Refining FCCU and Hydrotreater Emissions Minimization Workflow

This custom workflow automates the continuous, multi-variable optimization of Fluid Catalytic Cracking Units (FCCUs) and hydrotreaters—core profit centers with strict emissions permits. It ingests real-time data from CEMS, analyzers, and Distributed Control Systems (DCS) to dynamically adjust catalyst injection rates, reactor temperatures, and treat gas flows. The operational upside comes from reducing reagent consumption, avoiding regulatory excursions, and maintaining optimal yield, directly impacting fuel cost and margin. Implementation requires integration with existing Advanced Process Control (APC) layers and robust safety interlocks.

The architecture is built on an orchestration engine, like LangGraph, that sequences specialized agents for data validation, predictive modeling, and control signal generation. Critical controls include approval gates for major setpoint changes, continuous observability dashboards, and automated exception routing to process engineers. Rollout requires phased testing against the plant's digital twin to de-risk interactions before live deployment. This workflow turns emissions compliance from a reactive constraint into a lever for operational efficiency and cost control.

This workflow directly targets the multi-variable optimization bottleneck where operators manually balance catalyst activity, reactor temperatures, and treat gas rates against shifting feedstocks and environmental constraints. The operational upside comes from reducing reagent consumption (e.g., SCR ammonia), avoiding permit excursions, and improving unit stability, which translates to lower operating costs and reduced carbon intensity. Implementation integrates with the refinery's distributed control system (DCS), real-time optimizer (RTO), and historian (e.g., OSIsoft PI) to ingest live process data and execute setpoint adjustments within defined safety envelopes.

The architecture requires robust controls, including a human-in-the-loop console for non-routine adjustments and a physics-informed digital twin for safe scenario testing. Observability is built into the orchestration layer, logging all agent decisions, constraint checks, and performance deltas for audit and continuous retraining. Rollout is sequenced agent-by-agent, starting with advisory mode before progressing to closed-loop control, ensuring operational trust and managing the integration complexity with legacy refinery systems like Honeywell Experion or Emerson DeltaV.

Phase 1 establishes a non-invasive digital twin by ingesting real-time data from the refinery's Distributed Control System (DCS), historian, and CEMS. This creates a high-fidelity simulation of the FCCU reactor-regenerator and hydrotreater treat gas systems. Agents are deployed in shadow mode, running parallel to human operators to predict optimal catalyst injection rates and reactor temperatures for emissions reduction. This phase validates model accuracy against historical data, builds operator trust, and quantifies the potential margin uplift from yield protection and reagent savings without touching live controls.

Phase 2 moves to a closed-loop advisory system, integrating the orchestration layer with the refinery's Advanced Process Control (APC) system. Agents now recommend setpoint changes, which are routed through a mandatory human-in-the-loop approval gate in the operator console. This phase hardens exception handling for sensor drift and process upsets while collecting performance data on emissions reduction and yield stability. Phase 3 transitions to full, guarded autonomy for predefined operating envelopes, with agents directly writing to the APC. Robust approval gates for major feedstock changes or unit startups remain, and comprehensive observability dashboards track emissions, efficiency, and agent decision rationale for continuous audit.

Deploying autonomous control for FCCU reactors and hydrotreaters requires a phased, governed rollout to mitigate operational risk. The architecture must integrate with existing Advanced Process Control (APC) layers and Distributed Control Systems (DCS) like Emerson DeltaV or Honeywell Experion. Initial phases deploy agents in a shadow mode, recommending setpoints for operator approval, before progressing to closed-loop control within pre-defined safety envelopes. This controlled approach validates the AI's decisions against process models and human expertise, building confidence while capturing early efficiency gains.

Governance is enforced through the orchestrator, which logs every agent decision, the rationale from the underlying model, and the resulting process outcome. This audit trail is critical for regulatory defense and continuous improvement. Rollout sequencing targets non-critical units first, with rigorous A/B testing against historical baselines to prove ROI in reduced reagent consumption and lower carbon intensity. Exception handling routes any setpoint exceeding pre-calibrated bounds to human engineers, ensuring the system operates within a digital twin-verified safe operating envelope before affecting physical controls.