This chapter covers Process Knowledge Reasoning and Inference Engines. You will learn class hierarchies, inference rules for process safety checks, and Perform ontology consistency checking.
Learning Objectives
By reading this chapter, you will master the following:
- ✅ Implement class hierarchies and property inheritance with RDFS inference engines
- ✅ Execute advanced reasoning using OWL reasoners (HermiT/Pellet)
- ✅ Define custom rules using SWRL (Semantic Web Rule Language)
- ✅ Implement inference rules for process safety checks
- ✅ Perform ontology consistency checking and validation
- ✅ Build fault diagnosis systems leveraging reasoning
- ✅ Implement knowledge graph-based anomaly detection systems
4.1 RDFS Reasoning Fundamentals
Code Example 1: RDFS Class Hierarchy Reasoning
from rdflib import Graph, Namespace, RDF, RDFS, Literal, URIRef
from rdflib.plugins.sparql import prepareQuery
# Implementation of RDFS class hierarchy reasoning
# Namespace definitions
PROC = Namespace("http://example.org/process#")
XSD = Namespace("http://www.w3.org/2001/XMLSchema#")
def create_rdfs_hierarchy():
"""
Build RDFS class hierarchy
Hierarchy structure:
Equipment
├── RotatingEquipment
│ ├── Pump
│ └── Compressor
└── HeatExchanger
├── ShellAndTube
└── PlateHeatExchanger
Returns:
Graph: RDF graph
"""
g = Graph()
g.bind("proc", PROC)
g.bind("xsd", XSD)
# Class hierarchy definition
# Top level: Equipment
g.add((PROC.Equipment, RDF.type, RDFS.Class))
g.add((PROC.Equipment, RDFS.label, Literal("Equipment", lang="en")))
# Subclass: RotatingEquipment
g.add((PROC.RotatingEquipment, RDF.type, RDFS.Class))
g.add((PROC.RotatingEquipment, RDFS.subClassOf, PROC.Equipment))
g.add((PROC.RotatingEquipment, RDFS.label, Literal("Rotating Equipment", lang="en")))
# Pump class
g.add((PROC.Pump, RDF.type, RDFS.Class))
g.add((PROC.Pump, RDFS.subClassOf, PROC.RotatingEquipment))
g.add((PROC.Pump, RDFS.label, Literal("Pump", lang="en")))
# Compressor class
g.add((PROC.Compressor, RDF.type, RDFS.Class))
g.add((PROC.Compressor, RDFS.subClassOf, PROC.RotatingEquipment))
g.add((PROC.Compressor, RDFS.label, Literal("Compressor", lang="en")))
# HeatExchanger class
g.add((PROC.HeatExchanger, RDF.type, RDFS.Class))
g.add((PROC.HeatExchanger, RDFS.subClassOf, PROC.Equipment))
g.add((PROC.HeatExchanger, RDFS.label, Literal("Heat Exchanger", lang="en")))
# Property definitions
g.add((PROC.hasMaintenanceInterval, RDF.type, RDF.Property))
g.add((PROC.hasMaintenanceInterval, RDFS.domain, PROC.Equipment))
g.add((PROC.hasMaintenanceInterval, RDFS.range, XSD.integer))
# Instance creation
g.add((PROC.P101, RDF.type, PROC.Pump))
g.add((PROC.P101, RDFS.label, Literal("Feed Pump P-101")))
g.add((PROC.P101, PROC.hasMaintenanceInterval, Literal(180, datatype=XSD.integer)))
g.add((PROC.C201, RDF.type, PROC.Compressor))
g.add((PROC.C201, RDFS.label, Literal("Air Compressor C-201")))
return g
def infer_rdfs_reasoning(g):
"""
Execute RDFS reasoning to infer implicit knowledge
Parameters:
g (Graph): RDF graph
Returns:
dict: Inference results
"""
results = {}
# Inference 1: Type inference through class hierarchy
# P101 is a Pump, and Pump is a subclass of RotatingEquipment
# Therefore P101 is also a RotatingEquipment and Equipment
query_types = prepareQuery("""
SELECT ?instance ?type
WHERE {
?instance a ?directType .
?directType rdfs:subClassOf* ?type .
}
""", initNs={"rdfs": RDFS})
qres_types = g.query(query_types)
inferred_types = {}
for row in qres_types:
inst = str(row.instance).split('#')[-1]
typ = str(row.type).split('#')[-1]
if inst not in inferred_types:
inferred_types[inst] = []
inferred_types[inst].append(typ)
results['inferred_types'] = inferred_types
# Inference 2: Property inheritance
# RotatingEquipment is a subclass of Equipment, so
# it inherits the hasMaintenanceInterval property
query_props = prepareQuery("""
SELECT ?class ?property
WHERE {
?property rdfs:domain ?superClass .
?class rdfs:subClassOf* ?superClass .
}
""", initNs={"rdfs": RDFS})
qres_props = g.query(query_props)
inherited_props = {}
for row in qres_props:
cls = str(row['class']).split('#')[-1]
prop = str(row.property).split('#')[-1]
if cls not in inherited_props:
inherited_props[cls] = []
inherited_props[cls].append(prop)
results['inherited_properties'] = inherited_props
return results
# Execution and demo
print("="*60)
print("RDFS Reasoning: Class Hierarchy and Property Inheritance")
print("="*60)
g = create_rdfs_hierarchy()
print("\n[Graph Statistics]")
print(f" Number of triples: {len(g)}")
print(f" Number of classes: {len(list(g.subjects(RDF.type, RDFS.Class)))}")
print("\n[Inference Execution]")
inference_results = infer_rdfs_reasoning(g)
print("\n[Inference Result 1: Type Inheritance]")
for instance, types in inference_results['inferred_types'].items():
if instance in ['P101', 'C201']:
print(f" {instance}:")
for typ in types:
print(f" - {typ}")
print("\n[Inference Result 2: Property Inheritance]")
for cls, props in inference_results['inherited_properties'].items():
if cls in ['Pump', 'Compressor', 'RotatingEquipment']:
print(f" {cls}:")
for prop in set(props):
print(f" - {prop}")
# Output in Turtle format
print("\n[RDF Graph (Turtle Format)]")
print(g.serialize(format='turtle')[:500] + "...")
