On an environmental level, energy-efficient manufacturing facilities emit fewer greenhouse gas emissions and offer reduced demand for energy production. Less energy consumption means lower operational costs and more proficient processes at the facility level.
Chemical manufacturers can and should leverage artificial intelligence (AI) to improve facilitywide energy efficiency. About 80% of chemical industry executives are confident AI will prove essential to their business over the next several years. Here’s how chemical manufacturers use AI to their advantage.
Chemical Manufacturing and Energy Consumption — How Bad Is It?
According to the 2018 Manufacturing Energy Consumption Survey — curated every four years by the Energy Information Administration (EIA) — manufacturing’s six highest energy-consuming subsectors make up 87% of all manufacturing energy consumption. Of that amount, 37% is attributed to chemical manufacturing processes and operations.
Just as saving energy benefits the environment and individual manufacturing facilities, consuming excess power can have equally harmful effects. High energy consumption correlates with more expensive operating costs, higher overhead, and negative global effects on air, water and ozone.
Energy-Saving AI Applications in Chemical Manufacturing
AI has surpassed its initial novelty and transition period, securing its place as the next natural progression of the internet and technology. It’s become more commonplace in product design and industrywide usage, and possibilities for harnessing these complex algorithms and machine learning are nearly limitless.
Borealis, a leading global chemical producer, confirmed AI’s usefulness after implementing a program to improve the facility’s energy usage, reduce emissions and lower costs by targeting predetermined energy values.
Chemical manufacturers use AI for more specific applications, as it’s only limited in its level of training with different data, patterns and relationships. Manufacturers across industries use AI for tasks as large-scale as decreasing overhead and increasing profits and as targeted as automating certain equipment.
These are some of the most promising AI applications for chemical manufacturing:
1. Predictive Analytics
AI sensors and cameras fit securely on machines and production lines to analyze operations in real-time, issuing instant alerts of a future malfunction at first sight.
This same detection process can use mathematical models and previously learned patterns to find parts of operations that could be more efficient based on whatever goal engineers have programmed that AI to prioritize, like a specific product output or safety consideration. Some AI can even make real-time adjustments without pausing operations or burdening an already busy employee.
Predictive analytics spare facilities from cost-draining downtime and shine a light on the parts of manufacturing that could benefit from improvement or a new approach.
2. Environmental Insights
Because of chemical manufacturing’s meticulous and often hazardous nature, every part of the production environment matters. AI helps site managers better utilize resources through generative design.
In chemical manufacturing, generative design might look like more energy-conscious sourcing processes for raw materials and compounds or safer resource handling according to specific fixed parameters in the AI’s algorithm.
Facilities can use these insights to improve equipment efficiency and broaden the scope of material sourcing, use, reuse and waste.
3. Waste Management and Reduction
Product waste, whether quality control concerns with raw materials or mismanaged hazardous chemicals, is detrimental to the environment and the facility’s bottom line. Reproduction consumes double the energy for material sourcing, processing and manufacturing labor.
AI and machine learning can help manufacturing facilities automate certain parts of the production process with real-time awareness of crucial factors that could impact production, like incorrect temperatures or disrupted flow.
Detecting these concerns early enables chemical manufacturers to preserve as much of the remaining product as possible, reducing overall waste and energy consumption.
The same algorithms can help monitor inventory levels for limited resources to optimize spending and sourcing while avoiding unexpected downtime.
4. Streamlined Building Design
More than 40% of all power consumed in the U.S. comes from building operations — this is especially true for energy-intensive industries like chemical manufacturing. As a result, the U.S. Department of Energy (DOE) oversees guidelines for appliances and equipment using energy and water, spanning more than 60 total product types across sectors.
Using AI in building design is one way to ensure more eco-friendly processes that can save plants significant costs. One of the best parts of AI design is that nothing is permanent. Algorithms are created to learn and adapt as manufacturing facilities and processes change.
Some ways to implement AI in a manufacturing facility include:
- Smarter heating and cooling systems that adapt to internal and external temperature fluctuations and operation hours
- Optimized lighting according to each room and window’s location, the availability of natural light, operation hours and changes to the external environment, like new buildings or trees that block existing light supplies
- Appliances, tools and machines that adapt to fluctuating performance demands, including peak operational hours and output changes
Chemical Manufacturers Use AI to Prompt Industrywide Shifts
Energy-efficient chemical manufacturing can be widespread as long as manufacturers prioritize research and development and ongoing optimization. Much of that success will come from plants that invest in AI-powered equipment, programs and processes led by skilled human engineers.
