Research
In addition to developing secondary recyclable markets and streamlining the recycling process, our researchers are focused on the following tasks:
The recycling industry for plastics is fragmented and there is a gap in knowledge on the flow of plastic recyclables after they leave the Material Recovery Facility (MRF). A better understanding of the plastics recycling industry through data collection/analysis/data envelopment analysis could pave the way for increased recycling rates.
Subtask 1.1: Map the reverse supply chain for plastics using case-based research
Subtask 1.2: Economic-modeling to investigate incentives for different players in the waste management industry
The supply chain is fragmented with several waste management companies, public and private MRFs, reclaimers, brokers, manufacturers, etc. There is an opportunity to collaborate with industry professionals to identify and resolve critical supply chain management issues (e.g., contamination, pricing, etc.).
Subtask 2.1: Study MRF performance and operational efficiency in New York State
Subtask 2.2: Investigate hub and spoke recycling to manage recycling in rural areas
Subtask 2.3: Study secondary-sortation based business models to divert plastics from landfill
Microbeads in the Great Lakes started much of the current plastics research, including the many issues associated with single-use materials. These issues are not solved, and new research directions continue to emerge (e.g., mask/COVID-19 personal protective equipment (PPE) pollution in waterways and terrestrial environments). Microplastics in NYS impact tourism (e.g., lakes, rivers) and agriculture/food. UB has expertise in environmental engineering, water resources, and analytical chemistry which, together with polymer and separations/membrane expertise, can make a real impact.
In New York State, the agriculture/food and medical industries consume a lot of plastic and generate correspondingly large amounts of plastic waste, which routinely is not recycled. For example, the agriculture/food sector produces significant film plastic and food-contaminated plastic. The medical sector uses/discards complex plastic-based materials and also generates medically-contaminated plastic waste. This task aims to generate reliable and current information on the status of plastic usage and disposal by the agriculture/food and medical industries, and provide recommendations to help these industries manage plastics more sustainably.
In order to design and test a communication campaign that will increase recycling knowledge and minimize contamination in recycling, we need to learn how New Yorkers feel about recycling and better understand their recycling behaviors.
Success of plastic recycling critically depends on developing cost-effective techniques to automate the entire process of sorting plastics according to their chemical compositions in a high throughput fashion. At present, robotic arms with imaging cameras can sort materials based only on their visible characteristics. No chemical and molecular compositions can be obtained with cameras. Although spectroscopic techniques that can be used in a standoff mode for chemical identification of plastics have been proposed and demonstrated, they are inherently slow because of the time requirement needed for wavelength tuning for selectivity. Many standoff techniques based on laser beams focus on plastic pieces on a conveyor belts are being developed. However, acquiring chemical signatures using laser beams on a point-by-point method is slow and may affect the overall speed of the sorting. Increasing the speed of point-by-point interrogation for sorting plastics by using multiple laser units makes it economically prohibitive. In addition, tunable lasers and optical arrangement for detecting scattered light make the system very bulky. Since random orientation of plastics on a conveyor belt affect the intensity of the scattered light, it can increase the false positives/negatives. Too many variables involved in the signal generation and collection require the development of advanced machine learning techniques for signal analysis. Therefore, currently available techniques and methods do not offer a clear path for implementation of cost-effective automated plastic sorting that is rapid and easy to use in real-world conditions. We will develop a novel, simple, cost-effective technique that can increase the sensitivity, molecular selectivity, and speed of plastic sorting that is based on superimposition of images. The overall goal is to increase the sorting speed while decreasing the overall cost and complexity while being extremely amenable for easy integration into plastic sorting facilities.