Sustainability Maintenance Planning for GHG-Constrained Enterprise Operations
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Solution Overview
Problem
Challenges exist in efficiently tracking and improving sustainability parameters across hydrocarbon enterprise operations, including greenhouse gas emissions, energy consumption, and waste management, while identifying opportunities for enhancing sustainability.
Innovation Solution
A sustainability platform system that collects data from various sources, simulates action plans, and implements maintenance or replacement operations to reduce greenhouse gas emissions by scheduling device downtime for maintenance, using IoT devices and engineering workflow systems to optimize sustainability parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance operations are performed frequently to improve equipment reliability, then equipment reliability is improved, but productivity decreases due to increased downtime
Solution Approach 1:
The system performs preliminary maintenance actions by predicting equipment failures before they occur using machine learning models that analyze historical and real-time data. This allows maintenance to be scheduled proactively rather than reactively, improving reliability while optimizing downtime scheduling to minimize productivity impact.
Solution Approach 2:
The system implements continuous feedback loops where equipment performance data is collected, analyzed by ML models, and used to adjust maintenance schedules dynamically. This feedback mechanism ensures maintenance is performed based on actual equipment conditions rather than fixed schedules, optimizing both reliability and productivity.
2Measurement precision
If real-time data collection and simulation are implemented to improve sustainability parameter precision, then measurement precision is improved, but computational cost increases
Solution Approach 1:
The system applies partial simulation by running full sustainability simulations only for selected equipment or time periods rather than continuously for all equipment. Machine learning models predict sustainability parameters for cases where full simulation would be computationally expensive, providing sufficient precision while reducing overall computational cost.
Solution Approach 2:
The system uses machine learning models as simplified copies or proxies for complex sustainability simulations. These ML models are trained on simulation data and can quickly predict sustainability parameters without requiring full computational simulation, maintaining measurement precision while dramatically reducing computational energy consumption.
Data Source
AI summary
A method may include receiving a sustainability model indicative of a plurality of sustainability parameters associated with enterprise operations corresponding to production data of a hydrocarbon production system or facility data of buildings associated with the enterprise. The method includes simulating implementing action plans via a plurality of devices that correspond to the enterprise operations over time to determine an amount of greenhouse gas (GHG) emissions associated with the enterprise, identifying at least one of a plurality of engineering workflow systems to reduce the amount of GHG emissions based on the GHG emissions, such that the plurality of engineering workflow systems determines maintenance or replacement operations for equipment in the enterprise. The method includes sending commands to devices of the devices based on the action plan, such that the commands are cause the devices to go offline at a schedule time period to perform maintenance or replacement of the devices.