diff --git a/appabuild/config/lca.py b/appabuild/config/lca.py
index d940aae..5a3b4b8 100644
--- a/appabuild/config/lca.py
+++ b/appabuild/config/lca.py
@@ -99,20 +99,6 @@ def parse_parameters(cls, parameters: List[dict]) -> List[ImpactModelParam]:
 
         return parsed_parameters
 
-    @field_validator("parameters", mode="after")
-    @classmethod
-    def eval_exprs(cls, parameters: List[ImpactModelParam]) -> List[ImpactModelParam]:
-        exprs_set = ParamsValuesSet.build(
-            {param.name: param.default for param in parameters},
-            ImpactModelParams.from_list(parameters),
-        )
-
-        values = exprs_set.evaluate()
-        for param in parameters:
-            param.update_default(values[param.name])
-
-        return parameters
-
     def dump_parameters(self) -> List[dict]:
         return list(map(lambda p: p.model_dump(), self.parameters))
 
diff --git a/appabuild/database/databases.py b/appabuild/database/databases.py
index aaa0a6a..95b94ec 100644
--- a/appabuild/database/databases.py
+++ b/appabuild/database/databases.py
@@ -1,6 +1,5 @@
 """
 Contains classes to import LCI databases in Brightway project.
-Initialize parameters_registry object which must be filled before Impact Model build.
 """
 
 from __future__ import annotations
@@ -9,12 +8,11 @@
 import json
 import os
 import re
-from collections import ChainMap
-from typing import Dict, List, Optional
+from typing import Optional
 
 import brightway2 as bw
 import yaml
-from apparun.parameters import ImpactModelParam
+from apparun.parameters import ImpactModelParams
 from lca_algebraic import resetParams, setForeground
 from lxml.etree import XMLSyntaxError
 from pydantic_core import ValidationError
@@ -25,8 +23,6 @@
 from appabuild.exceptions import BwDatabaseError, SerializedDataError
 from appabuild.logger import log_validation_error, logger
 
-parameters_registry = {}
-
 
 class Database:
     """
@@ -219,12 +215,12 @@ def __init__(self, name, path):
         """
         Database.__init__(self, name, path)
         self.fu_name = ""
-        self.parameters = {}
+        self.parameters = None
         self.context = UserDatabaseContext(
             serialized_activities=[], activities=[], database=BwDatabase(name=name)
         )
 
-    def set_functional_unit(self, fu_name: str, parameters: dict):
+    def set_functional_unit(self, fu_name: str, parameters: ImpactModelParams):
         self.fu_name = fu_name
         self.parameters = parameters
 
@@ -278,7 +274,6 @@ def execute_at_startup(self):
         self.find_activities_on_disk()
 
     def execute_at_build_time(self):
-        self.declare_parameters()
         self.import_in_project()
 
     def import_in_project(self) -> None:
@@ -313,15 +308,3 @@ def import_in_project(self) -> None:
         ]
         bw_database.write(dict(to_write_activities))
         setForeground(self.name)
-
-    def declare_parameters(self) -> None:
-        """
-        Initialize each object's parameter as a ImpactModelParam and store it in
-        parameters_registry object. parameters_registry is used to build Impact Model's
-        parameters section.
-        :return:
-        """
-        for parameter in self.parameters:
-            parameters_registry[parameter["name"]] = ImpactModelParam.from_dict(
-                parameter
-            )
diff --git a/appabuild/exceptions.py b/appabuild/exceptions.py
index 5c665e5..564a3e1 100644
--- a/appabuild/exceptions.py
+++ b/appabuild/exceptions.py
@@ -17,3 +17,9 @@ class SerializedDataError(Exception):
     """Raised when any problem concerning yaml/json dataset is encountered."""
 
     pass
+
+
+class ParameterError(Exception):
+    """Raised when any problem concerning impact model parameterization is encountered."""
+
+    pass
diff --git a/appabuild/model/builder.py b/appabuild/model/builder.py
index 33ddfb8..6e39d83 100644
--- a/appabuild/model/builder.py
+++ b/appabuild/model/builder.py
@@ -6,6 +6,7 @@
 from __future__ import annotations
 
 import itertools
+import logging
 import os
 import types
 from collections import OrderedDict
@@ -28,13 +29,19 @@
     _multiLCAWithCache,
     _replace_fixed_params,
 )
-from lca_algebraic.params import _fixed_params, newEnumParam, newFloatParam
+from lca_algebraic.params import (
+    _fixed_params,
+    _param_registry,
+    newEnumParam,
+    newFloatParam,
+)
 from pydantic import ValidationError
 from sympy import Expr, simplify, symbols, sympify
+from sympy.parsing.sympy_parser import parse_expr
 
 from appabuild.config.lca import LCAConfig
-from appabuild.database.databases import ForegroundDatabase, parameters_registry
-from appabuild.exceptions import BwDatabaseError, BwMethodError
+from appabuild.database.databases import ForegroundDatabase
+from appabuild.exceptions import BwDatabaseError, BwMethodError, ParameterError
 from appabuild.logger import logger
 
