Introducing multivariable commodity streams

examples/29_multivariable_streams/29_multivariable_streams.yaml

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+name: "H2Integrate_config"
+
+system_summary: >
+  This example demonstrates the multivariable streams feature in H2Integrate.
+  Multivariable streams allow users to connect multiple related variables with
+  a single connection specification. In this example, we connect a wellhead_gas
+  stream (containing gas_flow, hydrogen_fraction, oxygen_fraction, gas_temperature,
+  and gas_pressure) between a dummy gas producer and consumer.
+
+driver_config: "driver_config.yaml"
+technology_config: "tech_config.yaml"
+plant_config: "plant_config.yaml"

examples/29_multivariable_streams/driver_config.yaml

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+name: "driver_config"
+description: "Driver configuration for multivariable streams example"
+
+general:
+  folder_output: outputs
+  create_om_reports: false

examples/29_multivariable_streams/plant_config.yaml

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+name: "plant_config"
+description: "Demonstrates multivariable streams with a gas combiner"
+
+plant:
+  plant_life: 30
+  simulation:
+    n_timesteps: 8760
+    timezone: -6
+
+technology_interconnections: [
+  ["gas_producer_1", "gas_combiner", "wellhead_gas", "pipe"],
+  ["gas_producer_2", "gas_combiner", "wellhead_gas", "pipe"],
+  ["gas_combiner", "gas_consumer", "wellhead_gas"],
+]

examples/29_multivariable_streams/run_multivariable_streams.py

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+"""
+Example 29: Multivariable Streams with Gas Combiner
+
+This example demonstrates:
+1. Multivariable streams - connecting multiple related variables with a single connection
+2. Gas stream combiner - combining multiple gas streams with mass-weighted averaging
+
+Two gas producers with different properties feed into a combiner, which outputs
+a single combined stream to a consumer.
+
+The wellhead_gas stream includes:
+- gas_flow (kg/h): Total mass flow rate
+- hydrogen_fraction: Fraction of hydrogen
+- oxygen_fraction: Fraction of oxygen
+- gas_temperature (K): Temperature
+- gas_pressure (bar): Pressure
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from h2integrate.core.h2integrate_model import H2IntegrateModel
+
+
+# Create and setup the H2Integrate model
+model = H2IntegrateModel("29_multivariable_streams.yaml")
+
+model.setup()
+
+model.run()
+
+
+# Get outputs from gas producers
+print("\nGas Producer 1 Outputs:")
+flow1 = model.prob.get_val("gas_producer_1.gas_flow_out", units="kg/h")
+temp1 = model.prob.get_val("gas_producer_1.gas_temperature_out", units="K")
+pres1 = model.prob.get_val("gas_producer_1.gas_pressure_out", units="bar")
+print(f"  Flow: mean={flow1.mean():.2f} kg/h")
+print(f"  Temperature: mean={temp1.mean():.1f} K")
+print(f"  Pressure: mean={pres1.mean():.2f} bar")
+
+print("\nGas Producer 2 Outputs:")
+flow2 = model.prob.get_val("gas_producer_2.gas_flow_out", units="kg/h")
+temp2 = model.prob.get_val("gas_producer_2.gas_temperature_out", units="K")
+pres2 = model.prob.get_val("gas_producer_2.gas_pressure_out", units="bar")
+print(f"  Flow: mean={flow2.mean():.2f} kg/h")
+print(f"  Temperature: mean={temp2.mean():.1f} K")
+print(f"  Pressure: mean={pres2.mean():.2f} bar")
+
+# Get outputs from combiner
+print("\nGas Combiner Outputs (mass-weighted average):")
+flow_out = model.prob.get_val("gas_combiner.gas_flow_out", units="kg/h")
+temp_out = model.prob.get_val("gas_combiner.gas_temperature_out", units="K")
+pres_out = model.prob.get_val("gas_combiner.gas_pressure_out", units="bar")
+h2_out = model.prob.get_val("gas_combiner.hydrogen_fraction_out")
+print(f"  Total Flow: mean={flow_out.mean():.2f} kg/h (sum of inputs)")
+print(f"  Temperature: mean={temp_out.mean():.1f} K (weighted avg)")
+print(f"  Pressure: mean={pres_out.mean():.2f} bar (weighted avg)")
+print(f"  H2 Fraction: mean={h2_out.mean():.3f} (weighted avg)")
+
+# Get derived outputs from gas_consumer
+print("\nGas Consumer Derived Outputs:")
+h2_mass_flow = model.prob.get_val("gas_consumer.hydrogen_mass_flow", units="kg/h")
+total_consumed = model.prob.get_val("gas_consumer.total_gas_consumed", units="kg")
+avg_temp = model.prob.get_val("gas_consumer.avg_temperature", units="K")
+avg_pressure = model.prob.get_val("gas_consumer.avg_pressure", units="bar")
+print(f"  H2 Mass Flow: mean={h2_mass_flow.mean():.2f} kg/h")
+print(f"  Total Gas Consumed: {total_consumed[0]:,.0f} kg")
+print(f"  Avg Temperature: {avg_temp[0]:.1f} K")
+print(f"  Avg Pressure: {avg_pressure[0]:.2f} bar")
+
+print("\n" + "=" * 60)
+print("SUCCESS: Gas stream combiner with multivariable streams worked!")
+print("=" * 60)
+
+
+# Time axis in hours
+n_timesteps = len(flow1)
+time_hours = np.arange(n_timesteps)
+
+# Create figure with 3 subplots sharing x-axis
+fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10), sharex=True)
+fig.suptitle("Gas Stream Time History", fontsize=14, fontweight="bold")
+
+# Colors for the two streams
+color1 = "#1f77b4"  # Blue for stream 1
+color2 = "#ff7f0e"  # Orange for stream 2
+color_total = "#2ca02c"  # Green for total/combined
+
+# ------------------------------------------------------------
+# Plot 1: Mass Flow Rates (stacked area)
+# ------------------------------------------------------------
+# Stack the flows - stream 1 on bottom, stream 2 on top
+ax1.fill_between(time_hours, 0, flow1, color=color1, alpha=0.7, label="Stream 1")
+ax1.fill_between(time_hours, flow1, flow1 + flow2, color=color2, alpha=0.7, label="Stream 2")
+ax1.plot(time_hours, flow_out, color=color_total, linewidth=2, label="Total (Combined)")
+
+# Add in-area labels at the center of each region
+mid_time = n_timesteps // 2
+ax1.text(
+    mid_time,
+    flow1.mean() / 2,
+    "Producer 1",
+    ha="center",
+    va="center",
+    fontsize=10,
+    fontweight="bold",
+    color="white",
+)
+ax1.text(
+    mid_time,
+    flow1.mean() + flow2.mean() / 2,
+    "Producer 2",
+    ha="center",
+    va="center",
+    fontsize=10,
+    fontweight="bold",
+    color="white",
+)
+
+ax1.set_ylabel("Mass Flow Rate (kg/h)")
+ax1.set_title("Mass Flow Rates")
+ax1.legend(loc="upper right")
+ax1.grid(True, alpha=0.3)
+ax1.set_ylim(bottom=0)
+
+# ------------------------------------------------------------
+# Plot 2: Pressure
+# ------------------------------------------------------------
+ax2.plot(time_hours, pres1, color=color1, linewidth=1.5, label="Stream 1", linestyle="--")
+ax2.plot(time_hours, pres2, color=color2, linewidth=1.5, label="Stream 2", linestyle="--")
+ax2.plot(time_hours, pres_out, color=color_total, linewidth=2, label="Combined")
+
+ax2.set_ylabel("Pressure (bar)")
+ax2.set_title("Pressure")
+ax2.legend(loc="upper right")
+ax2.grid(True, alpha=0.3)
+
+# ------------------------------------------------------------
+# Plot 3: Temperature
+# ------------------------------------------------------------
+ax3.plot(time_hours, temp1, color=color1, linewidth=1.5, label="Stream 1", linestyle="--")
+ax3.plot(time_hours, temp2, color=color2, linewidth=1.5, label="Stream 2", linestyle="--")
+ax3.plot(time_hours, temp_out, color=color_total, linewidth=2, label="Combined")
+
+ax3.set_xlabel("Time (hours)")
+ax3.set_ylabel("Temperature (K)")
+ax3.set_title("Temperature")
+ax3.legend(loc="upper right")
+ax3.grid(True, alpha=0.3)
+
+# Adjust layout and save
+plt.tight_layout()
+plt.show()

