Description:The invention provides an AI-driven Cognitive Digital Twin in which a physical chemical process is continuously synchronized with a hybrid virtual representation. The CDT receives process and asset data through industrial connectivity, validates and contextualizes the data, estimates current and future process states, detects anomalous behavior, reasons over a process knowledge graph to identify likely initiating causes and downstream consequences, produces human-interpretable explanations and confidence measures, and generates bounded control recommendations or validated supervisory actions.
In one embodiment, the CDT is organized as seven interacting layers: a physical-process layer; a connectivity layer; a data-and-context layer; a hybrid-twin layer; a cognitive-intelligence layer; a prediction-and-control layer; and a human/enterprise layer. The cognitive-intelligence layer is a principal extension beyond a conventional digital twin because it links numerical model outputs to equipment context, operating modes, process constraints, causal dependencies, maintenance history and corrective actions.
The invention further provides a safety gate that rejects or downgrades a proposed AI control action when uncertainty is excessive, the current state lies outside a validated model domain, process or equipment constraints are predicted to be violated, or a required confidence criterion is not satisfied. In such circumstances, operation remains in advisory mode or returns to an approved baseline controller. The architecture thereby supports progression from monitoring and advisory analytics to bounded supervisory closed-loop operation without replacing low-level regulatory control or independent safety instrumented systems.
, Claims:
1. An AI-driven Cognitive Digital Twin system comprising physical-process, connectivity, data-contextualization, hybrid-twin, cognitive-intelligence, prediction-control, and human/enterprise layers operating in a synchronized closed-loop for intelligent monitoring, prediction, reasoning, and control of industrial chemical processes.
2. The system of claim 1, wherein process data are acquired from DCS, PLC, gateways, edge devices, analyzers, laboratory, maintenance, and operator systems using OPC UA and optionally MQTT.
3. The system of claim 1, wherein the hybrid-twin combines a physical process model with temporal AI models, including LSTM, GRU, TCN, or transformer models, and provides soft-sensor estimates of difficult-to-measure quality variables.
4. The system of claim 1, wherein a decision safety gate evaluates control actions against quality, stability, equipment limits, operating constraints, confidence, and uncertainty, and invokes fallback control when required.
5. The system of claim 1, wherein model adaptation is event-triggered using validated fault-free data, with model validation, version control, shadow testing, registry, and rollback provisions.
6. A method comprising acquiring and contextualizing plant data, synchronizing a hybrid digital twin, predicting future states, detecting anomalies, performing knowledge-graph-based root-cause reasoning, estimating uncertainty, validating corrective actions, applying operator-guided or supervisory control, and using plant feedback for subsequent cognitive cycles.