Measurement This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements

Authors: Jialu Wang, Zedong Peng, Ryan Hughes, Debangsu Bhattacharyya, David E. Bernal Neira, Alexander W. Dowling

This repository contains code and results for the paper: Measure This, Not That: Optimizing the Cost and Model-based Information Content of Measurements

Questions: Please contact Prof. Alex Dowling ([email protected]). This repository is primarily to archive the code. It may not be maintained. The version of software packages used to generate results are given in the paper.

Installation instructions

The following instructions assume you have anaconda installed. We suggest creating an environment with the following commands to run code:

Step 1: create a new environment

• create new environment, called for e.g. measurement_optimization, with conda with Python version 3.8 Update on May 14: Python 3.8 is not necessary (sometimes even causing problem.) Let's switch to the most updated Python. This is tested to have no problems.

conda create --name measurement_optimization

conda activate measurement_optimization

Step 2 (optional): install IDAES-PSE

• this step provides Ipopt solver, but this solver is not necessary for reproducing paper results if you already have Ipopt. If step 2 is conducted, step 3 can be skipped

pip install idaes-pse

idaes get-extensions

Step 3: install numpy, scipy, pandas, matplotlib

• If not installing IDAES-PSE, the following packages are needed:

conda install numpy

conda install scipy

conda install pandas

conda install matplotlib

Step 4: install Pyomo from specified branches

• install from the following branch for a generalization version of Mindtpy:

pip install git+https://github.com/ZedongPeng/pyomo.git@add_mindtpy_callback

Step 5: install GurobiPy

• this is needed only for solving mixed-integer problems

  conda install -c gurobi gurobi

  (By Feb. 25 2024) ND CRC users: CRC hasn't updated their gurobi license to version 11. If you install gurobi without specifying version 10, it will pop an error about not having license for version 11. To specify a version for CRC:

  conda install -c gurobi gurobi==10.0.3

Step 6: install CyIpopt

• this is needed only for D-optimality problems with grey-box modules

  conda install -c conda-forge cyipopt

Step 7: install jupyter notebook

• this is needed only for the draw_figure.ipynb notebook

  conda install jupyter notebook

(Optional) Cvxpy environment setting

• this is needed only for constructing and solving the problem with cvxpy.

  Step 1: same as step 1 above to create the environment

  Step 2: same as step 3 above to install dependencies

  Step 3: install cvxpy with:

  conda install -c conda-forge cvxpy

  Step 4: install Mosek. You need to validate a liscence for using this solver, see the link: https://docs.mosek.com/latest/install/installation.html

Software versions we use for the results

Python: 3.8

IDAES-PSE: 2.2.0

Pyomo: 6.7.0 dev 0

GurobiPy: 10.0.3

CyIpopt: 1.3.0

Cvxpy: 1.2.1

Code

• measure_optimize.py: Measurement optimization optimization framework

• greybox_generalize.py: Grey-box generalization

• kinetics_MO.py: Kinetics case study

• rotary_bed_MO.py: Rotary bed case study

• draw_figure.ipynb: Generates all results figures in the manuscript

• cvxpy_problem.py: Kinetics case study implemented with Cvxpy

Example to run code and reproduce figures for case studies

Setup the scripts to reproduce result files and figures from the paper:

Kinetics case study

• Step 1: run kinetics_MO.py

• Step 2: with mip_option and objective, choose to run the A-optimality or D-optimality, mixed-integer or relaxed problem

• Step 3: with rerun_all_paper_results, set up the budget ranges as you want to try.

  If rerun_all_paper_results: we use the budget range [1000, 5000] with a 400 discretization, i.e. [1000, 1400, 1800, ..., 5000] for mixed-integer problems.

  Otherwise, we use three budget [1000, 2200, 3800] to do a test run.

• Step 4: with linear_solver_opt, choose the linear solver for CyIpopt. If not specified, it will use the default linear solver, which is ma27 if you have HSL, otherwise mumps.

• Step 5: with initializer_option and curr_results, select initial solutions to initialize the problem, and provide file paths for these solutions

  You can choose from: A- and D-optimality, with mixed-integer or continuous options. In the paper, both objective functions and both mixed-integer and continuous frameworks are considered and solved. Refer to Eq. (11) in section 2.3 for the MO problem with mixed-integer and continuous options, Eq. (12), (13) in section 2.4 for A- and D-optimality.

• Step 6: store results for drawing figures

  To do this, define the param file_store_name with a string you given, for e.g., "MINLP_result_".

  Then both the solutions and the FIM of the results are stored separately.

  For e.g., if running in the range [1000, 5000], the stored files will be:

  MINLP_result_1000, MINLP_result_fim_1000,

  ...

