Paper-ash is used for remediation of heavily contaminated soils with metals, but remediation efficiency after longer periods has not been reported. To gain insights into the mechanisms of immobilization of cadmium (Cd), lead (Pb), and znic (Zn), a study was performed in the laboratory experiment in uncontaminated, artificially contaminated, and remediated soils, and these soils treated with sulfate, to mimic conditions in contaminated soil from zinc smelter site. Remediation was performed by mixing contaminated soil with paper-ash to immobilize Cd, Pb, and Zn in the geotechnical composite. Partitioning of Cd, Pb, and Zn was studied over one year in seven-time intervals applying the sequential extraction procedure and complementary X-ray diffraction analyses. This methodological approach enabled us to follow the redistribution of Cd, Pb, and Zn over time, thus, to studying immobilization mechanisms and assessing the remediation efficiency and stability of newly formed mineral phases. Cd, Pb, and Zn were effectively immobilized by precipitation of insoluble hydroxides after the addition of paper-ash and by the carbonization process in insoluble carbonate minerals. After remediation, Cd, Pb, and Zn concentrations in the water-soluble fraction were well below the limiting values for inertness: Cd by 100 times, Pb by 125 times, and Zn by 10 times. Sulfate treatment did not influence the remediation efficiency. Experimental data confirmed the high remediation efficiency and stability of insoluble Cd, Pb, and Zn mineral phases in geotechnical composites.

The programming language Python offers the opportunity to analyze and model the municipal sewage sludge (MSS) drying process in an illustrative chemical engineering practice. The drying process is performed on a flat plate while maintaining a uniform, parallel drying air speed. The Python program helps to analyze the digitalized weight measurements from each sample. The program enables the sorting of input data, determination of the drying critical point, and evaluation of the first and second drying periods. Moreover, the model calculates the fundamental drying parameter and forms a drying master curve to support the transfer to different drying conditions. The basic parameters calculated are mass transfer coefficient, heat transfer coefficient, and diffusion coefficient. The results are consistent with published data for those coefficients over the drying temperature range of 19.4–52.4 °C and relative humidity range of 8.2–33.6%. The findings of this study demonstrate the potential of Python as a powerful tool for analyzing experimental data and modeling chemical processes, which can lead to improved process design, optimization, and control.

The rapid growth of the quantity of generated electronic waste (e-waste), driven by the increasing demand for electrical and electronic equipment (EEE), has raised urgent concerns regarding its environmental and health impacts. E-waste is the fastest-growing global waste stream, with only a small fraction recycled sustainably. Printed circuit boards (PCBs), a major component of e-waste, contain valuable metals and hazardous substances, complicating recycling efforts. This study explores bioleaching as an environmentally friendly alternative to traditional recycling methods. Bioleaching, utilising microorganisms such as Acidithiobacillus ferrooxidans and A. thiooxidans, which can effectively extracts metals like copper, nickel, and zinc from e-waste, reducing environmental contamination. Our research, conducted under the EIT RawMaterials WEEE-NET9 project, focuses on bioleaching's potential for sustainable recovery of critical raw materials (CRMs) from e-waste. Results demonstrate the effectiveness of bioleaching in metal extraction, supporting the EU's goals of increasing CRM recycling and reducing reliance on primary sources for critical materials, which we have to import into the EU.

The environmental properties of three geotechnical composites made by recycling wastes were investigated on a laboratory scale and in the field with the use of lysimeters designated for the revitalization of degraded mining sites. Composites were prepared by combining the mine waste with paper-mill sludge and foundry sand (Composite 1), with digestate from municipal waste and paper ash (Composite 2), and with coal ash, foundry slag and waste incineration bottom ash (Composite 3). The results of laboratory leaching tests proved that Composites 1 and 3 are environmentally acceptable, according to the legislative limits, as the potentially hazardous substances were immobilized, while in Composite 2, the legislative limits were exceeded. In the field lysimeters, the lowest rate of leaching was determined for optimally compacted Composites 1 and 3, while for Composite 2 the leaching of Cu was high. This study proved that optimally installed Composites 1 and 3 are environmentally acceptable for use in construction as an alternative to virgin materials, for the revitalization of degraded mining sites or, along with Composite 2, for closure operations with landfills. In this way, locally available waste streams are valorised and channelized into a beneficial and sustainable recycling practice.

This study investigates the use of programming language Python to analyse and model chemical engineering processes. Using municipal sewage sludge (MSS) drying as an example, the study demonstrates how Python can support analyses and model drying data. The drying process was performed on a flat plate while maintaining a uniform, parallel drying air speed of 1.15 m s-1. The Python program helped to analyse the digitalised weight measurements from each sample. The program enables sorting input data, determining the drying critical point and evaluating the first and second drying periods.