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Data-Based Robust Multiobjective Optimization of Interconnected Processes: Energy Efficiency Case Study in Papermaking
- Source :
- IEEE Transactions on Neural Networks. 22:2324-2338
- Publication Year :
- 2011
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2011.
-
Abstract
- Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.
- Subjects :
- Paper
Decision support system
Mathematical optimization
Databases, Factual
Computer Networks and Communications
Computer science
Process (engineering)
General Medicine
Energy consumption
Models, Theoretical
Multi-objective optimization
Feedback
Computer Science Applications
Energy conservation
Energy Transfer
Artificial Intelligence
Data Mining
Gradient descent
Software
Efficient energy use
Subjects
Details
- ISSN :
- 19410093 and 10459227
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks
- Accession number :
- edsair.doi.dedup.....54bd307fe43ec30b62a98e55a8de8c00