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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