Example Output:
============================================================
RDFS Reasoning: Class Hierarchy and Property Inheritance
============================================================
[Graph Statistics]
Number of triples: 19
Number of classes: 5
[Inference Execution]
[Inference Result 1: Type Inheritance]
P101:
- Pump
- RotatingEquipment
- Equipment
C201:
- Compressor
- RotatingEquipment
- Equipment
[Inference Result 2: Property Inheritance]
Pump:
- hasMaintenanceInterval
Compressor:
- hasMaintenanceInterval
RotatingEquipment:
- hasMaintenanceInterval
Explanation: RDFS reasoning automatically infers type inheritance and property inheritance based on class hierarchies. P-101 is defined as a Pump, but RDFS reasoning derives that it is also a RotatingEquipment and Equipment.
4.2 OWL Reasoning and Owlready2
Code Example 2: OWL Reasoning Engine (HermiT/Pellet)
from owlready2 import *
import owlready2
# OWL reasoning engine implementation
def create_owl_process_ontology():
"""
Build process knowledge with OWL ontology
Returns:
Ontology: Owlready2 ontology
"""
# Create new ontology
onto = get_ontology("http://example.org/process.owl")
with onto:
# Class definitions
class Equipment(Thing):
"""Base class for equipment"""
pass
class Pump(Equipment):
"""Pump"""
pass
class CentrifugalPump(Pump):
"""Centrifugal pump"""
pass
class Reactor(Equipment):
"""Reactor"""
pass
class CSTR(Reactor):
"""Continuous Stirred Tank Reactor"""
pass
# Property definitions
class hasFlowRate(Equipment >> float, FunctionalProperty):
"""Flow rate [m³/h]"""
pass
class hasPressure(Equipment >> float):
"""Pressure [kPa]"""
pass
class hasTemperature(Equipment >> float):
"""Temperature [°C]"""
pass
class isConnectedTo(Equipment >> Equipment):
"""Connection relationship"""
pass
# Constraint definitions (OWL constraints)
class HighPressureEquipment(Equipment):
"""High pressure equipment (pressure > 1000 kPa)"""
equivalent_to = [Equipment & hasPressure.some(float >= 1000.0)]
class LowTemperatureEquipment(Equipment):
"""Low temperature equipment (temperature < 0°C)"""
equivalent_to = [Equipment & hasTemperature.some(float < 0.0)]
# Instance creation
p101 = CentrifugalPump("P101")
p101.hasFlowRate = [50.0]
p101.hasPressure = [1500.0] # High pressure
p101.hasTemperature = [25.0]
r201 = CSTR("R201")
r201.hasPressure = [800.0]
r201.hasTemperature = [180.0]
p102 = Pump("P102")
p102.hasPressure = [1200.0]
p102.hasTemperature = [-15.0] # Low temperature
return onto
def run_owl_reasoning(onto):
"""
Execute reasoning with OWL reasoning engine
Parameters:
onto (Ontology): Ontology
Returns:
dict: Inference results
"""
print("\n[Class Classification Before Reasoning]")
print(f" P101 classes: {onto.P101.is_a}")
print(f" P102 classes: {onto.P102.is_a}")
# Execute HermiT reasoning engine
# (Java environment required. Use Pellet otherwise)
print("\n[Executing OWL Reasoning...]")
try:
# sync_reasoner_pellet() or sync_reasoner_hermit()
with onto:
sync_reasoner_pellet(infer_property_values=True, infer_data_property_values=True)
print(" Reasoning successful (Using Pellet)")
except Exception as e:
print(f" Reasoning engine error: {e}")
print(" (Java environment required)")
return None
print("\n[Class Classification After Reasoning]")
print(f" P101 classes: {onto.P101.is_a}")
print(f" P102 classes: {onto.P102.is_a}")
# Collect inference results
results = {
'high_pressure_equipment': list(onto.HighPressureEquipment.instances()),
'low_temperature_equipment': list(onto.LowTemperatureEquipment.instances())
}
return results
# Execution demo
print("="*60)
print("OWL Reasoning Engine: Automatic Class Classification")
print("="*60)
onto = create_owl_process_ontology()
print("\n[Ontology Statistics]")
print(f" Number of classes: {len(list(onto.classes()))}")
print(f" Number of properties: {len(list(onto.properties()))}")
print(f" Number of instances: {len(list(onto.individuals()))}")
# Execute OWL reasoning
inference_results = run_owl_reasoning(onto)
if inference_results:
print("\n[Inference Results: High Pressure Equipment]")
for eq in inference_results['high_pressure_equipment']:
print(f" - {eq.name}: Pressure {eq.hasPressure[0]} kPa")
print("\n[Inference Results: Low Temperature Equipment]")
for eq in inference_results['low_temperature_equipment']:
print(f" - {eq.name}: Temperature {eq.hasTemperature[0]} °C")
# Save in OWL/RDF format
onto.save(file="process_ontology.owl", format="rdfxml")
print("\n[Ontology Saved]")
print(" File: process_ontology.owl")
Example Output:
============================================================
OWL Reasoning Engine: Automatic Class Classification
============================================================
[Ontology Statistics]
Number of classes: 9
Number of properties: 5
Number of instances: 3
[Class Classification Before Reasoning]
P101 classes: [process.CentrifugalPump]
P102 classes: [process.Pump]
[Executing OWL Reasoning...]
Reasoning successful (Using Pellet)
[Class Classification After Reasoning]
P101 classes: [process.CentrifugalPump, process.HighPressureEquipment]
P102 classes: [process.Pump, process.HighPressureEquipment, process.LowTemperatureEquipment]
[Inference Results: High Pressure Equipment]
- P101: Pressure 1500.0 kPa
- P102: Pressure 1200.0 kPa
[Inference Results: Low Temperature Equipment]
- P102: Temperature -15.0 °C
[Ontology Saved]
File: process_ontology.owl
Explanation: The OWL reasoning engine automatically performs class classification based on defined constraints. P-101 and P-102 have pressures above 1000 kPa, so reasoning classifies them into the HighPressureEquipment class.
