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Multi-Scenario Analysis Tutorial ​

Overview ​

This tutorial demonstrates how to use ErgoPlanner's powerful multi-scenario analysis features to perform sensitivity testing, compare design alternatives, evaluate upgrade options, and optimize operational strategies under uncertainty.

What You'll Learn:

  • Creating and managing multiple scenarios
  • Performing sensitivity analysis on key parameters
  • Comparing design alternatives side-by-side
  • Monte Carlo uncertainty analysis
  • Optimization under multiple objectives
  • Scenario reporting and documentation

Prerequisites:

  • Completed Basic Simulation Tutorial
  • Understanding of process fundamentals
  • Familiarity with statistical concepts
  • Basic knowledge of optimization

Time Required: 60-90 minutes

Part 1: Understanding Scenario Analysis ​

1.1 What is Multi-Scenario Analysis? ​

Definition: Multi-scenario analysis is the systematic evaluation of different operational configurations, design alternatives, or parameter assumptions to understand system behavior, quantify uncertainty, and identify optimal solutions.

Use Cases:

Operational Planning:
- Peak flow vs. average flow conditions
- Summer vs. winter operation
- Wet weather vs. dry weather
- High vs. low load periods

Design Evaluation:
- Compare technology alternatives (AS vs. MBR vs. MBBR)
- Evaluate capacity expansion options
- Optimize basin volumes and configurations
- Select equipment sizing

Regulatory Compliance:
- Test against different effluent limits
- Evaluate permit scenarios
- Assess treatment reliability
- Plan for future regulations

Uncertainty Analysis:
- Input parameter variability
- Model uncertainty
- Climate change impacts
- Future flow/load projections

1.2 Types of Analysis ​

Sensitivity Analysis:

  • Vary one parameter at a time
  • Identify critical variables
  • Understand parameter influence
  • Example: How does SRT affect effluent quality?

Scenario Comparison:

  • Evaluate distinct alternatives
  • Side-by-side comparison
  • Multi-criteria decision analysis
  • Example: BNR Option A vs. Option B vs. Option C

Optimization:

  • Find best parameter values
  • Single or multi-objective
  • Subject to constraints
  • Example: Minimize cost while meeting effluent limits

Monte Carlo Analysis:

  • Multiple random parameter combinations
  • Probabilistic outcomes
  • Risk assessment
  • Example: 95% confidence interval for effluent TN

Part 2: Creating and Managing Scenarios ​

2.1 Baseline Scenario Setup ​

Step 1: Define Baseline

  1. Navigate to Simulation → Scenarios
  2. Click Create New Scenario
  3. Enter scenario details:
Scenario Name: Baseline - Current Operations
Description: Existing facility configuration and typical operating conditions
Category: Baseline
Tags: current, summer, avg-flow

Step 2: Configure Baseline Parameters

Influent Conditions:
- Flow: 10 MGD average
- Temperature: 20°C (summer)
- BOD: 200 mg/L
- TSS: 220 mg/L
- TKN: 40 mg/L
- TP: 6 mg/L

Process Configuration:
- Anaerobic zone: 1.5 MG
- Anoxic zone: 2.0 MG
- Aerobic zone: 4.5 MG
- Total SRT: 12 days
- MLSS: 3,500 mg/L
- DO setpoint: 2.0 mg/L
- RAS ratio: 0.75
- Internal recycle: 3.0

Effluent Targets:
- BOD: < 10 mg/L
- TSS: < 10 mg/L
- TN: < 8 mg/L
- TP: < 1 mg/L

Step 3: Run Baseline Simulation

  1. Click Run Simulation
  2. Wait for completion (30-60 seconds)
  3. Review results in Results tab
  4. Save scenario

2.2 Creating Variant Scenarios ​

Method 1: Duplicate and Modify

  1. Select baseline scenario
  2. Click Duplicate Scenario
  3. Rename: "Winter - Low Temperature"
  4. Modify parameters:
Changed Parameters:
- Temperature: 12°C (from 20°C)
- Flow: 11 MGD (from 10 MGD, wet season)
- SRT: 15 days (from 12 days, maintain nitrification)

Method 2: Batch Create from Template

  1. Click Batch Create Scenarios
  2. Select parameters to vary
  3. Define value ranges
  4. ErgoPlanner creates all combinations
Example: Seasonal Analysis
Parameters:
- Season: [Summer, Fall, Winter, Spring]
- Temperature: [22°C, 16°C, 10°C, 18°C]
- Flow: [9.5, 10.5, 11.0, 10.0] MGD

Result: 4 scenarios automatically created

2.3 Scenario Organization ​

Grouping Scenarios:

Folder Structure:
└── Baseline Conditions
    ├── Summer Average Flow
    ├── Summer Peak Flow
    ├── Winter Average Flow
    └── Winter Peak Flow
└── Design Alternatives
    ├── Option A - 5-Stage Bardenpho
    ├── Option B - MLE Process
    └── Option C - Oxidation Ditch
└── Upgrade Scenarios
    ├── Phase 1 - Fine Bubble Diffusers
    ├── Phase 2 - Additional Anoxic Volume
    └── Phase 3 - Filtration + UV

Color Coding:

  • 🟢 Green: Meets all requirements
  • 🟡 Yellow: Marginal performance
  • 🔴 Red: Fails requirements
  • 🔵 Blue: Optimized/recommended

