h2integrate.converters.iron.iron_dri_base

Classes

IronReductionCostBaseConfig(...)	Configuration baseclass for IronReductionPlantBaseCostComponent.
IronReductionPerformanceBaseConfig(...[, ...])	Configuration baseclass for IronReductionPlantBasePerformanceComponent.
IronReductionPlantBaseCostComponent(**kwargs)	Cost component for direct reduced iron (DRI) plant using the Rosner cost model.
IronReductionPlantBasePerformanceComponent(...)

class h2integrate.converters.iron.iron_dri_base.IronReductionPerformanceBaseConfig(pig_iron_production_rate_tonnes_per_hr, water_density=1000)

Configuration baseclass for IronReductionPlantBasePerformanceComponent.

Parameters:

• pig_iron_production_rate_tonnes_per_hr (float)

• water_density (float)

pig_iron_production_rate_tonnes_per_hr

capacity of the iron processing plant in units of metric tonnes of pig iron produced per hour.

Type:

float

water_density

water density in kg/m3 to use to calculate water volume from mass. Defaults to 1000.0

Type:

float

pig_iron_production_rate_tonnes_per_hr: float

water_density: float

class h2integrate.converters.iron.iron_dri_base.IronReductionPlantBasePerformanceComponent(**kwargs)

initialize()

Perform any one-time initialization run at instantiation.

setup()

Declare inputs and outputs.

Available attributes:

name pathname comm options

format_coeff_df(coeff_df)

Update the coefficient dataframe such that feedstock values are converted to units of feedstock unit / unit pig iron, e.g., ‘galUS/t’. Also convert values to standard units and that units are compatible with OpenMDAO Units. Filter the dataframe to include only the data necessary for the specified type of reduction.

Parameters:

coeff_df (pd.DataFrame) – performance coefficient dataframe.

Returns:

pd.DataFrame – filtered performance coefficient dataframe

compute(inputs, outputs)

Computation for the OM component.

For a template class this is not implement and raises an error.

class h2integrate.converters.iron.iron_dri_base.IronReductionCostBaseConfig(pig_iron_production_rate_tonnes_per_hr, cost_year, skilled_labor_cost, unskilled_labor_cost)

Configuration baseclass for IronReductionPlantBaseCostComponent.

Parameters:

• pig_iron_production_rate_tonnes_per_hr (float)

• cost_year (int)

• skilled_labor_cost (float)

• unskilled_labor_cost (float)

pig_iron_production_rate_tonnes_per_hr

capacity of the iron processing plant in units of metric tonnes of pig iron produced per hour.

Type:

float

cost_year

This model uses 2022 as the base year for the cost model. The cost year is updated based on target_dollar_year in the plant config to adjust costs based on CPI/CEPCI within this model. This value cannot be user added under cost_parameters.

Type:

int

skilled_labor_cost

Skilled labor cost in 2022 USD/hr

Type:

float

unskilled_labor_cost

Unskilled labor cost in 2022 USD/hr

Type:

float

pig_iron_production_rate_tonnes_per_hr: float

cost_year: int

skilled_labor_cost: float

unskilled_labor_cost: float

class h2integrate.converters.iron.iron_dri_base.IronReductionPlantBaseCostComponent(**kwargs)

Cost component for direct reduced iron (DRI) plant using the Rosner cost model.

config

configuration class

Type:

IronReductionCostBaseConfig

coeff_df

cost coefficient dataframe

Type:

pd.DataFrame

steel_to_iron_ratio

steel/pig iron ratio

Type:

float

setup()

Declare inputs and outputs.

Available attributes:

name pathname comm options

format_coeff_df(coeff_df)

Update the coefficient dataframe such that values are adjusted to standard units

and units are compatible with OpenMDAO units. Also filter the dataframe to include only the data necessary for natural gas DRI type.

Parameters:

coeff_df (pd.DataFrame) – cost coefficient dataframe.

Returns:

pd.DataFrame – cost coefficient dataframe

compute(inputs, outputs, discrete_inputs, discrete_outputs)

Computation for the OM component.

For a template class this is not implement and raises an error.