(127b) Predictive Asset Optimization and Intelligent Decision Making Utilizing Big Data to Enable Smarter Manufacturing | AIChE

(127b) Predictive Asset Optimization and Intelligent Decision Making Utilizing Big Data to Enable Smarter Manufacturing


Vaidhyanathan, R. - Presenter, IBM Chemical & Petroleum Center of Competency

The concept of Big Data is not new to oil & gas, downstream and petrochemical industries. In any operating facility there are thousands of sensors that are measuring process data every few seconds. This huge volume of data that is collected is typically stored in information management systems. In addition, large amounts of events data such as alarms, alerts and plant maintenance & reliability information such as issues, notifications, reports, work orders, failures are stored in the alarm / alert management systems and maintenance management systems.

The oil & gas, downstream and petrochemicals industries are facing challenges such as aging workforce with wide gaps in experience, aging asset base with more complex operations, market variability and speed of change requiring faster and better decisions at real-time, increased competition driving operational excellence, production and energy optimization and sustainability, increasing environmental regulations requiring effective controls, safe, secure and reliable operations for compliance.

The limited resources available are more focused on day-to-day routine operations, maintenance and response. There are no spare resources available to invest the time to analyze the vast amount of existing sensor and event data / information to proactively identify / discover trends and patterns and utilize those insights to optimize manufacturing operations and to improve the reliability of critical assets.

Given these challenges and the context of the emerging convergence of Information Technology and Operations Technologies, we will discuss using various example use cases from oil & gas, downstream and petrochemicals industries, how emerging technologies such as Predictive Analytics and Cognitive Computing (e.g.  Watson) utilize big data to improve performance, profitability, efficiency, quality, safety and asset availability. Utilizing these technologies, we can analyze and correlate a combination of all available data and information in various formats to identify trends and patterns and / discover insights. Based on this improved predictive operational intelligence visualized through advanced operations and maintenance dashboards, the operations and maintenance resources can be more effective and enabled to make the right decisions in real-time to increase the availability and utilization of critical equipment leading to optimized production, reduced unplanned capacity loss, reduced quality impacts, improved safety and reduced overall maintenance cost.