watertap.flowsheets.METAB package

Submodules

watertap.flowsheets.METAB.input_space_generation module

watertap.flowsheets.METAB.input_space_generation.create_samples(method='LHS', input_var_info=None, sample_numbers=10, csv_file='./results/input_data.csv')[source]

Generate input samples over a defined variable space and write them to a CSV file

Parameters:

  • method (str, optional) – Sampling strategy to use. This currently only supports the Latin Hypercube Sampling (LHS) method

  • input_var_info (dict, optional) – Ordered mapping of input variable names

  • sample_numbers (int, optional) – Number of sample points to generate

  • csv_file (str, optional) – Path to the output CSV file

Returns:

None. Results are written to csv_file and a confirmation message is printed on success.

watertap.flowsheets.METAB.model_evaluation module

watertap.flowsheets.METAB.model_evaluation.collect_results(case=None, results=None, mass=True)[source]

Gathers flowrates from four output streams — effluent, methane biogas (reactor 2 extraction), hydrogen biogas (reactor 1 extraction), and reactor 1 extracted biogas — and concatenates them into a single wide-format row. Column names are prefixed to distinguish streams:

  • eff_ : effluent digestate (no prefix applied)

  • bge2_ : biogas extraction from reactor 2 (CH4-rich)

  • bgr2_ : biogas from reactor 2 hydrogen stream

  • bge1_ : biogas extraction from reactor 1

Parameters:

  • case – An EXPOsan METAB system object whose outs streams will be read.

  • results (pd.DataFrame, optional) – Accumulator DataFrame from previous iterations.

  • mass (bool, optional) – If True, use get_mass_flowrate for all streams. If False, use the molar flowrate helpers (get_eff_fr, get_ch4_fr, get_h2_fr, get_r1_ex_biogas_fr).

Returns:

Updated accumulator with the current run’s output appended as a new row

Return type:

pd.DataFrame

watertap.flowsheets.METAB.model_evaluation.export_output_data(df, filename=None)[source]

Export a DataFrame of output results to a CSV file.

Parameters:

  • df – Output data to export

  • filename (str, optional) – Full path (including filename and extension) for the output CSV file. Passed directly to df.to_csv(). Defaults to None.

Returns:

None

watertap.flowsheets.METAB.model_evaluation.get_ch4_fr(case=None, df=None)[source]

Extract methane stream molar flowrates from a METAB system and append them to a DataFrame

Parameters:

  • case – An EXPOsan METAB system object with an outs attribute

  • df – Existing DataFrame to append the new row to. If None, a new DataFrame is created from the extracted flowrates.

Returns:

DataFrame containing one row of CH4 stream molar flowrates, with component names as columns

Return type:

pd.DataFrame

watertap.flowsheets.METAB.model_evaluation.get_eff_fr(case=None, df=None)[source]

Extract effluent molar flowrates from a METAB system and append them to a DataFrame

Parameters:

  • case – An EXPOsan METAB system object with an outs attribute

  • df – Existing DataFrame to append the new row to. If None, a new DataFrame is created from the extracted flowrates.

Returns:

DataFrame containing one row of effluent molar flowrates, with component names as columns

Return type:

pd.DataFrame

watertap.flowsheets.METAB.model_evaluation.get_h2_fr(case=None, df=None)[source]

Extract hydrogen stream molar flowrates from a METAB system and append them to a DataFrame.

Parameters:

  • case – An EXPOsan METAB system object with an outs attribute

  • df – Existing DataFrame to append the new row to.

Returns:

DataFrame containing one row of H2 stream molar flowrates, with component names as columns

Return type:

pd.DataFrame

watertap.flowsheets.METAB.model_evaluation.get_input_data(filename=None)[source]

Load input variable data from a CSV file or return a hardcoded default dataset. If no filename is provided, a small default dataset with three sample points is returned, covering influent flowrate, temperature, and hydraulic retention time.

Parameters:

filename (str, optional) – Path to a CSV file containing input variable columns

Returns:

DataFrame with one row per sample point

Return type:

pd.DataFrame

watertap.flowsheets.METAB.model_evaluation.get_mass_flowrate(case=None, df=None, stream=None)[source]

Extract mass flowrates and volumetric flowrate from a stream and append them to a DataFrame.

Reads component mass flowrates directly from a stream’s state vector, using the stream’s component list as column names. A Volumetric Flowrate column is appended from the last element of the state vector. Columns where all values are zero are dropped from the result.

Parameters:

  • case – An EXPOsan METAB system object

  • df – Existing DataFrame to append the new row to

  • stream – An EXPOsan stream object with components and state attributes

Returns:

DataFrame with one row of mass flowrates and volumetric flowrate, with zero-only columns removed.

Return type:

pd.DataFrame

watertap.flowsheets.METAB.model_evaluation.get_r1_ex_biogas_fr(case=None, df=None)[source]

Extract reactor 1 extracted biogas molar flowrates from a METAB system and append them to a DataFrame.