 act_symbols = {}  # Cache of  act = > symbol
@@ -76,7 +83,7 @@ def __init__(
         output_path: str,
         metadata: Optional[ModelMetadata] = ModelMetadata(),
         compile_models: bool = True,
-        parameters: Optional[dict] = None,
+        parameters: Optional[ImpactModelParams] = None,
     ):
         """
         Initialize the model builder
@@ -110,20 +117,16 @@ def from_yaml(lca_config_path: str) -> ImpactModelBuilder:
         lca_config = LCAConfig.from_yaml(lca_config_path)
 
         builder = ImpactModelBuilder(
-            lca_config.scope.fu.database,  # lca_config["scope"]["fu"]["database"],
-            lca_config.scope.fu.name,  # lca_config["scope"]["fu"]["name"],
-            lca_config.scope.methods,  # lca_config["scope"]["methods"],
-            # os.path.join(
-            #     lca_config["outputs"]["model"]["path"],
-            #     f"{lca_config['outputs']['model']['name']}.yaml",
-            # ),
+            lca_config.scope.fu.database,
+            lca_config.scope.fu.name,
+            lca_config.scope.methods,
             os.path.join(
                 lca_config.model.path,
                 lca_config.model.name + ".yaml",
             ),
-            lca_config.model.metadata,  # lca_config["outputs"]["model"]["metadata"],
-            lca_config.model.compile,  # lca_config["outputs"]["model"]["compile"],
-            lca_config.model.dump_parameters(),  # lca_config["outputs"]["model"]["parameters"],
+            lca_config.model.metadata,
+            lca_config.model.compile,
+            ImpactModelParams.from_list(lca_config.model.parameters),
         )
         return builder
 
@@ -183,6 +186,9 @@ def build_impact_tree_and_parameters(
         method format is Appa Run method keys.
         :return: root node (corresponding to the reference flow) and used parameters.
         """
+        # lcaa param registry can be populated if a model has already been built
+        _param_registry().clear()
+
         methods_bw = [to_bw_method(MethodFullName[method]) for method in methods]
         tree = ImpactTreeNode(
             name=functional_unit_bw["name"],
@@ -192,7 +198,45 @@ def build_impact_tree_and_parameters(
         # print("computing model to expression for %s" % model)
         self.actToExpression(functional_unit_bw, tree)
 
-        # Find required parameters by inspecting symbols
+        # Check if each symbol corresponds to a known parameter
+
+        # TODO move that in a FloatParam method
+        params_in_default = [
+            parameter.default
+            for parameter in self.parameters
+            if parameter.type == "float"
+            and (
+                isinstance(parameter.default, str)
+                or isinstance(parameter.default, dict)
+            )
+        ]
+        while (
+            len(
+                [
+                    parameter
+                    for parameter in params_in_default
+                    if isinstance(parameter, dict)
+                ]
+            )
+            > 0
+        ):
+            params_in_default_str = [
+                parameter
+                for parameter in params_in_default
+                if isinstance(parameter, str)
+            ]
+            params_in_default_dict = [
+                [value for value in parameter.values()]
+                for parameter in params_in_default
+                if isinstance(parameter, dict)
+            ]
+            params_in_default = (
+                list(itertools.chain.from_iterable(params_in_default_dict))
+                + params_in_default_str
+            )
+        params_in_default = [
+            parameter for parameter in params_in_default if isinstance(parameter, str)
+        ]  # there can be int params at this point
         free_symbols = set(
             list(
                 itertools.chain.from_iterable(
@@ -211,19 +255,29 @@ def build_impact_tree_and_parameters(
                     ]
                 )
             )
+            + [
+                str(symb)
+                for symb in list(
+                    itertools.chain.from_iterable(
+                        [
+                            parse_expr(params_in_default).free_symbols
+                            for params_in_default in params_in_default
+                        ]
+                    )
+                )
+            ]
         )
+
         activity_symbols = set([str(symb["symbol"]) for _, symb in act_symbols.items()])
 
         expected_parameter_symbols = free_symbols - activity_symbols
 
-        known_parameters = ImpactModelParams.from_list(parameters_registry.values())
-
         forbidden_parameter_names = list(
             itertools.chain(
                 *[
                     [
                         elem.name
-                        for elem in known_parameters.find_corresponding_parameter(
+                        for elem in self.parameters.find_corresponding_parameter(
                             activity_symbol, must_find_one=False
                         )
                     ]
@@ -234,35 +288,39 @@ def build_impact_tree_and_parameters(
 