examples/29_multivariable_streams/tech_config.yaml

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+name: "technology_config"
+description: >
+  Technology configuration for multivariable streams example.
+  Demonstrates two gas producers feeding into a combiner, which then
+  feeds a consumer. The combiner uses mass-weighted averaging.
+
+technologies:
+  gas_producer_1:
+    performance_model:
+      model: "dummy_gas_producer_performance"
+    cost_model:
+      model: "dummy_gas_producer_cost"
+    model_inputs:
+      performance_parameters:
+        base_flow_rate: 150.0  # kg/hr
+        base_temperature: 310.0  # K
+        base_pressure: 12.0  # bar
+        flow_variation: 0.3  # ±30% variation
+        temp_variation: 15.0  # ±15 K variation
+        pressure_variation: 2.0  # ±2 bar variation
+        random_seed: 42  # for reproducibility
+      cost_parameters:
+        capex: 1000000.0  # $1M
+        opex: 50000.0  # $50k/year
+        cost_year: 2024
+
+  gas_producer_2:
+    performance_model:
+      model: "dummy_gas_producer_performance"
+    cost_model:
+      model: "dummy_gas_producer_cost"
+    model_inputs:
+      performance_parameters:
+        base_flow_rate: 100.0  # kg/hr - different flow rate
+        base_temperature: 350.0  # K - hotter stream
+        base_pressure: 8.0  # bar - lower pressure
+        flow_variation: 0.25  # ±25% variation
+        temp_variation: 20.0  # ±20 K variation
+        pressure_variation: 1.5  # ±1.5 bar variation
+        random_seed: 123  # different seed for different random pattern
+      cost_parameters:
+        capex: 800000.0  # $800k
+        opex: 40000.0  # $40k/year
+        cost_year: 2024
+
+  gas_combiner:
+    performance_model:
+      model: "gas_stream_combiner"
+    model_inputs:
+      performance_parameters:
+        stream_type: "wellhead_gas"
+        in_streams: 2
+
+  gas_consumer:
+    performance_model:
+      model: "dummy_gas_consumer_performance"
+    cost_model:
+      model: "dummy_gas_consumer_cost"
+    model_inputs:
+      performance_parameters: {}
+      cost_parameters:
+        capex: 2000000.0  # $2M
+        opex: 100000.0  # $100k/year
+        cost_year: 2024