CO2 Model Fixes (#617)

* units and connecting variables

* fix doc page

* catch if annual_input_energy is 0

* remove base class

* fix oae test

* failing doc test

* fix value call

* final touchs

Author: kbrunik

CHANGELOG.md

@@ -21,6 +21,8 @@
   - Bugfix in tests of pyomo control strategies with `StoragePerformanceModel` so that the pathname attribute is correct
   - Added `demand_profile` as an input to `StoragePerformanceModel` and `PySAMBatteryPerformanceModel`
   - Renamed `xx_charge_fraction` to `xx_soc_fraction`
+- Bugfix input energy to OAE financial model [PR 617](https://github.com/NatLabRockies/H2Integrate/pull/617)
+  - Remove `MarineCarbonCapture` base classes
 
 ## 0.7.1 [March 13, 2026]
 

docs/technology_models/co2.md

@@ -1,14 +1,14 @@
 # Marine Carbon Dioxide Capture Models
 Marine carbon dioxide (CO₂) capture technologies aim to remove CO₂ from the ocean or enhance the ocean’s natural capacity to store atmospheric CO₂. These approaches provide additional pathways for managing carbon in the marine environment and can complement land-based strategies for resource management and ocean health.
 
-This section provides an overview of the marine carbon dioxide capture models integrated into H2Integrate. The models are adapted from and maintained in NREL's [MarineCarbonManagement Repository](https://github.com/Nrel/MarineCarbonManagement) and have been integrated here for ease of scenario analysis and system-level optimization.
+This section provides an overview of the marine carbon dioxide capture models integrated into H2Integrate. The models are adapted from and maintained in NLR's [MarineCarbonManagement Repository](https://github.com/NatLabRockies/MarineCarbonManagement) and have been integrated here for ease of scenario analysis and system-level optimization.
 
 
 ## Direct Ocean Capture (DOC) Model
 
 Direct Ocean Capture extracts dissolved CO₂ directly from seawater using engineered processes. By reducing the concentration of dissolved inorganic carbon, the ocean naturally reabsorbs an equivalent amount of atmospheric CO₂. The resultant CO₂ can then be used for downstream processes and conversion or storage.
 
-The DOC model is built on electrodialysis-based separation and includes both performance and cost components, allowing users to explore a wide range of system configurations, operational scenarios, and infrastructure options. It is designed for process design, optimization, and cost evaluation of marine carbon capture systems and is integrated from NREL's [MarineCarbonManagement Repository](https://github.com/Nrel/MarineCarbonManagement). Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol7030052)
+The DOC models, `DOCPerformanceModel` and `DOCCostModel`, are built on electrodialysis-based separation and includes both performance and cost components, allowing users to explore a wide range of system configurations, operational scenarios, and infrastructure options. It is designed for process design, optimization, and cost evaluation of marine carbon capture systems and is integrated from NLR's [MarineCarbonManagement Repository](https://github.com/NatLabRockies/MarineCarbonManagement). Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol7030052)
 
