Efficient pyrolysis of ginkgo biloba leaf residue and pharmaceutical sludge (mixture) with high production of clean energy: Process optimization by particle swarm optimization and gradient boosting decision tree algorithm

Abstract

Production of sustainable clean energy can be achieved by co-pyrolysis of agricultural residues and wastewater sludge. Herein, non-additive thermal behaviour of co-pyrolysis of pharmaceutical sludge and ginkgo biloba leaf residues was investigated. Synergistic effect of co-pyrolysis was not obvious at elevated temperatures. Further, kinetics of co-pyrolysis was studied by fitting Coats-Redfern integration method to thermogravimetric (TG) curve. The change of heat and mass transfer in the reactor caused the change of dynamic parameters. Moreover, hybrid particle swarm optimization and gradient boosting decision tree (PSO–GBDT) algorithm was designed to boost the energy production at full-scale pyrolysis plant by monitoring TG curves. PSO–GBDT model well predicts mass loss rate of the mixture at different heating rates confirming that co-pyrolysis of PS and GBLR can results in high energy production by increasing PS pyrolysis. Designing PSO–GBDT model help to reduced waste production by resourceful treatment of waste in to energy.