non-linear controllers and for predicting the behavior of complex mathematical procedures of process automation, which are difficult or impossible to implement using standard tools.

    In addition, FuzzyControl++ enables fuzzy systems to be developed and effectively configured for the automation of technical processes. Empirical process knowledge and verbally described experiences can be implemented as fuzzy rules directly in open and closed-loop controls, pattern recognition, decision logic, etc., in order to solve optimization tasks by applying forecasting models.Associated functions are also easy to configure with the help of the FuzzyControl++ tool. The rules are fed either via a table or via a matrix editor. Dynamic changes of the rules base are identified immediately and, if no rule should be applicable, a value previously prescribed for each output will be used. The inference and defuzzification method used by FuzzyControl++ is the well-known Takagi-Sugeno method.

    FuzzyControl++ can be executed on SIMATIC S7 PLCs, the SIMATIC PCS7 process control system and the SIMATIC WinCC SCADA system and it provides special function blocks and image blocks. The fuzzy systems are configured and generated by means of a configuration tool. The runtime software will process the systems during normal operation[27].

    Figure 4.  Fuzzy controller of raw mill

    As an example, in raw mills, limestone, limestone and clay mixture and iron ore are mixed to get raw meal. The raw materials are crushed and sent to an elevator after that must be separated in a separator. If the raw meal condition is not achieved, it will be crushed again. Once in the desired state, the flour will be passed to the silos for homogenization. To control the raw mill workshop, several conditions must be verified.

    The operator can recognize critical values and tendencies at an early stage and can therefore react in time by taking necessary counter-measures. To imitate this behavior, it is necessary to use a fuzzy system instead of a binary control. The operator must examine the level of material in the feed hopper in order to restart the transmission, the conduct of acoustic equipment and load of the elevator for a quick possible intervention.

    Configuration of the fuzzy system includes defining the target-system (CFC, OPC, etc.) and the number of inputs and outputs of the fuzzy system. In addition, the program must specify minimum and maximum values for inputs and outputs. To control the raw mill workshop, the fuzzy controller contains two inputs, load of elevator and load of the mill and one output which represent raw mill food's percent coming from feeders.

    However, unlike the inputs, the outputs must be inserted as singletons and not as triangles. Therefore, each membership function only requires one point to define its position (Fig. 5).For the three values, the system has different alarm level (H:High and L: Low), HH sup 80%, H=80%, L=20% and LL inf 20%.

    Figure 5.  Inputs and outputs of the fuzzy controller

    The rule table (Fig. 6) contains all fuzzy rules. Twenty-five rules can ensure all states of the loop regulation to synchronize the circulation of the material between the feeders, the mill and the elevator. The rules are entered so that for every combination of inputs, one of the linguistic value is selected from the output list box. Fig. 7 shows trends of inputs and output variation for fuzzy and continuous control of the raw mill workshop.

    To display the surface generated by the fuzzy controller, we can use a 3D representation as showed in Fig. 8. The fuzzy system is created with FuzzyControl++. For SIMATIC S7 400, there is alarger 20 Kbyte data block available. For both DBs (data block), an FB (functional block) exists which is designed for the DB. It is possible to implement several fuzzy systems with one DB for each system and a common FB for all systems. The fuzzy function block (FB) contains all algorithms and procedures of the functional range of an effective fuzzy application, fuzzification of the inputs, processing of the rules, defuzzification and results at the outputs.

    Figure 6.  Fuzzy rules table

    Figure 7.  Fuzzy controller curve plotter

    Figure 8.  Fuzzy controller 3D-representation

    By clicking the icon "fuzzy control", the window in Fig. 9 will appear to display the values of inputs, load of elevator and load of the mill and the output which represents raw mill feeder's percent, the mode of operation, the error codes (INFO), the QSTATUS as well as the current project path of the fuzzy application. Inputs and outputs are displayed as grayed-out. In the combo box, the operator can switch the operating mode from auto to manual or vice versa. In manual mode, the operator can set point the percent of raw mill feeders. The fuzzy rule base is loaded from the file "fuzzy.fpl".