 ### Why Use This Model
 - Evaluate technology performance — quantify system throughput, process efficiencies, and resource usage under different operating conditions.
@@ -55,7 +55,7 @@ The Ocean Alkalinity Enhancement (OAE) Model simulates the process of increasing
 This model estimates the CO₂ absorption potential of the ocean after the OAE process. CO₂ is not a usable downstream product.
 ```
 
-The OAE model is adapted from NREL’s MarineCarbonManagement Repository and includes both performance and cost components, as well as a financial model enabling users to explore a variety of system designs, operational strategies, and infrastructure setups. Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol8010012)
+The OAE models, `OAEPerformanceModel` and `OAECostModel`, are adapted from NLR’s MarineCarbonManagement Repository and includes both performance and cost components, as well as a financial model enabling users to explore a variety of system designs, operational strategies, and infrastructure setups. Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol8010012)
 
 ### Why Use This Model
 - Assess system performance — determine processing capacity, flow characteristics, and operational profiles.
@@ -95,7 +95,7 @@ Computes capital (CapEx) and operational (OpEx) costs based on plant design and
 - Annual operating cost (USD/year)
 
 #### OAE Cost and Financial Model
-Extends the cost model to include financial metrics. Allows calculation of net present value (NPV) and determination of credit values required for financial viability.
+The `OAECostAndFinancialModel` extends the cost model to include financial metrics. Allows calculation of net present value (NPV) and determination of credit values required for financial viability.
 
 **Additional inputs include:**
 - Levelized cost of electricity (LCOE)

docs/user_guide/connecting_technologies.md

@@ -49,14 +49,17 @@ There are two connection formats:
 
 ##### Different shared parameter names
 ```yaml
-["source_tech", "destination_tech", ("source_parameter", "destination_parameter")]
+["source_tech", "destination_tech", ["source_parameter", "destination_parameter"]]
 ```
 
 - **source_tech**: Name of the technology providing the output
 - **destination_tech**: Name of the technology receiving the input
 - **source_parameter**: The name of the parameter within ``"source_tech"``
 - **destination_parameter**: The name of the parameter within ``"destination_tech"``
 
+```{note}
+The `source_parameter` and `destination_parameter` should be input into the array as another array. If it's input as a tuple the model will raise an error.
+```
 
 ### Internal connection logic
 

examples/09_co2/ocean_alkalinity_enhancement_financials/plant_config_financials.yaml

@@ -18,6 +18,7 @@ plant:
 technology_interconnections:
   - [wind, oae, electricity, cable]
   - [finance_subgroup_electricity, oae, LCOE]
+  - [wind, oae, [annual_electricity_produced, annual_input_electricity]]
   # etc
 resource_to_tech_connections:
   # connect the wind resource to the wind technology

examples/test/test_all_examples.py

@@ -850,8 +850,6 @@ def test_wind_wave_oae_example(subtests, temp_copy_of_example):
     model.post_process()
 
     # Subtests for checking specific values
-    # Note: These are placeholder values. Update with actual values after running the test
-    # when MCM package is properly installed and configured
     with subtests.test("Check LCOC"):
         assert (
             pytest.approx(
@@ -887,8 +885,6 @@ def test_wind_wave_oae_example_with_finance(subtests, temp_copy_of_example):
     model.post_process()
 
     # Subtests for checking specific values
-    # Note: These are placeholder values. Update with actual values after running the test
-    # when MCM package is properly installed and configured
     with subtests.test("Check LCOE"):
         assert (
             pytest.approx(
@@ -901,7 +897,7 @@ def test_wind_wave_oae_example_with_finance(subtests, temp_copy_of_example):
     with subtests.test("Check Carbon Credit"):
         assert (
             pytest.approx(model.prob.get_val("oae.carbon_credit_value", units="USD/t")[0], rel=1e-3)
