MODELING AND OPTIMIZATION OF BATCH PROCESS THROUGH WAVELET ANALYSIS AND MULTIVARIATE ANALYSIS

An efficient method is developed to build a batch process model and optimize its operating conditions including time-dependent operation profiles. In the proposed method, referred to as wavelet regression and optimization (WRO), important wavelet coefficients of operation profiles are selected as input variables of a statistical model, and then further dimensionality reduction is achieved through multivariate analysis. Then, on the basis of the developed model, optimal operation profiles are derived through wavelet reconstruction. In addition, WRO is integrated with an indicator variable technique for trajectory alignment. A case study of lysine production based on a semi-batch fermentation process demonstrates the superiority of the proposed method over the conventional multiway method.