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).
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). 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. 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.
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 and applied in different domains, such as quad-rotor. 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. 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. 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 .
The control strategies behind ECS/PXP-Kiln are based on two decades of experience in cement kiln control and optimization project.
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, for addressing design issues and implementation of internet-based process control systems. In addition, other works use Wireless and Internet communications technologies for monitoring and control, ranging from modelling and control for wireless networked control system, for web-based SCADA display systems (WSDS) and access via Internet. Some initial works even in laboratory experiments tried to apply web technologies to acquire information remotely or for remote supervision of industrial processes, using self-organizing maps. Another work represent a development of remote control and monitoring of web-based distributed OPC system.
Actually, different works focussed on networked control systems as well as their progress and new technologies integrated in these networks. 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, for development of remote monitoring and control system for mechatronics engineering practice in the case of flexible manufacturing system. Another work represents a Web-based Supervisory Control System based on Raspberry Pi. 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.
2.1. Cement manufacturing plant
2.2. Cement industry in the world
2.3. Cement industry in Algeria
2.4. Fuzzy logic in cement industry
2.5. Web-based process control related works
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.
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. 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.
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%.
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.
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".