Biogas production from organic waste could be an option to reduce landfill and pollutant emissions into air, water, and soil. These fuels contain several trace compounds that are crucial for highly efficient energy generators or gas injection into the grid. The ability of adsorbents to physically remove such adsorbates was investigated using adsorption isotherms at a constant temperature. We experimentally modelled isotherms for siloxane removal. Siloxanes were considered due to their high impact on energy generators performance even at low concentrations. Octamethylcyclotetrasiloxane was selected as a model compound and was tested using commercially available carbon and char derived from waste materials. The results show that recyclable material can be used in an energy production site and that char must be activated to improve its removal performance. The adsorption capacity is a function of specific surface area and porous volume rather than the elemental composition. The most common adsorption isotherms were employed to find the most appropriate isotherm to estimate the adsorption capacity and to compare the sorbents. The Dubinin-Radushkevich isotherm coupled with the Langmuir isotherm was found to be the best for estimating the adsorption capacity.

The confined uniaxial tests of packings with discrete particle size distribution (PSD) were modeled with the discrete element method. Ternary packings of spheres with PSD uniform or nonuniform by number of particles were examined in three-dimensional (3D) system. The study addressed an effect of the particle size ratio and the particle size fraction on structural and micromechanical properties of mixtures. A study of packing structure included porosity and coordination numbers, while the investigation of micromechanical properties included distribution of normal contact forces and stress transmission through the packing. A micro-scale investigation of the effect of particle size ratio on structure and mechanics of the ternary packings revealed a strong relationship between the properties of sample and the value of parameter till its critical value was reached. A further increase in particle size ratio did not significantly affect properties of packings. Contrary to the porosity and coordination numbers, the partial stresses were highly affected by the fraction of particle size classes in ternary mixtures. The contribution of the partial stress into the global stress was determined by number fraction of particles in packings with small particle size ratio, while it was mainly determined by particle size ratio in packings with small particle size ratio.

One of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification. Defect recognition is a key in online computer-assisted apple sorting machines. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. Thus, the current study focuses on a highly accurate and feasible methodology for stem and calyx recognition based on Niblack thresholding and a machine learning technique using k-nearest neighbor (k-NN) classifiers associated with a locally designed small-scale apple sorting machine. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. It is found that the 5-nearest neighbor classifier and the Euclidean distance using 80 training samples produced the best accuracy rates, at 100% for stem and 97.5% for calyx. The significance of the result is very promising in fabricating an advanced small-scale and low-cost sorting machine with a high accuracy for the horticultural industry.