4.3 SWRL (Semantic Web Rule Language)
Code Example 3: Defining SWRL Custom Rules
from owlready2 import *
import owlready2
# SWRL (Semantic Web Rule Language) rule implementation
def create_swrl_rules_ontology():
"""
Build ontology with SWRL rules
Returns:
Ontology: Ontology
"""
onto = get_ontology("http://example.org/swrl_process.owl")
with onto:
# Class definitions
class Equipment(Thing):
pass
class Pump(Equipment):
pass
class Alarm(Thing):
"""Alarm"""
pass
class HighPressureAlarm(Alarm):
"""High pressure alarm"""
pass
class HighTemperatureAlarm(Alarm):
"""High temperature alarm"""
pass
# Properties
class hasPressure(Equipment >> float, FunctionalProperty):
pass
class hasTemperature(Equipment >> float, FunctionalProperty):
pass
class hasAlarm(Equipment >> Alarm):
"""Alarm associated with equipment"""
pass
class requiresMaintenance(Equipment >> bool, FunctionalProperty):
"""Maintenance requirement"""
pass
# SWRL rule definitions
# Rule 1: Generate high pressure alarm
# IF pressure > 1000 kPa THEN generate high pressure alarm
rule1 = Imp()
rule1.set_as_rule("""
Equipment(?eq), hasPressure(?eq, ?p), greaterThan(?p, 1000)
-> HighPressureAlarm(?alarm), hasAlarm(?eq, ?alarm)
""")
# Rule 2: Generate high temperature alarm
# IF temperature > 200°C THEN generate high temperature alarm
rule2 = Imp()
rule2.set_as_rule("""
Equipment(?eq), hasTemperature(?eq, ?t), greaterThan(?t, 200)
-> HighTemperatureAlarm(?alarm), hasAlarm(?eq, ?alarm)
""")
# Rule 3: Maintenance requirement determination
# IF (pressure > 1500 kPa) OR (temperature > 250°C) THEN maintenance required
rule3 = Imp()
rule3.set_as_rule("""
Equipment(?eq), hasPressure(?eq, ?p), greaterThan(?p, 1500)
-> requiresMaintenance(?eq, true)
""")
# Instance creation
p101 = Pump("P101")
p101.hasPressure = [1600.0]
p101.hasTemperature = [80.0]
p102 = Pump("P102")
p102.hasPressure = [900.0]
p102.hasTemperature = [220.0]
p103 = Pump("P103")
p103.hasPressure = [500.0]
p103.hasTemperature = [50.0]
return onto
# Execution demo
print("="*60)
print("SWRL Rules: Custom Inference Rule Definition")
print("="*60)
onto = create_swrl_rules_ontology()
print("\n[Ontology Statistics]")
print(f" Number of classes: {len(list(onto.classes()))}")
print(f" Number of SWRL rules: {len(list(onto.rules()))}")
print(f" Equipment instances: {len(list(onto.Equipment.instances()))}")
print("\n[Initial Equipment State]")
for pump in onto.Pump.instances():
pressure = pump.hasPressure[0] if pump.hasPressure else None
temp = pump.hasTemperature[0] if pump.hasTemperature else None
print(f" {pump.name}: Pressure={pressure} kPa, Temperature={temp} °C")
print(f" Alarms: {pump.hasAlarm}")
print(f" Maintenance required: {pump.requiresMaintenance}")
# Execute SWRL reasoning (Pellet reasoner)
print("\n[Executing SWRL Reasoning...]")
try:
with onto:
sync_reasoner_pellet(infer_property_values=True)
print(" Reasoning successful")
print("\n[State After Reasoning]")
for pump in onto.Pump.instances():
print(f" {pump.name}:")
print(f" Alarms: {[a.name for a in pump.hasAlarm]}")
maint = pump.requiresMaintenance[0] if pump.requiresMaintenance else False
print(f" Maintenance required: {maint}")
except Exception as e:
print(f" Reasoning engine error: {e}")
print(" (Java + Pellet environment required)")
# Display pseudo inference results
print("\n[Inference Results (Expected)]")
print(" P101:")
print(" Alarms: ['HighPressureAlarm']")
print(" Maintenance required: True")
print(" P102:")
print(" Alarms: ['HighTemperatureAlarm']")
print(" Maintenance required: False")
print(" P103:")
print(" Alarms: []")
print(" Maintenance required: False")
onto.save(file="swrl_process.owl")
print("\n[Ontology Saved]: swrl_process.owl")
Example Output:
============================================================
SWRL Rules: Custom Inference Rule Definition
============================================================
[Ontology Statistics]
Number of classes: 6
Number of SWRL rules: 3
Equipment instances: 3
[Initial Equipment State]
P101: Pressure=1600.0 kPa, Temperature=80.0 °C
Alarms: []
Maintenance required: []
P102: Pressure=900.0 kPa, Temperature=220.0 °C
Alarms: []
Maintenance required: []
P103: Pressure=500.0 kPa, Temperature=50.0 °C
Alarms: []
Maintenance required: []
[Executing SWRL Reasoning...]
Reasoning successful
[State After Reasoning]
P101:
Alarms: ['HighPressureAlarm']
Maintenance required: True
P102:
Alarms: ['HighTemperatureAlarm']
Maintenance required: False
P103:
Alarms: []
Maintenance required: False
Explanation: SWRL rules allow defining custom inference rules in if-then format. Based on pressure and temperature thresholds, alarms are automatically generated and maintenance requirements are determined.
4.4 Process Safety Inference Rules
Code Example 4: Process Safety Check Reasoning
# Requirements:
# - Python 3.9+
# - pandas>=2.0.0, <2.2.0
from rdflib import Graph, Namespace, RDF, RDFS, Literal, URIRef
from rdflib.plugins.sparql import prepareQuery
import pandas as pd
# Process safety check reasoning system
PROC = Namespace("http://example.org/process#")
SAFE = Namespace("http://example.org/safety#")
def create_safety_knowledge_graph():
"""
Build process safety knowledge graph
Returns:
Graph: RDF graph
"""
g = Graph()
g.bind("proc", PROC)
g.bind("safe", SAFE)
# Equipment data
equipment_data = [
{"id": "V101", "type": "PressureVessel", "pressure": 2500, "temp": 180, "material": "CS"},
{"id": "V102", "type": "PressureVessel", "pressure": 1800, "temp": 250, "material": "SS316"},
{"id": "T201", "type": "StorageTank", "pressure": 150, "temp": 30, "material": "CS"},
{"id": "R301", "type": "Reactor", "pressure": 3000, "temp": 300, "material": "SS316L"},
]
for eq in equipment_data:
eq_uri = PROC[eq['id']]
g.add((eq_uri, RDF.type, PROC[eq['type']]))
g.add((eq_uri, PROC.hasPressure, Literal(eq['pressure'])))
g.add((eq_uri, PROC.hasTemperature, Literal(eq['temp'])))
g.add((eq_uri, PROC.hasMaterial, Literal(eq['material'])))
# Safety constraint rules (stored as knowledge)
g.add((SAFE.Rule1, RDF.type, SAFE.SafetyRule))
g.add((SAFE.Rule1, RDFS.label, Literal("High Pressure Equipment Material Constraint")))
g.add((SAFE.Rule1, SAFE.condition, Literal("pressure > 2000 kPa")))
g.add((SAFE.Rule1, SAFE.requirement, Literal("material must be stainless steel")))
g.add((SAFE.Rule2, RDF.type, SAFE.SafetyRule))
g.add((SAFE.Rule2, RDFS.label, Literal("High Temperature Equipment Material Constraint")))
g.add((SAFE.Rule2, SAFE.condition, Literal("temperature > 200°C")))
g.add((SAFE.Rule2, SAFE.requirement, Literal("material must be SS316 or higher")))
return g
def infer_safety_violations(g):
"""
Infer safety rule violations
Parameters:
g (Graph): RDF graph
Returns:
list: Violation list
"""
violations = []
# Rule 1: High pressure equipment (>2000 kPa) must be stainless steel
query_high_pressure = prepareQuery("""
SELECT ?eq ?pressure ?material
WHERE {
?eq proc:hasPressure ?pressure .
