Post-consumer plastic sorting infrastructure improvements planning: Scenario-based modeling of greenhouse gas savings with sustainable costs.

With the increasing share of waste material recovery, household plastic waste is one of the biggest problems. In most countries, mainly manual sorting is used. Meanwhile, new automated technologies are being developed to expand the range of classifiable types to increase material recovery. The overall automation of the sorting process can help the EU's established recycling targets to be effectively met. However, the new technologies are feasible only in the case of large-capacity centers, which must be conveniently located in the existing infrastructure. This paper presents a two-stage model aiming to modernize the current sorting infrastructure for plastic waste. The approach uses multi-criteria optimization to minimize environmental impact at a reasonable price. The result is the optimal location of new automatic sorting centers, and waste stream flows using existing manual sorting facilities. The model is applied through an initial case study inspired by the Czech Republic data. Optimization output proposes four new automatic sorting lines with a total capacity of 158 kt per year. In most cases, manual sorting is used to reduce the transported weight of plastic waste, while automatic sorting lines separate the remaining, hardly recognized part. More than 60% of separately collected plastic is sorted and determined for material recovery.

Tool for optimization of energy consumption of membrane-based carbon capture.

The reduction of CO2 emissions is a very challenging issue. The capture of CO2 from combustion processes is associated with high energy consumption and decreases the efficiency of power-producing facilities. This can affect the economy and in specific cases, such as waste-to-energy plants, also their classification according to legislation. To allow the minimization of energy consumption, an optimization tool for membrane-based post-combustion capture was developed. The approach allows finding optimal membrane properties, membrane areas, and pressures for individual separation stages from the point of view of energy consumption. The core of the approach is represented by a mathematical model of the separation system that is based on a network flow problem. The model utilizes external simulation modules for non-linear problems to enable finding globally optimal results. These external modules approximate non-linear dependencies with any desired precision and allow using different mathematical descriptions of individual membrane stages without making changes to the model. Moreover, it allows easy substitution of the external module by experimental data and the model can be easily modified for specific purposes such as decision making, designing the separation process, as well as for regulation of process parameters in the case of dynamic operation. The ability of the model to optimize the process was verified on a case study and the results show that the optimization can significantly reduce the energy consumption of the process. For separation of 90% of CO2 at the purity of 95% from initial flue gas with 13% CO2 with state-of-the-art membranes based on the Robeson upper bound and three-stage process, the minimum power consumption was 1.74 GJ/tCO2 including final CO2 compression.

Multi-level stratification of territories for waste composition analysis.

The fundamental knowledge at all levels of decision-making related to waste management is the quantity and composition of waste. Many articles deal with methods for estimating the composition of municipal waste, but most details are given as to how many categories should be chosen and what technical procedure should be followed. In order to obtain a broader view and a reasonable evaluation of the results, it is necessary to select the areas where the analyzes will be performed effectively. Current approaches have insufficiently addressed this issue at the regional and national levels. This paper presents a method that uses multi-level stratification to divide municipalities into similar groups to reduce the number of observations needed to obtain an estimate of the composition of waste in a selected area (region or state level). The method combines expert knowledge with statistical considerations and makes use of cluster analysis. Socio-economic and waste-related parameters are used within the individual steps. Regarding the available financial resources and the required accuracy of the results, the municipalities in which the analyzes should take place are selected. These representative municipalities represent other municipalities in the created groups, and thanks to them, it is possible to estimate the composition of waste in any municipality, region, or larger territory. Waste analysis planning is an essential procedure for waste management, but the respective costs represent a crucial factor at the national level and even more for individual municipalities. Estimating waste composition impacts the transition to sustainable waste management and is thus a key element for further development in this sector. The presented method demonstrates the selection of 10 representative municipalities from the Czech Republic, but an arbitrary number can be set respecting the financial resources. Estimating the composition of the mixed municipal waste for the Czech Republic should cost around 72,000 euros for ten representatives with different distribution of dwelling types. The method is described in general and can be applied to any territory/country in the world, considering local conditions and possibilities.

