In-line quantitative estimation of ammonium polyphosphate flame retardant in polyolefins via industrial hyperspectral imaging system and machine learning.

Due to developments in European legislation, several halogenated flame retardants are banned due to their toxicity, and the use of phosphor-based flame retardants in plastics is increasing. A revision of ammonium polyphosphate (APP) flame retardant revealed that it is an eye irritant and toxic, thus posing a health issue. Hence APP identification is needed for enabling safe recycling of plastic waste streams. Herein an industrial in-line method for quantitative estimation of APP in low density polyethylene (LDPE) and polypropylene (PP) is demonstrated, by using an industrial hyperspectral imaging system (955 to 1700 nm) and principal component analysis (PCA). Spectra of plastic samples with varying concentrations of APP were applied to build and calibrate a quantitative determination method. PCA and band area ratios (of selected bands) were made and fitted with continuous functions for concentration determination. The plastic samples were characterised by elemental analysis, attenuated total reflection, differential scanning calorimetry, and thermogravimetric analysis. The PCA model outperforms the band area ratio model and predicts APP concentrations between 24.3 and 1.5 wt% in LDPE (R = 0.98) and 20.0 and 1.7 wt% in PP (R = 0.97). Unknown samples with APP ranging from 23.7 to 2.7 wt% in LDPE and from 18.6 to 2.3 wt% in PP were predicted and correlated to the actual concentrations. The proposed approach is valuable for the plastic recyclers and waste management industries where inline concentration determination of flame retardants is key.