Grain-size assessment of fine and coarse aggregates through bipolar area morphology

This paper presents a new methodology for computing grey-scale granulometries and estimating the mean size of fine and coarse aggregates. The proposed approach employs area morphology and combines the information derived from both openings and closings to determine the size distribution. The method, which we refer to as Bipolar Area Morphology (BAM), is general and can operate on particles of different size and shape. The effectiveness of the procedure was validate on a set of 13 classes of aggregates of size ranging from 0.125mm to 16mm and made a comparison with standard, fixed- shape granulometry. In the experiments our model con- sistently outperformed the standard approach and pre- dicted the correct size class with overall accuracy over 92%. Tests on three classes from real samples also con- firmed the potential of the method for application in real scenarios

Development of a method to classify and analyse the composition of mixed waste materials in real-time.

Philip Longhurst - Associate SupervisorThere is a need for innovative technologies to classify and monitor the composition of solid waste in real-time. This research project has highlighted which information is required to improve current process designs. It also identified visible spectrum cameras as the solution that can better inform waste composition and quality without requiring complementing technologies. The experiments applied deep learning methods to classify the materials based on their images, and a method to analyse the composition of mixed waste was developed. There is a high variability in the appearance of waste materials in the context of a material recovery facility. An image capture setup using multiple cameras and light sources was implemented and tested to acquire a representative set of images. The hardware captures images from different angles, with enhanced shadow details, and different exposure levels. Image processing software further augmented the data by rotating and changing the images resolutions. The images were converted to greyscale to increase the method robustness without affecting classification performance. Deep convolutional neural networks were trained on the augmented datasets. The trained networks obtained state-of-the-art performance when tested and validated for the task of waste material classification. Based on this, a composition analysis methodology was developed and tested with mixed material samples. The methodology provides results as accurate as current manual solutions, but it can analyse a waste stream on a conveyor belt in real-time. The findings and observations from the experimental results contribute to knowledge in three main areas: data capture, data processing, and deep learning. This thesis presents the progressive development of the methodology and discusses different applications for waste management. The composition analysis can provide real-time waste data to improve the overall efficiency of the waste treatment industry. This information can be also used by stakeholders for better decision-making in the future.PhD in Energy and Powe