Refinery Operations Optimization AI

AI to optimize refinery crude-to-product value chains, augment APC, im

Refineries operate intricate units under strict constraints regarding safety, product quality, and economic viability. Artificial intelligence can improve planning processes, augment Automated Process Control (APC), manage energy consumption, and ensure quality assurance – all while maintaining operator oversight and regulatory compliance.

The goal is to maximize profit margins, enhance production yields, reduce energy usage and emissions, guarantee products meeting specified standards, and operate safely with clear, transparent recommendations and controls.

DCS/APC signals, lab results, crude assays, blend recipes, energy mete

AI applications within refineries focus on areas like property prediction, augmenting APC systems, creating ‘soft sensors’ for continuous monitoring, optimizing energy usage, forecasting yields, detecting anomalies, and estimating emissions. These capabilities are visualized through operator dashboards.

These dashboards provide advisory setpoints for energy and yield optimization, QA alerts regarding product quality, and real-time monitoring of emissions – facilitating informed decision-making throughout the refinery operation.

live demo · related simulation● LIVE

Detect anomalies and avoid excess emissions.

The implementation of this AI system begins with establishing a strong foundation: ensuring data quality, maintaining instrument health, creating baseline measurements for key performance indicators (energy, yields, product quality), and defining safety limits.

This foundational work allows the AI to accurately identify deviations from expected behavior and proactively mitigate potential issues like excessive emissions or process instability.

Frequently asked questions

What are hard limits, interlocks, and manual override in a refinery control system?

Hard limits, interlocks, and manual override represent layers of safety controls designed to prevent hazardous situations and ensure operator intervention when necessary.

Can you explain the purpose of shadow mode, what-if tests, and operator sign-off within the AI system?

Shadow mode allows the AI to simulate proposed changes without directly impacting the process, while ‘what-if’ tests explore potential outcomes. Operator sign-off ensures human approval before implementing any recommendations.

What information is captured in decision logs, model versions, and outcome records for the AI system?

Decision logs document all AI-driven decisions, model versions track the evolution of the AI algorithms, and outcome records capture the results – including any overrides made by operators.

What steps are involved in preparing for emissions reporting and creating relevant documentation?

Preparing for emissions reporting requires establishing comprehensive data collection procedures and generating detailed documentation to demonstrate compliance with environmental regulations.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation