D03 · LIQUID SEPARATION · ULTRAFILTRATION
Intelligent Ultrafiltration Line Operations
Predict permeability loss, optimise filtration and cleaning decisions, and protect separation performance.
NOVYRON is software-first and vendor-neutral: this is configurable software, integration and lifecycle service, not process equipment. The existing PLC or DCS keeps interlocks and regulatory control, and every recommendation stays inside a site-approved operating envelope under human oversight.
THE INDUSTRIAL PROBLEM
Ultrafiltration performance moves with feed quality, temperature, viscosity, fouling and cleaning effectiveness. Operators balance flux, transmembrane pressure, recovery and product retention at the same time, and pushing one target can shorten run length or add a chemical clean.
THE NOVYRON LAYER
NOVYRON creates a membrane-train intelligence layer that normalises permeability, predicts the fouling trajectory and estimates time to a clean-or-stop threshold. It separates hydraulic drift from feed change, recommends bounded operating windows, and records the evidence behind each filtration and cleaning decision.
TYPICAL DATA INPUTS
- Feed, permeate and retentate flow
- Inlet, outlet and permeate pressure
- Temperature and calculated transmembrane pressure
- Turbidity, conductivity, TOC or product concentration where available
- Valve, pump and backwash states
- CIP recipe, chemical concentration, duration and post-clean permeability
MODELS AND DECISIONS
- Temperature-normalised permeability model
- Remaining-run-length forecast to a pressure or permeability threshold
- Multivariate fouling and integrity anomaly detection
- CIP effectiveness scoring and cleaning-sequence comparison
- Constrained flux and recovery optimisation by feed family
- Digital twin for train balancing, backwash and production scheduling
What each deployment reads, which models it runs, how it connects and how much authority it is given are designed for the site. None of it is pre-installed hardware, and none of it is a guaranteed autonomous function.
CORE CAPABILITIES
- TRAIN-BY-TRAIN HEALTH SCORE
- FEED-FAMILY & MEMBRANE-AGE CONTEXT
- REVERSIBLE VS PERSISTENT FOULING
- CIP GENEALOGY & RECOVERY CURVE
- INTEGRITY-TEST & ALARM CORRELATION
- WHAT-IF RUN PLANNING
HOW IT WORKS
- S1Create the membrane-train asset and tag model, train by train
- S2Calculate transmembrane pressure, recovery, flux and normalised permeability consistently
- S3Segment filtration, backwash, relaxation and cleaning states automatically
- S4Learn the fouling curve for each feed family and operating mode
- S5Validate run-length and cleaning-effectiveness predictions against history
- S6Deploy advisory operating windows and connect them to maintenance planning
INTEGRATION POINTS
- Skid PLC/SCADA and plant historian
- Online water or product-quality analysers
- LIMS and membrane integrity records
- CIP system and chemical inventory
- CMMS and production scheduling
NOVYRON connects read-only and runs in shadow mode first. Any write-back is designed, tested and approved separately. Interfaces are chosen to fit the architecture already on site rather than forcing a new hardware stack.
MEASUREMENT EXAMPLES
These are measures for a baseline and target-setting workshop, agreed from your own data. None of them is a statement of a result NOVYRON has achieved.
- Normalised permeability decline rate
- Run length between effective cleans
- Cleaning recovery and repeat-clean rate
- Specific energy per cubic metre of permeate
- Water and chemical use per production unit
- Permeate quality and product-retention compliance
TYPICAL APPLICATIONS
- INDUSTRIAL WATER REUSE
- FOOD, DAIRY & BEVERAGE CONCENTRATION
- BIOPROCESS & PHARMACEUTICAL SUPPORT
- SURFACE-WATER PRETREATMENT
- PROCESS STREAM RECOVERY
DEPLOYMENT PATH
- 01Membrane and instrumentation baseline
- 02Derived-variable definition and state segmentation
- 03Fouling and cleaning analytics
- 04Historical replay by feed family
- 05Shadow run-length forecasting
- 06Governed advisory with maintenance integration
WHAT STAYS OUTSIDE
Operating advice stays inside membrane-vendor limits, validated cleaning procedures and product-quality rules. Where a feed regime is not represented in the training data, the system says so rather than extrapolating, and pressure, pump and chemical protection remain with the control system.
SAFETY, QUALITY AND GOVERNANCE
- The deployment architecture can be designed to segregate customer data by tenant and deployment boundary, subject to the hosting, access-control and contractual terms agreed with you. Edge, on-premises, private-cloud and managed-cloud patterns are all evaluated against your constraints.
- Role-based access, encryption in transit and at rest, audit logging, model and version traceability and a secure update process are defined during solution design.
- Data provenance, retention and permitted model-training use are documented contractually. Customer data is not silently pooled into general models.
- Predictions carry quality flags and uncertainty where that is technically meaningful. Drift and out-of-distribution conditions trigger review rather than false precision.
Bring us the process boundary
Share the process, the signals you already collect, the control architecture, the constraints and the outcome you need. An engineer turns that into a scoped data assessment, a shadow pilot and a deployment plan.