         try:
             if len(forbidden_parameter_names) > 0:
-                raise ValueError(
+                raise ParameterError(
                     f"Parameter names {forbidden_parameter_names} are forbidden as they "
                     f"correspond to background activities."
                 )
-        except ValueError:
-            logger.exception("ValueError")
-            raise
-
-        used_parameters = [
-            known_parameters.find_corresponding_parameter(expected_parameter_symbol)
-            for expected_parameter_symbol in expected_parameter_symbols
-        ]
-        unique_used_parameters = []
-        [
-            unique_used_parameters.append(i)
-            for i in used_parameters
-            if i not in unique_used_parameters
-        ]
-        unique_used_parameters = ImpactModelParams.from_list(unique_used_parameters)
+        except ParameterError as e:
+            logger.exception(e)
+            raise ParameterError(e)
+        for expected_parameter_symbol in expected_parameter_symbols:
+            try:
+                self.parameters.find_corresponding_parameter(expected_parameter_symbol)
+            except ValueError:
+                e = (
+                    f"ValueError : {expected_parameter_symbol} is required in the impact"
+                    f" model but is unknown in the config. Please check in the LCA "
+                    f"config."
+                )
+                logger.error(e)
+                raise ParameterError(e)
 
         # Declare used parameters in conf file as a lca_algebraic parameter to enable
         # model building (will not be used afterwards)
-        for parameter in unique_used_parameters:
+
+        for parameter in self.parameters:
+            if parameter.name in _param_registry().keys():
+                e = f"Parameter {parameter.name} already in lcaa registry."
+                logging.error(e)
+                raise ParameterError(e)
             if isinstance(parameter, FloatParam):
                 newFloatParam(
                     name=parameter.name,
                     default=parameter.default,
                     save=False,
-                    dbname="",
+                    dbname=self.user_database_name,
                     min=0.0,
                 )
             if isinstance(parameter, EnumParam):
@@ -270,7 +328,7 @@ def build_impact_tree_and_parameters(
                     name=parameter.name,
                     values=parameter.weights,
                     default=parameter.default,
-                    dbname="",
+                    dbname=self.user_database_name,
                 )
 
         # Create dummy reference to biosphere
@@ -299,7 +357,7 @@ def build_impact_tree_and_parameters(
                     }
                 )
                 node.direct_impacts[method] = model_expr.xreplace(sub)
-        return tree, unique_used_parameters
+        return tree, self.parameters
 
