Analytical Case Study on a Dedicated Pigment in Recycling Processes of Polyethylene

The ability to track & trace materials is a key feature in the entire chain, and it ensures circularity principles. Examples from plastic recycling show the enormous added value that analytical technology can have for the circular economy. During polymer production and recycling processes, pigments can be added for different purposes;e.g. as colouring agent of the polymeric product but also as tracer for tracking process development and control in the final recycle products versus possible by-products. An analytical method for tracking the pigment Solvent Blue 15 in input materials, in intermediates as well as in recyclates was developed by tracing and quantifying an indicator metal which is copper (Cu). Therefore, suitable digestion procedures and a quantification method by high resolution inductively coupled plasma mass spectrometry (HR-ICP-MS) were developed and used for measuring the polymeric digests. The method was tested on relevant samples from chemical recycling processes. The background concentrations in base/raw material are in the range of 0.05 - 0.1 mg·kg-1 Cu. The processing concentrations are in the range of 4.2 to 28 mg·kg-1 Cu, while the pigment starting material (polyethylene, PE) has a concentration of around 50 mg·kg-1 Cu.

Object Tracking and Tracing:Hidden Semi-Markov Model Based Probabilistic Location Determination

The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always function reliably under complex and variable deployment environment.In many cases,RFID systems provide only probabilistic observations of object states.Thus,an approach to predict,record and track real world object states based upon probabilistic RFID observations is required.Hidden Markov model(HMM) has been used in the field of probabilistic location determination.But the inherent duration probability density of a state in HMM is exponential,which may be inappropriate for modeling of object location transitions.Hence,in this paper,we put forward a hidden semi-Markov model(HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach.The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.

E-Commerce-Objected E-Stamp Systems

Bioosting e-stamp's advantages are conduced to promote logisticsto go forward and help postal depart- ments to improve the quality ofservice, many other businesses add-valued can be developed in e-stampsystems. This paper analyzeds, designs e-commerce-object e-stampsystem, and discussed crucial technologies involved in detail.