title: Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.
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.
authors.affiliation: Control Systems Centre, School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK. p.afshar@ieee.org
authors.forename: Puya
authors.initials: P
authors.lastname: Afshar
authors.forename: Martin
authors.initials: M
authors.lastname: Brown
authors.forename: Jan
authors.initials: J
authors.lastname: Maciejowski
authors.forename: Hong
authors.initials: H
authors.lastname: Wang
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D004735:Energy Transfer; D005246:Feedback; D008962:Models, Theoretical; D010209:Paper