     @staticmethod
     @with_db_context
diff --git a/pyproject.toml b/pyproject.toml
index 0676e19..48184c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,7 +42,7 @@ dependencies = [
     "kaleido",
     "tqdm",
     "ruamel.yaml",
-    "apparun==0.3.6",
+    "apparun==0.3.7",
     "typer==0.15.1",
     "ipython>=7.6.0,<=8.34.0",
     "mermaid-py==0.7.1"
diff --git a/requirements.txt b/requirements.txt
index 6a58470..724a69e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -20,7 +20,7 @@ aenum
 kaleido
 tqdm
 ruamel.yaml
-apparun==0.3.6
+apparun==0.3.7
 ipython>=7.6.0,<=8.34.0
 pre-commit
 hatchling
diff --git a/samples/conf/nvidia_ai_gpu_chip_lca_conf.yaml b/samples/conf/nvidia_ai_gpu_chip_lca_conf.yaml
index 0b32752..e5a4ee3 100644
--- a/samples/conf/nvidia_ai_gpu_chip_lca_conf.yaml
+++ b/samples/conf/nvidia_ai_gpu_chip_lca_conf.yaml
@@ -20,14 +20,17 @@ model:
     report:
       link: https://appalca.github.io/
       description: "A mock example of Appa LCA's impact model corresponding to a fictive AI chip accelerator based on NVIDIA GPU."
-      date: 03/11/2023
+      date: 07/10/2025
       version: "1"
       license: proprietary
-      appabuild_version: "0.2"
+      appabuild_version: "0.3.6"
   parameters:
     - name: cuda_core
       type: float
-      default: 512
+      default:
+        architecture:
+          Maxwell: 1344
+          Pascal: 1280
       min: 256
       max: 4096
     - name: architecture
@@ -44,7 +47,10 @@ model:
         EU: 1
     - name: energy_per_inference
       type: float
-      default: 110.0
+      default:
+        architecture: #we model energy by inference using a model of the TDP function of the number of cuda cores. We suppose that TDP power corresponds to running inferences at 90fps.
+          Maxwell: "0.0878*cuda_core*1000/(90*3600)"
+          Pascal: "0.0679*cuda_core*1000/(90*3600)"
       pm_perc: 0.2
     - name: lifespan
       type: float
@@ -52,6 +58,6 @@ model:
       pm: 1
     - name: inference_per_day
       type: float
-      default: 86400000
+      default: "30*3600*8" #30fps 8 hours a day
       min: 0
       max: 86400000
\ No newline at end of file
diff --git a/samples/datasets/user_database/nvidia_ai_gpu/nvidia_ai_gpu_chip.yaml b/samples/datasets/user_database/nvidia_ai_gpu/nvidia_ai_gpu_chip.yaml
index ead8610..9aa9783 100644
--- a/samples/datasets/user_database/nvidia_ai_gpu/nvidia_ai_gpu_chip.yaml
+++ b/samples/datasets/user_database/nvidia_ai_gpu/nvidia_ai_gpu_chip.yaml
@@ -7,7 +7,7 @@ parameters:
 - cuda_core
 - architecture # Only Maxwell or Pascal supported
 - usage_location # Only FR or EU supported
-- energy_per_inference # In mW
+- energy_per_inference # In mWh
 - inference_per_day 
 - lifespan # in years
 include_in_tree: True
diff --git a/samples/datasets/user_database/nvidia_ai_gpu/nvidia_gpu_die_manufacturing.yaml b/samples/datasets/user_database/nvidia_ai_gpu/nvidia_gpu_die_manufacturing.yaml
index 0336144..f5a0715 100644
--- a/samples/datasets/user_database/nvidia_ai_gpu/nvidia_gpu_die_manufacturing.yaml
+++ b/samples/datasets/user_database/nvidia_ai_gpu/nvidia_gpu_die_manufacturing.yaml
@@ -17,14 +17,14 @@ exchanges:
     options:
     - name: Pascal
       parameters_matching:
-        defect_density: 0.05
+        defect_density: 0.0005
         technology_node: 16 # is actually 14 for 2 chips, and 16 for 4 chips.
         fab_location:
           TW: 1
         area: 0.13184623155305694*cuda_core + 21.707425626610416
     - name: Maxwell # also includes Maxwell 2.0
       parameters_matching:
-        defect_density: 0.02
+        defect_density: 0.0001
         technology_node: 28
         fab_location:
           TW: 1
diff --git a/tests/data/appalca_confs/valids/appalca_conf_lca_confs.yaml b/tests/data/appalca_confs/valids/appalca_conf_lca_confs.yaml
new file mode 100644
index 0000000..8ae15b9
--- /dev/null
+++ b/tests/data/appalca_confs/valids/appalca_conf_lca_confs.yaml
@@ -0,0 +1,5 @@
+project_name: cmd_build_lca_confs_test
+databases:
+  foreground:
+    name: user_database
+    path: './tests/data/foreground_datasets/valids/'
diff --git a/tests/data/cmd_build/appalca_conf.yaml b/tests/data/cmd_build/appalca_conf.yaml
index fdc6e70..01b579c 100644
--- a/tests/data/cmd_build/appalca_conf.yaml
+++ b/tests/data/cmd_build/appalca_conf.yaml
@@ -1,4 +1,4 @@
-project_name: cmd_build_ete_test2
+project_name: cmd_build_ete_test
 databases:
   foreground:
     name: user_database
diff --git a/tests/data/cmd_build/expected_scores.yaml b/tests/data/cmd_build/expected_scores.yaml
new file mode 100644
index 0000000..eb1b181
--- /dev/null
+++ b/tests/data/cmd_build/expected_scores.yaml
@@ -0,0 +1,3 @@
+ai_use_phase: 4.8272749055999995
+nvidia_ai_gpu_chip: 13.988936117453397
+nvidia_gpu_chip_manufacturing: 9.161661211853398
diff --git a/tests/data/cmd_build/foreground_datasets/nvidia_ai_gpu_chip.yaml b/tests/data/cmd_build/foreground_datasets/nvidia_ai_gpu_chip.yaml
index ead8610..9aa9783 100644
--- a/tests/data/cmd_build/foreground_datasets/nvidia_ai_gpu_chip.yaml
