rotary bed notebooks

README.md

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-# fixed_bed_adsorption
+# MBDoE and MO analysis for co-current and counter-current RPB 
+
+Authors: Jialu Wang, Ryan Hughes, Debangsu Bhattacharrya, Alexander Dowling 
+
+## Installation instructions 
+
+The following instructions assume you have anaconda installed. We suggest create an environment with the following commands to run code: 
+
+### Step 1: create a new environment 
+- create new environment, called for e.g. `rotary`, with `conda`
+
+`conda create --name rotary python==3.8`
+
+`conda activate rotary`
+
+### Step 2: install `IDAES-PSE`
+- this step is necessary for running `homotopy` solve for the model 
+
+`pip install idaes-pse` 
+
+`idaes get-extensions`
+
+### Step 3: install `Pyomo` from specified branches
+- install from the following branch for a modified version of `Pyomo`:
+
+`pip install git+https://github.com/jialuw96/pyomo.git@RPB_MBDOE`
+
+
+### Step 4: install `jupyter notebook`
+- this is needed only for the draw_figure.ipynb notebook
+
+  `conda install jupyter notebook`
+
+### Software versions we use for the results 
+
+`Python`: 3.8
+
+`IDAES-PSE`: 2.2.0
+
+`Pyomo`: 6.7.0 dev 0
+
+## Code content 
+
+### Co-current flow model 
+
+- `RPB_model_cocurrent.py`: Co-current flow RPB model provided by Ryan, modified for running `k_aug`
+
+- `Cocurrent_flow_MBDoE.ipynb`: Apply `Pyomo.DoE` to the RPB model with `compute_FIM` and `run_grid_search`. `stochastic_program` is also tried but not debugged
+
+### Counter-current flow model (Results are represented in Jialu's thesis chapter 4)
+
+- `RPB_model_countercurrent_kaug.py`: Conter-current flow RPB model provided by Ryan, modified for running `k_aug`
+
+- `Countercurrent_MBDoE.ipynb`: MBDoE analysis applying `Pyomo.DoE` and `k_aug` to the RPB model with `compute_FIM` and `run_grid_search`. `stochastic_program` is also tried but not debugged
+
+- `Counterflow_MO_data_process.ipynb`: Process the sensitivity data for MO analysis
+
+- `Counterflow_Jacobian_process.ipynb`: Reformulate the data structure for MO analysis
+
+- `countercurrent_MO.py`: Measurement optimization analysis to the RPB model
+
+- `counterflow_finite_difference_analysis.ipynb`: MBDoE analysis to the RPB model with finite difference method, not using `Pyomo.DoE`
+
+- `draw_figure.ipynb`: Generates all results figures in the manuscript
+
+## Results content 
+
+### Cocurrent flow model
+
+- `Cocurrent_results/MO_results`: folder, contains the MO results at different budgets. For e.g., Sep17_2000_a means budget=2000, optimized with A-optimality 
+
+- `Cocurrent_results/MBDoE_results`: folder, contains the process models ran with different variables. For e.g., Nov25_368_388 means ads.T = 368K, des.T=388K 
+
+- `Countercurrent_results/MO_MILP_solution`: folder, contains the MO results at different budgets. For e.g., updated_2000 means budget=2000, optimized with A-optimality 
+
+- `Countercurrent_results/MBDoE_results/MBDOE_sens`: folder, contained the process models ran with different variables. For e.g., .Tgin_des_391/Tgin_ads_368 means ads.T = 368K, des.T=391K
+
+
+## Run Models
+
+Model code and how to run them are provided by Ryan Hughes in `Ryan-Hughes-8/fixed_bed_adsorption` repository: 
+
+Co-current flow model: https://github.com/Ryan-Hughes-8/fixed_bed_adsorption
+
+Counter-current flow model:  https://github.com/Ryan-Hughes-8/fixed_bed_adsorption/tree/counter-current-configuration
+
+## Run measurement optimization results 
+
+The rerun instructions are for counter-current flow model, which is the model we use for the paper / thesis results. 
+
+### Step 1: Achieve sensitivity information (Jacobian) and error variance matrix
+
+- Run `Counterflow_MO_data_process.ipynb` to generate all the sensitivity data
+
+### Step 2: Process the data for MO analysis
+
+- Run `Counterflow_Jacobian_process.ipynb` to organize the sensitivity data to .csv files that MO code use
+
+### Step 3: Run MO analysis 
+
+- In `Countercurrent_MO.py`, find the toggles in line 17 - 27
+- Choose mixed-integer option by `mip_option`
+- Choose objective by `objective`
+- Choose budget set. It is default to be the budget ranges we use for the paper, from 2000 to 10000
+
+## Run MBDoE analysis
+
+### Run MBDoE with `k_aug` 
+
+- `Countercurrent_MBDoE.ipynb`: MBDoE analysis applying `Pyomo.DoE` and `k_aug` to the RPB model with `compute_FIM` and `run_grid_search`
+
+### Run MBDoE with finite difference 
+
+- `Counterflow-finite-difference-analysis.ipynb`: MBDoE analysis applying finite difference method to the RPB model. It runs the grid search, and generates the thesis results.
+
+
+## Reproduce figures 
+
+- `draw_figures.ipynb`: include the functions and the path of results to reproduce Fig. 4.2, 4.3, 4.4, 4.5 in the Chapter 4 of Jialu's thesis. 
+
+
+