    Figure 9.  Fuzzy controller monitored in the operator station

  • The system provides a set of analytical tools for interactive data display and analysis of current process states and historical data. The client evaluates and displays process values in data server or long-term archive server system. The user-name and the password of the operator are configured using user administrator as showed in Fig. 10.

    Figure 10.  WinCC/User administrator

    All commands executed by the operator on the web client are automatically logged with the user-name and the password of the operator as shown on Fig. 11.

    Figure 11.  Access of users to the remote-based process control

  • Raw mill process contains different steps. Clinker and cement production is an intensive process in terms of natural raw material and energy input. Where limestone (primary source of calcium carbonate CaCO$_{3}$) and clay (primary source of silica SiO$_{2}$, alumina Al$_{2}$O$_{3}$ and iron oxide Fe$_{2}$O$_{3}$)are typically mined in company-owned quarries and pre-blended to a target chemical material composition. Corrective materials like sand, iron ore, bauxite or industrial waste materials (alternative raw materials) are then used to fine-tune and correct the chemical composition of this pre-blend material in the raw mill. Fine ground raw meal is then stored and further homogenized in the raw meal silo.

    Operation station (OS) web server stores all views and scripts necessary to enable them to display and run on the web client. All views and scripts that must be prepared (published) for this purpose are done using the web view publisher tool. The operator can connect to the web client and access data from OS web server via a transmission control protocol/Internet protocol (TCP/IP)connection. The user interface corresponds to the operator station(OS) and is displayed in the Internet Explorer, with overview area, work area and function keys. The system provides a set of analytical tools for interactive data display and analysis of current process states and historical data. The web client can evaluate and display process values in data server. In the OS client system, following functions and analysis tools can be implemented according to the needs of the system.

    The process screens tool: Used for supervision and navigation between process screens using Internet Explorer browser. Moreover, it will be used to do the same operation as the server for communication, user management and representation of graphical data. Fig. 12 shows the Internet-based system realized for a cement manufacturing plant.

    Figure 12.  Internet-based remote control of the cement factory

    The Webcenter function: Serves as a center for various parameters, connection setup and for creation and administration of web pages. The user can compose and record in the webcenter page objects displayed called web parts, to make screen previews.As shown in Fig. 13, and after verifying all conditions, the operator can start all the equipment of the raw mill workshop.After that, the operator must start the materials feeders for grinding where, fuzzy controller ensures synchronization of the regulation loop between the feeders, the mill and the elevator.

    Figure 13.  Remote and fuzzy control of the raw mill workshop

    Trends and Alarms: They are used to display and analyze historical data from the runtime central archive Server or server's long-term archive. The data can be represented as tables or graphs as shown in Fig. 14. Trends are used to display the most recent historical values on a graph. The graph's $y$-axis represents the point value and the $x$-axis represents time. The entire trend window covers a period called the trend horizon. The right most fourth part of the trend is called the update horizon. The trend window is updated with new values each time a pre-assigned update period expires. Periodically, the system saves the values of all A-points and the statistical information for all points to a historical data file.

    Figure 14.  Data analyzing

    The "Excel Workbooks" function: Integrates messages, archive data and current process values in MS Excel and thus allows analysis on-line. The Excel workbooks may be created, published and made available for the Internet/Intranet.

    "Reports" function of Excel Workbooks can be published as an example for creating reports in WinCC DataMonitor. Fig. 15 presents how to transfer Excel reports.

    Figure 15.  Exporting excel reports

    The "Reports" function: Users can launch reports controlled by timer or event based, and print jobs to PDF or Excel files published work. For example, statistics and data analysis of specific process or as historical data are available as shown in Fig. 16.