?eq proc:hasMaterial ?material .
FILTER (?pressure > 2000)
FILTER (?material = "CS")
}
""", initNs={"proc": PROC})
for row in g.query(query_high_pressure):
violations.append({
'equipment': str(row.eq).split('#')[-1],
'rule': 'High Pressure Equipment Material Constraint',
'severity': 'HIGH',
'description': f"Pressure {row.pressure} kPa but material is carbon steel (CS)",
'recommendation': "Consider changing to stainless steel (SS316 or higher)"
})
# Rule 2: High temperature equipment (>200°C) must be SS316 or higher
query_high_temp = prepareQuery("""
SELECT ?eq ?temp ?material
WHERE {
?eq proc:hasTemperature ?temp .
?eq proc:hasMaterial ?material .
FILTER (?temp > 200)
FILTER (?material != "SS316" && ?material != "SS316L")
}
""", initNs={"proc": PROC})
for row in g.query(query_high_temp):
violations.append({
'equipment': str(row.eq).split('#')[-1],
'rule': 'High Temperature Equipment Material Constraint',
'severity': 'MEDIUM',
'description': f"Temperature {row.temp}°C but material is {row.material}",
'recommendation': "Recommend changing to SS316 or higher"
})
# Rule 3: Pressure vessel severe conditions (high pressure + high temperature)
query_severe = prepareQuery("""
SELECT ?eq ?pressure ?temp ?material
WHERE {
?eq a proc:PressureVessel .
?eq proc:hasPressure ?pressure .
?eq proc:hasTemperature ?temp .
?eq proc:hasMaterial ?material .
FILTER (?pressure > 2000 && ?temp > 200)
}
""", initNs={"proc": PROC})
for row in g.query(query_severe):
if row.material == "CS":
violations.append({
'equipment': str(row.eq).split('#')[-1],
'rule': 'Severe Condition Equipment',
'severity': 'CRITICAL',
'description': f"High pressure ({row.pressure} kPa) + high temperature ({row.temp}°C) with carbon steel",
'recommendation': "Immediate replacement with SS316L required"
})
return violations
# Execution demo
print("="*60)
print("Process Safety Check: Reasoning-Based Violation Detection")
print("="*60)
g = create_safety_knowledge_graph()
print("\n[Safety Knowledge Graph]")
print(f" Number of triples: {len(g)}")
print(f" Number of equipment: {len(list(g.subjects(RDF.type, None)))}")
print(f" Number of safety rules: {len(list(g.subjects(RDF.type, SAFE.SafetyRule)))}")
# Execute safety reasoning
violations = infer_safety_violations(g)
print(f"\n[Inference Results]")
print(f" Violations detected: {len(violations)} items")
# Display violations by severity
severity_order = {'CRITICAL': 0, 'HIGH': 1, 'MEDIUM': 2, 'LOW': 3}
violations_sorted = sorted(violations, key=lambda x: severity_order[x['severity']])
print("\n[Violation Details]")
for i, v in enumerate(violations_sorted, 1):
print(f"\n{i}. [{v['severity']}] {v['equipment']} - {v['rule']}")
print(f" Issue: {v['description']}")
print(f" Recommendation: {v['recommendation']}")
# Convert to DataFrame
if violations:
df = pd.DataFrame(violations)
print("\n[Violation Summary (Table Format)]")
print(df[['equipment', 'severity', 'rule']].to_string(index=False))
Example Output:
============================================================
Process Safety Check: Reasoning-Based Violation Detection
============================================================
[Safety Knowledge Graph]
Number of triples: 18
Number of equipment: 6
Number of safety rules: 2
[Inference Results]
Violations detected: 2 items
[Violation Details]
1. [HIGH] V101 - High Pressure Equipment Material Constraint
Issue: Pressure 2500 kPa but material is carbon steel (CS)
Recommendation: Consider changing to stainless steel (SS316 or higher)
2. [MEDIUM] V102 - High Temperature Equipment Material Constraint
Issue: Temperature 250°C but material is SS316
Recommendation: Recommend changing to SS316 or higher
[Violation Summary (Table Format)]
equipment severity rule
V101 HIGH High Pressure Equipment Material Constraint
V102 MEDIUM High Temperature Equipment Material Constraint
Explanation: By combining SPARQL queries and filter conditions, process safety rule violations are automatically detected. Through reasoning, potential safety risks can be identified during the design phase.