Parameters:

  • case – An EXPOsan METAB system object with an outs attribute

  • df – Existing DataFrame to append the new row to

Returns:

DataFrame containing one row of reactor 1 biogas molar flowrates, with component names as columns

Return type:

pd.DataFrame

watertap.flowsheets.METAB.model_evaluation.run_model(df)[source]

Iterates over all rows of the input DataFrame, configures a two-stage fluidized-bed METAB system with membrane gas extraction for each set of conditions, simulates it using the BDF solver, and accumulates the output stream flowrates into a single results DataFrame.

Fixed model parameters applied to every run:

  • n_stages : 2

  • reactor_type : "FB" (fluidized bed)

  • gas_extraction : "M" (membrane)

  • t_span : 200 (simulation time in days)

Parameters:

df (pd.DataFrame) – Input DataFrame with one row per simulation run. Must contain columns inf_fr (influent flowrate, m³/d), temp (reactor temperature, °C), and hrt (total hydraulic retention time, d).

Returns:

Accumulated output flowrates from all simulation runs, as returned by collect_results. Returns an empty dict if exposan is not installed or no rows are processed.

Return type:

pd.DataFrame

watertap.flowsheets.METAB.performance_estimation module

watertap.flowsheets.METAB.performance_estimation.display_plot(surrogate, dataframe, method='poly', path=None, show=True)[source]

Produces one parity plot per output variable and consolidates into a PDF

Parameters:

  • surrogate – IDAES surrogate object

  • dataframe – DataFrame containing input and output columns corresponding to the surrogate’s labels

  • method (str, optional) – Surrogate method identifier used to name the output PDF file (e.g. "poly", "kri", "rbf")

  • path (str, optional) – Directory in which to save the PDF

  • show (bool, optional) – Whether to display each figure interactively via matplotlib

Returns:

One figure per output variable

Return type:

list[matplotlib.figure.Figure]

watertap.flowsheets.METAB.performance_estimation.performance_estimation(surrogate=None, dataframe=None)[source]

Evaluates the surrogate against the provided dataframe and returns a DataFrame summarising fit metrics for each output variable.

Parameters:

  • surrogate – IDAES surrogate object Must implement input_labels(), output_labels(), and evaluate_surrogate()

  • dataframe (pd.DataFrame) – DataFrame containing both input and output columns corresponding to the surrogate’s input_labels() and output_labels()

Returns:

One row per output variable with columns:

  • Comp : output variable name

  • R2 : coefficient of determination

  • RMSE : root mean squared error

  • MSE : mean squared error

  • MAE : mean absolute error

  • maxAE : maximum absolute error

  • SSE : sum of squared errors

Return type:

pd.DataFrame

watertap.flowsheets.METAB.surrogate_model_generator module

watertap.flowsheets.METAB.surrogate_model_generator.gen_surrogate_model(tool='idaes', method='poly', feed_data=None, input_data=None, output_data=None)[source]

Train an IDAES surrogate model and save it to disk alongside a parity plot.

Method-specific configuration:

  • "poly"PySMO polynomial up to order 6, multinomials enabled,

    80/20 train/test split, 3-fold cross-validation.

  • "kri" : PySMO Kriging with numerical gradients and regularization.

  • "rbf" : PySMO RBF with cubic basis function.

  • "alamo"ALAMO with constant, linear, exponential, log, sin, cos

    basis functions and monomial/multi-linear powers up to 3.

Parameters:

  • tool (str, optional) – Surrogate toolbox identifier

  • method (str, optional) – Surrogate modelling method

  • feed_data – Combined input and output DataFrame used for training

  • input_data (pd.DataFrame, optional) – Input variables DataFrame. Used to derive input labels and bounds

  • output_data (pd.DataFrame, optional) – Output variables DataFrame.

Returns:

  • <method>_surrogate.json : serialised surrogate model.

  • <method>_parity.pdf : parity plot for all output variables.

Return type:

None. Writes the following files to the module directory

watertap.flowsheets.METAB.surrogate_model_generator.get_data(input_data_file='/home/docs/checkouts/readthedocs.org/user_builds/watertap/checkouts/1.7.0/watertap/flowsheets/METAB/input_data.csv', output_data_file='/home/docs/checkouts/readthedocs.org/user_builds/watertap/checkouts/1.7.0/watertap/flowsheets/METAB/output_data.csv')[source]

Load input and output data from CSV files and combine them into a single feed DataFrame.

Parameters:

  • input_data_file – Path to the input variables CSV file.

  • output_data_file – Path to the output variables CSV file.

Returns:

  • feed_data : Combined input and output DataFrame used for training.

  • input_data : Input variables only.

  • output_data : Output variables only (first column dropped).

Return type:

tuple

watertap.flowsheets.METAB.surrogate_model_generator.outputs_selections(output_data)[source]

Filter and reorder output data columns to the standard ADM1 component set.

The selected outputs are (in order): S_su, S_aa, S_fa, S_va, S_bu, S_pro, S_ac, S_h2, S_ch4, S_IC, S_IN, S_I, X_c, X_ch, X_pr, X_li, X_su, X_aa, X_fa, X_c4, X_pro, X_ac, X_h2, X_I, VolumetricFlowrate.

Parameters:

output_data (pd.DataFrame) – Raw output DataFrame as loaded from CSV.

Returns:

Output DataFrame restricted to the standard ADM1 component columns in the order listed above

Return type:

pd.DataFrame

Module contents