+++ b/tests/data/cmd_build/foreground_datasets/nvidia_ai_gpu_chip.yaml
@@ -7,7 +7,7 @@ parameters:
 - cuda_core
 - architecture # Only Maxwell or Pascal supported
 - usage_location # Only FR or EU supported
-- energy_per_inference # In mW
+- energy_per_inference # In mWh
 - inference_per_day 
 - lifespan # in years
 include_in_tree: True
diff --git a/tests/data/cmd_build/foreground_datasets/nvidia_gpu_die_manufacturing.yaml b/tests/data/cmd_build/foreground_datasets/nvidia_gpu_die_manufacturing.yaml
index 0336144..f5a0715 100644
--- a/tests/data/cmd_build/foreground_datasets/nvidia_gpu_die_manufacturing.yaml
+++ b/tests/data/cmd_build/foreground_datasets/nvidia_gpu_die_manufacturing.yaml
@@ -17,14 +17,14 @@ exchanges:
     options:
     - name: Pascal
       parameters_matching:
-        defect_density: 0.05
+        defect_density: 0.0005
         technology_node: 16 # is actually 14 for 2 chips, and 16 for 4 chips.
         fab_location:
           TW: 1
         area: 0.13184623155305694*cuda_core + 21.707425626610416
     - name: Maxwell # also includes Maxwell 2.0
       parameters_matching:
-        defect_density: 0.02
+        defect_density: 0.0001
         technology_node: 28
         fab_location:
           TW: 1
diff --git a/tests/data/cmd_build/nvidia_ai_gpu_chip_expected.yaml b/tests/data/cmd_build/nvidia_ai_gpu_chip_expected.yaml
index ea33b4d..1d8d74d 100644
--- a/tests/data/cmd_build/nvidia_ai_gpu_chip_expected.yaml
+++ b/tests/data/cmd_build/nvidia_ai_gpu_chip_expected.yaml
@@ -11,10 +11,10 @@ metadata:
     link: https://appalca.github.io/
     description: A mock example of Appa LCA's impact model corresponding to a fictive
       AI chip accelerator based on NVIDIA GPU.
-    date: 03/11/2023
+    date: 07/10/2025
     version: '1'
     license: proprietary
-    appabuild_version: '0.2'
+    appabuild_version: 0.3.6
 parameters:
 - name: architecture
   default: Maxwell
@@ -23,39 +23,45 @@ parameters:
     Maxwell: 1.0
     Pascal: 1.0
 - name: cuda_core
-  default: 512.0
+  default:
+    architecture:
+      Maxwell: 1344
+      Pascal: 1280
   type: float
-  min: 460.8
-  max: 563.2
+  min: 256.0
+  max: 4096.0
   distrib: linear
   pm: null
-  pm_perc: 0.1
+  pm_perc: null
 - name: energy_per_inference
-  default: 0.05
+  default:
+    architecture:
+      Maxwell: 0.0878*cuda_core*1000/(90*3600)
+      Pascal: 0.0679*cuda_core*1000/(90*3600)
   type: float
-  min: 0.01
-  max: 0.1
+  min: null
+  max: null
   distrib: linear
   pm: null
-  pm_perc: null
+  pm_perc: 0.2
 - name: inference_per_day
-  default: 3600.0
+  default: 30*3600*8
   type: float
-  min: 3240.0
-  max: 3960.0
+  min: 0.0
+  max: 86400000.0
   distrib: linear
   pm: null
-  pm_perc: 0.1
+  pm_perc: null
 - name: lifespan
-  default: 3.6
+  default: 2.0
   type: float
-  min: 3.24
-  max: 3.96
+  min: null
+  max: null
   distrib: linear
-  pm: null
-  pm_perc: 0.1
+  pm: 1.0
+  pm_perc: null
 - name: usage_location
-  default: EU
+  default: FR
   type: enum
   weights:
     FR: 1.0
@@ -64,12 +70,12 @@ tree:
   name: nvidia_ai_gpu_chip
   models:
     EFV3_CLIMATE_CHANGE: 12500.0*architecture_Maxwell*(4.6212599075297227e-9*cuda_core
-      + 7.37132179656539e-6) + 289.6776199311062*architecture_Maxwell*(0.009702834627645097*cuda_core
-      + 1)**2/((1 - 0.6773699850611761*exp(-0.003779619385733156*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
+      + 7.37132179656539e-6) + 0.007241940498277656*architecture_Maxwell*(0.009702834627645097*cuda_core
+      + 1)**2/((1 - 0.9980542072708582*exp(-1.889809692866578e-5*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
       + 19.47688243064738) - 106.7778184271516*sqrt(2)/sqrt(0.009702834627645097*cuda_core
       + 1))) + 12500.0*architecture_Pascal*(4.6891975579761074e-9*cuda_core + 7.808281424221127e-6)
-      + 2626.882558417281*architecture_Pascal*(0.0060737847877931227*cuda_core + 1)**2/((1
-      - 0.33777635255702983*exp(-0.0065923115776528474*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
+      + 0.26268825584172803*architecture_Pascal*(0.0060737847877931227*cuda_core +
+      1)**2/((1 - 0.98920497620445418*exp(-6.5923115776528471e-5*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
       + 21.707425626610416) - 101.14318001667067*sqrt(2)/sqrt(0.0060737847877931227*cuda_core
       + 1))) + 0.00036525*energy_per_inference*inference_per_day*lifespan*(0.005*usage_location_EU
       + 0.021*usage_location_FR)
@@ -94,32 +100,32 @@ tree:
   - name: nvidia_gpu_chip_manufacturing
     models:
       EFV3_CLIMATE_CHANGE: 12500.0*architecture_Maxwell*(4.6212599075297227e-9*cuda_core
-        + 7.37132179656539e-6) + 289.6776199311062*architecture_Maxwell*(0.009702834627645097*cuda_core
-        + 1)**2/((1 - 0.6773699850611761*exp(-0.003779619385733156*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
+        + 7.37132179656539e-6) + 0.007241940498277656*architecture_Maxwell*(0.009702834627645097*cuda_core
