|Abstract Title:||Optimizing the nexus between data, technology and environment|
|Session Choice:||Environmental Monitoring of Air|
|Presenter Name:||Mr Andrew Shek|
Abstract Information :
The purpose of this presentation is to examine the nexus between real-time data, technology and the environment, as it applies to holistic optimization of oil refining facility operations.
The oil refining industry is faced with competing operational objectives of production optimization and environmental performance. Traditionally this has been managed via specific process control systems tied to core production unit operations and targeted emission monitoring for key air emissions, supported by sampling and routine inspections.
This management approach has improved environmental compliance over time in line with regulatory requirements. However, it is often not correlated to business performance, nor does it paint a clear picture of a facility's interaction with the local environment and community as a whole.
Rapid improvements in real-time sensor and software technologies have opened up opportunities for refinery operators to look beyond discrete process control and source monitoring to whole-facility intelligent predictive management. This allows performance requirements and regulatory objectives to be linked to business performance, with the added benefit of improved environmental performance and enhanced community relations.
A case study using real-time sensor technology, cloud-based technology platforms and industry best-practice weather forecasting and modelling shows how an increased level of understanding of process operations can be achieved. By utilizing weather modelling and sensor data, operators are able to apply methods that identify emission sources in real time, hence increasing process performance and reducing production losses from fugitive emission sources. By capturing previously discrete data sets from production, continuous emissions monitoring and ambient sensor systems, an intelligent data set can be built that learns where fugitive emissions and losses are occurring. Compared to conventional management frameworks, such approaches provide a mechanism positive feedback between production (and financial) performance and environmental performance.