Production of diesel-range oil through pyrolysis of polyolefins recovered from municipal solid waste

Pyrolysis is an effective method to valorize plastic waste and obtain value-added fuels. This study adopted the ANN-GA (artificial neural network-genetic algorithm) coupled with a central composition factorial design to optimize the oil production from the pyrolysis of waste polyolefins (WP). The interactive effects of PE mass fraction (20-80 wt%), residence time (20-60 min), and carrier gas flow rate (0-100 mL/min) on the yields of WP pyrolysis products were investigated extensively by ANN. Moreover, the highest WP pyrolysis oil production of 78.87 wt%, optimized by GA, was obtained under 80 wt% PE, 60 min, and 0 mL/min. It was found that the different conditions of PE mass fraction, residence time, and carrier gas flow rate did not change the types of oil's main functional groups (-CH2-, -C=C-, -C=CH2, -CH3, and =C-H). The conditions affected the WP pyrolysis oil fractions significantly. The highest diesel selectivity of 91.42% was obtained under 20 wt% PE, 20 min, and 0 mL/min. Additionally, according to the interactive effects of different conditions on the productions of WP pyrolysis products, the pyrolysis pathways were proposed to understand the pyrolysis mechanism of WP better.

Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis

In the present study, the waste polyethylene (PE) pyrolysis under different non-isothermal conditions was investigated to estimate the optimal conversions and pyrolysis rates. The pyrolysis study was carried out using Thermogravimetry (TG) of the virgin and the waste PE under different heating rates of 5, 10, 15 and 20 °C/min. The TG experiments indicated that the virgin and the waste PE pyrolysis processes mainly underwent in the temperature range of 390-510 °C. Subsequently, the adaptive neural fuzzy model was adopted to predict the conversions and the pyrolysis rates of the virgin and the waste PE. The optimal operating conditions in different temperature ranges were optimized by the simulated annealing algorithm (SA). Moreover, the R-squared values of the virgin PE conversions (~ 1) and pyrolysis rates (> 0.999), and the waste PE conversions (~ 1) and pyrolysis rates (> 0.999) revealed the high accuracy of the adaptive neural fuzzy model predicted results.

An improved hydrodynamic model for percolation and drainage dynamics for household and agricultural waste beds

This study focuses on the hydrodynamic modelling of percolation and drainage cycles in the context of solid-state anaerobic digestion and fermentation (VFA platform) of household solid wastes (HSW) in leach bed reactors. Attention was given to the characterization of the water distribution and hydrodynamic properties of the beds. The experimental procedure enabled the measurement of water content in waste beds at different states of compaction during injection and drainage, and this for two types of HSW and for two other type of wastes. A numerical model, set up with experimental data from water content measurements, highlighted that a capillary-free dual-porosity model was not able to correctly reproduce all the hydrodynamic features and particularly the drainage dynamics. The model was improved by adding a reservoir water fraction to macroporosity which allowed to correctly simulate dynamics. This model, validated with data obtained from agricultural wastes, enabled to explain more precisely the water behaviour during percolation processes and these results should be useful for driving either solid-state anaerobic digestion or fermentation reactors. Indeed, this implies that the recirculation regime will impact the renewal of the immobile water fraction in macroporosity, inducing different concentration levels of fermentation products in the leachate.

Assessment of percolation through a solid leach bed in dry batch anaerobic digestion processes

This work aimed at assessing water percolation through a solid cow manure leach bed in dry batch AD processes. A laboratory-scale percolation column and an experimental methodology were set up. Water behaviour was modelled by a double porosity medium approach. An experimental procedure was proposed to determine the main hydrodynamic parameters of the multiphase flow model: the porosity, the permeability and the term for water exchange from macro- to micro-porosity. Micro- and macro-porosity values ranged from 0.42 to 0.70 m(3) m(-3) and 0.18 to 0.50 m(3) m(-3). Intrinsic permeability values for solid cow manure ranged from 5.55·10(-11) to 4.75·10(-9) m(2). The term for water exchange was computed using a 2nd order model. The CFD tool developed was used to simulate successive percolation and drainage operations. These results will be used to design leachate recirculation strategies and predict biogas production in full-scale dry AD batch processes.

The impact of compaction, moisture content, particle size and type of bulking agent on initial physical properties of sludge-bulking agent mixtures before composting

This study aimed to experimentally acquire evolution profiles between depth, bulk density, Free Air Space (FAS), air permeability and thermal conductivity in initial composting materials. The impact of two different moisture content, two particle size and two types of bulking agent on these four parameters was also evaluated. Bulk density and thermal conductivity both increased with depth while FAS and air permeability both decreased with it. Moreover, depth and moisture content had a significant impact on almost all the four physical parameters contrary to particle size and the type of bulking agent.