By Emily Newton, revolutionized.com
Energy management is crucial for any industrial operation, but chemical processing facilities face exceptionally high demands. Balancing sustainability, cost efficiency and sufficient production volumes is challenging, but new technologies can help. One of the most beneficial of these innovations is AI energy management.
Artificial intelligence has gained a lot of attention lately and its potential goes far beyond automating back-office tasks. Here’s how it can improve energy management in chemical processing facilities.
Responding to Real-Time Data
One of AI’s key benefits in energy management is it can adjust operations according to real-time data. Smart thermostats demonstrate this potential on a small scale, saving consumers hundreds of pounds annually by minimising energy waste, but industrial applications can save even more.
AI can analyse energy consumption in real time and compare it to factors like temperatures and production output. It can then lower or shut off power to maintain ideal conditions with as little electricity as possible.
These energy savings may seem minimal by themselves, but they add up over time. Applying these real-time responses to an entire facility over a year will prevent considerable power waste. Chemical manufacturers will experience lower operating costs and more sustainability as a result.
Highlighting Inefficiencies
AI energy management solutions can analyse workflows to find areas of electrical waste. This waste can stem from many factors, from incorrect machine sizing to using a less-efficient process to produce the same chemical. Because there are so many factors to consider, optimal production can be difficult for humans to determine, but AI excels in these decisions.
Machine learning algorithms can spot connections and trends in data faster and more accurately than humans. Consequently, they’re ideal for finding more efficient alternatives to current processes.
Chemical processing facilities can start by collecting data across their workflows to create a digital twin — a virtual representation of their real-world operation. AI can then simulate various changes in this twin to find which new processes yield the most improvements. Following this guidance will help chemical plants find the most energy-efficient way forward.
Finding New Feedstocks
In addition to finding potential inefficiencies, AI can help find new possibilities for energy efficiency. One of this application’s best specific use cases is determining new feedstocks.
Many energy initiatives in the chemical sector focus on industrial processes, but a chemical’s raw materials impact efficiency, too. Some feedstocks may produce the same chemicals through the same process but require less power. Others may enable manufacturers to employ more energy-efficient production methods. In either case, AI can identify ideal candidates the same way it determines the best processing techniques.
Simulating how different feedstocks will impact production efficiency is an easy-to-miss but crucial business advantage. More than three-fourths of AI leaders in the chemical industry use the technology to discover new materials, compared to just 31% of those who’ve achieved less value from AI.
Designing Energy-Efficient Chemical Products
Chemical manufacturers can apply AI energy management to the product development phase. Retrofitting more efficient processes to existing products is beneficial, but it’s sometimes better to ensure a product is efficient from the beginning. AI provides the necessary insight to achieve that.
Manufacturers can perform AI energy analysis of potential feedstocks and production methods for a theoretical product the same way they can for an existing one. That way, they can determine which chemical is the most viable to produce while minimising energy consumption before manufacturing anything.
Organisations can also use AI to analyse client purchasing habits to decide what kinds of products will perform best economically. Comparing these insights to their earlier, energy-focused research will help them identify the best products to focus on in the future.
Improving Machine Efficiency Through PdM
AI can improve energy management in less direct ways as well. One of the most advantageous is predictive maintenance (PdM). This practice uses AI to analyse machine health factors like temperatures and vibrations to predict when equipment will need repair. While PdM may be most widely known as a way to reduce maintenance costs, it’s also an excellent way to minimise energy waste.
Industrial equipment works less efficiently the more it falls behind proper maintenance schedules. As manufacturers keep it in top condition for longer through PdM, they ensure it wastes as little energy as possible.
These AI applications also help manufacturers take more control over their equipment’s service lives. Components like transformers can be recycled to promote sustainability, but knowing when to recycle them and when to repair them can be difficult without AI. AI can track potential returns and energy trends to inform manufacturers when it’s time to repair and when to replace.
Managing Renewable Energy
AI energy management can make renewable power a more viable solution. Renewables like wind and solar decrease energy-related emissions but don’t produce power on demand. Consequently, electrical systems must store or distribute energy differently depending on current generation and usage trends. AI enables those adaptations.
AI-enabled smart transformers can analyse current energy consumption levels to distribute electricity where it’s needed most at any moment. By diverting power away from where it’s not required and toward where it is, AI minimises waste, despite not having on-demand energy generation.
This adaptive energy direction makes renewables a viable business solution where they may not be otherwise. Chemical manufacturers can then capitalise on them to appeal to eco-conscious markets and protect the planet.
Suggesting Supply Chain-Level Changes
Chemical processors can also use AI to improve energy efficiency in their broader supply chains. In-house processes may take the most attention, but up to 80% of total emissions stem from indirect workflows in organisations’ supply networks. AI can minimise these, too.
Machine learning tools can analyse digital twins of supply chains the same way they do for specific facilities. They can then find where the most significant inefficiencies and areas of potential improvement lie.
Adapting to these suggestions from AI can help chemical manufacturers ensure their partner companies use less energy, too. The chemical sector as a whole will become more efficient as a result.
Chemical Manufacturers Need AI Energy Management
The chemical industry consumes a lot of power and releases considerable carbon emissions. If these companies hope to reduce their environmental impact and operating expenses, they must become more efficient. AI energy management enables those changes.
As more chemical producers embrace AI, it will quickly become a key factor in remaining competitive. Familiarity and research into the technology today will help ensure success tomorrow.
