دانلود مقاله ISI انگلیسی شماره 5566
عنوان فارسی مقاله

توسعه سیستم های هوشمند مبتنی بر عامل برای پشتیبانی از تصمیم گیری در صنعت فرایند شیمیایی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
5566 2009 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Agent-based intelligent system development for decision support in chemical process industry
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 36, Issue 8, October 2009, Pages 11099–11107

کلمات کلیدی
سیستم چند عامله - هوش مصنوعی - یکپارچه سازی اطلاعات - هماهنگی تولید - پشتیبانی تصمیم گیری
پیش نمایش مقاله
پیش نمایش مقاله توسعه سیستم های هوشمند مبتنی بر عامل برای پشتیبانی از تصمیم گیری در صنعت فرایند شیمیایی

چکیده انگلیسی

This paper presents an agent-based intelligent system to support coordinate manufacturing execution and decision-making in chemical process industry. A multi-agent system (MAS) framework is developed to provide a flexible infrastructure for the integration of chemical process information and process models. The system comprise of a process knowledge base and a group of functional agents. Agents in the system can communicate and cooperate with each other to exchange and share information, and to achieve timely decisions in dealing with various scenarios in process operations and manufacturing management. Process simulation, artificial intelligent technique, rule-based decision supports are integrated in this system for process analysis, process monitoring, process performance prediction and operation suggestion. The implementation of this agent-based system was illustrated with two case studies, including one application in process monitoring and process performance prediction for a chemical process and one application in de-bottlenecking of a site utility system.

مقدمه انگلیسی

In chemical process industry, data and information are important resources apart from materials and energy. The effective management for the variety of information resources becomes necessary for better access and sharing of information, which are vital to collaborative product development and integrated manufacturing (McGuire et al., 1993). However, most of technical and operational information resources of chemical engineering systems are kept in the electronic format, in the distributed network environment. On the other aspect, many software products are used to solve engineering design and operation problems, most of these programs provide their users with significant value when used in isolation, but the interoperation between programs is difficult because of the heterogeneity of these programs, as they might be written at different times and in different programming languages. Due to the increasing volume and distributed nature of the information resources in the chemical industry, and the complexity of the software solution, it requires a software system with flexible infrastructure to support the knowledge sharing and exchange, as well as software interoperation. Agent technique has been proposed as a way to help people better cope with the increasing volume and the complexity of information and computing resources (Bradshaw, Dutfield, Benoit, & Woolley, 1997). By contrast to traditional software programs, software agents are programs that help people solve problems by collaborating with other software agents and other resources in the network (Bradshaw et al., 1997 and Peng et al., 1998). Multi-agent system can be utilized to integrate diversity and heterogeneity of information sources, cooperates the distinct but complementary tasks, and facilitates the interoperation of software or application programs (Sycara, 1998). Software agents have been proved to be a useful technique in designing distributed and cooperative systems in many industrial and business sectors, including telecommunication, air traffic control, traffic and transportation management (Wahle & Schreckenberg, 2001), supply chain management (Julka, Srinivasan, & Karimi, 2002), and medical care (Jennings et al., 1996). They have also been used for developing distributed systems that can collaboratively solve domain problems over the Internet (Clark & Lazarou, 1997). In this paper, we present an agent-based intelligent system in support of interoperation of data and services to facilitate information integration, coordinate manufacturing execution, and to help engineers make decisions on the basis of up-to-date information. This paper is organized as follows. Section 2 introduces the basic concept of agent-based system. Section 3 presents the design of multi-agent system for decision-support in chemical process industry, and the implementation methodology of the multi-agent system will be described in Section 4. The system application is discussed in Section 5 through some example scenarios.

نتیجه گیری انگلیسی

An agent-based intelligent system has been presented which is capable of supporting information integration and decision-making for chemical process industry. The system architecture comprise of a process knowledge base and a group of functional agents. In this system, agents communicate and cooperate with each other to exchange and share information, and to achieve timely decisions in dealing with various scenarios. Artificial intelligent techniques such as artificial neural network, rule-base decision support are integrated in this system for process monitoring, process performance prediction and operation suggestion. Java Agent Development Framework (JADE) is used as the basis to develop such an intelligent system. Ontologies are used to specify the infrastructure of the agent-based system, and to characterize the information content in the exchanged messages between agents. The implementation of this agent-based intelligent system provides an environment for process monitoring, coordinate manufacturing execution as well as decision-making support. The paper illustrates a process monitoring and maintenance problem that requires the monitoring of process data, comparisons with design solutions, predict trends and evaluate alternative process operation conditions in order to cope with process deviations. The system has also been tested in a negotiation problem that involve utility networks and the trading of energy and power.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.