An integrated design for batch process using optimal average profit control with unfixed terminal time

Batch processes are flexible to meet the needs of the fluctuated market and have become very important in the modern chemical industry. Two-level control structure is widely adjusted in the operation of batch process. The higher level is the end-point property control and the low level tracks the time-varying reference trajectories designed by the high level. The two-level control structure does not take the dynamics of the low level into consideration in the high level that the low level cannot track the given trajectories perfectly because of model-plant mismatches. Also, this structure does not take the duration of the batch process into consideration which may lead to economic loss in the end. An integrated control structure is proposed in this paper to deal with the drawbacks of two-level structure. Also, different from the traditional objective of the high level, it is a pure economic objective in the integrated control structure using average profit criteria. In order to gain higher economic performance, the predictive horizon is relaxed here to achieve larger feasible region.

[1]  Elaine Martin,et al.  Particle filters for state and parameter estimation in batch processes , 2005 .

[2]  W. Fred Ramirez,et al.  Optimal fed‐batch control of induced foreign protein production by recombinant bacteria , 1994 .

[3]  David L. Ma,et al.  Worst‐case performance analysis of optimal batch control trajectories , 1999 .

[4]  Michel Perrier,et al.  Optimization of fed-batch culture of hybridoma cells using dynamic programming: single and multi feed cases , 1992 .

[5]  David Angeli,et al.  Economic optimization using model predictive control with a terminal cost , 2011, Annu. Rev. Control..

[6]  Prashant Mhaskar,et al.  Latent Variable Model Predictive Control (LV-MPC) for trajectory tracking in batch processes , 2010 .

[7]  Babu Joseph,et al.  Shrinking horizon model predictive control applied to autoclave curing of composite laminate materials , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[8]  John F. MacGregor,et al.  Latent variable MPC for trajectory tracking in batch processes , 2005 .

[9]  Francis J. Doyle,et al.  Nonlinear model-based control of a batch reactive distillation column , 1998 .

[10]  Z. Nagy,et al.  Robust nonlinear model predictive control of batch processes , 2003 .

[11]  Jay H. Lee,et al.  Model predictive control technique combined with iterative learning for batch processes , 1999 .

[12]  Branko Ristic,et al.  Particle Filters for Random Set Models , 2013 .

[13]  Prashant Mhaskar,et al.  Latent Variable MPC for trajectory tracking in batch processes: Role of the model structure , 2009, 2009 American Control Conference.

[14]  Zoltan K. Nagy,et al.  Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor , 2007 .

[15]  Prashant Mhaskar,et al.  Integrating data‐based modeling and nonlinear control tools for batch process control , 2012 .