Energy & Oil/Gas · Refinery process optimization
Your historian records the giveaway. Your operators can't watch it all
Banao builds AI advisory systems over refinery historian data that hold distillation, cracking, and treating units closer to economic optimum around the clock — flagging yield giveaway, energy overconsumption, and off-spec drift before the shift change.
The model does not write to the DCS. It surfaces setpoint guidance to the operator and tracks whether acting on it closes the gap — so the control-room team stays in charge and the economics are measurable.
Indian Oil— AI applied to downstream operations data, integrated with existing plant systems.
The first call is free · 45 minutes · no obligation
What we build
What a Banao process optimization deployment covers
A process AI advisory is not a dashboard. It is a model trained on your historian, wired into your control room's workflow, and measured against your margin.
Yield giveaway detection by unit and shift
Models over historian and lab data that identify where product is being fractionated beyond specification requirements — the difference between what you yield and what you could, shift by shift and column by column.
Energy draw optimization
Steam, fuel gas, and electricity consumption modelled against production rate and feed quality. The system flags over-firing, excessive reflux, and pump inefficiency with the setpoint move that recovers it.
Feedstock quality adaptation
When crude blend or feed quality shifts, the model revises its setpoint guidance before downstream product specs drift off-target — closing the lag between the lab result and the operator's response.
Distillation column advisory
Continuous guidance on reflux ratio, side-draw rates, reboiler duty, and pressure to hold the column at economic optimum without waiting for a process engineer to intervene manually.
Operator advisory interface
Guidance surfaces in the control room as a recommended move and the margin impact of acting. The operator approves, overrides, or ignores — the system tracks which calls were accepted and monitors the result.
Shift-continuity model
Optimum operating practice captured in the model rather than in a person's head. Night shift and weekend runs hold the same setpoint discipline as the senior day operator — without a shadow programme.
Receipts
Deployed, with names attached
Metrics shown dotted (··) are being finalised in our case-study metrics pack. We do not publish a number before it is verified.
AI advisory over downstream process historian data
One of India's largest downstream operators runs high-volume refinery and process units. Banao applies pattern and advisory models to historian data to surface setpoint opportunities, integrated with existing plant systems rather than a parallel data stack.
Dogfooding
We operate on our own AI before you do
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens our engineering hires; Vikaas runs our own demand generation. Both run in production every working day, which is the standard we hold a process advisory model to.
A vendor who depends on AI to run their own operation approaches your historian data differently from one who has only sold it. We know what it costs when the model gives a bad steer.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When refinery process optimization AI doesn't pay
We will tell you before you commit to a build — that conversation is why refinery operations heads take our second call:
- Units already under tight APC: if a Honeywell or Aspen controller is already holding the unit within two percent of optimum, an AI layer adds coordination cost with marginal gain. We'll say so.
- Very short run-length between turnarounds: a model needs repeated operating cycles to learn what good looks like. A unit that runs three weeks then shuts is hard to model reliably.
- Instrumentation gaps: we don't need a clean data lake, but we need a signal. A column with no flow transmitters and one thermocouple is an instrumentation project before it is an AI project.
How we start
How we start — prove the margin before you build the model
We do not quote a process AI advisory off a brochure. We look at your historian first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We pull a historian sample and map the cleanliness and coverage of your process tags. You leave with a ranked list of optimization opportunities by unit, baseline ROI maths per opportunity, and an honest go/no-go. The Sprint fee is credited against the build if you proceed.
- 02
Build
Tag mapping, data engineering, and model training on your historian. The advisory interface integrates with your existing control-room displays and the DMS your operators already use — no new screen to learn.
- 03
Shadow mode and go-live
The advisory runs in shadow for four weeks alongside normal operations so operators can validate the guidance before it counts. Once trust is established, the model is live and retrains on each new data batch.
FAQ
Frequently asked questions
We already run advanced process control. What does AI add?
APC holds setpoints within a feasible region. A historian AI model identifies whether that region is the economic optimum given today's feed, product prices, and energy costs — and suggests where to move the APC targets, not override them. The two are complementary, not competing.
Our historian data has bad tags and significant gaps. Can you still build on it?
That is the normal starting point. No refinery has clean historian data. The Discovery Sprint audits tag coverage and data quality by unit and tells you what is buildable. Tag cleaning and gap-handling are part of the build deliverable, not a precondition you must solve first.
Will the AI write setpoints to the DCS automatically?
No — this is a deliberate design choice. The model surfaces a recommended move and the margin impact to the operator, who approves or overrides. Closed-loop control requires a separate safety instrumented system review that is outside the scope of an advisory build. We can scope that conversation separately if the refinery wants to progress there.
How do we quantify the opportunity before committing to a full build?
That is exactly what the AI Discovery Sprint produces. In two weeks we pull your historian, map the tag coverage, run a baseline analysis by unit, and hand you ROI maths for each opportunity — yours to keep whether or not you continue. Worst case: a free assessment. Best case: your capital business case.
How long until a live advisory is running on a CDU or FCC?
A typical path is two weeks of Discovery, six to eight weeks of build and integration, then four weeks of shadow mode before the advisory is live and trusted. Banao's ~300-engineer bench means the engagement starts in weeks, not the months a new internal hire would require.
Get started
Put your worst unit in front of our process model
Bring a historian sample and your toughest yield or energy problem. In 45 minutes we will tell you whether process optimization AI is worth building and what ROI you could realistically expect.
Book a Discovery Sprint