Tagging:

Tag Categories:
- Operating condition: avg-flow, peak-flow, min-flow
- Season: summer, winter, spring, fall
- Purpose: baseline, design, optimization, what-if
- Status: active, archived, for-review

Part 3: Sensitivity Analysis ​

3.1 One-at-a-Time (OAT) Sensitivity ​

Objective: Determine how effluent TN responds to SRT variation

Setup:

  1. Simulation → Sensitivity Analysis → New Analysis
  2. Configure parameters:
Analysis Name: SRT Sensitivity - Effluent TN
Base Scenario: Baseline - Current Operations

Variable Parameter:
- Parameter: SRT (Solids Retention Time)
- Range: 5 to 20 days
- Increment: 1 day
- Total scenarios: 16

Output Metrics:
- Effluent TN (mg/L)
- Effluent TP (mg/L)
- MLSS (mg/L)
- Aeration energy (kWh/day)
- Methanol dose (kg/day)

Run Analysis:

  1. Click Run Sensitivity Analysis
  2. ErgoPlanner creates 16 scenarios with SRT = 5, 6, 7... 20 days
  3. Runs all simulations automatically
  4. Generates sensitivity plots

Interpreting Results:

Effluent TN vs. SRT Plot:
- SRT < 8 days: TN > 15 mg/L (nitrification failure)
- SRT = 8-10 days: TN = 8-12 mg/L (marginal)
- SRT = 10-15 days: TN = 5-8 mg/L (target range)
- SRT > 15 days: TN = 4-6 mg/L (diminishing returns)

Sensitivity Coefficient:
dTN/dSRT = -0.42 mg/L per day (at SRT = 12 days)

Interpretation:
Increasing SRT by 1 day reduces effluent TN by 0.42 mg/L.
Moderate sensitivity. SRT is important but not extreme.

Export Results:

  1. Results → Export Sensitivity Data
  2. Select CSV or Excel format
  3. Data includes all scenarios and metrics

3.2 Multi-Parameter Sensitivity ​

Objective: Analyze combined effects of SRT and DO on nitrification

Setup:

Analysis Name: SRT + DO Sensitivity Grid
Base Scenario: Baseline - Current Operations

Variable Parameters:
1. SRT: 8 to 16 days, increment 2 (5 levels)
2. DO: 1.0 to 3.0 mg/L, increment 0.5 (5 levels)

Total Scenarios: 5 × 5 = 25
Output Metric: Effluent NH4-N (mg/L)

Run and Visualize:

  1. Click Run Grid Analysis
  2. View 3D Surface Plot or Contour Map
  3. Identify optimal region

Interpreting Contour Plot:

Effluent NH4-N Contours:

DO (mg/L)
3.0 |  0.1   0.1   0.1   0.1   0.1
2.5 |  0.2   0.1   0.1   0.1   0.1
2.0 |  0.5   0.2   0.1   0.1   0.1
1.5 |  2.0   0.8   0.3   0.2   0.1
1.0 |  8.0   5.0   2.5   1.2   0.5
    +---------------------------
      8    10    12    14    16  SRT (days)

Findings:
- Target: NH4-N < 0.5 mg/L (shaded region)
- Trade-off visible: high DO or high SRT both work
- Optimal: SRT = 12 days, DO = 2.0 mg/L (balance)
- Critical threshold: DO < 1.5 mg/L causes problems

3.3 Tornado Diagrams ​

Objective: Rank parameters by influence on effluent TN

Setup:

Analysis Name: Effluent TN Tornado Analysis
Base Scenario: Baseline - Current Operations

Parameters to Test:
1. SRT: ±20% (9.6 to 14.4 days)
2. DO: ±20% (1.6 to 2.4 mg/L)
3. Internal Recycle: ±20% (2.4 to 3.6)
4. RAS Ratio: ±20% (0.6 to 0.9)
5. Temperature: ±10% (18 to 22°C)
6. Influent TKN: ±10% (36 to 44 mg/L)
7. Flow: ±10% (9 to 11 MGD)

Output Metric: Effluent TN (mg/L)

Run Analysis:

ErgoPlanner varies each parameter individually by ±% and measures impact.

Tornado Diagram Results:

Parameter               Low    Base   High   Range
─────────────────────────────────────────────────
Influent TKN      |═══════ 5.2   6.5   7.8  | 2.6
SRT               |═════ 5.5     6.5   7.5  | 2.0
Internal Recycle  |════ 5.8      6.5   7.2  | 1.4
Temperature       |═══ 6.0       6.5   7.0  | 1.0
DO                |══ 6.2        6.5   6.8  | 0.6
RAS Ratio         |═ 6.3         6.5   6.7  | 0.4
Flow              |█ 6.4         6.5   6.6  | 0.2

Interpretation:
1. Influent TKN: Most influential (range = 2.6 mg/L)
2. SRT: Second most influential (range = 2.0 mg/L)
3. Flow: Least influential (range = 0.2 mg/L)

Action Items:
- Tighten control on SRT (high sensitivity)
- Monitor influent TKN variations
- Flow variations less critical for TN performance

Part 4: Design Alternative Comparison ​

4.1 Technology Selection Study ​

Objective: Compare three BNR configurations for 15 MGD expansion

Alternatives:

