Global optimization for refinery and chemical planning
Find the plans local solvers miss. Answer every what‑if in under a minute.
One model of the plant for engineers, planners and traders, built to replace the planning LP, the process simulator and the spreadsheets between them.
- Global optimumon every classic pooling benchmark with a known optimum
- Same planevery time, so a case delta is the scenario, not the solver
- GPUaccelerated, built for the largest models
Planning today
Three groups, three tools, three versions of the same plant.
Engineers
Model the plant rigorously. Nobody downstream uses it.
Planners
Plan in a separate model, kept by hand and refreshed every few months. Each case takes minutes to hours, so few what-ifs get asked.
Traders and analysts
Work on their own copy of the numbers, pasted from last week's run.
Prometheus Studio: one model, from the plant to the trade.
Instead of Aspen Plus, Aspen HYSYS or AVEVA PRO/II
Facilities
Design the plant and its processes. Flowsheets solve, simulate and optimize in the browser, and their solves give the yields.
From flowsheet to planning model2:30
Instead of Aspen PIMS or AVEVA Spiral Suite
Plans
Plan on those yields: multiperiod, with cases, solved by a global engine, every what-if in under a minute.
Setting up a plan3:35
Instead of copies in Excel
Apps coming
Trade on the plans: traders and analysts run cases and read results on the same plan, never a copy.
See it work
See the plant become the plan.
Facilities become plans. Drag a unit from the library, wire it in and solve it. Then turn the solve into the planning unit’s operating modes, with their yields.
Optimize the plan. The units bring their operating modes from the facility. One solve picks the modes, the crudes and the products, and the profit and loss shows where the margin comes from.
Explain the Base plan: what drives the margin?
Base makes 211.1 MUSD over 180 days. The crude unit runs full in every period, so the margin is set by the crude slate.
Medium - sweet is the best buy: one more barrel of it would earn 0.96 USD over its price, so the plan takes all 40 kbbl/d on offer. Heavy - sweet and Heavy - sour fill the rest of the unit.
Vacuum gas oil and diesel earn the most: 425 and 297 MUSD of sales.
Ask the plan. The assistant reads the plan and its last solve, re-plans what-ifs on a copy in seconds, and proposes a case when one is worth keeping. Nothing changes until you add it.
A planner's week
Four requests, four answers, each in under a minute.
On a synthetic Gulf Coast conversion refinery: 160 kbbl/d, 12 crudes, three months. Each request is re-planned from the solved plan and ends as a case the planner can keep.
-
Monday
An email from operations
"The reformer will be down for the second period for a catalyst change." The assistant sets up the turnaround, re-plans and explains what it costs and why. The turnaround shows up in Capacity as a bar, just as if the planner had drawn it.
-
Tuesday
An offer from a trader
"Basrah Heavy at $3/bbl below its usual price. Take it?" The assistant tries the offer on a copy and answers yes or no, with the amount. The plan stays as it was.
-
Wednesday
Actuals from the plant
Attach the first week's file. The assistant holds the days that already happened and re-plans the rest of the horizon from them.
-
Thursday
A question before the planning meeting
"What limits us, and how does the turnaround compare with Base?" The assistant gives notes for the meeting and the margin's bridge, line by line.
The solver
Better plans, and the same plan every time you ask.
Blending and pooling make refinery planning nonconvex: a pool's quality depends on what flows into it, and every downstream spec depends on that quality. The pooling problem is NP-hard and has been studied for nearly 50 years, from Haverly (1978) to Ben-Tal et al. (1994) and Adhya et al. (1999). Local methods such as SLP and distributive recursion climb from a starting guess and stop at the first optimum they reach, and on the report a local optimum looks exactly like the true one.
What-ifs that don't add up
Adding a constraint can only cost money, or leave the margin where it was. With a local optimizer, it can come back better: the new case just landed on a better local optimum than the base did. So every case-versus-base delta mixes the scenario's real effect with the solver's luck, and the planner can't tell which is which. A turnaround that "makes money" or a cargo that "costs nothing" is the solver talking, not the plant.
