Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation

Feiran Sun; Tao Liu; Yi Cao; Xiongwei Ni; Zoltan Kalman Nagy

doi:10.1016/j.cjche.2020.03.007

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Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation
- Chinese Journal of Chemical Engineering Issue 6, Pages: 1637-1651(2020)
- Affiliations：
  
  1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology,Dalian,China,116024
  2. School of Control Science and Engineering, Dalian University of Technology,Dalian,China,116024
  3. College of Chemical and Biological Engineering, Zhejiang University,Hangzhou,China,310027
  4. School of Engineering and Physical Science, Heriot-Watt University,Edinburgh,UK
  5. Davidson School of Chemical Engineering, Purdue University, West Lafayette,Indiana,United States,47907
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2020.03.007
  CLC：
- Published：2020
- Accepted：
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Feiran Sun, Tao Liu, Yi Cao, et al. Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation[J]. Chinese Journal of Chemical Engineering, 2020, (6): 1637-1651.
DOI：

Feiran Sun, Tao Liu, Yi Cao, et al. Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation[J]. Chinese Journal of Chemical Engineering, 2020, (6): 1637-1651. DOI： 10.1016/j.cjche.2020.03.007.

摘要

Abstract

In this paper

a cell average technique (CAT) based parameter estimation method is proposed for cooling crystallization involved with particle growth

aggregation and breakage

by establishing a more efficient and accurate solution in terms of the automatic differentiation (AD) algorithm. To overcome the deficiency of CAT that demands high computation cost for implementation

a set of ordinary differential equations (ODEs) entailed from CAT based discretized population balance equation (PBE) are solved by using the AD based high-order Taylor expansion. Moreover

an AD based trust-region reflective (TRR) algorithm and another interior-point (IP) algorithm are established for estimating the kinetic parameters associated with particle growth

aggregation and breakage. As a result

the estimation accuracy can be further improved while the computation cost can be significantly reduced

compared to the existing algorithms. Benchmark examples from the literature are used to illustrate the accuracy and efficiency of the AD-based CAT

TRR and IP algorithms in comparison with the existing algorithms. Moreover

seeded batch cooling crystallization experiments of β form L-glutamic acid are performed to validate the proposed method.

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