A novel index for the study of synergistic effects during the co-processing of coal and biomass

In this study, synergistic interaction between coal and biomass and its intensity were investigated systematically using a low rank coal and its blends with different biomass samples at various blending ratios. The catalytic effects of minerals originated from biomass were also studied. It was found that some of the minerals existing in the ash derived from oat straw catalysed the combustions process and contributed to synergistic interactions. However, for the coal and rice husk blends, minimal improvements were recorded even when the biomass and coal blending ratio was as high as 30 wt%. Biomass volatile also influenced the overall combustion performance of the blends and contributed to synergistic interactions between the two fuels in the blends. Based on these findings, a novel index was formulated to quantify the degree of synergistic interactions. This index was also validated using data extracted from literature and showed satisfactory correlation coefficients. It was found that at a blending ratio of 30 wt% oat straw in the blend, the degree of synergistic interaction between coal and oat straw showed an additional SF value of 0.25 with non-catalytic and catalytic synergistic effect contributing 0.16 (64%) and 0.09 (36%) respectively. This index could be used in the selection of proper biomass and proper blending ratio for co-firing at coal-fired power stations aiming at improving the combustion performance of poor quality coals via enhancing synergistic interactions during co-processing

Thermal degradation kinetics of real-life reclaimed plastic solid waste (PSW) from an active landfill site:The mining of an unsanitary arid landfill

Landfilling is viewed nowadays as a serious threat associated with various burdens and stressors on the urban environment. To date, there is little information available on actual value of landfilled waste namely plastic solid waste (PSW) resulting from mining operations. In this work, PSW reclaimed from an active unsanitary landfill site (MAB) has been studied with the aim of determining its thermal profile and degradation behaviour for future utilisation in thermo-chemical conversion (TCC) processes. The materials were characterised by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) in accordance with internationally approved test methods in a simulated pyrolytic environment. In addition, chemical analysis using Fourier Transform Infrared Spectroscopy (FTIR) was applied to study the nature of the materials reclaimed. The degradation kinetics of the reclaimed PSW were studied with the aim of determining the apparent activation energy (Ea) of the pyrolytic reactions. The Ea values determined ranged from 199 to 266 kJ mol−1 which is in-line with pyrolytic reactions applicable for future use in fuel recovery units. TGA showed a clear shift in thermograms indicating a clear change in the degradation mechanism. The physico-chemical studies conducted on the materials also favours TCC treatment over other conventional end of life options such as physical (mechanical) recycling or incineration. The degradation mechanism was also determined from the Criado method showing that Avarami-Erofeve was the model that best represents PSW degradation. Overall, this work points towards future intervention schemes for reclaimed municipal solid waste (MSW) and in particular PSW favouring TCC technologies

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

Adaptive neural fuzzy model Simulated annealing algorithm A B S T R A C T 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