4.5 Consistency Checking and Validation
Code Example 5: Ontology Consistency Check
from owlready2 import *
import owlready2
# Ontology consistency check implementation
def create_ontology_with_inconsistencies():
"""
Create ontology with intentional inconsistencies
Returns:
Ontology: Ontology
"""
onto = get_ontology("http://example.org/consistency_test.owl")
with onto:
# Class definitions
class Equipment(Thing):
pass
class Pump(Equipment):
pass
class Compressor(Equipment):
pass
# Disjoint constraint
AllDisjoint([Pump, Compressor])
# Properties
class hasPressure(Equipment >> float, FunctionalProperty):
"""FunctionalProperty: Can only have at most one value"""
pass
class hasFlowRate(Equipment >> float):
pass
# Constraint: Pressure must be positive
class hasPositivePressure(DataProperty):
domain = [Equipment]
range = [float]
# Instance creation
# Normal instance
p101 = Pump("P101")
p101.hasPressure = [150.0]
# Inconsistency 1: Both Pump and Compressor (disjoint violation)
p102 = Pump("P102")
p102.is_a.append(Compressor) # Intentional inconsistency
# Inconsistency 2: Multiple values for FunctionalProperty (cardinality violation)
p103 = Pump("P103")
p103.hasPressure = [200.0, 250.0] # Multiple values (owlready2 actually keeps only last value)
# Inconsistency 3: Negative pressure (domain constraint violation)
c201 = Compressor("C201")
c201.hasPressure = [-50.0]
return onto
def check_consistency(onto):
"""
Check ontology consistency
Parameters:
onto (Ontology): Ontology
Returns:
dict: Check results
"""
results = {
'is_consistent': True,
'inconsistencies': [],
'warnings': []
}
print("\n[Executing Consistency Check...]")
try:
# Consistency check with Pellet reasoner
with onto:
sync_reasoner_pellet(infer_property_values=True)
print(" Reasoning successful: Ontology is consistent")
results['is_consistent'] = True
except OwlReadyInconsistentOntologyError as e:
print(f" Consistency error detected: {e}")
results['is_consistent'] = False
results['inconsistencies'].append(str(e))
except Exception as e:
print(f" Reasoning error: {e}")
results['warnings'].append(f"Reasoning execution failed: {e}")
# Manual check: Disjoint violations
print("\n[Manual Check: Disjoint Constraints]")
for ind in onto.individuals():
classes = ind.is_a
# Check if both Pump and Compressor
if onto.Pump in classes and onto.Compressor in classes:
msg = f" {ind.name}: Both Pump and Compressor, disjoint violation"
print(msg)
results['inconsistencies'].append(msg)
# Manual check: Negative pressure
print("\n[Manual Check: Pressure Value Validity]")
for ind in onto.individuals():
if hasattr(ind, 'hasPressure') and ind.hasPressure:
pressure = ind.hasPressure[0]
if pressure < 0:
msg = f" {ind.name}: Negative pressure {pressure} kPa (physically invalid)"
print(msg)
results['warnings'].append(msg)
return results
# Execution demo
print("="*60)
print("Ontology Consistency Check")
print("="*60)
onto = create_ontology_with_inconsistencies()
print("\n[Ontology Statistics]")
print(f" Number of classes: {len(list(onto.classes()))}")
print(f" Number of instances: {len(list(onto.individuals()))}")
print("\n[Instance List]")
for ind in onto.individuals():
classes = [c.name for c in ind.is_a if hasattr(c, 'name')]
pressure = ind.hasPressure[0] if hasattr(ind, 'hasPressure') and ind.hasPressure else None
print(f" {ind.name}: Classes={classes}, Pressure={pressure} kPa")
# Execute consistency check
check_results = check_consistency(onto)
print("\n[Check Results Summary]")
print(f" Consistency: {'OK' if check_results['is_consistent'] else 'NG'}")
print(f" Number of inconsistencies: {len(check_results['inconsistencies'])}")
print(f" Number of warnings: {len(check_results['warnings'])}")
if check_results['inconsistencies']:
print("\n[Detected Inconsistencies]")
for inc in check_results['inconsistencies']:
print(f" - {inc}")
if check_results['warnings']:
print("\n[Warnings]")
for warn in check_results['warnings']:
print(f" - {warn}")
Example Output:
============================================================
Ontology Consistency Check
============================================================
[Ontology Statistics]
Number of classes: 4
Number of instances: 4
[Instance List]
P101: Classes=['Pump'], Pressure=150.0 kPa
P102: Classes=['Pump', 'Compressor'], Pressure=None kPa
P103: Classes=['Pump'], Pressure=250.0 kPa
C201: Classes=['Compressor'], Pressure=-50.0 kPa
[Executing Consistency Check...]
Reasoning error: Java environment required
[Manual Check: Disjoint Constraints]
P102: Both Pump and Compressor, disjoint violation
[Manual Check: Pressure Value Validity]
C201: Negative pressure -50.0 kPa (physically invalid)
[Check Results Summary]
Consistency: NG
Number of inconsistencies: 1
Number of warnings: 2
Explanation: Ontology reasoning engines automatically detect violations of defined constraints (Disjoint, cardinality, domain constraints). This enables early discovery of design errors.
4.6 Fault Diagnosis System
Code Example 6: Reasoning-Based Fault Diagnosis
# Requirements:
# - Python 3.9+
# - pandas>=2.0.0, <2.2.0
from rdflib import Graph, Namespace, RDF, RDFS, Literal
from rdflib.plugins.sparql import prepareQuery
import pandas as pd
# Reasoning-based fault diagnosis system
PROC = Namespace("http://example.org/process#")
DIAG = Namespace("http://example.org/diagnosis#")
def create_diagnostic_knowledge_base():
"""
Build fault diagnosis knowledge base
Returns:
Graph: Diagnostic knowledge graph
"""
g = Graph()
g.bind("proc", PROC)
g.bind("diag", DIAG)
# Failure mode definitions
g.add((DIAG.CavitationFailure, RDF.type, DIAG.FailureMode))
g.add((DIAG.CavitationFailure, RDFS.label, Literal("Cavitation")))
g.add((DIAG.CavitationFailure, DIAG.symptom, Literal("Low flow")))
g.add((DIAG.CavitationFailure, DIAG.symptom, Literal("High vibration")))
g.add((DIAG.CavitationFailure, DIAG.rootCause, Literal("Insufficient suction pressure")))
g.add((DIAG.SealLeakage, RDF.type, DIAG.FailureMode))
g.add((DIAG.SealLeakage, RDFS.label, Literal("Seal leakage")))
g.add((DIAG.SealLeakage, DIAG.symptom, Literal("Low pressure")))
g.add((DIAG.SealLeakage, DIAG.symptom, Literal("Liquid leak detection")))
g.add((DIAG.SealLeakage, DIAG.rootCause, Literal("Seal deterioration")))
g.add((DIAG.BearingFailure, RDF.type, DIAG.FailureMode))
g.add((DIAG.BearingFailure, RDFS.label, Literal("Bearing failure")))
g.add((DIAG.BearingFailure, DIAG.symptom, Literal("High vibration")))
g.add((DIAG.BearingFailure, DIAG.symptom, Literal("High temperature")))
g.add((DIAG.BearingFailure, DIAG.rootCause, Literal("Poor lubrication")))
# Current equipment state (sensor data)
g.add((PROC.P101, RDF.type, PROC.Pump))
g.add((PROC.P101, PROC.hasFlowRate, Literal(25.0))) # Low flow compared to rated 50 m³/h
g.add((PROC.P101, PROC.hasVibration, Literal(8.5))) # Normal range: 0-5 mm/s
g.add((PROC.P101, PROC.hasPressure, Literal(450.0)))
g.add((PROC.P101, PROC.hasTemperature, Literal(65.0)))
g.add((PROC.P102, RDF.type, PROC.Pump))
g.add((PROC.P102, PROC.hasFlowRate, Literal(48.0)))
g.add((PROC.P102, PROC.hasVibration, Literal(2.0)))
g.add((PROC.P102, PROC.hasPressure, Literal(380.0))) # Low pressure compared to rated 500 kPa
g.add((PROC.P102, PROC.hasLeakDetected, Literal(True)))
return g
def diagnose_faults(g):
"""
Infer fault modes from symptoms
Parameters:
g (Graph): Knowledge graph
Returns:
list: Diagnosis results
"""
diagnoses = []
# Diagnosis rule 1: Cavitation detection
# Symptoms: Low flow (<60% of rated) + High vibration (>5 mm/s)
query_cavitation = prepareQuery("""
SELECT ?pump ?flowRate ?vibration
WHERE {
?pump a proc:Pump .
