Special issue on "advanced technology of waste treatment" [Editorial]

The protection of human health and the environment (representing the main reason for waste management), as well as the sustainable use of natural resources, requires chemical, biological, physical and thermal treatment of wastes [...

Landfill mining: resource potential of Austrian landfills – evaluation and quality assessment of recovered municipal solid waste by chemical analyses

Since the need for raw materials in countries undergoing industrialisation (like China) is rising, the availability of metal and fossil fuel energy resources (like ores or coal) has changed in recent years. Landfill sites can contain considerable amounts of recyclables and energy-recoverable materials, therefore, landfill mining is an option for exploiting dumped secondary raw materials, saving primary sources. For the purposes of this article, two sanitary landfill sites have been chosen for obtaining actual data to determine the resource potential of Austrian landfills. To evaluate how pretreating waste before disposal affects the resource potential of landfills, the first landfill site has been selected because it has received untreated waste, whereas mechanically–biologically treated waste was dumped in the second. The scope of this investigation comprised: (1) waste characterisation by sorting analyses of recovered waste; and (2) chemical analyses of specific waste fractions for quality assessment regarding potential energy recovery by using it as solid recovered fuels. The content of eight heavy metals and the net calorific values were determined for the chemical characterisation tests. </jats:p

Landfill mining: development of a theoretical method for a preliminary estimate of the raw material potential of landfill sites

In recent years, the rising need for raw materials by emerging economies (e.g. China) has led to a change in the availability of certain primary raw materials, such as ores or coal. The accompanying rising demand for secondary raw materials as possible substitutes for primary resources, the soaring prices and the global lack of specific (e.g. metallic) raw materials pique the interest of science and economy to consider landfills as possible secondary sources of raw materials. These sites often contain substantial amounts of materials that can be potentially utilised materially or energetically. To investigate the raw material potential of a landfill, boreholes and excavations, as well as subsequent hand sorting have proven quite successful. These procedures, however, are expensive and time consuming as they frequently require extensive construction measures on the landfill body or waste mass. For this reason, this article introduces a newly developed, affordable, theoretical method for the estimation of landfill contents. The article summarises the individual calculation steps of the method and demonstrates this using the example of a selected Austrian sanitary landfill. To assess the practicality and plausibility, the mathematically determined raw material potential is compared with the actual results from experimental studies of excavated waste from the same landfill (actual raw material potential)

The “ReWaste4.0” Project—A Review

ReWaste4.0 is an innovative and cooperative K-Project in the period 2017–2021. Through ReWaste4.0 the transformation of the non-hazardous mixed municipal and commercial waste treatment industry towards a circular economy has started by investigating and applying the new approaches of the Industry 4.0. Vision of the ReWaste4.0 is, among others, the development of treatment plants for non-hazardous waste into a “Smart Waste Factory” in which a digital communication and interconnection between material quality and machine as well as plant performance is reached. After four years of research and development, various results have been gained and the present review article summarizes, links and discuss the outputs (especially from peer-reviewed papers) of seven sub-projects, in total, within the K-project and discusses the main findings and their relevance and importance for further development of the waste treatment sector. Results are allocated into three areas, namely: contaminants in mixed waste and technical possibilities for their reduction as well as removal; secondary raw and energy materials in mixed waste and digitalization in waste characterization and treatment processes for mixed waste. The research conducted in ReWaste4.0 will be continued in ReWaste F for further development towards a particle-, sensor- and data-based circular economy in the period 2021–2025

Distribution-Independent Empirical Modeling of Particle Size Distributions—Coarse-Shredding of Mixed Commercial Waste

Particle size distributions (PSDs) belong to the most critical properties of particulate materials. They influence process behavior and product qualities. Standard methods for describing them are either too detailed for straightforward interpretation (i.e., lists of individual particles), hide too much information (summary values), or are distribution-dependent, limiting their applicability to distributions produced by a small number of processes. In this work the distribution-independent approach of modeling isometric log-ratio-transformed shares of an arbitrary number of discrete particle size classes is presented. It allows using standard empirical modeling techniques, and the mathematically proper calculation of confidence and prediction regions. The method is demonstrated on coarse-shredding of mixed commercial waste from Styria in Austria, resulting in a significant model for the influence of shredding parameters on produced particle sizes (with classes: &gt;80 mm, 30–80 mm, 0–30 mm). It identifies the cutting tool geometry as significant, with a p-value &lt; 10−5, while evaluating the gap width and shaft rotation speed as non-significant. In conclusion, the results question typically chosen operation parameters in practice, and the applied method has proven to be valuable addition to the mathematical toolbox of process engineers

Empirical modeling of compositions in chemical engineering

Many of the data used in chemical engineering are com­positional. When mass balance based theoretical models cannot be used, and empirical statistical modeling is ap­plied, the compositional, dependent, multivariate nature of the data must be considered. Log-ratios are a powerful approach for transforming compositional data to an uncon­strained vector space of linearly independent coordinates, allowing the application of standard statistical methods. Three of them, the additive, centered, and isometric log-ratios, as well as amalgamations, are introduced in this work, to the chemical engineering community, discussing their potentials, advantages, and possible pitfalls when using them.DiV4-(01) page 1DiV4-(01) page 44FFG - Austrian Research Promotion Agenc

Investigation of particle descriptors for size characterisation of solid waste particles for treatment process control

Real-time control of the particle size distribution of coarsely shredded mixed solid waste has a large potential for improving the performance of mechanical processing plants. In addition to controllers and actuators, online metrology for particle size distributions is needed. In this work, 2D-images of single waste particles from the material fractions wood and plastic are investigated. The materials were gained by handsorting of shredded mixed commercial waste and the individual particles were described through different descriptors, which were used in regression models for particle size determination. It is shown that univariate models are not very likely to perform well due to the overlapping of the descriptor values for different particle size classes. Though, using a Partial Least Squares regression, that considers many different descriptors, an accuracy of over 70 % was reached in most of the considered particle size classes for detecting the correct particle size in the investigated material fractions. Therefore, the potential of the method was proven, while further research is needed, to reach an accuracy level that is suitable for process control. Additionally, the evaluated particle size class must be combined with the particle weight to determine the effects of assigned particles in a particle size distribution.DiV5-(02) page 1DiV5-(02) page 5