AI Control Algorithms

Five advanced AI algorithms for autonomous control of 500 MW electrolyzer plants. Each algorithm addresses a specific control challenge with state-of-the-art machine learning techniques.

ASLB

Adaptive Stack Load Balancer using Deep Reinforcement Learning for dynamic load distribution

Benefit: +15-20% stack lifespan

Response: 1-10 seconds

PDC

Predictive Degradation Controller using LSTM with Attention for RUL prediction

Benefit: -30% unplanned downtime

Accuracy: >90% at 6-month horizon

DEO

Dynamic Efficiency Optimizer using Model Predictive Control + Neural Networks

Benefit: +3-5% efficiency gain

Savings: $2-3M annually

ADFI

Anomaly Detection and Fault Isolation using Variational Autoencoder

Benefit: 95% detection rate

Response: <30 seconds

RIC

Renewable Integration Coordinator using RNN + Mixed-Integer Linear Programming

Benefit: 90%+ renewable use

Savings: -40-50% curtailment

Hybrid Architecture

All five algorithms work together in a coordinated control system

Total Benefit: $7.6M/year OPEX reduction

ROI: 4-month break-even

Interactive Algorithm Demonstrations

Explore each algorithm with live simulations and real-time visualizations

Algorithm 1: Adaptive Stack Load Balancer (ASLB)

Deep Reinforcement Learning (Proximal Policy Optimization) for dynamic load distribution

Stopped

Grid Demand400 MW

74.2%

Fleet Efficiency

+0.0%

Lifespan Extension

0.0k

MWh/year Saved

Stack Load Distribution

Stack 180 MW → 80 MW

95%

Efficiency: 76.0%Temp: 75°C

Stack 280 MW → 75 MW

88%

Efficiency: 74.0%Temp: 78°C

Stack 380 MW → 82 MW

92%

Efficiency: 75.0%Temp: 76°C

Stack 480 MW → 70 MW

85%

Efficiency: 72.0%Temp: 80°C

Stack 580 MW → 78 MW

90%

Efficiency: 74.0%Temp: 77°C

Training Approach: Simulation-based training using Digital Twin, with transfer learning fine-tuning on real operational data

Expected Benefit: 15-20% extension of fleet-average stack lifespan through intelligent load distribution

Response Time: 1-10 seconds for load rebalancing decisions

Algorithm 2: Predictive Degradation Controller (PDC)

LSTM with Attention Mechanism + Physics-Informed Constraints for RUL prediction

Select Stack

Prediction Horizon

Months RUL

0.42%

Decay/Month

LOW

Risk Level

94%

Confidence

Predicted Degradation Trajectory

Jan 2026

94.6%

Jan 2026

94.2%

Mar 2026

93.8%

Apr 2026

93.4%

May 2026

93.0%

May 2026

92.5%

Jun 2026

92.0%

Continue Monitoring

Low Priority

Stack health is good. Continue normal operations with regular monitoring.

Next Scheduled Maintenance

Optimal maintenance window

July 15, 2026

Accuracy Target: >90% RUL prediction accuracy within ±10% error margin at 6-month horizon

Expected Benefit: 30% reduction in unplanned maintenance events, 25% reduction in maintenance costs

Input Data: Historical performance, operating conditions, maintenance history, EIS data

Algorithm 3: Dynamic Efficiency Optimizer (DEO)

Model Predictive Control + Neural Network Surrogate Model for efficiency maximization

Stopped

Power Availability400 MW

Electricity Price$50/MWh

H₂ Demand8000 kg/h

+0.0%

Efficiency Gain

-0.0%

Cost Reduction

+0.0%

Output Increase

Operating Setpoints

Temperature (°C)

Current:75.0

Optimal:75.0

Pressure (bar)

Current:30.0

Optimal:30.0

Current Density (A/cm²)

Current:1.80

Optimal:1.80

System Efficiency

74.0%

Target: 74.0%

H₂ Output

8000

kg/h

Energy Cost

$20.0k

per hour

Optimization Horizon: 15-minute rolling window with 1-minute resolution

Expected Benefit: 3-5% improvement in overall system efficiency, $2-3M annual energy cost savings

Constraints: Power availability, safe operating limits, hydrogen demand fulfillment

Algorithm 4: Anomaly Detection and Fault Isolation (ADFI)

Variational Autoencoder + Isolation Forest Ensemble for real-time anomaly detection

Stopped

175

Total Sensors

Active Anomalies

95.3%

Detection Rate

5.2%

False Alarm Rate

Real-time Sensor Monitoring

Stack 1 Temperature

Value: 75.20 | Anomaly Score: 12%

Stack 2 Pressure

Value: 30.50 | Anomaly Score: 8%

Stack 3 Voltage

Value: 1.85 | Anomaly Score: 15%

Stack 4 Flow Rate

Value: 850.00 | Anomaly Score: 10%

Stack 5 Vibration

Value: 2.30 | Anomaly Score: 7%

Anomaly Alerts

No anomalies detected. All systems operating normally.

Response Time: <30 seconds from anomaly onset to operator notification

Expected Benefit: 85% reduction in false alarm rate, 95% detection rate for incipient faults

Data Input: 10,000+ data points per second from 175+ sensors

Algorithm 5: Renewable Integration Coordinator (RIC)

RNN + Mixed-Integer Linear Programming for variable renewable energy coordination

Stopped

Time: 12:00

Time of Day12:00

Wind Speed8.0 m/s

Solar Irradiance800 W/m²

87.5%

Renewable Use

12.5%

Grid Dependency

45%

Curtailment Reduction

$3.2M

Annual Savings

Power Flow Distribution

Solar Power

150 MW

Wind Power

200 MW

Grid Power

50 MW

Battery Storage

50% charged

Hydrogen Production

8000

kg/h

Energy Curtailment

MW wasted

Prediction Horizon: 4-hour rolling forecast with 15-minute resolution

Expected Benefit: 40-50% reduction in renewable curtailment, 90%+ renewable energy utilization

Optimization Objective: Maximize renewable use, minimize grid dependency and curtailment

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