Energy produced from plant residue composting has stimulated great interest in heat recovery and utilization. Composting is an exothermic process often controlled through temperature measurements. However, energy analysis of the overall composting system, especially the rotary bioreactors, is generally not well known and very limited. This study presents detailed energy analysis in a laboratory-scale, batch-operated, rotary bioreactor used for composting tomato plant residues. The bioreactor was considered as a thermodynamic system operating under unsteady state conditions. The composting process was described, the input generated and lost energy terms as well as the relative importance of each term were quantitatively evaluated, and the composting phases were clearly identified. Results showed that the compost temperature peaked at 72 h of operation reaching 66.7 • C with a heat generation rate of 9.3 W•kg −1 of organic matter. During the composting process, the accumulated heat generation was 1.9 MJ•kg −1 of organic matter; only 4% of this heat was gained by the composting material, and 96% was lost outside the bioreactor. Contributions of thermal radiation, aeration, cylindrical, and side-walls surfaces of the reactor on the total heat loss were 1%, 2%, 69%, and 28%, respectively. The information obtained is applicable in the design, management, and control of composting operations and in improvement of bioreactor effectiveness and productivity.
Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated training data for deep learning. However, there is currently no free specialized software available that can efficiently annotate large 3D point clouds. We fill this gap by introducing PC-Annotate-a public annotation tool for 3D point cloud research. The proposed tool not only enables systematic annotation with a variety of fundamental volumetric shapes, but also provides useful functionalities of point cloud registration and the generation of volumetric samples that can be readily consumed by contemporary deep learning point cloud models. We also introduce a large outdoor public dataset for 3D semantic segmentation. The proposed dataset, PC-Urban is collected in a civic setup with Ouster LiDAR and labeled with PC-Annotate. It has over 4.3 billion points covering 66K frames and 25 annotated classes. Finally, we provide baseline semantic segmentation results on PC-Urban for popular recent techniques. INDEX TERMS 3D point cloud, Point Cloud Dataset, Annotation tool, Semantic Segmentation, LiDAR.
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