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Author: Ramprasad Yelchuru, PhD student, NTNU, Trondheim, Jan 2008 - Dec 2011.
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Background:

Selecting appropriate controlled variables is a key issue in Plantwide Control area
to operate the plant close to optimum in the presence of disturbances.
The PROST Group, NTNU, Trondheim have piloted self optimizing control ideas to address 
the controlled variables selection problem in Plantwide control

The files in this folder are to solve self optimizing control problem defined in

	I. J. Halvorsen, S. Skogestad, J. C. Morud, V. Alstad, Optimal selection
	of controlled variables, Ind. Eng. Chem. Res. 42 (2003).

This problem for a full H with given measurement set is solved as a constrained QP in 

	V. Alstad, S. Skogestad, E. Hori, Optimal measurement combinations
	as controlled variables, Journal of Process Control 19 (2009) 138-148.

These problems are solved using customized routines to find optimal measurements subsets in 

	V. Kariwala, Y. Cao, Bidirectional branch and bound for controlled
	vaiable selection. Part II: Exact local method for self-optimizing control,
	Computers and Chemical Engineering 33 (2009) 1402-1414.

	V. Kariwala, Y. Cao, Bidirectional branch and bound for controlled vaiable
	selection. Part III: Local average loss minimization, IEEE Transactions
	on Industrial Informatics 6 (2010) 54-61.


In my PhD, I solved these problems using MIQP formulations and standard cplex solvers. 
These MIQPs can solve all the problems handled in the customized routines and 
in addition can handle a few important structural constraints.

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Details of the programs and data files in this folder:

Prerequisite: 
You should have the freely available IBM ILOG CPLEX solver (IBM academic initiative) 
installed in your computer.


cplex_main_fullH_final.m    	- The main script file to find a full H matrix with 
				i number of optimal  measurements 


cplex_main_structuredH_final.m	- The main script file to find a full H matrix 
				that meets structural constraints with 
				i number of optimal  measurements 

cplex_miqp_full_final.m		- function to find optimal i measurement subsets 
				with full H	


cplex_miqp_full_structure.m	- function to find optimal i measurement subsets 
				with full H that meets structural constraints


OriginalData.mat		- The data for binary distillation column case study

				This case study details can be found in: 
				S. Skogestad, Dynamics and control of distillation columns:
				 A tutorial introduction, Chemical Engineering Research 
				and Design 75 (1997) 539 - 562. 


EvaporatorData.mat		- The data for evaporator case study
				
				This case study details can be found in 
				V. Kariwala, Y. Cao, S. Janardhanan, Local self-optimizing 
				control with average loss minimization, 
				Ind. Eng. Chem. Res. 47 (2008) 1150-1158.

VectorizationProcedure.pdf	- Describes the procedure to vectorize the matrix based
				optimization problem to vectorized optimization problem.
				
				The approach in the pdf is used in developing the functions
				cplex_miqp_full_final.m	and cplex_miqp_full_structure.m