ProOpter: An advanced platform for production analysis and optimization |
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| Affiliation: | 1. Jožef Stefan Institute, Ljubljana, Slovenia;2. University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia;1. College of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China;2. Jiangsu Province Biomass Energy and Materials Laboratary, Nanjing, Jiangsu 210042, China;3. Key Laboratory of Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430073, China;1. Instituto de Telecomunicações, University of Beira Interior, Polytechnic Institute of Castelo Branco, Rua Marquês D’Ávila e Bolama, 6201-001 Covilhã, Portugal;2. Instituto de Telecomunicações, University of Beira Interior, Rua Marquês D’Ávila e Bolama, 6201-001 Covilhã, Portugal;3. University ITMO, Kronverkskiy pr, 49, 197101 St. Petersburg, Russia;4. University of Haute Alsace – IUT, 34 rue du Grillenbreit, 68008 Colmar, France;5. CISTER Research Unit, ISEP/IPP, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal;1. Jožef Stefan Institute, Department of Systems and Control, Jamova 39, SI-1000 Ljubljana, Slovenia;2. Inea d.o.o. Stegne 11, SI-1000 Ljubljana, Slovenia;3. Helios, Tovarna barv, lakov in umetnih smol Količevo, d.o.o. Količevo 65, SI-1230 Domžale, Slovenia;4. Jožef Stefan International Postgraduate School, Jamova 39, SI-1000 Ljubljana, Slovenia;1. Department of Industrial and System Engineering/Engineering Research Institute, Gyeongsang National University, South Korea;2. Department of Industrial Engineering, Changwon National University, South Korea |
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| Abstract: | This paper presents the prototype of an advanced platform for production analysis and optimization, referred to as ProOpter. The platform was developed to support the recently derived concept of holistic production control (HPC), which relies on model-based control. The prototype is comprised of a set of off-line and on-line modules. The off-line modules support the definition of key performance indicators (KPIs), the selection of the most influential input (manipulative) variables, and the identification of a simple production model from historical data. The on-line modules enable KPI prediction and suggest actions to the production manager, employing model-based production control and/or optimization techniques. In this way, a new decision-support reasoning based on historical production data can be introduced. ProOpter has a modular design and can be used as an add-on to existing production IT systems since it relies on established industrial communication standards. The use of the platform is validated on the well-known Tennessee Eastman benchmark simulation process and on two industrial case studies. |
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| Keywords: | Production information technologies Production control Optimization Manufacturing intelligence |
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