Toggle Main Menu Toggle Search

Open Access padlockePrints

Modelling a Penicillin Fermentation Process Using Attention-based Echo State Networks Optimized by Covariance Matrix Adaption Evolutionary Strategy

Lookup NU author(s): Dr Jie ZhangORCiD



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Elsevier, 2020.

For re-use rights please refer to the publisher's terms and conditions.


Echo state network (ESN) has emerged as an effective alternative to conventional recurrent neural networks due to its simple training process and good modelling ability for solving a variety of problems, especially time-series modelling tasks. To improve modelling capability and to decrease the reservoir topology complexity, a new attention mechanism based ESN optimised by covariance matrix adaption evolutionary strategy (CMA-ES) is proposed in this paper. CMA-ES is a stochastic and derivative-free algorithm for solving non-linear optimization problems. Attention mechanism is incorporated to guide ESN to focus on regions of interest relevant to the modelling task. The proposed optimised ESN with attention mechanism is used to model a fed-batch penicillin fermentation process and the results are better than those from the standard ESN and ESN with attention mechanism.

Publication metadata

Author(s): Liu K, Zhang J

Editor(s): Pierucci S; Manenti F; Bozzano GL; Manca D

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 30th European Symposium on Computer Aided Chemical Engineering

Year of Conference: 2020

Pages: 1117-1122

Online publication date: 25/09/2020

Acceptance date: 08/01/2020

Date deposited: 27/03/2020

Publisher: Elsevier


DOI: 10.1016/B978-0-12-823377-1.50187-7

Notes: Conference website: ESCAPE 30 VIRTUAL SYMPOSIUM August 31- September 2, 2020 The ESCAPE 30 European Symposium on Computer Aided Process Engineering, originally scheduled live on August 31-September 2 in Milano, is going to be held virtually in the same date.

Library holdings: Search Newcastle University Library for this item

ISBN: 9780128233771