-            == 574.37466
+            == 1026.4684117
         )
 
 

h2integrate/converters/co2/marine/direct_ocean_capture.py

@@ -1,13 +1,9 @@
 from attrs import field, define
 from mcm.capture import echem_mcc
 
-from h2integrate.core.utilities import merge_shared_inputs
-from h2integrate.core.validators import must_equal
-from h2integrate.converters.co2.marine.marine_carbon_capture_baseclass import (
-    MarineCarbonCaptureCostBaseClass,
-    MarineCarbonCapturePerformanceConfig,
-    MarineCarbonCapturePerformanceBaseClass,
-)
+from h2integrate.core.utilities import BaseConfig, merge_shared_inputs
+from h2integrate.core.validators import gt_zero, contains, gte_zero, range_val, must_equal
+from h2integrate.core.model_baseclasses import CostModelBaseClass, PerformanceModelBaseClass
 
 
 def setup_electrodialysis_inputs(config):
@@ -29,10 +25,14 @@ def setup_electrodialysis_inputs(config):
 
 
 @define(kw_only=True)
-class DOCPerformanceConfig(MarineCarbonCapturePerformanceConfig):
+class DOCPerformanceConfig(BaseConfig):
     """Extended configuration for Direct Ocean Capture (DOC) performance model.
 
     Attributes:
+        number_ed_min (int): Minimum number of ED units to operate.
+        number_ed_max (int): Maximum number of ED units available.
+        use_storage_tanks (bool): Flag indicating whether to use storage tanks.
+        store_hours (float): Number of hours of CO₂ storage capacity (hours).
         power_single_ed_w (float): Power requirement of a single electrodialysis (ED) unit (watts).
         flow_rate_single_ed_m3s (float): Flow rate of a single ED unit (cubic meters per second).
         E_HCl (float): Energy required per mole of HCl produced (kWh/mol).
@@ -50,54 +50,55 @@ class DOCPerformanceConfig(MarineCarbonCapturePerformanceConfig):
         save_plots (bool, optional): If true, save plots of results. Defaults to False.
     """
 
-    power_single_ed_w: float = field()
-    flow_rate_single_ed_m3s: float = field()
-    E_HCl: float = field()
-    E_NaOH: float = field()
-    y_ext: float = field()
-    y_pur: float = field()
-    y_vac: float = field()
-    frac_ed_flow: float = field()
-    temp_C: float = field()
-    sal: float = field()
-    dic_i: float = field()
-    pH_i: float = field()
-    initial_tank_volume_m3: float = field()
+    number_ed_min: int = field(validator=gt_zero)
+    number_ed_max: int = field(validator=gt_zero)
+    use_storage_tanks: bool = field()
+    store_hours: float = field(validator=gte_zero)
+    power_single_ed_w: float = field(validator=gte_zero)
+    flow_rate_single_ed_m3s: float = field(validator=gt_zero)
+    E_HCl: float = field(validator=gte_zero)
+    E_NaOH: float = field(validator=gte_zero)
+    y_ext: float = field(validator=range_val(0, 1))
+    y_pur: float = field(validator=range_val(0, 1))
+    y_vac: float = field(validator=range_val(0, 1))
+    frac_ed_flow: float = field(validator=range_val(0, 1))
+    temp_C: float = field(validator=gte_zero)
+    sal: float = field(validator=gte_zero)
+    dic_i: float = field(validator=gte_zero)
+    pH_i: float = field(validator=gte_zero)
+    initial_tank_volume_m3: float = field(validator=gte_zero)
     save_outputs: bool = field(default=False)
     save_plots: bool = field(default=False)
 
 
-class DOCPerformanceModel(MarineCarbonCapturePerformanceBaseClass):
+class DOCPerformanceModel(PerformanceModelBaseClass):
     """
     An OpenMDAO component for modeling the performance of a Direct Ocean Capture (DOC) plant.
-
-    Extends:
-        MarineCarbonCapturePerformanceBaseClass
-
-    Computes:
-        - co2_out: Hourly CO2 capture rate (kg/h)
-        - co2_capture_mtpy: Annual CO2 capture (t/year)
-        - total_tank_volume_m3: Total tank volume (m^3)
-        - plant_mCC_capacity_mtph: Plant carbon capture capacity (t/h)
     """
 
     def initialize(self):
         super().initialize()
+        self.commodity = "co2"
+        self.commodity_rate_units = "kg/h"
+        self.commodity_amount_units = "kg"
 
     def setup(self):
         self.config = DOCPerformanceConfig.from_dict(
             merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"),