Option A: 5-Stage Bardenpho
- Anaerobic: 1.5 MG
- 1st Anoxic: 2.5 MG
- Aerobic: 5.0 MG
- 2nd Anoxic: 1.5 MG
- Re-aeration: 1.0 MG
- Total volume: 11.5 MG
- SRT: 14 days

Option B: Modified Ludzack-Ettinger (MLE)
- Anoxic: 3.5 MG
- Aerobic: 7.0 MG
- Total volume: 10.5 MG
- SRT: 12 days
- Internal recycle: 4.0

Option C: Oxidation Ditch
- Total volume: 12.0 MG
- SRT: 15 days
- Integrated anoxic/aerobic zones
- Horizontal shaft aerators

Create Scenarios:

  1. Scenarios → Create New → "Option A - Bardenpho"
  2. Configure basin volumes and parameters
  3. Repeat for Options B and C
  4. Ensure same influent conditions for fair comparison

Comparison Metrics:

Performance Metrics:
- Effluent quality (BOD, TSS, TN, TP)
- Treatment reliability (% time meeting limits)
- Nitrification capacity
- Denitrification efficiency

Economic Metrics:
- Capital cost ($ millions)
- Annual O&M cost ($/year)
- Energy consumption (kWh/year, $/year)
- Chemical cost (methanol, $/year)
- NPV over 20 years

Sustainability Metrics:
- Carbon footprint (tonnes CO2e/year)
- Land footprint (acres)
- Biogas production (if applicable)
- Resource recovery potential

Operational Metrics:
- Process complexity
- Operator attention required
- Maintenance intensity
- Flexibility/turndown capability

4.2 Running Comparison ​

Step 1: Batch Run

  1. Scenarios → Select Options A, B, C
  2. Click Run Selected Scenarios
  3. ErgoPlanner runs all three simultaneously

Step 2: View Comparison Table

Metric                    Option A   Option B   Option C   Target
─────────────────────────────────────────────────────────────────
Effluent BOD (mg/L)         3.2        3.8        2.9      < 10
Effluent TSS (mg/L)         4.1        4.5        3.8      < 10
Effluent TN (mg/L)          5.2        6.8        5.8      < 8
Effluent TP (mg/L)          0.6        0.8        0.7      < 1

Energy (MWh/year)         8,500      7,200      9,100        -
Methanol (tonnes/year)    1,200      1,850      1,400        -
Capital Cost ($M)          18.5       15.2       17.8        -
O&M Cost ($M/year)         1.85       1.62       1.78        -

Carbon (tonnes CO2e/yr)   5,200      5,800      5,500        -
Land (acres)               2.8        2.5        3.0        -

Performance Score            95         88         92      > 90

Step 3: Visual Comparison

ErgoPlanner generates:

  • Radar chart (spider diagram) showing all metrics
  • Bar charts for side-by-side comparison
  • Cost-effectiveness plot (performance vs. cost)

Radar Chart Interpretation:

        Effluent Quality
              │
     Cost ────┼──── Reliability
              │
         Sustainability
              │
           Flexibility

Legend:
━━━ Option A (Bardenpho)
─ ─ Option B (MLE)
··· Option C (Ox Ditch)

Analysis:
- Option A: Best effluent quality, higher cost
- Option B: Lowest cost, marginal TN performance
- Option C: Balanced, good reliability

4.3 Multi-Criteria Decision Analysis ​

Weighting Criteria:

Stakeholder Priorities (assign weights):

Criteria                 Weight   Option A   Option B   Option C
──────────────────────────────────────────────────────────────
Effluent Quality          30%       95         82         88
Capital Cost              25%       75         92         80
O&M Cost                  20%       82         88         85
Reliability               15%       92         78         88
Sustainability            10%       88         75         82
──────────────────────────────────────────────────────────────
Weighted Score           100%      86.1       84.4       85.0

Recommendation: Option A (Bardenpho)
- Highest weighted score
- Best meets effluent quality (top priority)
- Acceptable cost premium for performance gain

Sensitivity to Weights:

Test different stakeholder perspectives:

Scenario 1: Cost-Focused Utility
- Capital cost: 40%, O&M: 30%, Quality: 20%, Other: 10%
- Winner: Option B (MLE) - Score 87.2

Scenario 2: Quality-Focused Regulator
- Quality: 50%, Reliability: 25%, Other: 25%
- Winner: Option A (Bardenpho) - Score 91.8

Scenario 3: Sustainability-Focused Board
- Sustainability: 35%, Quality: 30%, Cost: 25%, Other: 10%
- Winner: Option A (Bardenpho) - Score 88.5

Robust Winner: Option A wins in 2/3 scenarios

Part 5: Optimization Studies ​

5.1 Single-Objective Optimization ​

Objective: Minimize energy consumption while meeting TN < 8 mg/L

Setup:

  1. Optimization → New Study
  2. Configure:
Optimization Name: Minimize Energy for TN Compliance
Base Scenario: Baseline - Current Operations

Decision Variables:
1. DO Setpoint: 1.0 to 3.0 mg/L
2. SRT: 8 to 18 days
3. Internal Recycle Ratio: 2.0 to 5.0
4. RAS Ratio: 0.5 to 1.2