    Figure 16.  Print PDF reports work

  • In the OS web client, several alarms occur during running of the raw mill workshop, in materials feeders, grinding or storage. Each occurrence of an alarm is displayed as a maintenance action to be performed. For example, if an alarm indicates that the mill outlet depression is high, the alarm stops the mill and the elevator and finally the separator. The alarm is displayed in the top bar of the view and appears in the alarm table. Alarms are created using WinCC alarm logging and several types of alarms could occur, among them include system alarms and process alarms. Each color indicates a category of an alarm or its level of danger (high "red"), or of other types according to the diversity of alarms (warning, intervention, etc.). Fig. 17 illustrates some alarms and maintenance actions.

    Figure 17.  E-diagnostic and e-maintenance of the raw mill workshop

  • Rapid expansion in communication areas that were not served by traditional data networks is increasing the need to embed security into the network architecture. As a result, much effort is being devoted to this area of research and development. In the meantime, many tools and procedures are being implemented to combat inherent security flaws in the industrial network architecture. Securing the network infrastructure includes physical securing of devices that provide network connectivity and preventing unauthorized access to the management software that resides on them. Some tools proposed are Siemens SOFTNET Security and SCALANCE S equipment.

    With SIMATIC NET SCALANCE S and SIMATIC NET SOFTNET Security Client, the SIEMENS security concept meets the exacting requirements of secure communication in industrial automation engineering. With a combination of different security measures such as firewall, NAT/NAPT routers and VPN (virtual private network) over IPsec tunnels, the SCALANCE S devices protect individual devices or even entire automation cells from data espionage, data manipulation and unauthorized access. With the SOFTNET security client PC software, secure remote access is possible from PCs/PGs (ProGramming device) in automation systems protected by SCALANCE S via public networks[28].

  • SCALANCE S is a security module used for protection of automation networks and security during data exchange between automation systems. The security module of SCALANCE S range can be used to protect all devices of an Ethernet network against unauthorized access. In addition, SCALANCE S also protects the data transmission between devices or network segments (e.g., automation cells) against data manipulation and espionage. It can also be used for secure remote access over the Internet.

    The security modules can be operated not only in bridge mode but also in router mode, and can thus be used directly at IP sub-network borders. Secure remote access over the Internet or GPRS/UMTS/LTE is possible with the GPRS/UMTS/LTE routers. SCALANCE S is optimized for use in automation and industrial environments and meets the specific requirements of automation systems, such as easy upgrades of existing systems, simple installation and minimal downtimes in the event of a fault.

    The firewall can be used as an alternative or to supplement VPN with flexible access control. The firewall filters data packets and disables or enables communication links in accordance with the filter list and filtering inspection. Both incoming and outgoing communication can be filtered, either according to IP and MAC addresses as well as communication protocols (ports) or they can be user-specic. Access data are saved by the security module in a log file which enables detection of how, when and by whom the network has been accessed as well as detecting access attempts and enabling appropriate preventative measures to be taken[28].

  • The SOFTNET security client is a component of the industrial security concept for protecting programmable controllers and for security during data exchange between automation systems. It is a VPN client for programming devices, PCs and notebooks in industrial environments and supports secure client access via local area network (LAN) or even wireless area network (WAN) (e.g., for remote maintenance via Internet) to automation systems protected by security integrated devices with VPN functionality. Data transmission is protected against operator error, eavesdropping/espionage and manipulation. Communication can only take place between authenticated and authorized devices. The SOFTNET security client uses field-proven IPsec mechanisms for setting up and operating VPNs. In addition, it guarantees the avoidance of system disruptions through exclusive access to programmable controllers or complete automation cells using approved programming devices or notebooks. It also presents a high flexibility when used on mobile PCs as no hardware is required for safeguarding the communication.

    The security modules of the SCALANCE S family are provided specially for use in automation, yet they can connect seamlessly with the security structures of the office and IT world. They provide security and meet the specific requirements of automation technology, such as simple upgrades of existing systems, simple installation and minimum downtime in case of fault. Depending on the particular security needs, various security measures can be combined. The SOFTNET security client allows programming devices, PCs, and notebooks access to devices with IPSec VPN functionality.