+        + 1)**2/((1 - 0.9980542072708582*exp(-1.889809692866578e-5*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
         + 19.47688243064738) - 106.7778184271516*sqrt(2)/sqrt(0.009702834627645097*cuda_core
         + 1))) + 12500.0*architecture_Pascal*(4.6891975579761074e-9*cuda_core + 7.808281424221127e-6)
-        + 2626.882558417281*architecture_Pascal*(0.0060737847877931227*cuda_core +
-        1)**2/((1 - 0.33777635255702983*exp(-0.0065923115776528474*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
+        + 0.26268825584172803*architecture_Pascal*(0.0060737847877931227*cuda_core
+        + 1)**2/((1 - 0.98920497620445418*exp(-6.5923115776528471e-5*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
         + 21.707425626610416) - 101.14318001667067*sqrt(2)/sqrt(0.0060737847877931227*cuda_core
         + 1)))
     direct_impacts:
       EFV3_CLIMATE_CHANGE: 12500.0*architecture_Maxwell*(4.6212599075297227e-9*cuda_core
-        + 7.37132179656539e-6) + 289.6776199311062*architecture_Maxwell*(0.009702834627645097*cuda_core
-        + 1)**2/((1 - 0.6773699850611761*exp(-0.003779619385733156*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
+        + 7.37132179656539e-6) + 0.007241940498277656*architecture_Maxwell*(0.009702834627645097*cuda_core
+        + 1)**2/((1 - 0.9980542072708582*exp(-1.889809692866578e-5*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
         + 19.47688243064738) - 106.7778184271516*sqrt(2)/sqrt(0.009702834627645097*cuda_core
         + 1))) + 12500.0*architecture_Pascal*(4.6891975579761074e-9*cuda_core + 7.808281424221127e-6)
-        + 2626.882558417281*architecture_Pascal*(0.0060737847877931227*cuda_core +
-        1)**2/((1 - 0.33777635255702983*exp(-0.0065923115776528474*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
+        + 0.26268825584172803*architecture_Pascal*(0.0060737847877931227*cuda_core
+        + 1)**2/((1 - 0.98920497620445418*exp(-6.5923115776528471e-5*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
         + 21.707425626610416) - 101.14318001667067*sqrt(2)/sqrt(0.0060737847877931227*cuda_core
         + 1)))
     scaled_direct_impacts:
       EFV3_CLIMATE_CHANGE: 12500.0*architecture_Maxwell*(4.6212599075297227e-9*cuda_core
-        + 7.37132179656539e-6) + 289.6776199311062*architecture_Maxwell*(0.009702834627645097*cuda_core
-        + 1)**2/((1 - 0.6773699850611761*exp(-0.003779619385733156*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
+        + 7.37132179656539e-6) + 0.007241940498277656*architecture_Maxwell*(0.009702834627645097*cuda_core
+        + 1)**2/((1 - 0.9980542072708582*exp(-1.889809692866578e-5*cuda_core))**2*(70685.775/(0.1889809692866578*cuda_core
         + 19.47688243064738) - 106.7778184271516*sqrt(2)/sqrt(0.009702834627645097*cuda_core
         + 1))) + 12500.0*architecture_Pascal*(4.6891975579761074e-9*cuda_core + 7.808281424221127e-6)
-        + 2626.882558417281*architecture_Pascal*(0.0060737847877931227*cuda_core +
-        1)**2/((1 - 0.33777635255702983*exp(-0.0065923115776528474*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
+        + 0.26268825584172803*architecture_Pascal*(0.0060737847877931227*cuda_core
+        + 1)**2/((1 - 0.98920497620445418*exp(-6.5923115776528471e-5*cuda_core))**2*(70685.775/(0.13184623155305694*cuda_core
         + 21.707425626610416) - 101.14318001667067*sqrt(2)/sqrt(0.0060737847877931227*cuda_core
         + 1)))
     children: []
diff --git a/tests/data/cmd_build/nvidia_ai_gpu_chip_lca_conf.yaml b/tests/data/cmd_build/nvidia_ai_gpu_chip_lca_conf.yaml
index 06e20f8..e5a4ee3 100644
--- a/tests/data/cmd_build/nvidia_ai_gpu_chip_lca_conf.yaml
+++ b/tests/data/cmd_build/nvidia_ai_gpu_chip_lca_conf.yaml
@@ -20,18 +20,19 @@ model:
     report:
       link: https://appalca.github.io/
       description: "A mock example of Appa LCA's impact model corresponding to a fictive AI chip accelerator based on NVIDIA GPU."
-      date: 03/11/2023
+      date: 07/10/2025
       version: "1"
       license: proprietary
-      appabuild_version: "0.2"
+      appabuild_version: "0.3.6"
   parameters:
     - name: cuda_core
       type: float
       default:
         architecture:
-          Maxwell: 512
-          Pascal: 560
-      pm_perc: 0.1
+          Maxwell: 1344
+          Pascal: 1280
+      min: 256
+      max: 4096
     - name: architecture
       type: enum
       default: Maxwell
@@ -40,20 +41,23 @@ model:
         Pascal: 1
     - name: usage_location
       type: enum
-      default: EU
+      default: FR
       weights:
         FR: 1
         EU: 1
     - name: energy_per_inference
       type: float
-      default: 0.05
-      min: 0.01
-      max: 0.1
-    - name: inference_per_day
-      type: float
-      default: 3600
-      pm_perc: 0.1
+      default:
+        architecture: #we model energy by inference using a model of the TDP function of the number of cuda cores. We suppose that TDP power corresponds to running inferences at 90fps.
+          Maxwell: "0.0878*cuda_core*1000/(90*3600)"
