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

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]  Li-Chih Wang,et al.  A multi-agent based agile manufacturing planning and control system , 2009, Comput. Ind. Eng..

[2]  Kevin K. Jurrens,et al.  Manufacturing planning and predictive process model integration using software agents , 2005, Adv. Eng. Informatics.

[3]  Payam Hanafizadeh,et al.  Designing fuzzy-genetic learner model based on multi-agent systems in supply chain management , 2009, Expert Syst. Appl..

[4]  Ming Lim,et al.  A multi-agent based manufacturing control strategy for responsive manufacturing , 2003 .

[5]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[6]  Stéphane Galland,et al.  ASPECS: an agent-oriented software process for engineering complex systems , 2010, Autonomous Agents and Multi-Agent Systems.

[7]  Theodor Borangiu,et al.  An implementing framework for holonic manufacturing control with multiple robot-vision stations , 2009, Eng. Appl. Artif. Intell..

[8]  Parisa A. Bahri,et al.  A methodology for the development of multi-agent systems using the JADE platform , 2006, Comput. Syst. Sci. Eng..

[9]  Dimitris Tsamatsoulis Modeling of raw material mixing process in raw meal grinding installations , 2010 .

[10]  Jorge J. Gómez-Sanz,et al.  Agent Oriented Analysis Using Message/UML , 2001, AOSE.

[11]  Mark A. Musen,et al.  The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility , 2000, EKAW.

[12]  Fugee Tsung,et al.  IMPROVING AUTOMATIC-CONTROLLED PROCESS QUALITY USING ADAPTIVE PRINCIPAL COMPONENT MONITORING , 1999 .

[13]  Shaoxiong Wu Intelligence Statistical Process Control in Cellular Manufacturing Based on SVM , 2011, ISNN.

[14]  M. Nikraz,et al.  A methodology for the analysis and design of multi-agent systems using JADE , 2006 .

[15]  David James Retallack,et al.  On-line X-ray diffraction for quantitative phase analysis: Application in the Portland cement industry , 2001, Powder Diffraction.

[16]  Weiming Shen,et al.  Applications of agent-based systems in intelligent manufacturing: An updated review , 2006, Adv. Eng. Informatics.

[17]  Navid Sahebjamnia,et al.  Modeling an e-based real-time quality control information system in distributed manufacturing shops , 2008, Comput. Ind..

[18]  Kari Koskinen,et al.  Extending process automation systems with multi-agent techniques , 2009, Eng. Appl. Artif. Intell..

[19]  Kuldeep Kumar,et al.  Agent-based negotiation and decision making for dynamic supply chain formation , 2009, Eng. Appl. Artif. Intell..

[20]  Nael H. El-Farra,et al.  Quasi-decentralized model-based networked control of process systems , 2008, Comput. Chem. Eng..

[21]  Miguel A. G. Aranda,et al.  Accuracy in Rietveld quantitative phase analysis of Portland cements , 2003 .

[22]  Christine W. Chan,et al.  Artificial intelligence for monitoring and supervisory control of process systems , 2007, Eng. Appl. Artif. Intell..

[23]  Yi-Sheng Dong,et al.  Role-based Context-specific Multiagent Q-learning , 2007 .

[24]  Valerie Barr Rule‐base coverage analysis applied to test case selection , 1997, Ann. Softw. Eng..

[25]  Franco Zambonelli,et al.  Developing multiagent systems: The Gaia methodology , 2003, TSEM.

[26]  David Naso,et al.  A soft computing approach for task contracting in multi-agent manufacturing control , 2003, Comput. Ind..

[27]  Xiaojun Zhou,et al.  Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA , 2008, Comput. Ind..

[28]  Nicholas R. Jennings,et al.  The Gaia Methodology for Agent-Oriented Analysis and Design , 2000, Autonomous Agents and Multi-Agent Systems.

[29]  Michael Wooldridge,et al.  Revised Papers and Invited Contributions from the Second International Workshop on Agent-Oriented Software Engineering II , 2001 .

[30]  Thomas O. Boucher,et al.  A multi-agent architecture for control of AGV systems , 2004 .

[31]  Wei Chen,et al.  Modeling for Robotic Soccer Simulation Team Based on UML , 2008, CSSE.

[32]  Shobha Venkataraman,et al.  Context-specific multiagent coordination and planning with factored MDPs , 2002, AAAI/IAAI.

[33]  Á. G. Torre,et al.  Quantitative Phase Analysis of Laboratory-Active Belite Clinkers by Synchrotron Powder Diffraction , 2007 .

[34]  Ming-Chyuan Lin,et al.  Using AHP and TOPSIS approaches in customer-driven product design process , 2008, Comput. Ind..

[35]  Paul Valckenaers,et al.  An agent architecture for manufacturing control: manAge , 2001, Comput. Ind..

[36]  Dimitris Tsamatsoulis Modeling of cement milling process based on long term industrial data , 2011 .

[37]  Christine W. Chan An expert decision support system for monitoring and diagnosis of petroleum production and separation processes , 2005, Expert Syst. Appl..

[38]  Zofia Lukszo,et al.  Performance analysis of a multi-plant specialty chemical manufacturing enterprise using an agent-based model , 2010, Comput. Chem. Eng..