Detection of Hotspots During Domestic Composting by Direct Microwave Imaging Algorithms: A Numerical Assessment

Having homogeneous temperature profiles is desirable in composting, especially in low-volume-mass production, a less self-regulated and unstable scenario, where the biomass maturation cycle is more sensitive to the detrimental effects of hotspot disruptions that could annihilate the process. This study numerically assesses the performance of microwave imaging (MWI) as an alternative for detecting these hotspots by using four non-iterative algorithms: truncated singular value decomposition (TSVD) with a Born approximation, delay-and-sum (DAS), multiple signal classification imaging (MUSIC-I), and multifrequency bifocusing (MFBF), and two non-invasive multiview operation setups, partial and full, to map temperaturerelated dielectric contrasts within a domestic composter. Overall, the study demonstrates that direct MWI is a feasible approach for compost hotspot detection.