document: The average-case identifiability and controllability of large scale systems Needs for increased product quality , reduced pollution , and reduced energy and material consumption are driving enhanced process integration . This increases the number of manipulated and measured variables required by the control system to achieve its objectives . This paper addresses the question of whether processes tend to become increasingly more difficult to identify and control as the process dimension increases . Tools and results of multivariable statistics are used to show that , under a variety of assumed distributions on the elements , square processes of higher dimension tend to be more difficult to identify and control , whereas the expected controllability and identifiability of nonsquare processes depends on the relative numbers of measured and manipulated variables . These results suggest that the procedure of simplifying the control problem so that only a square process is considered is a poor practice for large scale systems
doc_bio_tags: O O O O O O B I I O O O O O O O O O O O O O O O O O B I I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I I
extractive_keyphrases: large scale systems enhanced process integration multivariable statistics nonsquare processes measured variables manipulated variables
abstractive_keyphrases: average-case identifiability average-case controllability process control high dimension square processes process identification monte carlo simulations chemical engineering