SCENARIO 03 · PROCESS WATER · DEMINERALISATION & POLISHING
Breakthrough forecasting for a demineralisation and polishing train
Capacity estimation from ionic load, breakthrough forecasting per quality attribute, and lead/lag switching inside demand and storage constraints.
ILLUSTRATIVE CASE STUDYIllustrative scenario. This is not a named customer project and claims no achieved result. It shows how NOVYRON would structure a technically governed engagement.
OPERATING CONTEXT
A utility runs lead and lag demineralisation vessels followed by a polishing bed. Influent ionic load varies through the year, while regeneration is scheduled mainly by treated volume and conservative experience.
CHALLENGE
- Conductivity alone gives limited early warning of silica or sodium leakage
- Changing influent quality consumes resin capacity at different rates
- Regeneration effectiveness varies with chemical strength and with sequence
- Switching early wastes capacity; switching late puts water quality at risk
SYSTEM BOUNDARY
NOVYRON supplies the software intelligence, integration and lifecycle layer. The process equipment, instruments, PLC/DCS logic, safety systems and approved operating procedures stay under the control of the customer and their equipment vendors.
WHAT NOVYRON WOULD BUILD
- Create vessel state models from flow, conductivity, silica and sodium and regeneration history
- Estimate cumulative ionic load and remaining capacity, with uncertainty attached
- Forecast breakthrough probability for each monitored quality attribute
- Score each regeneration by chemical mass balance, rinse duration and restored capacity
- Recommend lead/lag switching inside storage, demand and quality constraints
MODELLING & DECISION LOGIC
A state-space capacity estimator is corrected by online and laboratory observations. Survival models forecast breakthrough, while sensor-fusion logic flags analyser disagreement or probable valve leakage before it is read as a process change.
DELIVERY WORKFLOW
- S1Topology, resin and quality-rule workshop
- S2Data reconciliation and analyser-health assessment
- S3Historical service and regeneration model
- S4Back-test on known breakthrough and quality events
- S5Shadow recommendations across seasonal conditions
- S6Operator and utility-quality acceptance
DATA THE SCENARIO WOULD USE
Vessel topology and lead/lag state; service and regeneration valve sequences; flow, pressure, conductivity, pH, silica, sodium and any other approved quality measurement; influent laboratory chemistry; regenerant concentration and quantity; rinse volume and duration; treated-water storage and demand; resin age, maintenance and replacement history; and recorded quality holds or breakthrough events. Laboratory and analyser timestamps are reconciled with vessel residence time.
MEASUREMENT PLAN
A pilot starts from agreed definitions, baseline windows and exclusion rules. These are the measures that would be agreed, not results that have been obtained.
- Treated volume per regeneration
- Regenerant and rinse water per treated volume
- Breakthrough warning lead time
- Off-specification events and quality holds
- Restored capacity after regeneration
Results would be evaluated against comparable products, feed conditions and operating constraints, with model predictions, operator actions and external disturbances all kept traceable.
MEASURED RESULTS — STATUS
Requirements of this kind reach us as genuine process needs, which is evidence of demand — not of a delivered project or an achieved result. This therefore stays an illustrative scenario. A real pilot would measure remaining-capacity forecast error, breakthrough warning lead time, treated volume per regeneration, chemical and rinse-water intensity, restored capacity and off-specification events against an agreed baseline.
WHAT THE PILOT WOULD LEARN
Which ionic-load indicators best predict each breakthrough mode; whether analyser disagreement can be told apart from true leakage; how regeneration quality carries into the next service run; and when the polishing bed, rather than the vessel upstream of it, becomes the limiting risk. It also sets the abstention rule for when a key analyser is unavailable.
DATA & GOVERNANCE
- Read-only and shadow-mode deployment come before any approved write-back.
- Access is role-based, and data and model versions are logged.
- Site safety systems and validated quality release stay authoritative.
- Customer data use, retention and model-training rights are documented.
- Every recommendation shows its assumptions, its confidence and its constraint status.
EXPECTED OUTCOME CATEGORY
More defensible regeneration and switching decisions, with earlier visibility of quality risk. This scenario asserts no realised customer result.
DOMAINS USED IN THIS SCENARIO
Discuss a comparable process
Give us the process boundary, the tags you already have, the quality measures, the constraints and your current baseline. We will propose a data assessment and a shadow pilot — without a hardware-vendor change.