Propylene recovery looks straightforward on paper, yet the propane/propylene splitter is one of the most energy-intensive columns in the entire complex, driving up steam demand and operating costs while every minor feed swing chips away at yield and purity.
When volatile feedstock prices and logistics disruptions squeeze margins, small inefficiencies translate into millions in lost profit. AI-driven optimization can help cut operating costs by up to 50% in these energy-intensive areas — a game-changing lever when margins are under pressure.
Traditional models can’t tune away this complexity. The relationships among pressure, temperature, and catalyst activity are too nonlinear. Industrial AI learns those patterns in real-time, continuously steering setpoints to maximize recovery while reducing energy use. The six-step framework below shows exactly how to make that happen on your unit.
Prerequisites: Is Your PRU Ready for AI?
Before building any model, confirm you can feed it quality data. Comprehensive high-frequency data from key equipment, including towers, exchangers, and compressors, is crucial, but a full twelve months is not strictly required before developing AI optimization models.
Gaps or noisy streams undermine the statistical power of any algorithm, so confirm that your process historians manage second- or minute-level data, but typically use compression techniques that may introduce minimal losses.
A robust distributed control system (DCS) backed by stable PID and advanced process control (APC) layers provides the execution muscle. Without it, even the smartest model can only watch from the sidelines. When you integrate AI, the closed loop must write setpoints safely and predictably.
Use this readiness checklist to evaluate your system:
- Sensor health and recent calibrations
- Lab sample frequency matched to process dynamics
- Historian tag latency below a few seconds
- Firewalls configured for secure data bridges
- IT/OT connectors that expose real-time data to the AI platform
Well-documented data tags build transparency that operators can trust. Quick wins, recalibrating drifting transmitters, flagging stale lab tags, and tightening historian deadbands, often unlock more value than new hardware. Verify you have sufficient on-prem compute or a secure cloud edge to run inference without interrupting critical operations.
Step 1 – Benchmark Current Performance & Data Quality
Start by pulling high-frequency data from your DCS historian, ideally, with short sampling intervals, but trim out shutdown or turnaround periods. With that clean slice, calculate a baseline for propylene recovery, reboiler steam per metric tonne, and unplanned-downtime hours. Heat maps and box plots quickly reveal feed-swing bias and other hidden patterns that dilute recovery.
While scoring data tags on a 0 to 5 scale for completeness, accuracy, resolution, and latency can be a useful internal heuristic, it is not a formally recognized standard in process data validation. Even a single unreliable flow meter can skew the model. Robust baselines depend on reliable process historians, and poor data quality will sabotage any closed-loop effort before it starts.
Step 2 – Define Objectives & Success Metrics
Before you dive into modeling, establish what success looks like for your unit. Start by translating corporate goals into concrete targets the control room can track: a 2% bump in propylene recovery, a 5% cut in reboiler steam, or a 1°C tighter product-purity cushion.
Attach dollars to every target. Pull the latest planner price set so the model can convert each extra kilogram of propylene or saved kilogram of steam into margin. Linking objectives to real economics keeps everyone focused, especially when feedstock prices fluctuate with regional logistics pressures.
Lock in a frozen baseline window, typically 60–90 days of steady operation, before you activate the AIO model. Use the same KPI definitions after activation, then let simple t-tests verify whether improvements beat natural variability. A clear, immutable baseline prevents post-project debates about shifting goalposts.
Secure alignment across operations, planning, and leadership. Daily huddles that show how each control move supports energy-intensity targets in the propane/propylene splitter connect strategic decarbonization goals with front-line actions. When everyone sees that higher recovery also lowers emissions intensity, your objectives turn into sustained performance gains.
Step 3 – Integrate & Monitor Data
You start by wiring your distributed control system (DCS) and process historians to the AI platform through open protocols such as OPC-UA or MQTT. High-frequency tags stream directly into the platform, so even subtle pressure blips in the splitter or momentary feed swings are captured. Solidifying a single tag dictionary comes next; reconciling “C301_P” and “C-301_Press” into one standard name prevents model blind spots and speeds future troubleshooting.
