Synthetic bacterial consortium for degradation of plastic pyrolysis oil waste

The plasic crisis is ominipresent, from littering macroplastic to reports that document plastic in every niche of this planet, including the human body. In order to achieve higher recycling quotas, especially of mixed plastic waste, pyrolysis seems to be a viable option. However, depending on the process parameters, plastic pyrolysis oil waste is encountered, which is difficult to valorize, due to the enormous spread of the molecules included. To reduce the molecular heterogeneity, we here artificially compounded, monitored, and optimized the performance of a bacterial consortium, which has the ability to tolerate organic pollutants and use them as energy and carbon sources for their own metabolic activity. The primary constituents of the here used plastic pyrolysis oil waste (PPOW) were alkanes and {varepsilon}-caprolactam. The bacterial community exhibited noteworthy efficacy in eliminating alkanes of diverse chain lengths ranging from 71% to 100%. Additionally, within 7-days, the microbial community demonstrated a removal efficiency surpassing 50% for various aromatic hydrocarbons, along with complete eradication of {varepsilon}-caprolactam and naphthalene. Besides, a back-propagation (BP) neural network method is applied to evaluate O2 consumption as a measure of microbial activity. The insights gained were used to build a model, which is able to predict O2 depletion in long-time experiments and other experimental conditions. The results are discussed in the context of a developing (open) circular plastic economy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/590079v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@628f68org.highwire.dtl.DTLVardef@b5274eorg.highwire.dtl.DTLVardef@1278ceaorg.highwire.dtl.DTLVardef@194887a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightO_LISynthetic bacterial communities are used to remove plastic hydrolysis oil waste C_LIO_LIThe optimized biphase reaction system can remove the majority of pollutants C_LIO_LIThe biodegradation process can be monitored in a real-time bioprocess software C_LIO_LINeural network techniques are used to model and predict the removal process C_LI