The industrial world has been associated with volatile supply and demand scenarios, dynamic pricing, and changing trade opportunities, necessitating the need for industrial artificial intelligence, or Industrial AI. To be able to thrive in an industrial environment, organizations need agility and a mindset to adapt to ever-changing realities. While agility and flexibility play a vital role in succeeding in the highly competitive industrial world, the more immediate and decisive challenge in every capital-intensive today is Sustainability. Due to the carbon-intensive operations of heavy industries, the amount of scrutiny by regulators, stakeholders as well as customers is at an all-time high. The industrial sector is the second-largest carbon emitter after the energy sector with industries like steel, cement, and chemical accounting for 30% of global carbon emissions. The sustainability strategy of industrial organizations will be a pivotal factor in determining their present and future success. Still, balancing efficiency, profits, and sustainability is often a daunting task.
The game-changing technology which can help industries face their sustainability challenges is Industrial AI. The most real-world impact of AI is here, and it has the power to create a greener future for companies and industries. AI is not an isolated technology to be applied in advanced labs. On the contrary, Industrial AI consists of an amalgamation of technologies that make industrial assets and processes more efficient.
Here are the top five reasons why Industrial AI is turning out to be a powerful sustainability tool for companies.
1. Industrial AI makes processes more reliable
Advanced industrial AI tools involve a combination of domain expertise, data science, AI, and engineering. The algorithms deployed in the Industrial AI solutions like Eugenie involve not only domain expertise but also, physics-based models, and satellite data processing.
For example, if an industrial organization needs to reduce emissions from a specific process, Industrial AI can enable process engineers to simulate various scenarios in a virtual replica – A digital twin of the process. This can save time and costs associated with manual efforts and wrong decisions.
2.Complex machine-learning algorithms in the form of Digital Twins
With Industrial AI solutions, operators can get AI-based insights in domain-specific terms without the support of data scientists and experts. The technical skill gap and the retiring workforce are issues that can be addressed effectively through real-time data-based digital twins – leading to better operational decision-making.
3. Reducing industrial energy wastage
Unplanned downtimes are the most draining industrial events in terms of resources as well as energy. Frequently failing assets and equipment can go undetected in a typical manufacturing scenario – leading to more energy usage, almost like a weak heart working harder to keep the blood circulation going. Energy used to restart the assets/processes shut down in downtime is a sheer waste. Additionally, a few instances of equipment failure can lead to the emission of hazardous gases in large quantities. Advanced Industrial AI solutions can provide timely alerts and traceability to detect which assets and processes are prone to failure in the near future and how an operational mishap can be prevented.
4. Reducing resource consumption with accurate anomaly detection
AI-driven predictive analytics provide near-accurate failure predictions and anomaly detection through complex operational data and failure point analysis. The insights of Industrial AI solutions can help with improving maintenance practices and avoiding looming asset failures, leading to excessive resource consumption.
5.Predicting emission risk assessment with AI-driven simulations
An important benefit of AI-powered solutions is the ability to track and calculate industrial emissions. Machine learning algorithms of Industrial AI can calculate greenhouse gas emissions as well as sustainability risk assessment. Simulations can provide the comparison of different variations of sustainability goals of companies.
For example, Eugenie’s Industrial AI product offers setting up various sustainability goals such as energy consumption, and process emissions tracking to be able to trace the high emitting assets and processes. Not only tracking, but Eugenie’s offerings also enable operators to immediately delegate high-priority tasks with an intent to improvise productivity and sustainability.
Industrial AI can support various sustainability activities without the need for additional expertise, which many enterprises currently lack and regard as a barrier to advancing AI capabilities. Advanced Industrial AI solutions like Eugenie enable businesses to strike a balance between profitability and sustainability, without investing in additional technical expertise. Our plug-and-play solution starts generating insights in a few days after a swift deployment.
Accelerate your sustainability journey with Eugenie. Contact us for a product demo or write to us at firstname.lastname@example.org