  MINLP_result_5000, MINLP_result_fim_5000,

• Step 7: use draw_figure.ipynb to read stored FIM and solutions

  • read_fim receives the string name, for.e.g. MINLP_result_, and budget ranges, returns a list of A- and D-optimality values of the given FIMs

  • plot_data receives both the A- and D-optimality of all four optimization strategies, and draw two figures like Fig. 3 in paper

  • read_solution receives the string name, for e.g. MINLP_result_, and budget ranges, returns 6 lists: CA, CB, CC solutions as SCM and DCM, each list contains four lists as results from four strategies

  • plot_one_solution receives and draws the solution of one measurement under four strategies. To reproduce result figure like Fig. S-2 in paper, call it 6 times to draw all 6 figures and combine to a panel figure.

Kinetics case study with Cvxpy

• Step 1: run cvxpy_problem.py

• Step 2: with mip_option and objective, choose to run the A-optimality or D-optimality, mixed-integer or relaxed problem

• Step 3: with test, set up the budget ranges as you want to try.

  If test is False: we use the budget range [1000, 5000] with a 400 discretization, i.e. [1000, 1400, 1800, ..., 5000] for mixed-integer problems.

  Otherwise, we use three budget [3000, 5000] to do a test run.

• Step 4: store results for drawing figures

  To do this, define the param file_store_name with a string you given, for e.g., "MINLP_result_".

  Then both the solutions and the FIM of the results are stored separately.

  For e.g., if running in the range [1000, 5000], the stored files will be:

  MINLP_result_1000, MINLP_result_fim_1000,

  ...

  MINLP_result_5000, MINLP_result_fim_5000,

• Step 7: use draw_figure.ipynb to read stored FIM and solutions

  • read_fim receives the string name, for.e.g. MINLP_result_, and budget ranges, returns a list of A- and D-optimality values of the given FIMs

  • plot_data receives the Cvxpy solution and Pyomo solution, and draws them on the same figure

Rotary bed case study

• Step 1: run rotary_bed_MO.py

• Step 2: with mip_option and objective, choose to run the A-optimality or D-optimality, mixed-integer or relaxed problem

• Step 3: with rerun_all_paper_results, set up the budget ranges as you want to try.

  If rerun_all_paper_results: In our results, we use the budget range [1000, 25000] with a 1000 discretization, i.e. [1000, 11000, ..., 25000], for relaxed problems

  Otherwise, we use three budget [1000, 5000, 15000] to do a test run.

• Step 4: with linear_solver_opt, choose the linear solver for CyIpopt. If not specified, it will use the default linear solver, which is ma27 if you have HSL, otherwise mumps.

• Step 5: with initializer_option and curr_results, select initial solutions to initialize the problem, and provide file paths for these solutions

  You can choose from: A- and D-optimality, with mixed-integer or continuous options.

• Step 6: store results for drawing figures

  To do this, define the param file_store_name with a string you given, for e.g., "MINLP_result_".

  Then both the solutions and the FIM of the results are stored separately.

  For e.g., if running in the range [1000, 25000], the stored files will be:

  MINLP_result_1000, MINLP_result_fim_1000,

  ...

  MINLP_result_25000, MINLP_result_fim_25000,

• Step 7: use draw_figure.ipynb to read stored FIM and solutions

  • read_fim receives the string name, for.e.g. MINLP_result_, and budget ranges, returns a list of A- and D-optimality values of the given FIMs

  • plot_data receives both the A- and D-optimality of all four optimization strategies, and draw two figures like Fig. 6 in paper

Source files

Kinetics case study

• ./kinetics_source_data/reactor_kinetics.py: kinetics case study model

• ./kinetics_source_data/Q_drop0.csv: contain Jacobian for this case study, data structure as the following:

  0 | A1 | A2 | E1 | E2 |

  1 | num | num | num | num |

  ...

  24 | num | num | num | num |

  Rows: measurements (C_A, C_B, C_C, each measurement has 8 time points) Columns: parameters (4 parameters)

Rotary bed case study

• ./rotary_source_data/RotaryBed-DataProcess.ipynb: process rotary bed measurements data from Aspen Custom Modeler, generate Jacobian

• ./rotary_source_data/Q110_scale.csv: contain Jacobian for this case study, data structure as the following:

  0 | MTC | HTC | DH | ISO1 | ISO2 |

  1 | num | num | num | num | num |

  ...

  1540 | num | num | num | num | num |

  Rows: measurements (14 measurements, each has 110 time points) Columns: parameters (5 parameters)

Result files

Kinetics case study

At each budget, the FIM result and the optimal solution are stored separately in pickle files. Computational details including solver time and numbers of operations are also stored separately in pickle files.