Predictive modelling as a tool for effective municipal waste management policy at different territorial levels.

Nowadays, the European municipal waste management policy based on the circular economy paradigm demands the closing of material and financial loops at all territorial levels of public administration. The effective planning of treatment capacities (especially sorting plants, recycling, and energy recovery facilities) and municipal waste management policy requires an accurate prognosis of municipal waste generation, and therefore, the knowledge of behavioral, socio-economic, and demographic factors influencing the waste management (and recycling) behavior of households, and other municipal waste producers. To enable public bodies at different territorial levels to undertake an effective action resulting in circular economy we evaluated various factors influencing the generation of municipal waste fractions at regional, micro-regional and municipal level in the Czech Republic. Principal components were used as input for traditional models (multivariable linear regression, generalized linear model) as well as tree-based machine learning models (regression trees, random forest, gradient boosted regression trees). Study results suggest that the linear regression model usually offers a good trade-off between model accuracy and interpretability. When the most important goal of the prediction is supposed to be accuracy, the random forest is generally the best choice. The quality of developed models depends mostly on the chosen territorial level and municipal waste fraction. The performance of these models deteriorates significantly for lower territorial levels because of worse data quality and bigger variability. Only the age structure seems to be important across territorial levels and municipal waste fractions. Nevertheless, also other factors are of high significance in explaining the generation of municipal waste fractions at different territorial levels (e.g. number of economic subjects, expenditures, population density and the level of education). Therefore, there is not one single effective public policy dealing with circular economy strategy that fits all territorial levels. Public representatives should focus on policies effective at specific territorial level. However, performance of the models is poor for lower territorial levels (municipality and micro-regions). Thus, results for municipalities and micro-regions are weak and should be treated as such.

Impact assessment of pollutants from waste-related operations as a feature of holistic logistic tool.

Waste management has still been a developing and progressing field, which demands continual improvements in waste transportation as well as proper selection of locations and technical operation of new treatment facilities. Most of research papers on waste management planning have been dealing with optimisation of network flows, thus minimising the cost and improving economic criteria. The shortest paths to treatment facilities are considered together with detailed analysis of their operation including heat and electricity demands in their vicinity. The tasks sometimes include social and global environmental criterions, however, the direct local consequences also play an important role and should be examined. A decision-making strategy in waste management updated with the local emission impact on the population is proposed in this paper. The paper focuses on the first move in analysing the production, dispersion, and impact of pollutants, originating in transport, with regards to the population living close to routes. The calculation of emission produced during the transport of waste takes into consideration the altitude profiles of routes, container loads, and specific types of vehicles. The consecutive estimated impact on the population reckons with the distances between routes and municipalities as well as their sizes in terms of the numbers of inhabitants, where the transportation routes are divided into smaller segments and dispersion is limited with threshold value. The proposed approach describing the emission effect has been tested using real-life operating data corresponding to the specific, 81 km long route along which approximately 25 t of waste is transported 800 times a year. The impact of pollutants on the population was evaluated and discussed. Results of the analysis were quantified for this route to create an edge characterisation needed for further calculations. This approach applied to the whole network then yields input data needed for future research of novel strategies in facility location problems. Other possible extensions of the presented approach include more accurate dispersion function or detailed calculation of the impact of pollutants with respect to specific locations of residential houses.

Material analysis of Bottom ash from waste-to-energy plants.

Bottom ash (BA) from waste-to-energy (WtE) plants contains valuable components, particularly ferrous (Fe) and non-ferrous (NFe) metals, which can be recovered. To assess the resource recovery potential of BA in the Czech Republic, it was necessary to obtain its detailed material composition. This paper presents the material composition of BA samples from all three Czech WtE plants. It was found that the BA contained 9.2-22.7% glass, 1.8-5.1% ceramics and porcelain, 0.2-1.0% unburnt organic matter, 10.2-16.3% magnetic fraction, 6.1-11.0% Fe scrap, and 1.3-2.8% NFe metals (in dry matter). The contents of individual components were also studied with respect to the BA granulometry and character of the WtE waste collection area.