Python Reactor Modeling Tools

Python Reactor Modeling Tools (PyREMOT) is an open-source package which can be used for process simulation, optimization, and parameter estimation. The current version consists of homogenous models for steady-state and dynamic conditions.

You can visit dashboard to build model input and load examples!

Installation

You can install this package

  pip install PyREMOT

Documentation

The main method is called as:

    from PyREMOT import rmtExe



    # model inputs

    # using dashboard to build model inputs

    modelInput = {...}



    # run

    res = rmtExe(modelInput)

Check component list available in the current version:

    from PyREMOT import rmtCom



    # display component list

    res = rmtCom()

    print(res)

PyREMOT UI dashboard conatins some panels as:

1- MODEL SELECTION

2- COMPONENTS

H2;CO2;H2O;CO;CH3OH;DME

this code is automaticly converted to python as:

  compList = ["H2","CO2","H2O","CO","CH3OH","DME"]

3- REACTIONS

define reacions as:

CO2 + 3H2 <=> CH3OH + H2O;

CO + H2O <=> H2 + CO2;

2CH3OH <=> DME + H2O

then:

    reactionSet = {

    "R1":"CO2+3H2<=>CH3OH+H2O",

    "R2":"CO+H2O<=>H2+CO2",

    "R3":"2CH3OH<=>DME+H2O"

    }

In order to define reaction rate expressions, there are two code sections as

a) define parameters:

"RT": x['R_CONST']*x['T'];

"K1": 35.45*math.exp(-1.7069e4/x['RT']);

"K2": 7.3976*math.exp(-2.0436e4/x['RT']);

"K3": 8.2894e4*math.exp(-5.2940e4/x['RT']);

"KH2": 0.249*math.exp(3.4394e4/x['RT']);

"KCO2": 1.02e-7*math.exp(6.74e4/x['RT']);

"KCO": 7.99e-7*math.exp(5.81e4/x['RT']);

"Ln_KP1": 4213/x['T'] - 5.752 *     math.log(x['T']) - 1.707e-3*x['T'] + 2.682e-6 *     (math.pow(x['T'], 2)) - 7.232e-10*(math.pow(x['T'], 3)) + 17.6;

"KP1": math.exp(x['Ln_KP1']);

"log_KP2": 2167/x['T'] - 0.5194 *     math.log10(x['T']) + 1.037e-3*x['T'] - 2.331e-7 *     (math.pow(x['T'], 2)) - 1.2777;

"KP2": math.pow(10, x['log_KP2']);

    "Ln_KP3":  4019/x['T'] + 3.707 *     math.log(x['T']) - 2.783e-3*x['T'] + 3.8e-7 *     (math.pow(x['T'], 2)) - 6.56e-4/(math.pow(x['T'], 3)) - 26.64;

"KP3":  math.exp(x['Ln_KP3']);

"yi_H2":  x['MoFri'][0];

"yi_CO2":  x['MoFri'][1];

"yi_H2O":  x['MoFri'][2];

"yi_CO":  x['MoFri'][3];

"yi_CH3OH":  x['MoFri'][4];

"yi_DME":  x['MoFri'][5];

"PH2":  x['P']*(x['yi_H2'])*1e-5;

"PCO2":  x['P']*(x['yi_CO2'])*1e-5;

"PH2O":  x['P']*(x['yi_H2O'])*1e-5;

"PCO": x['P']*(x['yi_CO'])*1e-5;

"PCH3OH":  x['P']*(x['yi_CH3OH'])*1e-5;

"PCH3OCH3":  x['P']*(x['yi_DME'])*1e-5;

"ra1":  x['PCO2']*x['PH2'];

"ra2":  1 + (x['KCO2']*x['PCO2']) + (x['KCO']*x['PCO']) + math.sqrt(x['KH2']*x['PH2']);

"ra3": (1/x['KP1'])*((x['PH2O']*x['PCH3OH'])/(x['PCO2']*(math.pow(x['PH2'], 3))));

"ra4":  x['PH2O'] - (1/x['KP2'])*((x['PCO2']*x['PH2'])/x['PCO']);

"ra5": (math.pow(x['PCH3OH'], 2)/x['PH2O'])-(x['PCH3OCH3']/x['KP3'])

then converted:

   varis0 = {

   "RT": lambda x: x['R_CONST']*x['T'],

   "K1": lambda x: 35.45*math.exp(-1.7069e4/x['RT']),

   "K2": lambda x: 7.3976*math.exp(-2.0436e4/x['RT']),

   "K3": lambda x: 8.2894e4*math.exp(-5.2940e4/x['RT']),

   "KH2": lambda x: 0.249*math.exp(3.4394e4/x['RT']),

   "KCO2": lambda x: 1.02e-7*math.exp(6.74e4/x['RT']),

   "KCO": lambda x: 7.99e-7*math.exp(5.81e4/x['RT']),

   "Ln_KP1": lambda x: 4213/x['T'] - 5.752 *     math.log(x['T']) - 1.707e-3*x['T'] + 2.682e-6 *     (math.pow(x['T'], 2)) - 7.232e-10*(math.pow(x['T'], 3)) + 17.6,

   "KP1": lambda x: math.exp(x['Ln_KP1']),

   "log_KP2": lambda x: 2167/x['T'] - 0.5194 *     math.log10(x['T']) + 1.037e-3*x['T'] - 2.331e-7 *     (math.pow(x['T'], 2)) - 1.2777,

