Dynamic Simulation

Time-varying process response simulation

Overview

Dynamic simulation models how your wastewater treatment plant responds to changes over time. It captures transient behavior, diurnal patterns, and control system response.

When to Use

Ideal For

• Diurnal variation analysis
• Storm event response
• Controller tuning
• Startup/shutdown procedures
• Process optimization
• Operator training scenarios

Considerations

• Requires more computational time
• Needs initial conditions (from steady-state)
• More parameters to configure

Running Dynamic Simulation

Prerequisites

1. Complete process flow diagram
2. All units configured
3. Steady-state converged (recommended)

Quick Start

1. Run steady-state first (provides initial conditions)
2. Click Run Simulation (F5)
3. Select Dynamic
4. Configure time settings:
   • Duration: 1-30 days
   • Time step: 1-15 minutes
   • Output interval: 5-60 minutes
5. Click Start

Time Settings

Setting	Range	Default	Description
Duration	1 hr - 30 days	1 day	Total simulation time
Time Step	0.1 - 15 min	1 min	Integration step
Output Interval	1 - 60 min	5 min	Results storage
Start Time	Any	0	Simulation start

Influent Patterns

Diurnal Profiles

Define hourly variation in influent:

Hour    Flow Factor    COD Factor
00:00   0.6           0.8
06:00   0.9           1.0
08:00   1.3           1.2
12:00   1.2           1.1
18:00   1.4           1.3
21:00   1.0           1.0

Pattern Types

Type	Application
Diurnal	Daily variation
Weekly	Weekend effects
Seasonal	Temperature, tourism
Custom	Site-specific data

Loading Patterns

Import from:

• CSV file
• SCADA data
• Historical records
• Theoretical profiles

Events and Disturbances

Event Types

Event	Description
Step Change	Instantaneous parameter change
Ramp	Gradual change over time
Pulse	Temporary change, then return
Profile	Custom time-series

Defining Events

Example: Flow spike event

{
  "type": "pulse",
  "parameter": "influent_flow",
  "startTime": "Day 2, 14:00",
  "duration": "4 hours",
  "magnitude": 1.5,
  "unit": "factor"
}

Common Scenarios

Scenario	Events
Storm	Flow + TSS spike
Industrial discharge	COD/toxicity pulse
Equipment failure	Blower off, RAS stop
Load variation	Diurnal + weekend

Solver Settings

Integration Method

Method	Accuracy	Speed	Use Case
Euler	Low	Fast	Quick estimates
RK4	High	Medium	Standard use
Adaptive	High	Variable	Stiff systems

Time Step Selection

• Shorter (1 min): Better accuracy, slower
• Longer (15 min): Faster, may miss dynamics
• Adaptive: Automatic adjustment

Stability

For stable simulation:

• Time step < 0.1 × smallest time constant
• Typically 1-5 minutes for biological systems
• 0.1-1 minute for fast controllers

Initial Conditions

From Steady-State

Recommended approach:

1. Run steady-state simulation
2. Use converged state as initial condition
3. Start dynamic simulation

From Previous Run

Continue from previous simulation:

1. Save state at end of run
2. Load as initial condition
3. Continue simulation

Custom Initial Conditions

Specify manually:

• MLSS concentration
• Sludge blanket depth
• Controller states

Results and Output

Time Series Data

Each output interval records:

• All stream compositions
• Unit states (volumes, masses)
• Controller outputs
• Performance metrics

Visualization

Chart Type	Use
Line plot	Parameter vs. time
Multi-axis	Compare parameters
Phase plot	Parameter correlations
Statistics	Mean, max, min, std

Export Options

• CSV for analysis
• Excel with multiple sheets
• JSON for programmatic use
• Charts as images

Controller Integration

Available Controllers

• PID (DO, SRT, level)
• Cascade (NH4→DO)
• Ratio (chemical dosing)
• Time-based (intermittent aeration)
• Advanced (MPC, feedforward)

Tuning with Dynamic Simulation

1. Set controller to manual
2. Apply step change
3. Observe response
4. Calculate tuning parameters
5. Switch to auto, verify

See Controllers Overview

Performance Optimization

Speed Tips

1. Increase time step: Within stability limits
2. Reduce output: Store only needed parameters
3. Simplify model: Basic mode where possible
4. Shorter runs: Build up duration

Memory Management

For long simulations:

• Enable output compression
• Store summary statistics only
• Export and clear periodically

Example: Storm Event

Setup

Base Case:

• 20,000 m3/d average flow
• Standard diurnal pattern
• MLE process

Storm Event:

• Start: Day 2, 14:00
• Duration: 6 hours
• Peak flow: 3× average
• TSS: 2× normal

Results

Parameter	Before	Peak	After 24h
Effluent TSS	10 mg/L	45 mg/L	12 mg/L
Effluent NH4	1.5 mg/L	8 mg/L	2 mg/L
Sludge Blanket	0.5 m	1.2 m	0.6 m
RAS TSS	8,000 mg/L	12,000 mg/L	8,500 mg/L

Insights

• Plant recovers within 24 hours
• Sludge blanket critical parameter
• Consider equalization basin

Troubleshooting

Common Issues

Issue	Cause	Solution
Simulation too slow	Small time step	Increase step size
Oscillations	Controller tuning	Detune controllers
Mass balance drift	Numerical errors	Reduce time step
Memory overflow	Long simulation	Export periodically
Negative values	Stiff kinetics	Use adaptive solver

Debugging

1. Run shorter duration first
2. Monitor key parameters
3. Check for unrealistic values
4. Compare with steady-state

Best Practices

1. Always start from steady-state
2. Verify diurnal patterns match real data
3. Test controllers before long runs
4. Save checkpoints during long simulations
5. Document scenarios for reproducibility

Related Topics

• Steady-State Simulation
• Controllers Overview
• Running Simulations
• Analyzing Results

References

1. Henze, M. "Activated Sludge Models"
2. Olsson, G. "ICA of Wastewater Treatment"
3. Rieger, L. "Guidelines for Dynamic Modelling"