Continuous Transport Module

The Continuous Transport module provides comprehensive modeling capabilities for steady-state and dynamic transport operations in process control applications. This module includes specialized models for liquid and solid transport systems.

Overview

The continuous transport module implements physics-based models for two distinct categories:

Continuous Liquid Transport (Transport Continuous Liquid Module) - Single-phase and multiphase liquid transport
Continuous Solid Transport (Continuous Solid Transport) - Bulk solid handling and conveying systems

All models provide both steady-state and dynamic analysis capabilities for comprehensive system characterization and control design.

Key Features

Continuous Operation - Models for steady-state transport processes
Dynamic Analysis - Transient behavior for control system design
Multi-Phase Support - Single-phase liquids, slurries, and solid handling
Physics-Based - Rigorous fluid mechanics and material handling models
Control Ready - Integration with process control systems

Applications

Continuous transport operations are essential for:

Process Industries - Chemical, petrochemical, and pharmaceutical plants
Mining Operations - Ore transport and processing facilities
Water Treatment - Municipal and industrial water systems
Power Generation - Coal, ash, and chemical handling systems
Manufacturing - Raw material and product transport

Quick Start

Liquid Transport Systems

from transport.continuous.liquid import PipeFlow, SlurryPipeline

# Clean liquid pipeline
pipe = PipeFlow(pipe_length=1000, pipe_diameter=0.2)
result = pipe.steady_state([300000, 293.15, 0.05])
pressure_out, temperature_out = result

# Slurry pipeline
slurry = SlurryPipeline(pipe_length=5000, pipe_diameter=0.3)
result = slurry.steady_state([400000, 0.15, 2.5])
pressure_out, concentration_out, critical_velocity = result

Solid Transport Systems

from transport.continuous.solid import PneumaticConveying, ConveyorBelt

# Pneumatic conveying
pneumatic = PneumaticConveying(pipe_length=200, pipe_diameter=0.15)
result = pneumatic.steady_state([150000, 15.0, 0.5])
pressure_out, min_velocity, power_required = result

# Belt conveyor
belt = ConveyorBelt(belt_length=100, belt_width=1.2)
result = belt.steady_state([10.0, 2.0, 0.8])
tension, power, efficiency = result

Process Integration

# Integrated continuous transport system
class ContinuousProcessLine:
    def __init__(self):
        # Liquid feed line
        self.feed_line = PipeFlow(pipe_length=500, pipe_diameter=0.2)

        # Solid material conveyor
        self.solid_conveyor = ConveyorBelt(belt_length=200, belt_width=1.0)

        # Product slurry line
        self.product_line = SlurryPipeline(pipe_length=1000, pipe_diameter=0.25)

    def process_analysis(self, liquid_flow, solid_rate, slurry_concentration):
        # Analyze entire process line
        liquid_result = self.feed_line.steady_state([200000, 293.15, liquid_flow])
        solid_result = self.solid_conveyor.steady_state([solid_rate, 1.5, 0.9])
        slurry_result = self.product_line.steady_state([250000, slurry_concentration, 3.0])

        return {
            'liquid_pressure_drop': 200000 - liquid_result[0],
            'solid_conveyor_power': solid_result[1],
            'slurry_transport_feasibility': slurry_result[2] > 2.0  # Above critical velocity
        }

Advanced Applications

Dynamic Process Control

from utilities.control_utils import tune_pid
from simulation.process_simulation import ProcessSimulation

# Design flow control system
pipe = PipeFlow(pipe_length=2000, pipe_diameter=0.25)

# Process identification
process_params = {'K': 1e-6, 'tau': 30.0, 'theta': 5.0}
pid_params = tune_pid(process_params, method='imc')

# Closed-loop simulation
sim = ProcessSimulation(pipe, controller_params=pid_params)
results = sim.run(time_span=3600, setpoint_changes=[(1800, 0.06)])

Multi-Objective Optimization

from optimization.parameter_estimation import optimize_design

def transport_system_optimization(design_params):
    pipe_diameter, conveyor_speed, slurry_concentration = design_params

    # Create transport models
    pipe = PipeFlow(pipe_length=1000, pipe_diameter=pipe_diameter)
    conveyor = ConveyorBelt(belt_length=100, belt_width=1.2)
    slurry = SlurryPipeline(pipe_length=800, pipe_diameter=pipe_diameter)

    # Analyze performance
    pipe_result = pipe.steady_state([300000, 293.15, 0.05])
    conveyor_result = conveyor.steady_state([10.0, conveyor_speed, 0.8])
    slurry_result = slurry.steady_state([250000, slurry_concentration, 2.5])

    # Multi-objective: minimize energy consumption and maximize throughput
    total_power = conveyor_result[1] + slurry_result[1] if len(slurry_result) > 1 else conveyor_result[1]
    throughput = conveyor_speed * slurry_concentration

    return total_power / throughput  # Power per unit throughput

optimal_design = optimize_design(
    transport_system_optimization,
    bounds=[(0.15, 0.4), (1.0, 3.0), (0.1, 0.3)],
    constraints={'min_throughput': 5.0}
)

System Monitoring and Diagnostics

# Continuous transport system monitoring
def monitor_transport_line(models, operating_data):
    """Monitor multiple transport units for performance degradation"""

    performance_metrics = {}

    for unit_name, model in models.items():
        expected_result = model.steady_state(operating_data[unit_name]['inputs'])
        actual_result = operating_data[unit_name]['outputs']

        # Calculate performance indicators
        if unit_name.startswith('pipe'):
            # Pressure drop analysis
            expected_dp = operating_data[unit_name]['inputs'][0] - expected_result[0]
            actual_dp = operating_data[unit_name]['inputs'][0] - actual_result[0]
            performance_ratio = expected_dp / actual_dp

        elif unit_name.startswith('conveyor'):
            # Power consumption analysis
            expected_power = expected_result[1]
            actual_power = actual_result[1]
            performance_ratio = expected_power / actual_power

        performance_metrics[unit_name] = {
            'performance_ratio': performance_ratio,
            'status': 'normal' if 0.8 <= performance_ratio <= 1.2 else 'abnormal'
        }

        # Generate alerts
        if performance_ratio < 0.8:
            print(f"ALERT: {unit_name} - Performance degradation detected")
            print(f"Performance ratio: {performance_ratio:.2f}")

            if unit_name.startswith('pipe'):
                print("Possible causes: fouling, corrosion, instrument drift")
            elif unit_name.startswith('conveyor'):
                print("Possible causes: belt wear, bearing issues, material buildup")

    return performance_metrics