ML Flare Event Prediction for Routine and Non-Routine Flaring

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing flaring management in industries such as oil and gas, petrochemical, and landfill relies heavily on manual approaches that are labor-intensive and lack scalability, leading to inefficiencies and environmental impacts, especially in distinguishing between routine and non-routine flaring events.

Innovation Solution

A machine learning (ML) model is employed to analyze real-time flare data from stacks, categorize flaring events as routine or non-routine, predict potential flare events, and provide parameter setpoints and advisory information to manage flaring effectively.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual approaches are used to distinguish between routine and non-routine flaring events, then operational expertise can be applied, but labor intensity increases and scalability is limited

Engineering Contradiction:

Improveaccuracy in distinguishing flaring eventsVSAvoidautomation of flaring management

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The patent replaces manual mechanical analysis by operational teams with an automated machine learning system that uses computational algorithms to analyze flare data, identify peaks, and categorize flaring events. This substitution eliminates labor-intensive manual processes while maintaining or improving accuracy through consistent algorithmic application.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the flaring management system to automatically categorize and analyze events without requiring continuous human intervention. The ML model independently processes flare data, identifies patterns, and generates insights, making the system self-sufficient while reducing dependency on operational expertise.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual flaring management is used, then existing operational knowledge can be leveraged, but productivity and scalability are reduced

Engineering Contradiction:

Improveefficiency of flaring managementVSAvoidtime for analyzing flare data

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces time-consuming manual analysis with automated computational processing. The ML system rapidly processes large volumes of flare data in real-time, identifying peaks and categorizing events instantaneously, thereby dramatically improving productivity and eliminating time losses associated with manual review processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary action by continuously monitoring and pre-processing flare data in real-time, so that when flaring events occur, they are already identified and categorized. This proactive approach eliminates delays in analysis and enables immediate response to flaring events.

Inventive Principle:

Principle #10Preliminary action

3Extent of automation

If real-time flare data analysis is implemented, then automated categorization is achieved, but computational complexity increases

Engineering Contradiction:

Improveautomation of flare event categorizationVSAvoidcomplexity of ML model system

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the complex task of flaring management into distinct modular components: data collection, peak identification, event categorization, and analysis. Each module performs a specific function independently, reducing overall system complexity while maintaining high automation capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary ML model layer that sits between raw flare data and operational decisions. This intermediary automatically processes and translates complex data patterns into actionable insights, managing computational complexity while enabling effective automation without requiring direct human intervention in the computational process.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260036950A1Method and system for managing flaring using machine learning (ML) model

Publication Date: 2026.02.05 HONEYWELL INTERNATIONAL INC

AI summary

A method for managing flaring using a machine learning (ML) model is disclosed. The method comprising receiving, via at least one processor, flare data from one or more flare stacks in real time; determining, via the at least one processor, one or more peaks within the flare data, using the ML model; categorizing, via the at least one processor, the flare data into a routine flaring and a non-routine flaring; predicting, via the at least one processor, flare events within the flare data, based at least on a historical data and a set of parameters; determining, via the at least one processor, one or more parameter setpoints and advisory information associated with the predicted flare events; and deploying, via the at least one processor, the determined one or more parameter setpoints and the advisory information on each of the one or more flare stacks, to manage the flaring.