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.

Sorting path optimization of parallel robot based on improved Genetic Algorithm

In industrial production, parallel robot is often used for sorting, battery clamp is divided into two structures, the classification and sorting, for multi-target sorting problems. The motion path of end-effector is planned, the overall sorting path is planned, and the practical problem is transformed into a similar traveling salesman problem. The improved genetic algorithm is proposed to optimize the sorting time sequence, and the optimized path shortens 10.23% total distance on average compared with random sorting and 5.37% total distance compared with fixed longitudinal sorting. The shortening of the total distance can lead to higher sorting efficiency in sorting and increase productivity.

Coastal Waste Detection Based on Deep Convolutional Neural Networks

Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.

Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds

Background Maize haploid breeding technology can be used to rapidly develop homozygous lines, significantly shorten the breeding cycle and improve breeding efficiency. Rapid and accurate sorting haploid kernels is a prerequisite for the large-scale application of this technology. At present, the automatic haploid sorting based on nuclear magnetic resonance (NMR) using a single threshold method has been realized. However, embryo-aborted (EmA) kernels are usually produced during in vivo haploid induction, and both haploids and EmA kernels have lower oil content and are separated together using a single threshold method based on NMR. This leads to a higher haploid false discrimination rate (FDR) and requires secondary manual sorting to select the haploid kernels from the mixtures, which increases the sorting cost and decreases the haploid sorting efficiency. In order to improve the correct discrimination rate (CDR) in sorting haploids, a method to distinguish EmA kernels is required. Results Single kernel weight and oil content were measured for the diploid, haploid, and EmA kernels derived from three maize hybrids and nine inbred lines by in vivo induction. The results showed that the distribution of oil content showed defined boundaries between the three types of kernels, while the single kernel weight didn't. According to the distribution of oil content in the three types of kernels, a double-threshold method was proposed to distinguish the embryo-aborted kernels, haploid and diploid kernels based on NMR and their oil content. The double thresholds were set based on the minimum oil content of diploid kernels and the maximum content of EmA kernels as the upper and lower boundary values, respectively. The CDR of EmA kernels in different maize materials was > 97.8%, and the average FDR was reduced by 27.9 percent. Conclusions The oil content is an appropriate indicator to discriminate diploid, haploid and EmA kernels. An oil content double-threshold method based on NMR was first developed in this study to identify the three types of kernels. This methodology could reduce the FDR of haploids and improve the sorting efficiency of automated sorting system. Thus, this technique represents a potentially efficient method for haploid sorting and provides a reference for the process of automated sorting of haploid kernels with high efficiency using NMR.

Development and Assessment of Cracking and Sorting Processes of Palm Kernel Nut Machine

This paper presents a research on the Development and Assessment of the cracking and sorting processes of palm kernels nuts in a mechanical cracking machine. The palm kernel oil is an important agricultural produce, it has a wide range of usage aside from cooking. In attempt to reduce energy exploitation, in conjunction with the regular high rate of demand for products been extracted from palm kernel nut, the need to improve on the manual method of cracking and sorting became eminent. The mechanical cracker was made up of two units: the cracking and sorting unit. Experimental studies of properties of machine components were considered with the purpose of avoiding fatigue failure and fracture on the palm nuts during the processing duration. The mechanical cracker was designed, fabricated and performance evaluation carried out using locally sourced materials. It was developed with a 5hp electric motor to drive the machine coupled with belts and pulleys. The average data assessment shows a 2.01% of un-cracked nuts, 2.36% of partially cracked nuts, 93.58% of un-cracked nut and 2.05% broken nuts. The cracking-sorting efficiency was estimated at 94% and throughput capacity was determined to be 80kg/h while the overall palm kernel recovery efficiency was 85%. These suggest that the machine is very suitable for separating the palm kernel nuts from the cracked shells clearly and completely.