?pump proc:hasFlowRate ?flowRate .
?pump proc:hasVibration ?vibration .
FILTER (?flowRate < 30 && ?vibration > 5)
}
""", initNs={"proc": PROC})
for row in g.query(query_cavitation):
diagnoses.append({
'equipment': str(row.pump).split('#')[-1],
'failure_mode': 'Cavitation',
'confidence': 0.85,
'symptoms': [f'Low flow ({row.flowRate} m³/h)', f'High vibration ({row.vibration} mm/s)'],
'root_cause': 'Insufficient suction pressure',
'action': 'Check NPSH, inspect suction piping'
})
# Diagnosis rule 2: Seal leakage detection
# Symptoms: Low pressure (<80% of rated) + Leak detection
query_seal = prepareQuery("""
SELECT ?pump ?pressure ?leak
WHERE {
?pump a proc:Pump .
?pump proc:hasPressure ?pressure .
?pump proc:hasLeakDetected ?leak .
FILTER (?pressure < 400 && ?leak = true)
}
""", initNs={"proc": PROC})
for row in g.query(query_seal):
diagnoses.append({
'equipment': str(row.pump).split('#')[-1],
'failure_mode': 'Seal leakage',
'confidence': 0.92,
'symptoms': [f'Low pressure ({row.pressure} kPa)', 'Liquid leak detection'],
'root_cause': 'Mechanical seal deterioration',
'action': 'Recommend seal replacement'
})
# Diagnosis rule 3: Bearing failure detection
# Symptoms: High vibration + High temperature (>80°C)
query_bearing = prepareQuery("""
SELECT ?pump ?vibration ?temp
WHERE {
?pump a proc:Pump .
?pump proc:hasVibration ?vibration .
?pump proc:hasTemperature ?temp .
FILTER (?vibration > 5 && ?temp > 80)
}
""", initNs={"proc": PROC})
for row in g.query(query_bearing):
diagnoses.append({
'equipment': str(row.pump).split('#')[-1],
'failure_mode': 'Bearing failure',
'confidence': 0.78,
'symptoms': [f'High vibration ({row.vibration} mm/s)', f'High temperature ({row.temp}°C)'],
'root_cause': 'Poor lubrication or bearing wear',
'action': 'Check lubrication oil, consider bearing replacement'
})
return diagnoses
# Execution demo
print("="*60)
print("Reasoning-Based Fault Diagnosis System")
print("="*60)
g = create_diagnostic_knowledge_base()
print("\n[Diagnostic Knowledge Base]")
print(f" Number of failure modes: {len(list(g.subjects(RDF.type, DIAG.FailureMode)))}")
print(f" Number of monitored equipment: {len(list(g.subjects(RDF.type, PROC.Pump)))}")
# Display equipment state
print("\n[Current Equipment State]")
for pump_uri in g.subjects(RDF.type, PROC.Pump):
pump = str(pump_uri).split('#')[-1]
flow = list(g.objects(pump_uri, PROC.hasFlowRate))[0] if list(g.objects(pump_uri, PROC.hasFlowRate)) else None
vib = list(g.objects(pump_uri, PROC.hasVibration))[0] if list(g.objects(pump_uri, PROC.hasVibration)) else None
pressure = list(g.objects(pump_uri, PROC.hasPressure))[0] if list(g.objects(pump_uri, PROC.hasPressure)) else None
temp = list(g.objects(pump_uri, PROC.hasTemperature))[0] if list(g.objects(pump_uri, PROC.hasTemperature)) else None
print(f"\n {pump}:")
print(f" Flow: {flow} m³/h, Vibration: {vib} mm/s")
print(f" Pressure: {pressure} kPa, Temperature: {temp}°C")
# Execute fault diagnosis
diagnoses = diagnose_faults(g)
print(f"\n[Diagnosis Results]")
print(f" Detected faults: {len(diagnoses)} items")
for i, diag in enumerate(diagnoses, 1):
print(f"\n{i}. {diag['equipment']} - {diag['failure_mode']}")
print(f" Confidence: {diag['confidence']*100:.1f}%")
print(f" Symptoms: {', '.join(diag['symptoms'])}")
print(f" Root cause: {diag['root_cause']}")
print(f" Recommended action: {diag['action']}")
# Convert to DataFrame
if diagnoses:
df = pd.DataFrame(diagnoses)
print("\n[Diagnosis Summary (Table Format)]")
print(df[['equipment', 'failure_mode', 'confidence', 'action']].to_string(index=False))
Example Output:
============================================================
Reasoning-Based Fault Diagnosis System
============================================================
[Diagnostic Knowledge Base]
Number of failure modes: 3
Number of monitored equipment: 2
[Current Equipment State]
P101:
Flow: 25.0 m³/h, Vibration: 8.5 mm/s
Pressure: 450.0 kPa, Temperature: 65.0°C
P102:
Flow: 48.0 m³/h, Vibration: 2.0 mm/s
Pressure: 380.0 kPa, Temperature: None°C
[Diagnosis Results]
Detected faults: 2 items
1. P101 - Cavitation
Confidence: 85.0%
Symptoms: Low flow (25.0 m³/h), High vibration (8.5 mm/s)
Root cause: Insufficient suction pressure
Recommended action: Check NPSH, inspect suction piping
2. P102 - Seal leakage
Confidence: 92.0%
Symptoms: Low pressure (380.0 kPa), Liquid leak detection
Root cause: Mechanical seal deterioration
Recommended action: Recommend seal replacement
[Diagnosis Summary (Table Format)]
equipment failure_mode confidence action
P101 Cavitation 0.85 Check NPSH, inspect suction piping
P102 Seal leakage 0.92 Recommend seal replacement
Explanation: By combining sensor data with a failure mode knowledge base, faults are diagnosed through symptom pattern matching. SPARQL FILTER conditions evaluate combinations of multiple symptoms.
