Search

The co-combustion characteristics and thermodynamics of waste biomasses, namely coconut shell and corncob, with anthracite were investigated using TG-DTG/DSC analysis and artificial neural networks. TG-DTG experiments were conducted at different ratios of CS-AN and CC-AN, and the samples were heated from 293 K to 1273 K at heating rates of 10, 15, 20, 25, and 30 K·min−1. The results indicated three main stages in the combustion process of the mixtures and the combustion profiles shifted to higher temperatures with increasing heating rates. As the mass fraction of biomass in the mixtures increased from 10 wt.% to 90 wt.%, the residual mass of the co-combustion mixture decreased by 19.50 wt.% and 21.78 wt.% for CS-AN and CC-AN, respectively. The comprehensive combustion index of CS-AN increased from 24.645%2·min−2·K−3 to 167.274%2·min−2·K−3, while that of CC-AN increased from 26.262%2·min−2·K−3 to 102.654%2·min−2·K−3. The DSC results showed a peak of heat absorption followed by an exotherm in the co-combustion process. As the mass fraction of anthracite in the mixtures increased, the curve shifted toward the high temperature region and the exothermic time increased. The apparent activation energies (E) for CS, CC, and AN were 75.36, 112.08, and 90.90 kJ·mol−1, respectively. Additionally, 50 wt.% CS and 50 wt.% CC were more conducive to CS-AN and CC-AN co-combustion processes. 63 ANN models were utilized to predict the post-combustion residual weight of CS5-AN5. ANN56 was identified as the best performer, with error results (RSME = 1.482, MBE = 1.120, R2 = 0.9982) and a topology of (3 × 15 * 5 × 1), which proved to be the most suitable model for the co-combustion characteristics of this mixture. This study provides theoretical and practical guidance for solving engineering problems related to the energy utilization of waste biomasses (coconut shell and corncob) with anthracite. [ABSTRACT FROM AUTHOR]

The rapid and stable monitoring of CO₂ emissions from point sources in localized regions remains a key challenge in energy conservation and emission reduction efforts. To address this challenge, the Gaussian plume model is adopted for the rapid prediction of carbon emission dispersion from multiple point sources, and an inversion model for carbon emission intensities is constructed based on the Simplex search algorithm. By incorporating elevation data, the Gaussian plume model is modified to adapt to undulating mountainous terrain, and the impacts of the Gaussian diffusion model on the CO 2 concentration diffusion of multiple point sources are analyzed under the conditions of the observation height, atmospheric stability and terrain correction. When the number of monitoring stations reach 10, the average inversion error ranges from 0.01 to 0.47% under various atmospheric conditions, together with an average inversion uncertainty in a range of [0.09%, 1.22%], indicating that enhancing the number of monitoring stations and selecting more stable atmospheric conditions can significantly improve the inversion accuracy of the carbon emission intensities from multiple point sources. This work provides a theoretical guidance for formulating the energy conservation and emission reduction policies together with monitoring and reducing the anthropogenic carbon emission.