How to Leverage AI for Feedstock Management

In today's world, the demand for recycled content is on the rise, driven by several factors, including stringent regulations, corporate pledges, and consumer preferences. To keep up with the demand, the waste and recycling industry will have to innovate to increase material recovery.

Increasing Demand for Recycled Materials

The global push for sustainability has led to a surge in demand for recycled content. Key drivers include:

• Regulations: Extended Producer Responsibility (EPR) laws and mandates for recycled content incentivize higher recycling and recovery rates. These regulations are pushing industries to adopt more sustainable practices.
• Corporate Pledges: A staggering 86% of consumer goods companies have pledged to incorporate more recycled content and circular packaging into their products.
• Consumer Willingness: About 70% of consumers are willing to pay a premium for products made from recycled materials and sustainable packaging. This consumer behavior is a powerful incentive for companies to switch to recycled content.
• Energy and Emission Benefits: Recycled feedstock requires 40-95% less energy than virgin material feedstock, potentially reducing 11 gigatons of CO2e by 2050. This significantly reduces greenhouse gas emissions.

Virgin vs. rPET

Despite the clear benefits, recycled PET (rPET) is currently more expensive than virgin plastic. The primary reasons are:

• Low Supply of Feedstock: The supply of high-quality recycled feedstock is limited, which drives up the price.
• Increased Processing Costs: The process of reclaiming and recycling PET involves higher costs compared to producing virgin plastic.

Challenges in the Recycling Supply Chain

While the demand for recycled content is increasing, current operational inefficiencies throughout the recycling system are causing a lack of supply and expensive recycled material. According to this McKinsey report, the demand for rPET is increasing by 15% annually, whereas the supply is only growing by 1%. This imbalance suggests that demand will soon outstrip supply. Several challenges are hindering the efficient supply of high-quality recycled feedstock:

1. Post-Collection Sorting Phase:
   
   • Manual Operations in MRFs: Material Recovery Facilities (MRFs) rely heavily on manual labor, making it difficult to increase recovery rates while keeping costs low.
   • Labor Challenges: High labor costs result in high operational costs.
   • Imprecise Sorting Equipment: Lack of precise sorting and visibility leads to sorting losses and poor bale quality, ultimately resulting in revenue loss.
2. Secondary Processing:
   
   • High Costs Due to Supply Shortage: The shortage of high-quality feedstock leads to increased processing costs and downtime.
   • Inconsistent Output: The inconsistency in the quality of the output means missed revenue opportunities. High operational costs make it difficult to compete with virgin materials.

Recovering all the lost material from the MRF and providing a consistent output of high-quality recycled feedstock to reclaimers would result in a high-quality output. This approach would lead to lower processing costs for the reclaimers and reduced downtime, ultimately resulting in a future where the cost of recycled material could be less than the cost of virgin material. This outcome is achievable through the utilization of AI and robotics.

How AI and Robotics Can Help

Material Characterization

When placing EverestLabs AI throughout your facility, we can provide visibility into plant operations like never before. EverestLabs AI can identify over 50 different recyclable material classes, which is continually expanding. With our material characterization data, we can identify every object going in and out of your plant. We can identify contaminants, break down materials by color distribution, and even identify FDA vs. non-FDA-grade material for polypropylene. MRFs and reclaimers can use this data to optimize their operations and make improvements to help increase material quantity and quality.

Bale Audits

Before AI, there was no good way to measure the quality of incoming bales. EverestLabs can use AI to do bale audits that can track contamination to keep suppliers accountable. Recyclers can use this data to understand their input feedstock and their output and provide feedback to their suppliers. The supplier can then use this to determine where in their processes they can remove contaminants to produce a higher yield of quality materials.

Robotic Arm Contamination Removal

With EverestLabs robotics, we can help facilities create a pure material stream and an efficient recycling plant. With robotics, facilities will be able to process materials more efficiently and produce a higher output. With our robotics, we are able to help recyclers in two ways:

1. Quality Control: Clean up low-quality bales as a cost-effective alternative to buying clean bales.
2. Reduce equipment downtime: remove contaminants that cause machines to shut down and delay processing

The rising demand for recycled content, driven by regulations, corporate pledges, and consumer preferences, is reshaping industries. However, inefficiencies in the recycling supply chain and high costs for recycled materials pose significant challenges. The integration of AI and robotics offers a promising solution. These technologies can enhance sorting accuracy, reduce contamination, and optimize feedstock quality, leading to lower processing costs and higher yields of recyclable feedstock. By improving the supply of high-quality recycled materials, the industry can make recycled content more competitive with virgin materials, supporting a sustainable future and advancing the circular economy.

Watch our video for more on how EverestLabs can help increase recycled feedstock.

Get in touch with an expert to learn how AI and robotics fit into your facility, or send us an email at hello@everestlabs.ai.