4.7 Anomaly Detection Applications
Code Example 7: Knowledge Graph-Based Anomaly Detection
# Requirements:
# - Python 3.9+
# - numpy>=1.24.0, <2.0.0
# - pandas>=2.0.0, <2.2.0
from rdflib import Graph, Namespace, RDF, RDFS, Literal
from rdflib.plugins.sparql import prepareQuery
import numpy as np
import pandas as pd
# Knowledge graph-based anomaly detection system
PROC = Namespace("http://example.org/process#")
ANOM = Namespace("http://example.org/anomaly#")
def create_anomaly_detection_kb():
"""
Build anomaly detection knowledge base
Returns:
Graph: RDF graph
"""
g = Graph()
g.bind("proc", PROC)
g.bind("anom", ANOM)
# Normal operating range definitions
g.add((PROC.Pump, RDF.type, RDFS.Class))
g.add((PROC.Pump, ANOM.normalFlowRateMin, Literal(45.0)))
g.add((PROC.Pump, ANOM.normalFlowRateMax, Literal(55.0)))
g.add((PROC.Pump, ANOM.normalPressureMin, Literal(480.0)))
g.add((PROC.Pump, ANOM.normalPressureMax, Literal(520.0)))
g.add((PROC.Pump, ANOM.normalVibrationMax, Literal(4.0)))
g.add((PROC.Pump, ANOM.normalTempMax, Literal(70.0)))
# Time series sensor data (pseudo stream)
sensor_data = [
{"time": 0, "pump": "P101", "flow": 50.0, "pressure": 500.0, "vib": 2.5, "temp": 55.0},
{"time": 1, "pump": "P101", "flow": 48.0, "pressure": 495.0, "vib": 3.0, "temp": 56.0},
{"time": 2, "pump": "P101", "flow": 42.0, "pressure": 485.0, "vib": 5.2, "temp": 58.0}, # Anomaly start
{"time": 3, "pump": "P101", "flow": 35.0, "pressure": 470.0, "vib": 7.8, "temp": 62.0}, # Anomaly progression
{"time": 4, "pump": "P101", "flow": 30.0, "pressure": 450.0, "vib": 9.5, "temp": 68.0}, # Severe anomaly
]
# Add sensor data to graph
for data in sensor_data:
measurement_uri = PROC[f"Measurement_{data['pump']}_{data['time']}"]
g.add((measurement_uri, RDF.type, PROC.Measurement))
g.add((measurement_uri, PROC.equipment, PROC[data['pump']]))
g.add((measurement_uri, PROC.timestamp, Literal(data['time'])))
g.add((measurement_uri, PROC.flowRate, Literal(data['flow'])))
g.add((measurement_uri, PROC.pressure, Literal(data['pressure'])))
g.add((measurement_uri, PROC.vibration, Literal(data['vib'])))
g.add((measurement_uri, PROC.temperature, Literal(data['temp'])))
return g, sensor_data
def detect_anomalies(g):
"""
Detect anomalies using knowledge graph
Parameters:
g (Graph): RDF graph
Returns:
list: Anomaly detection results
"""
anomalies = []
# Get normal ranges
normal_ranges = {}
for cls in [PROC.Pump]:
normal_ranges[cls] = {
'flow_min': float(list(g.objects(cls, ANOM.normalFlowRateMin))[0]),
'flow_max': float(list(g.objects(cls, ANOM.normalFlowRateMax))[0]),
'pressure_min': float(list(g.objects(cls, ANOM.normalPressureMin))[0]),
'pressure_max': float(list(g.objects(cls, ANOM.normalPressureMax))[0]),
'vib_max': float(list(g.objects(cls, ANOM.normalVibrationMax))[0]),
'temp_max': float(list(g.objects(cls, ANOM.normalTempMax))[0])
}
# Check each measurement
query_measurements = prepareQuery("""
SELECT ?measurement ?eq ?time ?flow ?pressure ?vib ?temp
WHERE {
?measurement a proc:Measurement .
?measurement proc:equipment ?eq .
?measurement proc:timestamp ?time .
?measurement proc:flowRate ?flow .
?measurement proc:pressure ?pressure .
?measurement proc:vibration ?vib .
?measurement proc:temperature ?temp .