FIM of final results

An example name: LP_fim_1000_a, the results of A-optimality LP problem of a budget of 1000

Data file type: pickle, storing a numpy array of FIM of the shape Np*Np, Np is the number of parameters

To replicate the results, iterate in the given budget range to retrieve the FIM stored in each data file

• A-optimality LP results: kinetics_results/LP_fim_x_a, x in the range [1000, 1100, 1200, ..., 5000]

• A-optimality MILP results: kinetics_results/MILP_fim_x_a, x in the range [1000, 1400, 1800, ..., 5000]

• D-optimality NLP results: kinetics_results/NLP_fim_x_d, x in the range [1000, 1100, 1200, ..., 5000]

• D-optimality MINLP results: kinetics_results/MINLP_fim_x_d_mip, x in the range [1000, 1400, 1800, ..., 5000]

• Operating cost results: kinetics_results/Operate_fim_x_d_mip, x in the range [1000, 1400, 1800, ..., 5000]

Optimal solutions

An example name: LP_1000_a, the results of A-optimality LP problem of a budget of 1000

Data file type: pickle, storing a numpy array of the solutions of the shape Nm*Nm, Nm is the number of all measurements

• A-optimality LP results: kinetics_results/LP_x_a, x in the range [1000, 1100, 1200, ..., 5000]

• A-optimality MILP results: kinetics_results/MILP_x_a, x in the range [1000, 1400, 1800, ..., 5000]

• D-optimality NLP results: kinetics_results/NLP_x_d, x in the range [1000, 1100, 1200, ..., 5000]

• D-optimality MINLP results: kinetics_results/MINLP_x_d, x in the range [1000, 1400, 1800, ..., 5000]

• Operating cost results: kinetics_results/Operate_x_d_mip, x in the range [1000, 1400, 1800, ..., 5000]

Computational details

The computational details are stored separately.

For A-optimality LP and MILP problems, the pickle files store a numpy array of the solver time of each budget

For D-optimality NLP and MINLP problems, the pickle files store a dictionary, where the keys are the budgets. An example is:

nlp_time={1000: {"t": 0.01, "n": 10}, ..., "5000": {"t": 0.01, "n": 10}}

For each budget, the value is a dictionary where the key t stores the solver time, n stores the number of iterations

• A-optimality LP solver time: "kinetics_time_lp"

• A-optimality MILP solver time: "kinetics_time_milp"

• D-optimality NLP iterations and solver time: "kinetics_time_iter_nlp"

• D-optimality MINLP iterations and solver time: "kinetics_time_iter_minlp"

Rotary bed case study

At each budget, the FIM result and the optimal solution are stored separately in pickle files. Computational details including solver time and numbers of operations are also stored separately in pickle files.

FIM of optimal solutions

An example name: LP_fim_1000_a, the results of A-optimality LP problem of a budget of 1000

Data file type: pickle, storing a numpy array of FIM of the shape Np*Np, Np is the number of parameters

To replicate the results, iterate in the given budget range to retrieve the FIM stored in each data file

• A-optimality LP results: rotary_results/LP_fim_x_a, x in the range [1000, 2000, 3000, ..., 25000]

• A-optimality MILP results: rotary_results/MILP_FIM_A_mip_x, x in the range [1000, 2000, 3000, ..., 25000]

• D-optimality NLP results: rotary_results/NLP_fim_x_d, x in the range [1000, 2000, 3000, ..., 25000]

• D-optimality MINLP results: rotary_results/MILP_fim_x_d_mip, x in the range [1000, 2000, 3000, ..., 25000]

Optimal solutions

An example name: LP_1000_a, the results of A-optimality LP problem of a budget of 1000

Data file type: pickle, storing a numpy array of the solutions of the shape Nm*Nm, Nm is the number of all measurements

• A-optimality LP results: rotary_results/LP_x_a, x in the range [1000, 2000, 3000, ..., 25000]

• A-optimality MILP results: rotary_results/MILP_A_mip_x, x in the range [1000, 2000, 3000, ..., 25000]

• D-optimality NLP results: rotary_results/NLP_x_d, x in the range [1000, 2000, 3000, ..., 25000]

• D-optimality MINLP results: rotary_results/MILP_x_d_mip, x in the range [1000, 2000, 3000, ..., 25000]

Computational details

The computational details are stored separately.

For A-optimality LP and MILP problems, the pickle files store a numpy array of the solver time of each budget

For D-optimality NLP and MINLP problems, the pickle files store a dictionary, where the keys are the budgets. An example is:

nlp_time={1000: {"t": 0.01, "n": 10}, ..., "5000": {"t": 0.01, "n": 10}}

For each budget, the value is a dictionary where the key t stores the solver time, n stores the number of iterations

• A-optimality LP solver time: rotary_time_lp

• A-optimality MILP solver time: rotary_time_milp

• D-optimality NLP iterations and solver time: rotary_time_iter_nlp

• D-optimality MINLP iterations and solver time: rotary_time_iter_minlp