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Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in
Puya Afshar1, Martin Brown, Jan Maciejowski
1Control Systems Centre, School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK. p.afshar@ieee.org
IEEE Transactions on Neural Networks
|December 8, 2011
Summary
This study introduces a robust multiobjective optimization system for energy-intensive industries, like papermaking. It effectively reduces energy consumption and enhances productivity while maintaining product quality.
Area of Science:
- Industrial Engineering
- Process Optimization
- Data-driven Decision Support
Background:
- Energy-intensive industries face challenges in reducing consumption.
- Multicriteria optimization is complex due to trade-offs and system interconnections.
- Data-driven models have limitations in local validity and extrapolation risks.
Purpose of the Study:
- To present a novel decision support system for robust multiobjective optimization of interconnected industrial processes.
- To address challenges in multicriteria optimization, complex dynamics, and model validity.
- To enable data-based optimization for energy saving in industries like papermaking.
Main Methods:
- Process modeling by dividing plants into serially connected units, incorporating product quality, energy consumption, and uncertainty.
- Utilizing a multiobjective gradient descent algorithm guided by user preferences.
- Implementing the system via a MATLAB-based interactive tool, DataExplorer.
Main Results:
- Demonstrated effectiveness in reducing steam consumption and increasing productivity in two UK paper mills.
- Successfully optimized vacuum pressures in forming and press sections while maintaining product quality.
- Validated the approach for robust multiobjective optimization in real-world industrial settings.
Conclusions:
- The proposed decision support system offers a viable solution for energy saving in papermaking.
- The method effectively handles multicriteria optimization challenges in complex industrial systems.
- Data-driven optimization, when robustly implemented, can lead to significant operational improvements.
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