دانلود رایگان ترجمه مقاله طراحی سیستم چندعاملی هوشمند برای نظارت بر کیفیت فرآیند تولید سیمان (نشریه الزویر 2013)

IMAQCS: طراحی و پیاده سازی سیستم چندعاملی هوشمند برای کنترل و نظارت بر کیفیت فرآیند تولید سیمان

IMAQCS: Design and implementation of an intelligent multi-agent system for monitoring and controlling quality of cement production processes

Abstract

In cement plant, since all processes are chemical and irreversible, monitoring and control is a critical factor. If the process is not controlled at any stage, the final product can be damaged or lost. Thus, in such environments, considering the quality of the product at each state is essential. Also, to control the process, communication among different parts of production line is essential. The wasted time in production line has a direct effect on process correction time and cement production performance. Here, a model of a new intelligent multi-agent quality control system (IMAQCS) for controlling the quality of cement production processes is suggested. This model, using of rule-based artificial intelligence technique, concentrates on relationship between departments in cement production line to monitor multi-attribute quality factors. With the presence of agents for controlling the quality of cement processes, real-time analyzing and decision making in a fault condition will be provided. In order to validate the proposed model, IMAQCS is deployed in real plants of a cement industries complex in Iran. The ability of the system in the process production environment is assessed. The effectiveness and efficiency of the system are demonstrated by reducing the process correction time and increasing the cement production performance. Finally, this system can effectively impact on factory resources and cost saving.

1 Introduction

The importance of process control in quality products is clear. Most manufacturing process such as chemical and industries process have automated process control systems. The majority of automated quality control systems are used to detect out-ofcontrol conditions [1]. Also they focused on the process output and control actions. Tsung to detect changes in a process, provided functions of the process output and control actions [1]. Wu in [2] with the help of probabilistic neural network (PNN) proposed a method for pattern recognition of control chart in cellular manufacturing. Yu et al. used a genetic algorithm based rule extraction approach to recognize the relationship between manufacturing parameters and product quality. They integrated a knowledge-based artificial neural network and a genetic algorithm rule extraction to improve the product quality in a japanning-line [3]. Moreover, intelligent systems for monitoring, control, and diagnosis of industries process are based on three main approaches: knowledge-base, analytical and data-driven as mentioned in [4]. Uraikul et al. provided an overview on intelligent systems for control and diagnosis of process [4].

Among several systems for process control and fault detection have been proposed, depending on the type of process, the quality control is different. The process control is more difficult in chemical process because of their irreversible nature. The product is completely wasted, if the process is out of control. Many technological methods in cement process quality control automation have been proposed in recent years. Most of these methods are about X-ray analysis at the different departments of cement production line. They focused on the control of the chemistry of cement production [5–7]. Apart from chemistry of the cement, Tsamatsoulis provided a reliable model of the dynamics among the chemical modules in the outlet of raw meal grinding systems in [8]. Also, he has developed a dynamical model of cement milling process in [9]. In these two works, each department is assessed separately. The whole plant has not been considered. In cement process, an integrated system for controlling the quality of process has received less attention. Along with the nature of the cement process, monitoring and interaction among departments are important too. A quality control system that monitors the process, controls the input and output of different departments, and detects fault conditions in cement industries complex is an issue.

The control of plants that are spatially distributed has been considered recently. Chan presented a system that monitors operations at the plant based on the input data. Then it detected abnormalities in the data and suggested some actions to the operator. It was an expert decision support system for monitoring, control and diagnosis of a petroleum production and separation plant [10]. Mahdavi et al. suggested a real-time quality control information system that improves control of the quality of products through an integrated monitoring of distributed shops [11]. Van Brussel et al. presented the architecture consists of three types of basic holons: order holons, product holons, and resource holons to reduce the complexity of the system and enable easy reconfiguration [12]. However, multi-agent systems (MASs) can be used to control the plant, and especially the control of process in distributed manufacturing. Seilonen et al. utilized MAS to design a process automation system. They applied agents to run supervisory control and make decisions [13]. A large number of researches on distributed manufacturing and MAS in industries focus on scheduling and planning tasks among different machines for optimizing their throughput [14–18]. Some other works on MAS are done in the area of supply chain management (SCM) systems [19]. A review of all related work to agent-based systems in manufacturing is provided in [20]. In addition, some other researchers have proposed different models of MAS and deployed them in manufacturing [18,21–24]. Finally, Behdani et al. in [25] proposed an agent-based system for modeling a complex network of a chemical manufacturing enterprise which can capture the interactions among the various constituents including the plants, functional departments, and external entities. Among the researches that have been referred to, the use of MAS to cope with the control of chemical process quality among different parts of plants has been less noticed.

In this paper, we proposed an automated process quality control system for cement process that is designed based on multiagent system. In our proposed model, we try to concentrate on the communication between sampling station, laboratory and different departments of the cement production process which are not extensively described in previous researches. Also, we transform statistical quality control into a new communication method for cement production. We found MAS technology to cope with sophisticated interaction among departments. Besides, we compare a manual system with our system in a part of cement plant to evaluate the model. With this method, we were able to reduce the time of correcting the process. This reduction in process correction time is lead to reduce wasted raw materials and has the financial impact for the factory.

The other parts of this paper are organized as follows: Section 2 gives an overview of problem domain. Our proposed model is presented in Section 3. In this section, agents in the system, their interaction and coordination approach, analysis and design method and implementation technique are explained. Next, in Section 4 the proposed system has been tested and evaluated. Finally, conclusions and future work are provided in Section 5.