Clean data is the lifeblood of industrial AI. Automated routines strip deadbands, flag slow drift, and align sample results with online measurements. This continuous scrubbing is essential because a model that learns from dirty inputs will push the column in the wrong direction.
Well-maintained process historians already store the required granularity, but you still need to weed out idle tags and sampling gaps before training begins; otherwise, you risk propagating errors through every layer of optimization.
Configure live dashboards that surface propylene recovery, steam rate, and anomaly alerts in real time. A quick rule of thumb: if an operator can’t see the same KPI the AI is optimizing, trust will erode fast. Keep an eye on three common integration hurdles that can derail your progress.
Firewall rules often block MQTT traffic, so whitelist only the required ports to maintain cybersecurity. Inconsistent tag conventions create confusion down the line; batch-rename with a script before ingestion, rather than retrofitting names later. Lab delay offset can throw off your models, so build a rolling buffer that shifts sample timestamps to match process time.
Addressing these issues up front lets the AIO solution—and your team—focus on driving higher recovery instead of fighting data fires.
Step 4 – Build Predictive & Control Models
Start by mapping every variable that influences propylene splitters, tower pressure, top temperature, reflux ratio, feed olefinicity, steam flow, condenser duty, and even ambient humidity. That complete feature list lets the algorithm see the same nuances you and your operators watch on the DCS.
The most effective approach combines mass- and energy-balance equations with deep learning networks. The physics keeps predictions grounded while the network learns the hard-to-model non-linearities that traditional control struggles with.
After cleansing, split data 70% for training, 15% for validation, and 15% for testing, then layer k-fold checks and early stopping to catch overfitting before it starts. This structured approach ensures your AIO solution performs reliably when feed composition shifts or ambient conditions change.
Model transparency matters for operator confidence. Generate SHAP plots and tornado charts so you can trace how a one-kilopascal pressure shift moves recovery or why certain steam trims pay back fastest.
Step 5 – Deploy Closed Loop AI Optimization
You start in advisory mode, where the AI observes plant behavior and surfaces recommended setpoints without writing to the DCS. Once the recommendations mirror, or improve on, operator intuition, you shift to operator-approved control and finally to fully autonomous closed-loop operation.
Because the controller relies on Reinforcement Learning (RL), it continuously learns from feed swings and ambient shifts, adjusting decisions in real time. Traditional APC remains intact; the AIO solution simply nudges manipulated variables inside existing limits, so operators can override at any moment.
Before cutover, validate every tag path, add soft limits for unreliable I/O, and run joint training sessions—these steps build trust and ensure a smooth transition to hands-off optimization.
Step 6 – Validate, Sustain & Scale
Once your system starts writing setpoints, freeze a 90-day baseline window and compare it against an equal period of post-deployment data. Run a simple 95% confidence t-test on key indicators—propylene recovery, reboiler steam per metric tonne, and unplanned downtime—to prove the uplift isn’t random noise. Your plant’s historian data makes these statistical checks fast and transparent, building operator trust in the results.
Long-term value demands discipline. Plan to retrain the model quarterly and run an economic re-validation once a year. This cadence lets the AI learn from drift in feed characteristics, catalyst age, or ambient conditions without losing its edge. Proactive monitoring dashboards flag data gaps, while scheduled operator re-training keeps front-line teams confident enough to act on AI moves.
With the propylene splitter running smoothly, turn your attention to the next bottleneck. Depropanizers, debutanizers, ethylene fractionators, or even the FCC main fractionator often share utilities and constraints. Optimizing them in sequence unlocks cross-unit synergies. As each additional unit comes online, the collective model behaves like a digital twin of your entire separation train, compounding energy savings and margin improvements over time through integrated optimization.
Imubit’s Closed Loop AI Optimization Will Fast-Track Your PRU Results
Imubit’s Closed Loop AI Optimization (AIO) gives your propylene recovery unit a fast path to measurable improvements.
The solution stands on three pillars: advanced technology, value sustainment that protects ROI, and workforce transformation that builds trust with front-line operations. Request a complimentary AIO consultation to discover how quickly Imubit can improve your PRU performance.