A density-based threshold model for evaluating the separation of particles in heterogeneous mixtures with curvilinear microfluidic channels

Particle separation techniques play an important role in biomedical research. Inertial focusing based microfluidics using nonlinear channels is one of the promising label-free technologies for biological applications. The particle separation is achieved as a result of the combination of inertial lift force (FL) and Dean drag force (FD). Although the mathematical expressions of FL and FD have been well derived in prior studies, they are still complicated, which limits their popularity in practice. Recent studies modified these expressions through experiments and proposed a threshold model, which assumes that only particles larger than the threshold will be well focused. Although this threshold model has been used in recent studies, two varying versions of the threshold model (TM1 and TM2) prevents standardisation in practice. In addition, both models were developed with regular low-density particles and may not be applicable to samples with higher density or samples with irregular shapes. Here, we evaluated the threshold models with samples of different densities. Based on these evaluations, we derived a modified model (TM4), which additionally considers the factor of particle density to improve the accuracy of existing models. Our results demonstrated that TM4 could more reliably predict the sorting efficiency of samples within a wider density range.

Cross-belt sorter - a model and analysis of selected mechanical loads

This article presents the results of numerical research on operational mechanical loads, carried out for the main structural elements of a cross-belt sorter: tracks and trolleys with trays. The goal of the research was to collect data required at the stage of designing new solutions of the sorters. A novelty is an analytical model that allows to determine the influence of the motion velocity of trolleys, frictional properties and mass of sorted objects on the forces transmitted from the trolleys to the track, the sorting efficiency and power required by the drive systems.

Potato cyst nematode egg viability assessment and pre-parasitic juvenile screening using large particle flow cytometer and sorter

Potato cyst nematode (PCN) cysts consist of heterogenous populations of eggs, juveniles, and eggshells which make manual sorting of individual life stages cumbersome. The number of viable egg is a major determinant of crop damage. An accurate high throughput PCN egg viability assay is useful for developing effective management and eradication plans. In this study, we present a method for rapid and precise enumeration and sorting of PCN eggs and juveniles, and an egg viability assessment by staining with the fluorescent stain, acridine orange and sorting with the Complex Object Parametric Analyzer and Sorter (COPAS) system. Both size and fluorescent sorting capabilities of the COPAS were explored. By using the COPAS, sorting efficiency for eggs and pre-parasitic second stage juveniles (J2s) was 97.6% and 97.2%, respectively, with a 99% recovery at a flow rate of 15 events/second. Purity of sorted live and dead eggs was 95.5% and 94.1%, respectively. Sorting ofJ2s by size indicated that for G. ellingtonae or G. pallida 15-16.4% had an average body length of 436.1 ± 3.4 µm compared to an average size of 512.9 ± 4.4 µm for the majority of the J2 population for both species. A red auto-fluorescing J2 population was also identified through sorting. Sorting of eggs by flow cytometry did not significantly affect hatching (55.1 ± 1.2% and 53.9 ± 1.6%, respectively for sorted or non-sorted eggs) or juvenile motility (91.3 ± 1.0% or 90.1 ± 1.1%, respectively), thus confirming the method does not impair the biological activity of the nematode

Investigation on Inertial Sorter Coupled with Magnetophoretic Effect for Nonmagnetic Microparticles

The sizes of most prokaryotic cells are several microns. It is very difficult to separate cells with similar sizes. A sorter with a contraction–expansion microchannel and applied magnetic field is designed to sort microparticles with diameters of 3, 4 and 5 microns. To evaluate the sorting efficiency of the designed sorter, numerical simulations for calculating the distributions of microparticles with similar sizes were carried out for various magnetic fields, inlet velocities, sheath flow ratios and structural parameters. The numerical results indicate that micro-particles with diameters of 3, 4 and 5 microns can be sorted efficiently in such a sorter within appropriate parameters. Furthermore, it is shown that a bigger particle size and more powerful magnetic field can result in a greater lateral migration of microparticles. The sorting efficiency of microparticles promotes a lower inlet velocity and greater sheath flow ratios. A smaller contraction–expansion ratio can induce a greater space between particle-bands. Finally, the micro particle image velocity (micro-PIV) experiments were conducted to obtain the bandwidths and spaces between particle-bands. The comparisons between the numerical and experimental results show a good agreement and make the validity of the numerical results certain.