Objective Function:
Minimize: Total Energy Consumption (kWh/day)

Constraints:
- Effluent TN ≤ 8.0 mg/L
- Effluent NH4-N ≤ 1.0 mg/L
- MLSS ≥ 2,500 mg/L
- MLSS ≤ 4,500 mg/L

Optimization Algorithm:
- Method: Genetic Algorithm
- Population: 50
- Generations: 100
- Convergence: 1e-4

Run Optimization:

  1. Click Start Optimization
  2. Monitor progress (may take 10-30 minutes)
  3. View convergence plot

Results:

Optimal Solution Found:

Decision Variables:
- DO Setpoint: 1.82 mg/L (↓ from 2.00)
- SRT: 14.3 days (↑ from 12.0)
- Internal Recycle: 3.85 (↑ from 3.00)
- RAS Ratio: 0.68 (↓ from 0.75)

Performance:
- Energy: 32,500 kWh/day (vs. 38,200 baseline)
- Energy reduction: 15% (5,700 kWh/day saved)
- Effluent TN: 7.8 mg/L (meets constraint)
- Effluent NH4-N: 0.4 mg/L (meets constraint)
- MLSS: 3,850 mg/L (within range)

Annual Savings:
- Energy: 2,080,500 kWh/year
- Cost: $208,050/year (at $0.10/kWh)
- Carbon: 868 tonnes CO2e/year reduced

Sensitivity Check:

Verify optimization robustness:

  • Run Monte Carlo with optimal parameters
  • Test against seasonal variations
  • Evaluate performance degradation scenarios

5.2 Multi-Objective Optimization ​

Objective: Minimize both energy AND methanol costs

Setup:

Optimization Name: Pareto Optimization - Energy vs. Chemicals
Base Scenario: Baseline - Current Operations

Decision Variables:
1. DO Setpoint: 1.5 to 3.0 mg/L
2. SRT: 10 to 18 days
3. Internal Recycle: 2.0 to 5.0
4. Anaerobic Volume Fraction: 0.10 to 0.20
5. Anoxic Volume Fraction: 0.15 to 0.30

Objective Functions:
1. Minimize: Energy (kWh/day)
2. Minimize: Methanol Cost ($/day)

Constraints:
- Effluent TN ≤ 8.0 mg/L
- Effluent TP ≤ 1.0 mg/L
- All other limits met

Algorithm: NSGA-II (Non-dominated Sorting GA)

Pareto Front Results:

Pareto Optimal Solutions (10 shown):

Solution   Energy      Methanol    Total     TN     TP
           (kWh/day)   ($/day)     ($/day)  (mg/L) (mg/L)
──────────────────────────────────────────────────────────
A          28,500      $850        $3,700    7.9    0.8
B          30,200      $680        $3,700    7.5    0.7
C          31,800      $580        $3,760    7.2    0.6
D          33,500      $490        $3,840    6.8    0.6
E          35,000      $420        $3,920    6.5    0.5
F          36,800      $360        $4,040    6.2    0.5
G          38,200      $310        $4,130    5.9    0.5
H          39,500      $270        $4,220    5.7    0.5
I          41,000      $240        $4,340    5.5    0.5
J          42,800      $210        $4,490    5.3    0.5

Energy cost assumed $0.10/kWh

Pareto Front Visualization:

Methanol Cost ($/day)
│
900│ A
   │
700│   B
   │     C
500│       D
   │         E
300│           F G H I J
   │
100└─────────────────────────────
   28  32  36  40  44  Energy (MWh/day)

Analysis:
- Solutions A-E: Balanced trade-off region
- Solutions F-J: Diminishing returns (high energy)
- Knee point: Solution D (best compromise)

Select Preferred Solution:

Recommended: Solution D

Decision Variables:
- DO: 2.1 mg/L
- SRT: 13.5 days
- Internal Recycle: 4.2
- Anaerobic fraction: 0.15
- Anoxic fraction: 0.22

Rationale:
- Near knee of Pareto curve
- 12% energy reduction vs. baseline
- 35% methanol reduction vs. baseline
- Excellent effluent quality (TN = 6.8 mg/L)
- Robust performance margin

5.3 Robust Optimization ​

Objective: Find solution that works well under uncertainty

Setup:

Optimization Name: Robust Design - Seasonal Variations
Base Scenario: Baseline - Current Operations

Decision Variables:
(Same as single-objective example)

Objective Function:
Minimize: Worst-Case Energy across scenarios

Uncertainty Scenarios (automatically evaluated):
1. Summer + Average Flow
2. Summer + Peak Flow
3. Winter + Average Flow
4. Winter + Peak Flow
5. Spring + High Load
6. Fall + Low Load

Constraints:
- Must meet TN ≤ 8 mg/L in ALL scenarios
- Must meet other limits in ALL scenarios

Algorithm: Minimax optimization

Results:

Robust Solution:

Decision Variables:
- DO: 2.2 mg/L (slightly higher for safety)
- SRT: 15 days (longer for winter)
- Internal Recycle: 4.0
- RAS: 0.75

Performance Across Scenarios:
Scenario                Energy    TN     Pass?
───────────────────────────────────────────────
Summer + Avg           33,800   6.2     ✓
Summer + Peak          39,200   7.1     ✓
Winter + Avg           36,500   7.5     ✓
Winter + Peak          42,800   7.9     ✓
Spring + High Load     38,100   7.3     ✓
Fall + Low Load        31,200   5.8     ✓