   "KP2": lambda x: math.pow(10, x['log_KP2']),

       "Ln_KP3": lambda x:  4019/x['T'] + 3.707 *     math.log(x['T']) - 2.783e-3*x['T'] + 3.8e-7 *     (math.pow(x['T'], 2)) - 6.56e-4/(math.pow(x['T'], 3)) - 26.64,

   "KP3": lambda x:  math.exp(x['Ln_KP3']),

   "yi_H2": lambda x:  x['MoFri'][0],

   "yi_CO2": lambda x:  x['MoFri'][1],

   "yi_H2O": lambda x:  x['MoFri'][2],

   "yi_CO": lambda x:  x['MoFri'][3],

   "yi_CH3OH": lambda x:  x['MoFri'][4],

   "yi_DME": lambda x:  x['MoFri'][5],

   "PH2": lambda x:  x['P']*(x['yi_H2'])*1e-5,

   "PCO2": lambda x:  x['P']*(x['yi_CO2'])*1e-5,

   "PH2O": lambda x:  x['P']*(x['yi_H2O'])*1e-5,

   "PCO": lambda x: x['P']*(x['yi_CO'])*1e-5,

   "PCH3OH": lambda x:  x['P']*(x['yi_CH3OH'])*1e-5,

   "PCH3OCH3": lambda x:  x['P']*(x['yi_DME'])*1e-5,

   "ra1": lambda x:  x['PCO2']*x['PH2'],

   "ra2": lambda x:  1 + (x['KCO2']*x['PCO2']) + (x['KCO']*x['PCO']) + math.sqrt(x['KH2']*x['PH2']),

   "ra3": lambda x: (1/x['KP1'])*((x['PH2O']*x['PCH3OH'])/(x['PCO2']*(math.pow(x['PH2'], 3)))),

   "ra4": lambda x:  x['PH2O'] - (1/x['KP2'])*((x['PCO2']*x['PH2'])/x['PCO']),

   "ra5": lambda x: (math.pow(x['PCH3OH'], 2)/x['PH2O'])-(x['PCH3OCH3']/x['KP3'])

   }

b) define the final form of reaction rate expressions:

"r1": 1000*x['K1']*(x['ra1']/(math.pow(x['ra2'], 3)))*(1-x['ra3'])*x['CaBeDe'];

"r2": 1000*x['K2']*(1/x['ra2'])*x['ra4']*x['CaBeDe'];

"r3": 1000*x['K3']*x['ra5']*x['CaBeDe']

then converted:

   rates0 = {

   "r1": lambda x: 1000*x['K1']*(x['ra1']/(math.pow(x['ra2'], 3)))*(1-x['ra3'])*x['CaBeDe'],

   "r2": lambda x: 1000*x['K2']*(1/x['ra2'])*x['ra4']*x['CaBeDe'],

   "r3": lambda x: 1000*x['K3']*x['ra5']*x['CaBeDe']

   }

4- PROPERTIES

feed properties:

   # species-concentration [mol/m^3]

   SpCoi = [574.8978, 287.4489, 1.15e-02, 287.4489, 1.15e-02, 1.15e-02]

   # flowrate @ P & T [m^3/s]

   VoFlRa = 0.000228

   # pressure [Pa]

   P = 5000000

   # temperature [K]

   T = 523

   # process-type [-]

   PrTy = "non-iso-thermal"

5- REACTOR

reactor and catalyst characteristics:

   # reactor-length [m]

   ReLe = 1

   # reactor-inner-diameter [m]

   ReInDi = 0.0381

   # bed-void-fraction [-]

   BeVoFr = 0.39

   # catalyst bed density [kg/m^3]

   CaBeDe = 1171.2

   # particle-diameter [m]

   PaDi = 0.002

   # particle-density [kg/m^3]

   CaDe = 1920

   # particle-specific-heat-capacity  [J/kg.K]

   CaSpHeCa = 960

6- HEAT-EXCHANGER

    # overall-heat-transfer-coefficient [J/m^2.s.K]

    U = 50

    # medium-temperature [K]

    Tm = 523

7- SOLVER

    # ode-solver [-]

    ivp = "default"

    # display-result [-]

    diRe = "True"

After setting all modules, you can find 'model input' in python format in the summary panel. Then, copy the content of this file in your python framework and run it!

You can also find an example on PyREMOT dashboard, load it and then have a look at the summary panel.

Run

As the downloaded python file contains modelInput varibale, you can directly run the model as:

    # model input

    modelInput = {...}

    # start modeling

    res = rmtExe(modelInput)

Result Format

For steady-state cases, the modeling result is stored in an array named dataPack:

    # res

    dataPack = []

    dataPack.append({

        "modelId": modelId,

        "processType": processType,

        "successStatus": successStatus,

        "computation-time": elapsed,

        "dataShape": dataShape,

        "labelList": labelList,

        "indexList": indexList,

        "dataTime": [],

        "dataXs": dataXs,

        "dataYCons1": dataYs_Concentration_DiLeVa,

        "dataYCons2": dataYs_Concentration_ReVa,

        "dataYTemp1": dataYs_Temperature_DiLeVa,

        "dataYTemp2": dataYs_Temperature_ReVa,

        "dataYs": dataYs_All

    })

And for dynamic cases,

    # res

    resPack = {

        "computation-time": elapsed,

        "dataPack": dataPack

    }

Concentration results:

dataYCons1: dimensionless concentration



dataYCons2: concentraton [mol/m^3.s]

Temperature results:

dataYTemp1: dimensionless temperature



dataYTemp2: Temperature [K]

All modeling results is also saved in dataYs.

FAQ

For any question, you can conatct me on LinkedIn or Twitter.

Authors

• @sinagilassi