I. Waste Quantification, Characterisation and Sorting Technology Development

1. Modelling of Mechanical Waste Treatment Plants

Our research laboratory is equipped with advanced machinery that enables us to model the processes used in full-scale municipal solid waste treatment and packaging waste sorting plants.

What does our laboratory offer?

• Process modelling – investigation and simulation of complete waste treatment lines.
• Waste fraction characterisation – detailed analysis of diverse solid waste streams.
• Technology development – development of optimised separation solutions for specific waste fractions.

Our equipment

• Shredders – waste preparation and size reduction.
• Screening unit (CZ SCREEN MS MICRO) – precise separation into particle size fractions.
• Optical sorting system (UNISORT PCM1000RBF) – advanced near-infrared (NIR) technology for separating plastics and other materials.
• Combined metal separator (Steinert MOH) – magnetic and eddy current separation.

2. Waste Quantification and Characterisation

Municipal solid waste and its B fractions, refuse-derived fuel (RDF), packaging waste, kitchen waste and textile waste.

Our laboratory has the advanced equipment required for comprehensive and detailed waste characterisation.

• Physical and compositional analysis: material composition, moisture content, ash content and volatile matter content.
• Elemental analysis: determination of As, Ba, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mo, Na, Ni, P, Pb, Sn and Zn using ICP-OES and FAAS.
• Anion determination: conventional analytical methods and ion chromatography.
• CHNS/O analysis: determination of carbon, hydrogen, nitrogen, sulphur and oxygen; measurement of total carbon (TC), total inorganic carbon (TIC) and total organic carbon (TOC).
• Biological testing: biochemical oxygen demand (BOD) and respiration activity.
• Thermal analysis: decomposition behaviour under oxidative and inert atmospheres, and determination of net calorific value.

3. Data-Driven Waste Stream Analysis and Forecasting

A key objective of our work is the systematic collection and analysis of waste management data from Hungary and abroad.
These data help us better understand waste generation patterns and identify optimal recycling and recovery pathways.

Why is a data-driven approach important?

• Understanding regional differences – waste quantities and composition depend strongly on economic and social conditions.
• Reliable estimates – essential for the efficient planning of collection routes, facilities and technologies.
• System-wide optimisation – using data to identify opportunities for improvement and develop recommendations.

Our methods

• Network analysis – assessing system resilience and identifying critical nodes.
• Forecasting – modelling municipal solid waste quantities using statistical and machine learning methods.
• Decision support – providing strategic recommendations for infrastructure development and waste treatment processes.