Our own search method, not an off‑the‑shelf solver
Today’s planning tools hand the hard part to a general-purpose solver: SLP or distributive recursion in PIMS-class tools, IPOPT, CONOPT or a generic global solver elsewhere. Those solvers know nothing about refineries. Local ones stop at the first optimum they reach. Generic global ones can prove an optimum on small models, but on a full refinery plan they can run out of time well short of the best plan.
We wrote our own. It is built on the structure of the plant itself, starts from a much tighter view of where the best plans are, and explores many directions at once, in parallel, on CPUs and GPUs.
Pooling benchmarks
Hours in the literature. Seconds with Prometheus.
The published pooling benchmarks, against the methods and runs that set their best-known values.
Sources and method
Hard instances: the large random set of Dey & Gupte, Operations Research 63(2), 2015, Table 2 and e-companion EC.2–EC.3: BARON 12.7.3 run for 6 h on NEOS; their MILP heuristic, CPLEX 12.2 single-threaded for 1 h; randstd22 and 26 proven optimal by BARON in 28 min 40 s and 55 min 2 s. Bars show the length of the run that produced each best-known value. Not shown: randstd55 and randstd58, where our runs end below the best known.
Classic instances: Jalilian & Kocuk, arXiv 2306.10810 (2023), Table 14: Gurobi 9.1 on the source-based formulation, 1 h limit; Adhya 1–4 end unproven at the limit with gaps of 2.0%, 0.07%, 2.5% and 4.0%. Other formulations and bound tightening solve them faster. Haverly 1–3, Ben-Tal 4–5, Foulds 2 and RT2, not shown: ours in 1.5–1.6 s.
Start with a pilot
Keep PIMS. Give it a better place to start.
Aspen PIMS stops at the local optimum nearest its starting point, which is why planners spend so much time tuning initial guesses (PGUESS). Give it a better starting point and it finds a better plan, in your own model.
-
Step 1
Install it where your model lives
On your own servers or in your own cloud tenant. Your model, prices and plans never leave your network.
-
Step 2
Run the global search
The engine reads your PIMS model as it is today and searches the whole problem, not just the neighborhood of your current guesses.
-
Step 3
See the margin in your own numbers
Load the plan into PIMS as a PGUESS, run it, and compare the margin with your current plan in your own reports.
Not convinced yet? We first show the benefit on a public refinery model, then you run the same check on your own model, behind your firewall.
No migration, no new workflow, no leap of faith: the benefit shows up in the tool your planners already trust.
About us
Scientists and planners who got tired of waiting for the answer.
Our mission
Our mission is to provide cutting-edge technology solutions that help our customers maximize their margins and achieve their operational, trading, compliance, and environmental goals. We measure our success by the tangible benefits we deliver to our customers.
Our story
For years, the energy industry has been forced to rely on antiquated software that's slow, obscure, and painful to work with. The science behind these tools is often incorrect, providing suboptimal plans and incorrect price signals. As scientists, experienced industry professionals, and technology experts, we knew there had to be a better way.
We founded our company to provide cutting-edge technology solutions that help energy and chemical companies maximize their profit margins and make better decisions. Our software is built on the latest technology and sound scientific principles, and we're committed to continuous improvement to stay ahead of the curve.
We don't just sell software; we provide tangible benefits that make a real difference in our customers' bottom line.
Experienced leadership
Our leadership team brings decades of experience in both the tech and energy industries.
In the tech industry, our leaders have worked with the world's leading companies, developing technology to design distribution networks and optimize supply chain operations.
In the energy industry, our leaders have led successful projects to improve raw material valuation and acquisition. They have been instrumental in the development of optimization technology in the operation of midstream and downstream.
With their combined expertise in both the tech and energy industries, our leadership team is uniquely qualified to lead our company as we work to create cutting-edge technology for our customers.
Get started
See it on your own refinery.
Book a live demo. When you want to see it on your own model, start a pilot: it runs inside your network, and your data never leaves it.