(125a) Modeling and Optimization of Batch Process Operation through Wavelet Analysis and Multivariate Analysis
AIChE Annual Meeting
Monday, November 13, 2006 - 3:15pm to 3:40pm
In the present work, a new regression method based on wavelet analysis and multivariate analysis is proposed. Referred to as wavelet coefficient regression (WCR), the proposed method can build a statistical model that relates operation profiles with product quality in a batch process. In WCR, selected wavelet coefficients of operation profiles are used as input variables of a statistical model, and thus time-related information such as timing of manipulation can be successfully modeled. In addition, by integrating multivariate analysis and wavelet analysis, WCR can cope with correlation of input variables. As a result, WCR enables us to build an accurate statistical model of a batch process.
On the basis of WCR, a data-driven method for improving product quality in a batch process is also proposed. The proposed method can determine operation profiles that can achieve the desired product quality and optimize the operation profiles under a given performance index and various constraints.
The usefulness of the proposed WCR and quality improvement method is demonstrated through a case study of lysine production based on a semi-batch fermentation process. A more accurate statistical model was built by using the proposed WCR than conventional methods such as multiway PLS. Furthermore, WCR-based optimization of operation profile functioned successfully for finding the best profile to achieve any given objective, such as maximizing throughput and minimizing operation cost, under various constraints.
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