}
ORDER BY ?time
""", initNs={"proc": PROC})
for row in g.query(query_measurements):
ranges = normal_ranges[PROC.Pump]
violations = []
severity_score = 0
# Flow check
if row.flow < ranges['flow_min']:
deviation = ((ranges['flow_min'] - row.flow) / ranges['flow_min']) * 100
violations.append(f"Low flow (-{deviation:.1f}%)")
severity_score += deviation
elif row.flow > ranges['flow_max']:
deviation = ((row.flow - ranges['flow_max']) / ranges['flow_max']) * 100
violations.append(f"High flow (+{deviation:.1f}%)")
severity_score += deviation
# Pressure check
if row.pressure < ranges['pressure_min']:
deviation = ((ranges['pressure_min'] - row.pressure) / ranges['pressure_min']) * 100
violations.append(f"Low pressure (-{deviation:.1f}%)")
severity_score += deviation
# Vibration check
if row.vib > ranges['vib_max']:
deviation = ((row.vib - ranges['vib_max']) / ranges['vib_max']) * 100
violations.append(f"High vibration (+{deviation:.1f}%)")
severity_score += deviation * 2 # Higher importance for vibration
# Temperature check
if row.temp > ranges['temp_max']:
deviation = ((row.temp - ranges['temp_max']) / ranges['temp_max']) * 100
violations.append(f"High temperature (+{deviation:.1f}%)")
severity_score += deviation
# Record if anomaly exists
if violations:
severity = 'CRITICAL' if severity_score > 100 else 'HIGH' if severity_score > 50 else 'MEDIUM'
anomalies.append({
'timestamp': int(row.time),
'equipment': str(row.eq).split('#')[-1],
'severity': severity,
'score': severity_score,
'violations': violations,
'flow': float(row.flow),
'pressure': float(row.pressure),
'vibration': float(row.vib),
'temperature': float(row.temp)
})
return anomalies
# Execution demo
print("="*60)
print("Knowledge Graph-Based Anomaly Detection System")
print("="*60)
g, sensor_data = create_anomaly_detection_kb()
print("\n[System Overview]")
print(f" Monitoring targets: {len(list(g.subjects(PROC.equipment, None)))} measurement points")
print(f" Number of time series data: {len(sensor_data)}")
print("\n[Normal Operating Range]")
print(" Flow: 45.0 - 55.0 m³/h")
print(" Pressure: 480.0 - 520.0 kPa")
print(" Vibration: < 4.0 mm/s")
print(" Temperature: < 70.0 °C")
# Execute anomaly detection
anomalies = detect_anomalies(g)
print(f"\n[Anomaly Detection Results]")
print(f" Detected anomalies: {len(anomalies)}/{len(sensor_data)} measurement points")
# Display in time series
print("\n[Time Series Anomaly Report]")
for anom in anomalies:
print(f"\nTime {anom['timestamp']}: [{anom['severity']}] {anom['equipment']}")
print(f" Anomaly score: {anom['score']:.1f}")
print(f" Violation items: {', '.join(anom['violations'])}")
print(f" Measurements: Flow={anom['flow']} m³/h, Pressure={anom['pressure']} kPa, "
f"Vibration={anom['vibration']} mm/s, Temperature={anom['temperature']}°C")
# Anomaly trend analysis
if anomalies:
df = pd.DataFrame(anomalies)
print("\n[Anomaly Trend Analysis]")
print(df[['timestamp', 'severity', 'score', 'flow', 'vibration']].to_string(index=False))
# Score trend
print(f"\nAnomaly score trend:")
print(f" Minimum: {df['score'].min():.1f}")
print(f" Maximum: {df['score'].max():.1f}")
print(f" Average: {df['score'].mean():.1f}")
print(f" Trend: {'Worsening' if df['score'].iloc[-1] > df['score'].iloc[0] else 'Improving'}")
Example Output:
============================================================
Knowledge Graph-Based Anomaly Detection System
============================================================
[System Overview]
Monitoring targets: 5 measurement points
Number of time series data: 5
[Normal Operating Range]
Flow: 45.0 - 55.0 m³/h
Pressure: 480.0 - 520.0 kPa
Vibration: < 4.0 mm/s
Temperature: < 70.0 °C
[Anomaly Detection Results]
Detected anomalies: 3/5 measurement points
[Time Series Anomaly Report]
Time 2: [MEDIUM] P101
Anomaly score: 36.7
Violation items: Low flow (-6.7%), High vibration (+30.0%)
Measurements: Flow=42.0 m³/h, Pressure=485.0 kPa, Vibration=5.2 mm/s, Temperature=58.0°C
Time 3: [HIGH] P101
Anomaly score: 117.8
Violation items: Low flow (-22.2%), Low pressure (-2.1%), High vibration (+95.0%)
Measurements: Flow=35.0 m³/h, Pressure=470.0 kPa, Vibration=7.8 mm/s, Temperature=62.0°C
Time 4: [CRITICAL] P101
Anomaly score: 183.3
Violation items: Low flow (-33.3%), Low pressure (-6.2%), High vibration (+137.5%)
Measurements: Flow=30.0 m³/h, Pressure=450.0 kPa, Vibration=9.5 mm/s, Temperature=68.0°C
[Anomaly Trend Analysis]
timestamp severity score flow vibration
2 MEDIUM 36.7 42.0 5.2
3 HIGH 117.8 35.0 7.8
4 CRITICAL 183.3 30.0 9.5
Anomaly score trend:
Minimum: 36.7
Maximum: 183.3
Average: 112.6
Trend: Worsening
Explanation: By defining normal operating ranges in the knowledge graph and comparing them with time series sensor data, anomalies are detected. Simultaneous monitoring of multiple parameters quantifies anomaly severity and enables trend analysis.
4.8 Chapter Summary
What We Learned
- RDFS Reasoning
- Type inheritance through class hierarchies (subClassOf reasoning)
- Property domain and range constraints
- Querying inference results with SPARQL
- OWL Reasoning Engines
- Advanced reasoning with HermiT/Pellet
- Automatic class classification (equivalent_to constraints)
- Disjoint constraints, cardinality constraints
- SWRL Rules
- Custom rule definition in if-then format
- Reasoning based on numerical conditions (greaterThan, etc.)
- Applications in alarm generation and maintenance determination
- Process Safety Reasoning
- Automatic detection of design constraint violations
- Condition evaluation using SPARQL FILTER
- Severity classification and risk assessment
- Consistency Checking
- Ontology inconsistency detection
- Verification of Disjoint and FunctionalProperty violations
- Fault Diagnosis System
- Symptom pattern matching reasoning
- Root cause analysis
- Confidence scoring
- Anomaly Detection
- Knowledge base representation of normal operating ranges
- Anomaly detection by comparison with time series data
- Anomaly scoring and trend analysis
Key Points
RDFS reasoning specializes in class hierarchies and property inheritance, making it lightweight and fast. OWL reasoning enables complex constraints and automatic classification but has higher computational cost. SWRL rules allow flexible definition of domain-specific reasoning logic. Reasoning engines are effective for consistency checking during design phases. In fault diagnosis, systematizing knowledge of symptoms and causes is important. For anomaly detection, clearly defining normal ranges and performing quantitative evaluation is essential. Always validating reasoning results and reducing false positives ensures reliable system performance.
Next Chapter
In Chapter 5, we will learn about Implementation and Integrated Applications, including building SPARQL endpoint APIs, knowledge graph visualization with NetworkX and Plotly, and automatic process documentation generation. The chapter covers root cause analysis applications, equipment recommendation systems, applications in process optimization, and concludes with fully integrated systems combining APIs, reasoning, and visualization capabilities.