This documentation covers NeqSim’s comprehensive Operational Risk Simulation Framework for equipment failure analysis, production impact assessment, and process topology analysis.
📚 Documentation Structure
Core Framework
| Section | Description |
|---|---|
| Overview | Framework architecture and key concepts |
| Equipment Failure Modeling | Failure modes, types, and reliability data |
| Risk Matrix | 5×5 risk matrix with probability, consequence, and cost |
| Monte Carlo Simulation | Stochastic simulation for availability analysis |
| Production Impact Analysis | Analyzing failure effects on production |
| Degraded Operation | Optimizing plant operation during outages |
| Process Topology | Graph structure extraction and analysis |
| STID & Functional Location | Equipment tagging following ISO 14224 |
| Dependency Analysis | Cascade failure and cross-installation effects |
| Mathematical Reference | Formulas and statistical methods |
| API Reference | Complete Java API documentation |
Advanced Risk Framework (P1-P7)
| Section | Description |
|---|---|
| Advanced Framework Overview | Overview of the implemented risk-analysis packages |
| P1: Dynamic Simulation | Monte Carlo with transient modeling |
| P2: SIS/SIF Integration | IEC 61508/61511, LOPA, SIL verification |
| P4: Bow-Tie Analysis | Barrier analysis, threat/consequence visualization |
| P6: Condition-Based Reliability | Health monitoring, RUL estimation |
Quick start: a traceable LOPA calculation
The example below is a complete Java 8 program. It applies a BPCS protection layer and a SIL 2 safety instrumented function (SIF) to an initiating-event frequency. The numerical result is transparent:
\[f_{\mathrm{mitigated}} = f_{\mathrm{IE}}\,\mathrm{PFD}_{\mathrm{BPCS}}\,\mathrm{PFD}_{\mathrm{SIF}} = 0.1 \times 0.1 \times 0.005 = 5.0 \times 10^{-5}\ \mathrm{yr}^{-1}\]import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import neqsim.process.safety.risk.RiskEvent;
import neqsim.process.safety.risk.sis.LOPAResult;
import neqsim.process.safety.risk.sis.SISIntegratedRiskModel;
import neqsim.process.safety.risk.sis.SafetyInstrumentedFunction;
public final class RiskFrameworkQuickStart {
private static final Logger logger = LogManager.getLogger(RiskFrameworkQuickStart.class);
private RiskFrameworkQuickStart() {}
public static void main(String[] args) {
String eventName = "HP vessel overpressure";
SISIntegratedRiskModel model = new SISIntegratedRiskModel("HP vessel LOPA");
model.addInitiatingEvent(eventName, 0.1, RiskEvent.ConsequenceCategory.MAJOR);
SISIntegratedRiskModel.IndependentProtectionLayer bpcs =
new SISIntegratedRiskModel.IndependentProtectionLayer(
"BPCS pressure control",
0.1,
SISIntegratedRiskModel.IndependentProtectionLayer.IPLType.BPCS);
bpcs.addApplicableEvent(eventName);
model.addIPL(bpcs);
SafetyInstrumentedFunction esd = SafetyInstrumentedFunction.builder()
.id("SIF-001")
.name("High-pressure ESD")
.description("Isolate the HP vessel on confirmed high pressure")
.sil(2)
.pfd(0.005)
.initiatingEvent(eventName)
.addProtectedEquipment("HP vessel")
.safeState("Isolated")
.build();
model.addSIF(esd);
LOPAResult result = model.performLOPA(eventName);
double expectedFrequency = 0.1 * 0.1 * 0.005;
if (Math.abs(result.getMitigatedFrequency() - expectedFrequency) > 1.0e-12) {
throw new IllegalStateException("LOPA layer multiplication did not close");
}
logger.info("Mitigated frequency: {} per year", result.getMitigatedFrequency());
logger.info("Total risk-reduction factor: {}", result.getTotalRRF());
}
}
This calculation demonstrates software behavior, not approval of an initiating-event frequency, IPL independence,
SIL target, test interval, or safe state. Those inputs require a traceable hazard study and accountable review. For a
process-coupled study, continue with Monte Carlo simulation, dynamic risk,
SIS integration, and process topology. Python users should access these Java
classes through the supported from neqsim import jneqsim gateway; see the
advanced risk notebook for the complete setup.
📊 Framework Architecture
┌─────────────────────────────────────────────────────────────────────┐
│ NeqSim Process Simulation │
│ ProcessSystem │
└──────────────────────────────┬──────────────────────────────────────┘
│
┌───────────────────┼───────────────────┐
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Equipment │ │ Production │ │ Process │
│ Failure │ │ Impact │ │ Topology │
│ Modeling │ │ Analysis │ │ Analysis │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Reliability │ │ Degraded │ │ Dependency │
│ Data │ │ Operation │ │ Analysis │
│ (OREDA) │ │ Optimizer │ │ │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
└────────────────────┼────────────────────┘
▼
┌───────────────────────────────┐
│ Risk Assessment │
│ ┌─────────────────────────┐ │
│ │ Monte Carlo │ │
│ │ Simulation │ │
│ └─────────────────────────┘ │
│ ┌─────────────────────────┐ │
│ │ Risk Matrix │ │
│ │ (5×5 Visualization) │ │
│ └─────────────────────────┘ │
└───────────────────────────────┘
📦 Package Structure
neqsim.process
├── equipment.failure
│ ├── EquipmentFailureMode - Failure type definitions
│ ├── ReliabilityDataSource - OREDA reliability data
│ └── package-info.java
├── safety.risk
│ ├── OperationalRiskSimulator - Monte Carlo engine
│ ├── OperationalRiskResult - Simulation results
│ └── RiskMatrix - 5×5 risk matrix
└── util
├── optimizer
│ ├── ProductionImpactAnalyzer - Impact analysis
│ ├── ProductionImpactResult - Impact results
│ ├── DegradedOperationOptimizer - Degraded optimization
│ └── DegradedOperationResult - Optimization results
└── topology
├── ProcessTopologyAnalyzer - Graph extraction
├── FunctionalLocation - STID parsing
├── DependencyAnalyzer - Cascade analysis
└── package-info.java