Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

  • Authors:

  • Guishan Xing;Jinliang Ding;Tianyou Chai;Puya Afshar;Hong Wang

  • Affiliations:

  • State Key Laboratory of Integrated Automation for Process Industry, Northeastern University, Shenyang 110004, PR China;State Key Laboratory of Integrated Automation for Process Industry, Northeastern University, Shenyang 110004, PR China;State Key Laboratory of Integrated Automation for Process Industry, Northeastern University, Shenyang 110004, PR China;Control System Centre, School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK;Control System Centre, School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2012

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Abstract

In this paper, a hybrid intelligent parameter estimation algorithm is proposed for predicting the strip temperature during laminar cooling process. The algorithm combines a hybrid genetic algorithm (HGA) with grey case-based reasoning (GCBR) in order to improve the precision of the strip temperature prediction. In this context, the hybrid genetic algorithm is formed by combining the genetic algorithm with an annealing and a local multidimensional search algorithm based on deterministic inverse parabolic interpolation. Firstly, the weight vectors of retrieval features in case-based reasoning are optimised using hybrid genetic algorithm in offline mode, and then they are used in grey case-based reasoning to accurately estimate the model parameters online. The hybrid intelligent parameter estimation algorithm is validated using a set of operational data gathered from a hot-rolled strip laminar cooling process in a steel plant. Experiment results show the effectiveness of the proposed method in improving the precision of the strip temperature prediction. The proposed method can be used in real-time temperature control of hot-rolled strip and has potential for parameter estimation of different types of cooling process.