A Fuzzy Multi-Criteria Model for Municipal Waste Treatment Systems Evaluation including Energy Recovery

One of the recent problems on waste sorting systems is their performance evaluation for proper decision making and management. For this purpose, multi-criteria methods can be used to evaluate the sorting system from both operational and financial perspectives. According to a recent literature review, there are no solutions for evaluating waste sorting systems that take into account: sorting point utilisation, sorting efficiency, waste stream irregularity, and technical system availability. In addition, the problem of data uncertainty and the need to use expert judgements indicate the need for the implementation of methods adjusted to the qualitative and quantitative assessment, such as the fuzzy approach. Following this, in order to overcome the presented limitations, the authors introduced the new assessment method for waste sorting systems based on multi-criteria model implementation and fuzzy theory use. Therefore, the developed model was based on a hierarchical fuzzy logic model for which appropriate membership function parameters and inference rules were defined. The specificity of the chosen assessment criteria and their justification was provided. The model has been implemented to evaluate one of the waste sorting plants in Wroclaw, Poland. Tests have been conducted for seven different configurations of waste sorting lines (with variable input parameters). The study focuses on analysing the amount of selected waste at each station in relation to the total stream size of each fraction. Efficiency was measured by the mass of the collected waste and the number of pieces of waste in each fraction. Based on the obtained results, estimations of particular parameters of the model were made, and the results were presented and commented on. It was shown that there is a significant relationship between the level of system evaluation and sorting efficiency and an inverse relationship with the level of RDF obtained. The analysis was based on Pearson’s linear correlation coefficient estimation and linear regression implementation.

Differentiation between normal and tumor mammary glands with depth-resolved attenuation coefficient from optical coherence tomography

Optical coherence tomography (OCT) is a well-established imaging technology for high-resolution, cross-sectional imaging of biological tissues. Imaging processing and light attenuation coefficient estimation allows to further improve the OCT diagnostic capability. In this paper we use a commercial OCT system, Telesto II-1325LR from Thorlabs, and demonstrate its ability to differentiate normal and tumor mammary mouse glands with the OCT attenuation coefficient. Using several OCT images of normal and tumor mammary mouse glands (n=26), a statistical analysis was performed. The attenuation coefficient was calculated in depth, considering a slope of 0.5 mm. The normal glands present a median attenuation coefficient of 0.403 mm-1, comparatively to 0.561 mm-1 obtained for tumor mammary glands. This translates in an attenuation coefficient approximately 39 % higher for tumor mammary glands when compared to normal mammary glands. The OCT attenuation coefficient estimation eliminates the subjective analysis provided by direct visualization of the OCT images.