Turning Magnetic Density Separation into Green Business Using the Cyclic Innovation Model

Upgrading of PVC rich wastes by magnetic density separation and hyperspectral imaging quality control

Polyvinylchloride (PVC) is one of the most produced polymers in Europe, with a share of 11% in terms of mass (8 milliontons) of total polymer consumption, but in 2010 only 5% of the total PVC production came from recycled materials, where other polymer recycling achieves a level of 15% on average. In order to find an innovative process to extract PVC from window frames waste, a combination of two innovative technologies was tested: magnetic density separation (MDS) and hyperspectral imaging (HSI). By its nature, MDS is a flexible high precision density separation technology that is applicable to any mixture of polymers and contaminants with non-overlapping densities. As PVC has a very distinctive high density, this technology was tested to obtain high-grade PVC pre-concentrates from window frame waste. HSI was used to perform a quality control of the products obtained by MDS showing that PVC was clearly discriminated from unwanted rubber particles of different colors. The results showed that the combined application of MDS and HSI techniques allowed to separate and to check the purity of PVC from window frame waste.

Multi-spectral infrared spectroscopy for robust plastic identification

The identification and classification of plastics plays an important role in waste management and recycling processes. Present electrical and optical sorting techniques lack the required resolution for accurate identification in a high throughput manner for a diverse set of plastics commonly found in municipal waste. In this work a multi-spectral infrared spectroscopic technique is employed to construct a unique fingerprint library of 12 plastic resin groups that are commonly encountered in municipal waste. We test the proposed method in a blind plastic identification experiment, which shows excellent unbiased identification accuracy. This simple optical technique in combination with the multi-spectral library will enable high throughput and accurate detection of various plastics from recovered solid waste.

CASE ANALYSIS OF INNOVATION IN THE PACKAGING INDUSTRY USING THE CYCLIC INNOVATION MODEL

This paper builds upon Berkhout et al.'s (2010) cyclic innovation model (CIM). This model was shown to provide an effective framework for understanding and managing the innovation process and to address many of the shortcomings of previous models. Building on that article we have applied CIM to an in-depth case study featuring a formable paperboard technology within the packaging industry. Using data gathered from 28 interviews conducted over a three year period, CIM, for the first time, is applied to a low technology industry. In so doing, this paper contributes to a growing body of literature exploring low technology industries and, in turn, demonstrates the wider applicability of CIM beyond technology intensive industries.