Canadian Journal of Chemical Engineering, Vol.95, No.4, 724-736, 2017
Run-to-Run Optimization of Batch Processes with Self-Optimizing Control Strategy
This paper deals with the run- to- run optimization problem of batch processes in the presence of uncertainty with a tailored self- optimizing control (SOC) strategy. Firstly, the dynamic programming problem for the batch process is transformed into a static nonlinear programming (NLP) problem using the control parameterization method. Then combinations of output measurements are selected as controlled variables (CVs), which are batch- wise controlled to account for uncertainties. However, although existing SOC methods appear directly applicable to such a static NLP formulation, a major problem therein is that the number of control parameters is generally large to maintain a satisfactory optimizing performance, which makes them inappropriate as being manipulated variables for closed- loop optimization. To circumvent this difficulty, it is proposed to alternatively use the so- called latent effective manipulated variables as the control system's manipulated variables, which are linear combinations of original control parameters, however, less in number whilst implicitly dominating optimal operation in the whole uncertain space. This way, the run- to- run self- optimizing control system is designed with less process- dependent CVs and operated with minimal complexity. A simulated fed- batch reactor is provided to illustrate the proposed methodology.