             additional_cls_name=self.__class__.__name__,
         )
         super().setup()
-        self.add_output(
-            "plant_mCC_capacity_mtph",
+
+        self.add_input(
+            "electricity_in",
             val=0.0,
-            units="t/h",
-            desc="Theoretical maximum CO₂ capture (t/h)",
+            shape=self.n_timesteps,
+            units="W",
+            desc="Hourly input electricity (W)",
         )
+
         self.add_output(
-            "total_tank_volume_m3",
+            "total_tank_volume",
             val=0.0,
             units="m**3",
         )
@@ -123,9 +124,7 @@ def compute(self, inputs, outputs):
         )
 
         outputs["co2_out"] = ed_outputs.ED_outputs["mCC"] * 1000  # kg/h
-        outputs["co2_capture_mtpy"] = max(ed_outputs.mCC_yr, 1e-6)  # Must be >0 #TODO: remove
-        outputs["total_tank_volume_m3"] = range_outputs.V_aT_max + range_outputs.V_bT_max
-        outputs["plant_mCC_capacity_mtph"] = max(range_outputs.S1["mCC"])  # TODO: remove
+        outputs["total_tank_volume"] = range_outputs.V_aT_max + range_outputs.V_bT_max
 
         outputs["rated_co2_production"] = (ed_outputs.mCC_yr_MaxPwr / 8760) * 1e3
         outputs["total_co2_produced"] = outputs["co2_out"].sum()
@@ -141,21 +140,17 @@ class DOCCostModelConfig(DOCPerformanceConfig):
     """Configuration for the DOC cost model.
 
     Attributes:
-        infrastructure_type (str): Type of infrastructure (e.g., "desal", "swCool", "new"").
+        infrastructure_type (str): Type of infrastructure (e.g., "desal", "swCool", "new").
         cost_year (int): dollar year corresponding to cost values
     """
 
-    infrastructure_type: str = field()
+    infrastructure_type: str = field(validator=contains(["desal", "swCool", "new"]))
     cost_year: int = field(default=2023, converter=int, validator=must_equal(2023))
 
 
-class DOCCostModel(MarineCarbonCaptureCostBaseClass):
+class DOCCostModel(CostModelBaseClass):
     """OpenMDAO component for computing capital (CapEx) and operational (OpEx) costs of a
-        direct ocean capture (DOC) system.
-
-    Computes:
-        - CapEx (USD)
-        - OpEx (USD/year)
+    direct ocean capture (DOC) system.
     """
 
     def initialize(self):
@@ -168,18 +163,27 @@ def setup(self):
         )
 
         super().setup()
+        plant_life = int(self.options["plant_config"]["plant"]["plant_life"])
 
         self.add_input(
-            "total_tank_volume_m3",
+            "total_tank_volume",
             val=0.0,
             units="m**3",
         )
 
         self.add_input(
-            "plant_mCC_capacity_mtph",  # TODO: replace with rated_co2_production
+            "annual_co2_produced",
+            val=0.0,
+            shape=plant_life,
+            units="t/year",
+            desc="Annual co2 captured",
+        )
+
+        self.add_input(
+            "rated_co2_production",
             val=0.0,
             units="t/h",
-            desc="Theoretical plant maximum CO₂ capture (t/h)",
+            desc="Theoretical plant maximum CO₂ capture",
         )
 
     def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
@@ -189,15 +193,13 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
         res = echem_mcc.electrodialysis_cost_model(
             echem_mcc.ElectrodialysisCostInputs(
                 electrodialysis_inputs=ED_inputs,
-                mCC_yr=inputs["co2_capture_mtpy"],  # TODO: replace with annual_co2_produced
-                total_tank_volume=inputs["total_tank_volume_m3"],
+                mCC_yr=inputs["annual_co2_produced"],
+                total_tank_volume=inputs["total_tank_volume"],
                 infrastructure_type=self.config.infrastructure_type,
-                max_theoretical_mCC=inputs[
-                    "plant_mCC_capacity_mtph"
-                ],  # TODO: replaced with rated_co2_production
+                max_theoretical_mCC=inputs["rated_co2_production"],
             )
         )
 
         # Calculate CapEx
         outputs["CapEx"] = res.initial_capital_cost
-        outputs["OpEx"] = res.yearly_operational_cost
+        outputs["OpEx"] = res.yearly_operational_cost[0]