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

Abstract

The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

What is claimed is: 
     
         1 . 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:
     J   o ( 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.   
     
     
         2 . The model-free online recursive optimization method for the batch process based on variable period decomposition according to  claim 1 , characterized in that the time intervals of batch process data acquisition in step (1) are equal or unequal. 
     
     
         3 . The model-free online recursive optimization method for the batch process based on variable period decomposition according to  claim 1 , characterized in that the interval division in step (3) is equal interval division or unequal interval division. 
     
     
         4 . The model-free online recursive optimization method for the batch process based on variable period decomposition according to  claim 1 , characterized in that the classification in step (6) comprises positive action, reverse action and no/micro action. 
     
     
         5 . The model-free online recursive optimization method for the batch process based on variable period decomposition according to  claim 1 , characterized in that the value of the included angle cosine symbol sign(i) is +1 when the included angle is smaller than 90 degrees, −1 when the included angle is greater than 90 degrees, or 0 when the included angle is equal to 90 degrees. 
     
     
         6 . The model-free online recursive optimization method for the batch process based on variable period decomposition according to  claim 1 , characterized in that the optimization variable curve is digitally filtered in step (8), so that the new optimization variable curve is smooth.