Worst-case: 42,800 kWh/day (Winter Peak)
Average: 36,933 kWh/day

Comparison to Non-Robust Optimum:
- Non-robust fails in 2/6 scenarios (winter)
- Robust solution: 0/6 failures
- Energy penalty: +8% average, but 100% reliable

Part 6: Monte Carlo Uncertainty Analysis ​

6.1 Setup Probability Distributions ​

Define Input Uncertainties:

  1. Simulation → Monte Carlo → New Analysis
  2. Configure distributions:
Analysis Name: Effluent TN Uncertainty - Annual Performance
Base Scenario: Baseline - Current Operations
Number of Runs: 1,000

Uncertain Parameters:

1. Influent Flow
   - Distribution: Log-Normal
   - Mean: 10 MGD
   - Std Dev: 1.5 MGD
   - Min: 6 MGD, Max: 18 MGD

2. Influent TKN
   - Distribution: Normal
   - Mean: 40 mg/L
   - Std Dev: 5 mg/L
   - Min: 25 mg/L, Max: 55 mg/L

3. Temperature
   - Distribution: Triangular
   - Min: 10°C, Mode: 18°C, Max: 24°C

4. DO Control Accuracy
   - Distribution: Uniform
   - Min: -0.3 mg/L, Max: +0.3 mg/L
   - (Deviation from setpoint)

5. SRT Variability
   - Distribution: Normal
   - Mean: 12 days
   - Std Dev: 1 day
   - Min: 8 days, Max: 18 days

Output Metrics:
- Effluent TN (mg/L)
- Effluent TP (mg/L)
- Energy consumption (kWh/day)
- Compliance rate (%)

6.2 Run Monte Carlo Simulation ​

Execution:

  1. Click Run Monte Carlo
  2. ErgoPlanner samples 1,000 parameter combinations
  3. Runs 1,000 simulations (may take 15-45 minutes)
  4. Aggregates results

Progress Monitoring:

Monte Carlo Progress:
[██████████████████████░░░░] 850/1000 (85%)
Estimated time remaining: 4 minutes
Convergence check: σ < 0.01 ✓

6.3 Analyze Results ​

Distribution of Effluent TN:

Statistical Summary:

Metric            Value
─────────────────────────
Mean              6.8 mg/L
Median            6.5 mg/L
Std Deviation     1.2 mg/L
Min               3.8 mg/L
Max               11.2 mg/L

Percentiles:
5th               5.1 mg/L
25th              5.9 mg/L
75th              7.6 mg/L
95th              9.2 mg/L

Compliance:
P(TN ≤ 8 mg/L)    82.3%
P(TN ≤ 10 mg/L)   96.8%

Histogram:

Frequency
│
120│         ┌─┐
   │      ┌─┐│ │┌─┐
 80│   ┌─┐│ ││ ││ │┌─┐
   │┌─┐│ ││ ││ ││ ││ │┌─┐
 40││ ││ ││ ││ ││ ││ ││ │
   ││ ││ ││ ││ ││ ││ ││ │
  0└─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴──
   3  4  5  6  7  8  9 10 11  Effluent TN (mg/L)
               ▲
            Limit (8 mg/L)

Non-compliance scenarios: 177/1000 (17.7%)

Sensitivity Rankings (from Monte Carlo):

Contribution to Output Variance:

Parameter          R² (%)   Rank
──────────────────────────────────
Influent TKN        45%      1
Temperature         28%      2
SRT Variability     15%      3
Flow                 8%      4
DO Control           4%      5

Interpretation:
- Influent TKN accounts for 45% of TN variation
- Top 2 parameters (TKN, Temp) explain 73% of variance
- Focus monitoring/control efforts accordingly

6.4 Risk Assessment ​

Reliability Analysis:

Question: What SRT ensures 95% compliance with TN < 8 mg/L?

Method: Run Monte Carlo for different SRT values

Results:
SRT (days)   P(TN ≤ 8)   95% CI
───────────────────────────────────
10           68.2%       [65.5, 70.9%]
12           82.3%       [79.9, 84.7%]
14           91.5%       [89.6, 93.4%]
16           96.8%       [95.4, 98.2%]  ← Target
18           98.9%       [97.9, 99.9%]

Recommendation: SRT ≥ 16 days for 95% reliability

Financial Risk:

Question: What is 95% confidence interval for annual energy cost?

