Machine learning is having a major impact on countless industries across the globe. The energy sector is a prime example.
The energy and utilities sector is essential to the global economy. The production and consumption of energy resources is imperative for powering nations and business sectors, including transportation and manufacturing. According to an analysis by CB Insights, machine learning and AI are having a large impact on this industry in many ways.
STEM, a California based energy startup, raised nearly $400 million to launch a new machine learning system to boost the efficiency of energy startups. The United States Department of Energy also worked closely with Stanford to develop an autonomous grid.
Machine Learning is Driving the Evolution of the Energy Industry
The industry is evolving. Once slow to adopt new technologies, industry leaders are now leveraging technology, automation, and AI to develop competitive advantages. The energy industry is seeing greater connectivity in operations and processes.
The use of digital and machine learning processes has helped foster a revival among the titans of energy through connecting and modernizing older systems, incorporating innovative technologies, and leveraging data in new ways.
Here are a few ways machine learning is changing the way energy organizations operate to gain efficiencies, realize operational cost savings, and achieve continuous improvement.
1. Helping to Minimize Turnaround Times
When it comes to the management and maintenance of critical assets, handling turnaround processes is often difficult. Large turnarounds for refineries, power plants, and steam crackers are planned years in advance. An hour or day of lost time during a shutdown can cost an organization millions. However, dated technologies and manual processes are used simultaneously, leading to inefficiencies for employees and contractors and often causing turnarounds to go beyond budget and past schedule.
With digital transformation, organizations can streamline these workflows and automate the processes. Machine learning simplifies the automation of these processes. A single platform can provide workers with a central hub from which to work and complete visibility into turnaround activities.
For example, platforms like Appian allow organizations to deliver modern applications that sit on top of data and existing technology systems. These applications streamline project intelligence into a centralized place, automate turnaround tasks, and provide the real-time data visibility to ensure complicated turnarounds stay on time and on budget.
This allows organizations to fix issues quicker, minimize downtime, and be more responsive when unexpected problems arise.
2. Machine Learning Leads to Visibility
Relying on multiple systems and data sources can hinder visibility during projects, field work, and turnarounds. Even worse is that for many energy organizations this information is still in paper form.
In a 2018 whitepaper, Appian’s Vice President of Sales Glenn Healy explained, “When we look back at historical turnarounds, often what happens is we have completed this turnaround and it was a 30-day turnaround and we missed our deadline by five days, so we ask why did we miss,” said Healey. “We don’t know. No one knows. We don’t have the answers. Everything is all paper-based, so we have to look back at all the paper documents and track back. It becomes an exercise of analysis. Analysis causes paralysis, and nothing gets done.”
Digital transformation with an emphasis on machine learning is the answer to these issues. Automation empowers organizations to work more effectively at a lower cost.
3. Minimizing Risk and Improving Safety
Machine learning in the energy sector has helped address safety issues as well. Although Bain & Company reports more than 50% of companies are not leveraging digital technology to improve safety, companies that do are seeing greater gains in safety and their bottom line.
Technology gives organizations the ability to monitor assets, employees, and projects in real-time for instant information about problems or incidents. This way organizations can be proactive instead of reactive. For example, the Internet of Things (IoT) leverages data produced by sensors and networks to trigger a proactive response, such as closing a security gate or initiating a valve shut off.
Digital is improving safety with better insight, response, and communication among the field, business, and external parties.
Energy’s Revival is Dependent on Machine Learning
Energy leaders are leveraging machine learning more than ever. Adopting new technologies doesn’t have to be a radical change, but it can lead to tremendous efficiency gains. Even small machine learning driven automations like mobile applications in the field and automatic reporting can help provide employees and supervisors with information to make better decisions. Using digital information, teams have the improved visibility to work smarter, reduce turnaround times, and enhance field safety.
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