+          Pascal: "0.0679*cuda_core*1000/(90*3600)"
+      pm_perc: 0.2
     - name: lifespan
       type: float
-      default: inference_per_day / 1000
-      pm_perc: 0.1
\ No newline at end of file
+      default: 2.0
+      pm: 1
+    - name: inference_per_day
+      type: float
+      default: "30*3600*8" #30fps 8 hours a day
+      min: 0
+      max: 86400000
\ No newline at end of file
diff --git a/tests/data/foreground_datasets/valids/ai_use_phase.yaml b/tests/data/foreground_datasets/valids/ai_use_phase.yaml
new file mode 100644
index 0000000..0bbdfb6
--- /dev/null
+++ b/tests/data/foreground_datasets/valids/ai_use_phase.yaml
@@ -0,0 +1,19 @@
+name: ai_use_phase
+location: RER
+type: process
+unit: unit
+amount: 1
+parameters:
+- usage_location # Only FR or RER supported
+- inference
+- energy_per_inference
+include_in_tree: True
+comment: "Use phase for AI inference."
+exchanges:
+- database: user_database
+  name: electricity
+  type: technosphere
+  amount: "inference * energy_per_inference / 1000000"
+  input:
+    uuid: "electricity_no_ei"
+    database: user_database
diff --git a/tests/data/foreground_datasets/valids/electricity_no_ei.yaml b/tests/data/foreground_datasets/valids/electricity_no_ei.yaml
new file mode 100644
index 0000000..916ef97
--- /dev/null
+++ b/tests/data/foreground_datasets/valids/electricity_no_ei.yaml
@@ -0,0 +1,25 @@
+name: electricity_no_ei
+location: GLO
+type: process
+unit: kwh
+amount: 1
+parameters:
+- usage_location # Only FR or RER supported
+comment: "Low voltage electricity using 2023 https://www.eea.europa.eu/ data."
+exchanges:
+- database: user_database
+  name: electricity
+  type: technosphere
+  switch:
+    name: usage_location
+    options:
+    - name: EU
+      amount: 0.005
+      input:
+        database: impact_proxies
+        uuid: "('EF v3.0', 'climate change', 'global warming potential (GWP100)')_technosphere_proxy"
+    - name: FR
+      amount: 0.021
+      input:
+        database: impact_proxies
+        uuid: "('EF v3.0', 'climate change', 'global warming potential (GWP100)')_technosphere_proxy"
\ No newline at end of file
diff --git a/tests/data/lca_confs/invalids/parameter_in_default_missing_in_conf.yaml b/tests/data/lca_confs/invalids/parameter_in_default_missing_in_conf.yaml
new file mode 100644
index 0000000..7b75052
--- /dev/null
+++ b/tests/data/lca_confs/invalids/parameter_in_default_missing_in_conf.yaml
@@ -0,0 +1,50 @@
+scope:
+  fu:
+    name: 'ai_use_phase'
+    database: "user_database"
+  methods:
+    - "EFV3_CLIMATE_CHANGE"
+model:
+  path: "."
+  name: "ai_use_phase"
+  compile: True
+  metadata:
+    author:
+      name: Maxime PERALTA
+      organization: CEA
+      mail: maxime.peralta@cea.fr
+    reviewer:
+      name: Mathias TORCASO
+      organization: CEA
+      mail:
+    report:
+      link: https://appalca.github.io/
+      description: "A mock example of Appa LCA's impact model corresponding to a fictive AI chip accelerator based on NVIDIA GPU."
+      date: 03/11/2023
+      version: "1"
+      license: proprietary
+      appabuild_version: "0.2"
+  parameters:
+    - name: energy_per_inference
+      type: float
+      default: 0.05*inference
+      min: 0.01
+      max: 0.1
+    - name: inference
+      type: float
+      default:
+        usage_location:
+          FR: inference_per_day*365.25*lifetime
+          EU: inference_per_day*365.25*lifetime
+      pm_perc: 0.1
+    - name: usage_location
+      type: enum
+      default: EU
+      weights:
+        FR: 1
+        EU: 1
+    - name: lifetime
+      type: float
+      default: 2
+      min: 0
+      max: 3
\ No newline at end of file
diff --git a/tests/data/lca_confs/invalids/parameter_missing_in_conf.yaml b/tests/data/lca_confs/invalids/parameter_missing_in_conf.yaml
new file mode 100644
index 0000000..0b9d96c
--- /dev/null
+++ b/tests/data/lca_confs/invalids/parameter_missing_in_conf.yaml
@@ -0,0 +1,36 @@
+scope:
+  fu:
+    name: 'ai_use_phase'
+    database: "user_database"
+  methods:
+    - "EFV3_CLIMATE_CHANGE"
+model:
+  path: "."
+  name: "ai_use_phase"
+  compile: True
+  metadata:
+    author:
+      name: Maxime PERALTA
+      organization: CEA
+      mail: maxime.peralta@cea.fr
+    reviewer:
+      name: Mathias TORCASO
+      organization: CEA
+      mail:
+    report:
+      link: https://appalca.github.io/
+      description: "A mock example of Appa LCA's impact model corresponding to a fictive AI chip accelerator based on NVIDIA GPU."
+      date: 03/11/2023
+      version: "1"
+      license: proprietary
+      appabuild_version: "0.2"
+  parameters:
+    - name: energy_per_inference
+      type: float
+      default: 0.05
+      min: 0.01
+      max: 0.1
+    - name: inference
+      type: float
+      default: "60*60"
+      pm_perc: 0.1
diff --git a/tests/end_to_end/test_cmd_build.py b/tests/end_to_end/test_cmd_build.py
index 30a532e..8979511 100644
--- a/tests/end_to_end/test_cmd_build.py
+++ b/tests/end_to_end/test_cmd_build.py
@@ -4,7 +4,9 @@
 
