EARLY RISK DETECTION
Identify abnormal trends before parameters enter critical ranges.
X-IRIS Insights
Operational intelligence for modern water treatment plants.
Practical articles on Web-SCADA, AIoT, AI Predictor, compliance data and cost optimization.
X-IRIS | AI FORECASTING & EARLY WARNING
ANTICIPATE RISKS. STAY AHEAD OF INCIDENTS
Choose a wastewater risk that can be evaluated on the facility’s own measurements and confirmed events.
BENEFITS
DON’T JUST KNOW WHAT HAS HAPPENED. AI HELPS YOU ANTICIPATE WHAT MAY HAPPEN NEXT.
MORE TIME TO PREPARE
Give teams more time to inspect, assess, and respond proactively.
DECISION SUPPORT
Provide risk levels and relevant information so users can prioritize the right issues.
FEATURED
ANTICIPATE. WARN EARLY. ACT PROACTIVELY.
X-IRIS combines real-time and historical data to analyze trends, identify anomalies, and forecast risks before parameters enter critical ranges, giving operations teams more time to inspect conditions and respond.
Understand Risks in the Right Operational Context
Multi-source data integration: Analyze data simultaneously from sensors, PLCs, SCADA, and operational history.
Understand the real operational context: Place each parameter within the correct process stage, equipment, and operating state.
Link multiple influencing factors: Consider water quality, flow rate, load, energy, and chemicals within a single view.
Build a foundation for forecasting: Standardize data to support anomaly detection and risk forecasting.
Detect Changes Before Thresholds Are Exceeded
Identify deviation trends: Detect abnormal signals as soon as the data begins to change.
Do not wait for thresholds to be exceeded: Help teams identify risks early before incidents become clearly established.
Pinpoint the anomaly location: Link alerts to the relevant process stage, tank, and equipment.
Prioritize issues that require action: Classify severity levels so teams can focus on the most important risks first.
Anticipate Incidents, Act Proactively
Forecast parameter trends:
Estimate the next developments of key operational indicators.
Risk-based alerts:
Classify conditions as normal, monitor, warning, and critical.
Display AI confidence:
Provide an additional basis for users to assess analysis results.
Deliver alerts to the right roles:
Help operators, managers, and supervisors access the information relevant to their responsibilities.
Ask the Data Directly, Get Answers Based on Your Needs
Ask questions directly: Quickly retrieve operational status, alerts, and forecasts.
Suggested common questions: Help users quickly access the information they need.
Answers based on actual data: AI uses data from the selected project to respond in context.
3 flexible response levels: Quick, Standard, and In-depth, depending on user needs.
Before implementation
Records to prepare for this solution
Choose a wastewater risk that can be evaluated on the facility’s own measurements and confirmed events.
Process data:Sources, units, location and available history.
Events:Confirmed disturbances and operating changes.
Quality:Gaps, sensor checks and sample times.
Response:Recipients and useful inspection time.
QUY TRÌNH TRIỂN KHAI
Bốn bước triển khai
Bắt đầu từ hệ thống hiện tại, mở rộng theo nhu cầu
01
Agree scope
Choose a defined operating question, owners and acceptance criteria.
02
Check records
Bring historical data, verified events, known data gaps and one risk that the operations team wants to investigate.
03
Pilot and review
Test source quality, permissions and representative tasks in the agreed scope.
04
Accept and hand over
Compare outcomes with the criteria, record open issues and agree support and expansion.
READY FOR END-TO-END DIGITAL TRANSFORMATION?
Discuss a scope that can be tested
Questions before implementation
Where should implementation start?
Choose a wastewater risk that can be evaluated on the facility’s own measurements and confirmed events.
Must existing equipment be replaced?
Replacement is not assumed. Check actual models, protocols, permissions and source quality. Document any necessary additions before agreeing the trial.
How are results accepted?
Agree measurable checks before the pilot. Retain source comparisons, user task results and open issues; define who confirms the outcome.
How are schedule and cost agreed?
They depend on sources, equipment, functions and intervention requirements. The assessment defines trial scope, responsibilities, cost items and acceptance evidence.
Can AI predict every incident?
No universal detection or warning-time guarantee applies. Evaluate correct alerts, false alerts, missed events and useful lead time on the facility data and operating modes.
Operational knowledge
Prepare before implementation
Practical guides to data, equipment and workflows for smart wastewater operations.
- AI wastewater risk forecasting: how to evaluate an operational pilotA wastewater forecast is useful when it identifies a relevant risk early enough for operators to verify conditions and respond.Read the guide
- Forecasting pH, COD, TSS and DO: sources and evaluation criteriapH, COD, TSS and DO records do not always represent the same kind of measurement.Read the guide
- Data quality before wastewater AI: a practical review checklistData quality affects how reliably a wastewater model can be evaluated and how operators interpret its output.Read the guide
- From threshold alarms to wastewater AI: a staged evaluation roadmapIntroducing AI works best when each stage has a question, evidence and acceptance criteria.Read the guide
- Wastewater recovery workflows: the roles of Web-SCADA and AIRecovery workflows connect detection, verification, approved action and confirmed outcome.Read the guide
- Traceable wastewater operation records for incident reviewAn incident record should connect what the system observed with what people checked and did.Read the guide
