watertap.flowsheets.lsrro package

Submodules

watertap.flowsheets.lsrro.lsrro module

class watertap.flowsheets.lsrro.lsrro.ABTradeoff(value)[source]

Bases: StrEnum

An enumeration.

class watertap.flowsheets.lsrro.lsrro.ACase(value)[source]

Bases: StrEnum

An enumeration.

class watertap.flowsheets.lsrro.lsrro.BCase(value)[source]

Bases: StrEnum

An enumeration.

watertap.flowsheets.lsrro.lsrro.feed_concentration_recovery_profile(m, number_of_stages, points_per_sweep=5, quick_start=True)[source]

Generate a cost-optimal feed concentration vs recovery profile for a given number of stages. :param m: Existing Pyomo model. If None, a new model is created. :type m: ConcreteModel, optional :param points_per_sweep: Number of points to sample in the sweep. :type points_per_sweep: int :param number_of_stages: Number of RO stages in the system. :type number_of_stages: int :param quick_start: If True, skip rigorous initialization for faster results. :type quick_start: bool

Returns:

DataFrame containing the sweep results. output_filename (str): Path to the CSV file where results are saved. fig, ax: Matplotlib figure and axis objects for the contour plot.

Return type:

results (pd.DataFrame)

watertap.flowsheets.lsrro.lsrro.optimize_set_up(m, set_default_bounds_on_module_dimensions=True, water_recovery=None, Cbrine=None, A_case=ACase.fixed, B_case=BCase.optimize, AB_tradeoff=ABTradeoff.none, A_value=None, permeate_quality_limit=None, AB_gamma_factor=None, B_max=None, assert_dof=False)[source]

Get the LSRRO flowsheet ready to optimize

watertap.flowsheets.lsrro.lsrro.B_case
Type:

‘single_optimum’ or anything else to optimize B value at every LSR stage

watertap.flowsheets.lsrro.lsrro.A_case
Type:

‘fixed’ or ‘optimize’ or ‘single_optimum’ A at every LSR stage

watertap.flowsheets.lsrro.lsrro.AB_tradeoff

‘equality_constraint’ B = function of A ‘none’ no constraint relating B value to A value

Type:

‘inequality_constraint’ B >= function of A

watertap.flowsheets.lsrro.lsrro.A_value
Type:

if A_case=’fixed’, then provide a value to fix A with

watertap.flowsheets.lsrro.lsrro.assert_dof
Type:

if True, will raise an error if the degrees of freedom are an unexpected value

Returns:

model (Pyomo ConcreteModel)

Return type:

The LSRRO flowsheet.

watertap.flowsheets.lsrro.lsrro.plot_lcow_breakdown(data_dict, scenario_labels=None)[source]

Plots a stacked bar chart from a dictionary of LCOW components.

Parameters: - data_dict: dict where keys are cost categories and values are lists of values per scenario.

watertap.flowsheets.lsrro.lsrro_ui module

watertap.flowsheets.lsrro.multi_sweep module

watertap.flowsheets.lsrro.multi_sweep.run_case(number_of_stages, nx, output_filename=None, quick_start=False)[source]

Run the parameter sweep tool on the LSRRO flowsheet, sweeping over feed concentration from 5 to 250 kg/m^3 and water recovery from 30% to 90%.

Parameters:

  • (int) (nx) –

  • (int) – total number of points swept will be nx^2.

  • (str (output_filename) – csv file. By default it is ./param_sweep_output/{number_of_stages}_stage/results_LSRRO.csv

  • optional) (The place to write the parameter sweeep results) – csv file. By default it is ./param_sweep_output/{number_of_stages}_stage/results_LSRRO.csv

Returns:

  • global_results (numpy array) (The raw values from the parameter sweep)

  • sweep_params (dict) (The dictionary of samples)

  • model (Pyomo ConcreteModel) (The LSRRO flowsheet used for parameter sweeps)

Module contents