    Since IP addresses can be falsified (IP spoofing), checking the IP address (of the client access) is not sufficient for reliable authentication. In addition to this, Client PCs may have changing IP addresses. For this reason, the authentication is performed using tried and tested VPN mechanisms. For data encryption, secure encryption is necessary to protect data traffic from espionage and manipulation. This means that the data traffic remains incomprehensible to any eavesdropper in the network.

    By using the associated configuration tool, it is possible to create and manage security rules even without special security knowledge. In the simplest case, only the SCALANCE S modules or SOFTNET security clients that will communicate with each other are created and configured. As soon as SOFTNET security client knows the programmable controllers to be accessed, communication can be established[28].

  • The availability and security of production plants is of great importance in industrial environments. Integration of the process control system into the corporate network increases the risk of damage by viruses or malware. In order to avoid production failures and downtime, data traffic between the networks must be checked, analyzed and selectively approved without impairing the function of the process control system. This is the only way to provide optimum protection to the plant without impairing productivity. Firewalls with supplementary services are most appropriate for this.

    The automation firewall from Siemens is a tested and validated standard. It has been tuned for use with SIMATIC PCS 7 and WinCC.The automation firewall works excellently with SIMATIC NET communication products. It features comprehensive hardware and software functions for SIMATIC PCS 7 and WinCC projects, e.g., stateful inspection packet filter, application layer firewall, VPN gateway, intrusion detection system (IDS), URL filtering or web proxy[29].

  • All aims described in this paper have been achieved. The system allows the fuzzy control of the material circulation between the elevator, the mill and the feeders. The continuous fuzzy control system ensures the nonstop working of the workshop and least intervention of the operator to modify the set point of regulators.The operator's station allows the operator to control, and supervise the system, but he must be in main control room all the time. he must be in main control room all the time. With economical viewpoint, the factory can reduce the number of operators in production unit.

  • To successfully compete in today's challenging world economy, companies often require innovative solutions to make their plant's operating systems function at peak efficiency while utilizing latest equipment, resources and materials. However, complex industrial processes are difficult to control because of inadequate knowledge of their behavior. This lack of knowledge is principally a lack of structural detail and this prevents the use of conventional control theory. However, these processes are often controlled with great skill by a human operator who makes decisions on the basis of inexact and linguistic measures of the process state. Fuzzy logic is considered as a superset of standard logic, which is extended to deal with the partial truth. It has become one of the most successful technologies for developing complex control systems.

    Sophisticated cement plant processes represent a complex task that requires knowhow and use of supporting advanced technologies such as artificial intelligence techniques. Fuzzy logic is an artificial intelligence design methodology that can be used to solve real life problems. Chemical engineering has employed fuzzy logic in the piping risk assessment, safety analysis, batch crystallizer, combustion process, food production, fluidized catalytic cracking unit and separation process. It has also been applied to process control and kinetics.

    Fuzzy logic is used for processing complicated and involved processes. Whereas, fuzzy systems are usually unable to adapt and learn if the system behavior changes. Indeed, given the encouraging results in every case study, the proposed approach solves the problem through fuzzy control giving satisfactory results. In addition, we propose the generalization of this approach to all cement factories. It is more efficiency to have a web-fuzzy system control, e-monitoring, alarms e-diagnosing and e-maintenance. Therefore, this new architecture allows us to execute all operations such as, fuzzy control, the diagnosis of various alarms generated during running of the process and alarm's maintenance.

    In addition, by using Internet, the system created in the application has several advantages, it allows providing data levels of process in the world of office, and it offers a set of powerful functions for the representation and analysis of production data. In addition, it allows direct access to production data using Internet or Intranet. It also allows effectively monitoring and analyzing production lines and writing PDF or Excel reports with client-server architecture to ensure usability on the LAN and the WLAN.

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