Monte Carlo Energy Results:
Mean: 35,200 kWh/day = 12,848,000 kWh/year
Std Dev: 3,100 kWh/day

Annual Cost (at $0.10/kWh):
Mean: $1,284,800/year
95% CI: [$1,098,000, $1,471,000]/year
Range: $373,000/year

Budget Recommendation:
- Budget for 95th percentile: $1,471,000/year
- Provides cushion for high-energy scenarios
- Avoid budget shortfalls

Part 7: Climate Change Scenario Planning ​

7.1 Future Climate Scenarios ​

Define Climate Projections (2050):

Scenario 1: RCP 2.6 (Low Emissions)
- Temperature: +1.5°C avg annual
- Precipitation: +5% annual, +15% winter
- Extreme events: +10% frequency

Scenario 2: RCP 4.5 (Moderate Emissions)
- Temperature: +2.5°C avg annual
- Precipitation: +8% annual, +25% winter
- Extreme events: +25% frequency

Scenario 3: RCP 8.5 (High Emissions)
- Temperature: +4.0°C avg annual
- Precipitation: +12% annual, +40% winter
- Extreme events: +50% frequency

Translate to WWTP Impacts:

Temperature Impacts:
- Wastewater temp increase: 70% of air temp increase
- RCP 2.6: +1.0°C → Benefits nitrification
- RCP 8.5: +2.8°C → May stress biological processes

Flow Impacts:
- Increased I&I from intense storms
- Average flow increase: 5-15%
- Peak wet weather flows: +20-50%

Load Impacts:
- Population growth: +20% by 2050
- Reduced per-capita loading: -10% (water conservation)
- Net load increase: +8%

Create Scenarios in ErgoPlanner:

  1. Scenario: "2050 - RCP 2.6"

    • Temp: 21°C (from 19°C)
    • Flow: 10.5 MGD (from 10 MGD)
    • TKN: 43 mg/L (from 40 mg/L)

  2. Scenario: "2050 - RCP 4.5"

    • Temp: 22.5°C
    • Flow: 11.8 MGD
    • TKN: 43 mg/L

  3. Scenario: "2050 - RCP 8.5"

    • Temp: 24.8°C
    • Flow: 13.0 MGD
    • TKN: 43 mg/L

7.2 Adaptive Capacity Planning ​

Test Current Design Under Future Conditions:

Run climate scenarios with current configuration:

Results:

Scenario        TN      TP    Hydraulic  Aeration   Compliance
               (mg/L)  (mg/L) Capacity   Capacity
────────────────────────────────────────────────────────────────
Baseline 2024   6.5    0.7      OK         OK          ✓
RCP 2.6 2050    5.8    0.6      OK         OK          ✓
RCP 4.5 2050    7.2    0.7    Marginal     OK          ✓
RCP 8.5 2050    8.9    0.8      FAIL     Marginal      ✗

Findings:
- Current design adequate for RCP 2.6, 4.5
- RCP 8.5 exceeds hydraulic capacity
- TN compliance fails under RCP 8.5
- Upgrade needed for high-emission scenario

Design Upgrade Alternatives:

Option 1: Modest Expansion (targets RCP 4.5)
- Add 1.5 MG aerobic volume
- Increase blower capacity 20%
- Cost: $5M
- Handles up to 12 MGD, 23°C

Option 2: Major Expansion (targets RCP 8.5)
- Add 3.0 MG total volume
- New blower building
- Enhanced controls
- Cost: $12M
- Handles up to 15 MGD, 26°C

Option 3: Adaptive Phased (flexible)
- Phase 1: Controls upgrade ($500K) - immediate
- Phase 2: Add 1.5 MG if needed ($5M) - by 2035
- Phase 3: Add 1.5 MG more if needed ($5M) - by 2045
- Total potential cost: $10.5M
- Defer investment until warranted

Decision Analysis:

Criteria: Minimize Regret

Strategy          RCP 2.6   RCP 4.5   RCP 8.5   Max Regret
────────────────────────────────────────────────────────────
Do Nothing         $0        $0        $50M*      $50M
Option 1           $5M       $5M       $55M*      $55M
Option 2          $12M      $12M       $12M       $12M
Option 3 Adaptive  $0.5M     $5.5M     $10.5M     $10.5M

*Regret from non-compliance penalties + emergency upgrades

Recommendation: Option 3 (Adaptive Phased)
- Lowest regret in all scenarios
- Preserves flexibility
- Defers major capital until needed
- Insurance policy against uncertainty

Part 8: Advanced Scenario Techniques ​

8.1 Batch Scenario Processing ​

Create Scenarios Programmatically:

ErgoPlanner supports CSV import for batch scenario creation:

scenarios.csv format:

Name,Flow,Temp,BOD,TKN,TP,SRT,DO,RAS
Summer_Low,8.5,22,180,35,5.5,12,2.0,0.75
Summer_Avg,10.0,20,200,40,6.0,12,2.0,0.75
Summer_High,11.5,21,220,45,6.5,12,2.0,0.75
Winter_Low,9.0,12,190,38,5.8,15,2.2,0.75
Winter_Avg,11.0,10,210,42,6.2,15,2.2,0.75
Winter_High,13.0,11,230,48,6.8,15,2.2,0.75

Import and Run:

  1. Scenarios → Import from CSV
  2. Select file
  3. Map columns to parameters
  4. Click Import (creates 6 scenarios)
  5. Run All to execute batch

8.2 Scenario Scripting ​

Python API for Automation:

python
from ergoplanner import Simulation, Scenario

# Load base scenario
base = Scenario.load("Baseline")

# Create parameter sweep
flows = [8, 9, 10, 11, 12, 13, 14, 15]
temps = [10, 12, 14, 16, 18, 20, 22]

results = []

for flow in flows:
    for temp in temps:
        # Clone and modify scenario
        scenario = base.clone()
        scenario.name = f"Flow_{flow}_Temp_{temp}"
        scenario.set_parameter("flow", flow)
        scenario.set_parameter("temperature", temp)

