Model-Free Online Recursive Optimization Method for Batch Process Based on Variable Period Decomposition

A model-free online recursive optimization method for a batch process based on variable period decomposition, characterized by comprising the following steps:

• (1) for operating a complete batch process, acquiring variables to be optimized and final quality or yield indicators in batches;
• (2) for the data acquired in step (1), performing principal component analysis on the variables in batches, and removing singular points from a principal component mode diagram, so as to enable all data points to be within one degree of credibility;
• (3) performing interval division on the remaining data after the singular points are removed on a time axis;
• expressing each batch of data included in each interval as a continuous variable, wherein these variables are referred to as decomposed period variables, and a value of the period variable is composed of each batch of data of the variable to be optimized in a specific time interval;
• (4) referring to each corresponding batch quality or yield indicator in step (3) as an indicator variable, wherein a value of the indicator variable is a continuous variable formed by the quality or yield of each batch;
• (5) combining the period variables and the indicator variables formed in step (3) and step (4) to form a combined data matrix of the period variables and the indicator variables, and performing principal component analysis on the combined data matrix to form a principal component load diagram;
• (6) classifying the action directions and magnitudes of the period variables on the indicator variables for the principal component load diagram in step (5);
• (7) calculating an optimization strategy for each period variable according to the following perturbation formula:
• J(i)=(i)+sign(i)×3σ(i)
• wherein J(i), M(i) and σ(i) herein are respectively optimization target value, mean value and standard deviation of the ith period variable;
• and sign(i) is a cosine symbol of an included angle formed by the ith period variable and the indicator variable;
• (8) constituting a basic optimization variable curve for the whole batch process by using the optimization target values of all periods obtained in step (7) according to a period sequence;
• (9) in the (i−1)th time period, calculating an error of an offline basic optimization target value J(i−1) and an actual measured value RV(i−1):
• E(i−1)=J(i−1)−RV(i−1);
• (10) on the offline basic optimization strategy, constituting a new optimization target value of next period:
• Jo(i)=J(i)+E(i−1);
• and
• (11) sequentially calculating step (9) and step (10) according to the period sequence i=1, 2, . . . , N and applying them to the process, till the operation of the whole batch process is over.