 import os
 
+import pytest
 import yaml
+from apparun.impact_model import ImpactModel
 from typer.testing import CliRunner
 
 from appabuild.cli.main import cli_app
@@ -19,6 +21,7 @@ def test_build_command():
     expected_file = os.path.join(
         DATA_DIR, "cmd_build", "nvidia_ai_gpu_chip_expected.yaml"
     )
+    expected_scores_file = os.path.join(DATA_DIR, "cmd_build", "expected_scores.yaml")
 
     result = runner.invoke(
         cli_app,
@@ -39,6 +42,19 @@ def test_build_command():
     with open("nvidia_ai_gpu_chip.yaml", "r") as stream:
         value = yaml.safe_load(stream)
 
-    os.remove("nvidia_ai_gpu_chip.yaml")
-
     assert expected == value, "result file not the same as expected file "
+
+    # Check that the generated impact model can be run by Appa Run
+    model = ImpactModel.from_yaml("nvidia_ai_gpu_chip.yaml")
+    scores = model.get_nodes_scores()
+    scores = {
+        score.name: score.lcia_scores.scores["EFV3_CLIMATE_CHANGE"][0]
+        for score in scores
+    }
+
+    with open(expected_scores_file, "r") as stream:
+        expected_scores = yaml.safe_load(stream)
+
+    assert scores == pytest.approx(expected_scores)
+
+    os.remove("nvidia_ai_gpu_chip.yaml")
diff --git a/tests/functional_no_db/test_lca_conf.py b/tests/functional_no_db/test_lca_conf.py
index 2ed2338..257b834 100644
--- a/tests/functional_no_db/test_lca_conf.py
+++ b/tests/functional_no_db/test_lca_conf.py
@@ -6,7 +6,10 @@
 import pytest
 from pydantic import ValidationError
 
+from appabuild import setup
+from appabuild.cli.lca import build
 from appabuild.config.lca import LCAConfig
+from appabuild.exceptions import ParameterError
 from tests import DATA_DIR
 
 
@@ -57,6 +60,40 @@ def test_parameter_empty_name():
             assert error["loc"] in invalid_params_loc
 
 
+def test_parameter_missing_in_conf():
+    """
+    Check that an exception is raised if a parameter is missing in the LCA conf.
+    """
+    appa_conf_file = os.path.join(
+        DATA_DIR, "appalca_confs", "valids", "appalca_conf_lca_confs.yaml"
+    )
+    lca_conf_file = os.path.join(
+        DATA_DIR, "lca_confs", "invalids", "parameter_missing_in_conf.yaml"
+    )
+
+    foreground_database = setup.initialize(appa_conf_file)
+    with pytest.raises(ParameterError):
+        setup.build(lca_conf_file, foreground_database)
+        os.remove("ai_use_phase.yaml")
+
+
+def test_parameter_in_default_missing_in_conf():
+    """
+    Check that an exception is raised if a parameter is missing in the LCA conf.
+    """
+    appa_conf_file = os.path.join(
+        DATA_DIR, "appalca_confs", "valids", "appalca_conf_lca_confs.yaml"
+    )
+    lca_conf_file = os.path.join(
+        DATA_DIR, "lca_confs", "invalids", "parameter_in_default_missing_in_conf.yaml"
+    )
+
+    foreground_database = setup.initialize(appa_conf_file)
+    with pytest.raises(ParameterError):
+        setup.build(lca_conf_file, foreground_database)
+        os.remove("ai_use_phase.yaml")
+
+
 def test_valid():
     """
     Check no exception is raised for a valid LCA configuration.