        # Run simulation
        sim = Simulation(scenario)
        result = sim.run()

        # Collect results
        results.append({
            'flow': flow,
            'temp': temp,
            'effluent_TN': result.effluent_TN,
            'energy': result.energy_total
        })

# Export results
import pandas as pd
df = pd.DataFrame(results)
df.to_csv("parametric_study_results.csv")

8.3 Scenario Templates ​

Save Reusable Templates:

Template: Seasonal Analysis
- Creates 4 scenarios: Spring, Summer, Fall, Winter
- Preset temp, flow, load profiles
- One-click deployment

Template: Capacity Analysis
- Creates scenarios for 80%, 100%, 120%, 150% design flow
- Tests capacity limits
- Identifies bottlenecks

Template: Upset Conditions
- Low temp nitrification upset
- High load event
- RAS pump failure
- Chemical feed interruption

Using Templates:

  1. Scenarios → New from Template
  2. Select template type
  3. Customize parameters if needed
  4. Click Generate Scenarios

Part 9: Reporting Multi-Scenario Studies ​

9.1 Comparison Reports ​

Auto-Generated Report Sections:

1. Executive Summary
   - Study objectives
   - Scenarios evaluated
   - Key findings
   - Recommendations

2. Methodology
   - Base assumptions
   - Parameter ranges
   - Constraints
   - Analysis methods

3. Results
   - Comparison tables
   - Performance charts
   - Sensitivity analysis
   - Statistical summaries

4. Economic Analysis
   - Capital costs
   - O&M costs
   - NPV comparison
   - Sensitivity to economic parameters

5. Risk Assessment
   - Compliance probability
   - Performance under uncertainty
   - Robustness evaluation

6. Recommendations
   - Preferred alternative
   - Implementation phasing
   - Monitoring requirements

9.2 Visual Dashboards ​

Interactive Dashboard Elements:

Performance Comparison Dashboard:

┌─────────────────────┬─────────────────────┐
│ Effluent Quality    │  Economics          │
│ (Radar Chart)       │  (Bar Chart)        │
├─────────────────────┼─────────────────────┤
│ Energy Consumption  │  Carbon Footprint   │
│ (Line Plot)         │  (Stacked Area)     │
├─────────────────────┴─────────────────────┤
│ Scenario Filters: [All] [Baseline] [Opt]  │
│ Metric Selector: [TN] [TP] [Energy]       │
└────────────────────────────────────────────┘

Export Options:

  • PDF report (static)
  • PowerPoint presentation
  • Interactive HTML dashboard
  • Raw data (CSV, Excel)

9.3 Stakeholder Communication ​

Tailored Views for Different Audiences:

Executive View:
- High-level summary
- Cost comparison
- Recommendation
- 1-2 pages

Technical View:
- Detailed methodology
- Full results tables
- Sensitivity analysis
- Process diagrams
- 10-20 pages

Regulatory View:
- Compliance demonstration
- Statistical reliability
- Monitoring plan
- QA/QC procedures
- 5-10 pages

Part 10: Best Practices and Tips ​

10.1 Scenario Design ​

✓ Start with a robust baseline

  • Well-calibrated model
  • Representative conditions
  • Validated against data

✓ Define clear objectives

  • What question are you answering?
  • What decision will this inform?
  • What metrics matter?

✓ Use realistic parameter ranges

  • Based on historical data
  • Account for operational constraints
  • Consider physical limits

✓ Organize systematically

  • Consistent naming convention
  • Logical folder structure
  • Comprehensive tagging

✓ Document assumptions

  • Record all modifications
  • Note data sources
  • Explain rationale

10.2 Analysis Approach ​

✓ Choose appropriate method

  • Sensitivity: parameter influence
  • Comparison: discrete alternatives
  • Optimization: best values
  • Monte Carlo: uncertainty/risk

✓ Validate results

  • Sanity checks on outputs
  • Cross-check with hand calculations
  • Compare to industry norms
  • Peer review

✓ Consider computational cost

  • Simple models for screening
  • Detailed models for final analysis
  • Use reduced-order models if needed

✓ Iterate and refine

  • Start broad, then narrow
  • Eliminate dominated alternatives
  • Focus on promising regions

10.3 Common Pitfalls ​

❌ Over-parameterization

  • Too many variables → noisy results
  • Focus on influential parameters
  • Use screening studies first

❌ Insufficient scenarios

  • Monte Carlo: need ≥500 runs for stability
  • Grid search: ensure adequate resolution
  • Don't miss important regions

❌ Ignoring constraints

  • Always include realistic limits
  • Consider operational feasibility
  • Check for practical implementability

❌ Confusing precision with accuracy

  • Model uncertainty > numerical precision
  • Report appropriate significant figures
  • Acknowledge limitations

❌ Analysis paralysis

  • Don't over-analyze
  • Good enough often beats perfect
  • Make decision and move forward

10.4 Efficiency Tips ​

Parallel Processing:

  • ErgoPlanner can run multiple scenarios simultaneously
  • Utilize multi-core CPU
  • Typical speedup: 4-8x on modern hardware

Scenario Caching:

  • ErgoPlanner caches intermediate results
  • Re-running with minor changes is fast
  • Clear cache if model structure changes

Simplification:

  • Use steady-state for screening
  • Dynamic simulation for final verification
  • Balance accuracy vs. speed

Incremental Refinement:

1. Rough screening (100 scenarios, simple model)
2. Down-select to 10 promising scenarios
3. Detailed analysis (complex model, long time horizon)
4. Final comparison of top 3 alternatives
5. Sensitivity on selected option

Part 11: Case Study - Expansion Planning ​

11.1 Problem Statement ​

City WWTP Expansion Project:
- Current capacity: 10 MGD
- Future flow (2045): 18 MGD
- Current treatment: Conventional AS
- Future requirements: TN < 8 mg/L, TP < 1 mg/L

Question: What upgrade strategy?

