Downstream

Refineries operate as tightly coupled, energy-intensive systems where small inefficiencies can quickly translate into large economic losses. We help downstream operators improve overall efficiency by building models that capture the physical behavior of units, utilities, and energy networks—providing clear visibility into how operating decisions impact cost, throughput, and product quality.

Our monitoring solutions focus on identifying abnormal and excessive energy consumption across process units, furnaces, compressors, and utilities. By combining first-principles relationships with data-driven anomaly detection, we enable early identification of inefficiencies, equipment degradation, and suboptimal operating modes—long before they show up in monthly energy or emissions reports.

We also develop tools to improve and continuously tune operating setpoints at the unit and system level. Physics-based models ensure recommendations remain safe and feasible, while machine learning captures nonlinear behavior, disturbances, and changing feedstock conditions. The result is more stable operation, improved yields, and reduced energy intensity without increasing operational risk.

At the planning and optimization layer, we enhance traditional linear programming (LP) refinery models by pairing them with machine learning and advanced analytics. ML-based forecasts, constraint softening, and scenario evaluation allow LP models to become more adaptive—responding dynamically to feed variability, unit performance, and market conditions rather than relying on static assumptions.

By integrating physics, optimization, and machine learning into a cohesive workflow, we help refineries move from reactive control to proactive optimization. Our solutions are computationally efficient, operationally grounded, and designed to deliver measurable improvements in profitability, energy efficiency, and operational reliability.