Running Real-Time 'What-If' Scenarios for Process Changes Workflow

This workflow eliminates the multi-day cycle of manual data gathering, spreadsheet modeling, and stakeholder review for process changes. By providing a conversational interface to a calibrated digital twin, engineers can test scenarios like a new fuel blend or equipment shutdown in minutes, not weeks. The business impact is faster adaptation to market conditions (e.g., fuel price shifts) and the ability to conduct more operational experiments within the same planning window, leading to continuous efficiency gains.

Manually forecasting the emissions impact of a process change is error-prone. This workflow automates the simulation, applying precise emission factors and plant-specific models to predict NOx, SOx, and CO2 outcomes against permit limits (including complex rolling averages). By surfacing potential violations before any physical change, it prevents costly compliance excursions, associated fines, and reputational damage. The architecture includes guardrail agents that flag high-risk scenarios for mandatory engineering review.

For plants with carbon capture, every operational change affects the capture energy penalty and credit generation potential. This workflow's agents model the trade-off between capture rate, energy cost, and real-time carbon market prices. Engineers can ask, 'What if we reduce capture to 70% during peak power prices?' and receive a net-present-value impact. This turns carbon management from a static compliance activity into a dynamic profit center, directly improving project ROI and supporting participation in demand response markets.

The implementation is a multi-agent system orchestrated via LangGraph. A Conversational Interface Agent parses the engineer's natural language query. A Query Planning Agent decomposes it into simulation parameters. A Digital Twin Agent executes the scenario on a physics-informed model (e.g., Aspen Plus, custom PDEs) hosted in a high-performance compute environment. A Reporting Agent synthesizes results into impact summaries for emissions, efficiency, and cost. The system integrates with the DCS/Historian (OSIsoft PI, Aveva) for real-time calibration and with CMMS/ERP (SAP, IBM Maximo) to contextualize maintenance impacts.

Phase 1: Build and calibrate the high-fidelity digital twin core for the flue gas train and capture unit, using 12+ months of historian data. Phase 2: Develop the agent orchestration layer (LangGraph) and a secure chat interface, integrating with plant SSO and role-based access. Phase 3: Implement the approval workflow: all simulation-triggered physical setpoint changes require a human-in-the-loop review and electronic sign-off in the system before being pushed to the DCS. Phase 4: Roll out with a controlled pilot group, using synthetic personas to stress-test the interface and exception handling before broad deployment.

Success depends on data quality; the workflow must include upstream agents for sensor validation and gap filling. Model drift is managed by continuous reconciliation of twin predictions against actual operating data, with alerts for growing deviation. The system requires full audit trails: every query, simulation result, and (if applicable) approval is logged with user, timestamp, and input data snapshot for compliance and root-cause analysis. Observability is built on OpenTelemetry traces across all agents and custom dashboards for simulation usage and outcome accuracy.

A secure chat or voice interface where engineers pose natural language queries (e.g., 'What if we blend 15% hydrogen into fuel stream A?'). An intent agent parses the query, extracts key variables (fuel type, blend ratio, equipment ID), and structures a formal simulation request for the orchestration layer. This agent handles clarification dialogues and enforces query permissions based on user role.

The core orchestration agent that receives the structured request. It retrieves the current live state of the plant from historians (OSIsoft PI, Aveva) and configures the physics-informed digital twin with the proposed changes. It then dispatches parallel simulation jobs, managing computational resources and ensuring the twin's boundary conditions (e.g., ambient temperature, current load) are correctly set for a valid comparison.

A specialized analytics agent that processes the raw simulation outputs. It calculates key delta metrics: changes in NOx/SOx/CO2 emissions (kg/hr), boiler efficiency (%), fuel consumption (MMBtu/hr), and potential carbon credit implications. It applies plant-specific business rules and regulatory thresholds to flag any simulated outcomes that would breach permit limits or economic guardrails.

This agent synthesizes the analysis into a concise, actionable impact report. It generates plain-English summaries, comparative charts, and data tables, highlighting trade-offs (e.g., '5% efficiency gain but 12% increase in natural gas cost'). Crucially, it cites the underlying model assumptions and data sources, creating an audit trail for the recommendation and building operator trust in the system's outputs.

The workflow's integration backbone. It provides secure, bidirectional APIs to the Distributed Control System (DCS - e.g., Emerson DeltaV, Siemens PCS 7) for live data and to the asset performance or production planning system (e.g., AspenTech, GE Digital). This allows the 'what-if' scenario to be seeded with actual plant conditions and, if approved, for optimized setpoints to be pushed back as candidate operating instructions.

A control layer that manages the lifecycle of a scenario. It logs all queries and users, routes high-impact or high-risk simulation results for mandatory engineering review before presentation, and can initiate a formal Management of Change (MOC) workflow if the user decides to implement the change. This ensures the speed of AI-driven simulation does not bypass required safety and operational governance.