A recycling pathway using flat glass waste as a supplementary cementitious material (SCM) leads to circularity in concrete production through industrial symbiosis. By incorporating glass waste, the environmental impact of cement can be reduced, contributing to the goal of climate-neutral concrete by 2045. This review study highlights the pozzolanic, chemical and physical properties of glass powder (GP) activated through mechanical process. The relationship between the strength activity index and particle size of GP as well as the hydration phases and the mitigation effect of alkali-silica reactions (ASR) is discussed.

A recycling pathway using flat glass waste as a supplementary cementitious material (SCM) leads to circularity in concrete production through industrial symbiosis. By incorporating glass waste, the environmental impact of cement can be reduced, contributing to the goal of climate-neutral concrete by 2045. This review study highlights the pozzolanic, chemical and physical properties of glass powder (GP) activated through a mechanical process. The relationship between particle size of GP and the strength activity index as well as the hydration phases and its potential to reduce alkali-silica reactions (ASR) are discussed.

Machine Learning - From Foundations to Advanced Architectures The field of Machine Learning can be overwhelming, and you might feel like you're losing control. Instead of giving up, let me share a secret with you: it's easier than you think. All you need is a kind of "Rosetta Stone" to fully accelerate your work with machine learning. This book offers such a guide. It takes you on an alternative route, starting with fundamental concepts from calculus, linear algebra, numerical methods, and optimization, and leading up to the state-of-the-art algorithms that have emerged over the last couple of decades. My background in industry, research, and teaching has given me deep insights into the common challenges of developing efficient algorithms for prediction. That experience is distilled here into a coding-oriented, "from-scratch" approach, where we focus on setting up the environment properly, defining datasets, configuring training and validation, and avoiding version-related pitfalls that often cause problems in real-world projects. Balancing conceptual clarity with practical implementation, the book combines detailed derivations, clear illustrations, and runnable Python code. It is designed for students, engineers, and researchers who want to not only understand the principles of machine learning but also build systems that work. Contents (main chapters): * Nomenclature * Foreword * Introduction * Numerical Methods - The Basis for ML * Neural Networks (ANN) * ResNet * Autograd * Convolutional Neural Networks (CNN) * Activation Functions * System Analysis - LeNet-5 Case Study * Transformers * Discrete Wavelet Transform * Support Vector Machines (SVM) * Principal Component Analysis (PCA) * Generative Adversarial Networks (GAN) * Spiking Neural Networks (SNN) * Equivariant CNNs * Data Structures for Object Detection * Glossary of AI and Machine Learning Terms * Bibliography 

The field of Machine Learning can be overwhelming, and you might feel like you're losing control. Instead of giving up, let me share a secret with you: it's easier than you think. All you need is a "Rosetta Stone" to fully accelerate your work with machine learning. This book takes you on an alternative route, starting with the fundamental concepts from calculus, linear algebra, numerical methods, and optimization, leading up to the state-of-the-art algorithms that have emerged over the last couple of decades. This is an ambitious promise, but my background in industry, research, and teaching has given me deep insights into the common challenges of developing efficient algorithms for prediction. This book will focus on the coding perspective-a "from-scratch" approach where the configuration includes setting up the environment properly, defining the dataset, and configuring training and validation. Moreover, this book will dedicate significant effort to eliminating version-related errors that could cause problems when maintaining the code for future development.

Research in warehouse optimization has gotten increased attention in the last few years due to e-commerce. The warehouse contains a waste range of different products. Due to the nature of the individual order, it is challenging to plan the picking list to optimize the material flow in the process. There are also challenges in minimizing costs and increasing production capacity, and this complexity can be defined as a multidisciplinary optimization problem with an IDF nature. In recent years the use of parallel computing using GPGPUs has become increasingly popular due to the introduction of CUDA C and accompanying applications in, e.g., Python. 

In the case study at the company in the field of retail, a case study including a system design optimization (SDO) resulted in an increase in throughput with well over 20% just by clustering different categories and suggesting in which sequence the orders should be picked during a given time frame. 

The options provided by implementing a distributed high-performance computing network based on GPUs for subsystem optimization have shown to be fruitful in developing a functioning SDO for warehouse optimization. The toolchain can be used for designing new warehouses or evaluating and tuning existing ones. 

Crusher models presented in research is calibrated using experimental data. This approach is everyday praxis and is made in many different ways. One method is to use two different compression tests. The single-particle breakage test (SPB) and the inter-particle breakage test (IPB). Models for predicting the breakage energy and size distribution for the SPB test and also to show the correlation between the SPB and IPB test is presented. The results show that Weibull analysis and Rosin-Rammer distributions are a successful way to model both energy and size distribution for SPB and IPB based compression crushing. The number of experiments conducted in both SPB and IPB tests can be drastically reduced by testing the lowest and highest compression ratio values used in the original SPB and IPB tests. The results also show a strong correlation between the SPB and IPB tests when evaluating the relationship between the consumed breakage energy and the coefficient of variance. The energy model is compared with Bond Work Index (BWI), and it was shown that the model parameter C shows a good correlation with BWI. A full-scale validation of a cone crusher is made, presenting a calculation scheme for addressing the use of the IPB and SPB models for predicting the size distribution.

Coarse comminution test-work and modeling are powerful tools in the design and optimization of mineral processing plants and provide information on energy consumption. Additional information on mineral liberation characteristics can be used for assessing the potential of pre-concentration stages or screens in the plant design. In ores of high-value metals (e.g., Ta, W), standard techniques—such as the mineralogical quantification of grain mounts by quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) or chemical analysis by X-ray fluorescence (XRF) can be challenging, due to the low relative abundance of such valuable minerals. The cost of QEMSCAN is also a limiting factor, especially considering the large number of samples required for the optimization of coarse comminution. In this study, we present an extended analytical protocol to a well-established mechanical test of interparticle breakage to improve the assessment of coarse mineral liberation characteristics. The liberation of ore minerals is a function of the rock texture and the difference in size and mechanical properties of the valuable minerals relative to gangue minerals and they may fraction in certain grain sizes if they behave differently during comminution. By analyzing the bulk-chemistry of the different grain size fractions produced after compressional testing, and by generating element by size diagrams, it is possible to understand the liberation characteristics of an ore. We show, based on a case study performed on a tantalum ore deposit, that element distribution can be used to study the influence of mechanical parameters on mineral liberation. This information can direct further mineralogical investigation and test work

Screening is a key unit operation for the large-scale separation of materials. There are certain different machine parameters and variables that affect the process of screening. The Discrete Element Method (DEM) is a suitable method to analyze parameters and variables. The main benefit of using the DEM for simulating the screening process is that, as a contact model, it provides the possibility of tracking each particle in the material flow and all collisions between particles and between particles and boundaries.

There are different types of materials used for screening media, such as rubber and polyurethane, which are used in modular systems as a panel, and such as steel, which are used as a wire in the mesh. This paper presents how different materials used in screen decks affect the screening process. The materials’ strength and elasticity have been examined in order to study how the aperture will change in different materials and how different shapes of the aperture and material of screening media affect the screening performance by analyzing the effect on material flow.

The modelling of breakage in compression breakage has traditionally been done using population balance modelling, and the research has been developed over the last decades into advanced dynamic models. This paper presents a model for predicting particle size distribution and energy consumption. The particle size distribution model is derived using a first-order differential equation for how the coefficient of variance depends on the compression length. The coefficient of variance model is combined with a bimodal Weibull distribution to predict the cumulative size distribution. The power consumption is modelled in a similar way using Weibull analysis to determine the relationship between power consumption and the coefficient of variance.