Iterative Learning Modelling and Control of Batch Fermentation Processes

Abstract In this paper a novel method for batch-to-batch modelling and optimization, Iterative Learning Partial Least Squares Optimization (IL-PLSO) is proposed. This method uses a recursive technique to update a multi-way PLS model so that it is able to track the varying dynamics from one batch to the next. Based on the model obtained at the end of one batch, a Quadratic Programme (QP) is used to identify the required trajectory for the primary manipulated variable in the subsequent batch to ensure that the target end-point quality is met. This target quality can be gradually increased to optimise the productivity, or yield of the process. The capabilities of the proposed IL-PLSO method are illustrated through its application to optimise the end-point product quality of a benchmark simulation of a fermentation process. In this application, the proposed algorithm is able to identify an optimal trajectory for the manipulated variable after approximately 10 batches. The results are shown to compare very favourably with alternative approaches.

[1]  Bhupinder S. Dayal,et al.  Recursive exponentially weighted PLS and its applications to adaptive control and prediction , 1997 .

[2]  Zhihua Xiong,et al.  Batch to batch iterative learning control of a fed-batch fermentation process using linearised models , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[3]  Jay H. Lee,et al.  Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables , 2003 .

[4]  Ulf Holmberg,et al.  Optimal Operation of Fed-Batch Fermentations via Adaptive Control of Overflow Metabolite , 2003 .

[5]  Francis J. Doyle,et al.  Survey on iterative learning control, repetitive control, and run-to-run control , 2009 .

[6]  Wolfgang Marquardt,et al.  Run‐to‐run control of membrane filtration processes , 2007 .

[7]  Ying-wei Zhang,et al.  Combining Kernel Partial Least-Squares Modeling and Iterative Learning Control for the Batch-to-Batch Optimization of Constrained Nonlinear Processes , 2010 .

[8]  David H. Owens,et al.  Iterative learning control - An optimization paradigm , 2015, Annu. Rev. Control..

[9]  Alberto Ferrer,et al.  Self-tuning run to run optimization of fed-batch processes using unfold-PLS , 2007 .

[10]  Christoph Herwig,et al.  Generally applicable fed-batch culture concept based on the detection of metabolic state by on-line balancing. , 2003, Biotechnology and bioengineering.

[11]  B. Srinivasan,et al.  Convergence analysis of run-to-run control for a class of nonlinear systems , 2003, Proceedings of the 2003 American Control Conference, 2003..

[12]  A. Ferrer,et al.  Dealing with missing data in MSPC: several methods, different interpretations, some examples , 2002 .

[13]  S. Jørgensen,et al.  A biochemically structured model for Saccharomyces cerevisiae. , 2001, Journal of biotechnology.

[14]  Pengcheng Fu,et al.  Simulation of an iterative learning control system for fed-batch cell culture processes , 2004, Cytotechnology.

[15]  J. Macgregor,et al.  Control of batch product quality by trajectory manipulation using latent variable models , 2004 .

[16]  H. De Battista,et al.  Sliding mode scheme for adaptive specific growth rate control in biotechnological fed-batch processes , 2005 .

[17]  Boutaieb Dahhou,et al.  Robust iterative learning control of an exothermic semi-batch chemical reactor , 2001 .