11.2 Scenarios Evaluated ​

Alternative 1: Expand Existing AS + Add BNR

Configuration:
- Expand existing to 12 MGD (conventional AS)
- Build new 6 MGD BNR train
- Total: 18 MGD capacity

Capital Cost: $35M
O&M: $2.8M/year
Energy: 0.42 kWh/m³

Alternative 2: Convert to Full BNR + Expand

Configuration:
- Retrofit existing 10 MGD to BNR
- Expand BNR to 18 MGD

Capital Cost: $42M
O&M: $3.1M/year
Energy: 0.38 kWh/m³

Alternative 3: New MBR Facility

Configuration:
- Existing becomes primary/equalization
- New 18 MGD MBR facility

Capital Cost: $65M
O&M: $3.8M/year
Energy: 0.55 kWh/m³

Alternative 4: Phased BNR Expansion

Configuration:
- Phase 1 (2030): Retrofit 10 MGD to BNR
- Phase 2 (2040): Add 8 MGD BNR if needed

Capital Cost: $28M + $18M (deferred)
O&M: $2.5M → $3.2M/year
Energy: 0.38 kWh/m³

11.3 Multi-Scenario Analysis ​

Performance Comparison:

Metric                Alt 1   Alt 2   Alt 3   Alt 4
──────────────────────────────────────────────────────
Effluent TN (mg/L)     6.5     5.8     4.2     5.8
Effluent TP (mg/L)     0.8     0.6     0.4     0.6
Reliability (%)         94      97      99      97
Footprint (acres)       8.5     7.2     5.5     7.2
Energy (kWh/m³)        0.42    0.38    0.55    0.38

Capital Cost ($M)       35      42      65      28*
NPV 30yr ($M)          105     115     155      98
O&M ($/m³)            $0.102  $0.113  $0.138  $0.108

*Phase 1 only, $46M total if both phases

Sensitivity to Growth:

Scenario: Flow only reaches 14 MGD by 2045 (22% lower)

Impact on Alternatives:
- Alt 1: Over-designed, $8M wasted capacity
- Alt 2: Over-designed, $12M wasted capacity
- Alt 3: Significantly over-designed, $22M wasted
- Alt 4: Build Phase 1 only, save $18M (adaptive)

Advantage: Alternative 4 (phased)

Sensitivity to Regulations:

Scenario: Future TN limit tightens to 3 mg/L

Impact on Alternatives:
- Alt 1: Requires further upgrade, $8M
- Alt 2: Requires optimization, $2M
- Alt 3: Meets without modification, $0
- Alt 4: Requires optimization, $2M

Advantage: Alternative 3 (MBR)

11.4 Decision ​

Multi-Criteria Scoring:

Criteria           Weight   Alt 1   Alt 2   Alt 3   Alt 4
────────────────────────────────────────────────────────────
Performance          20%      85      92      98      92
Capital Cost         25%      92      85      65      95
O&M Cost             20%      88      84      70      86
Flexibility          15%      75      78      82      95
Sustainability       10%      82      88      75      88
Risk Management      10%      78      85      90      92
────────────────────────────────────────────────────────────
TOTAL SCORE         100%    84.2    85.4    78.7    90.2

Recommendation: Alternative 4 (Phased BNR)

Rationale:

  • Highest overall score (90.2)
  • Excellent flexibility (highest score)
  • Defers risk ($18M can be avoided if growth slows)
  • Good performance (TN = 5.8 mg/L)
  • Reasonable economics (lowest NPV)
  • Can adapt to future regulatory changes

Summary ​

This tutorial covered comprehensive multi-scenario analysis capabilities:

Key Techniques:

  • ✓ Sensitivity analysis (OAT, multi-parameter, tornado)
  • ✓ Design alternative comparison
  • ✓ Single and multi-objective optimization
  • ✓ Monte Carlo uncertainty analysis
  • ✓ Climate scenario planning
  • ✓ Robust optimization

Best Practices:

  • ✓ Start with clear objectives
  • ✓ Use appropriate analysis methods
  • ✓ Organize scenarios systematically
  • ✓ Validate and verify results
  • ✓ Communicate findings effectively

Next Steps:

  1. Apply to your facility's planning study
  2. Explore advanced optimization algorithms
  3. Integrate with economic analysis tools
  4. Develop stakeholder presentation materials

Related Tutorials:

  • Economic Analysis & Life Cycle Costing
  • Carbon Footprint Analysis
  • Model Calibration Best Practices
  • Advanced Process Control Optimization

Tutorial Complete!

Multi-scenario analysis is essential for informed decision-making under uncertainty. Practice these techniques to become proficient in evaluating alternatives and optimizing WWTP performance.