• Energy Research

Abstract Municipal solid waste treatment leads to the production of a considerable amount of mixed municipal waste, in case of which material recovery is difficult. Its treatment represents a worldwide challenge since landfilling is still a major treatment method and the respective emissions of greenhouse gases are significant. Approximately 126 Mt of municipal solid waste were landfilled or incinerated within the EU-28 in 2017, while the waste management sector produced 3% of the overall greenhouse gases emissions. Regarding mixed municipal waste, Waste-to-Energy plants seem to be a suitable disposal option as they substitute both landfills and energy production from fossil fuels in combined heat and power plants. However, new treatment facilities of this type need to take into account also the heat and electricity demands in their vicinity to ensure economic stability. This paper therefore analyses the relationship between greenhouse gases emissions and the cost of mixed municipal waste treatment, while considering environmental impact of different treatment options. A reverse logistic (mixed integer programming) model has been developed to optimise future strategies of mixed municipal waste treatment in a large geographical area. The model is nonlinear because of the nonlinear nature of the cost of mixed municipal waste treatment as well as the economic incentive associated with the avoided greenhouse gases emissions. These, in turn, are influenced by plant capacities, locations, and other location-specific parameters (such as the yearly heat demand profile) that must be considered during the integration of a future plant into the existing district heating systems. The results are presented through a case study for the Czech Republic, with 206 micro-regions (waste producers), 148 landfills, 113 potential mechanical-biological treatment plants, 24 potential locations for plants utilising refuse-derived fuels, 4 existing Waste-to-Energy plants, and 32 candidate locations for new Waste-to-Energy plants have been considered. The proposed future concepts involving various processing chains (small Waste-to-Energy plant, large Waste-to-Energy plant with the necessary complex logistics, mechanical biological treatment prior to incineration), are compared with the current (2016) waste treatment strategy, in which 73% of mixed municipal waste is landfilled. The trade-off between economically viable and environmentally acceptable solution is also targeted. The obtained data suggest a possible reduction in greenhouse gases emissions by almost 150% with the cost of waste treatment being increased only by approx. 2.5 EUR/t.

Data recording is struggling with the occurrence of errors, which worsen the accuracy of follow-up calculations. Achievement of satisfactory results requires the data processing to eliminate the influence of errors. This paper applies a data reconciliation technique for mining of data from ecording movement vehicles. The database collects information about the start and end point of the route (GPS coordinates) and total duration.The presented methodology smooths available data and allows to obtain an estimation of transportation time through individual parts of the entire recorded route. This process allows obtaining valuable information which can be used for further transportation planning. First, the proposed mathematical model is tested on simplifled example. The real data application requires necessary preprocessing within which anticipated routes are designed. Thus, the database is supplemented with information on the probable speed of the vehicle. The mathematical model is based on weighted least squares data reconciliation which is organized iteratively. Due to the time-consuming calculation, the linearised model is computed to initialize the values for a complex model. The attention is also paid to the weight setting. The weighing system is designed to reflect the quality of specific data and the dependence on the frequency of trafic. In this respect, the model is not strict, which leaves the possibility to adapt to the current data. The case study focuses on the GPS data of shipping vehicles in the particular city in the Czech Republic with several types of roads.

This paper analyses factors affecting the production of greenhouse gases from the treatment of residual municipal waste. The analysis is conducted so that the environmentally-friendly decision-making criteria may be later implemented into an optimisation task, which allocates waste treatment capacities. A simplified method of life cycle assessment is applied to describe environmental impact of the allocation. Global warming potential (GWP) is employed as a unit to quantify greenhouse gases (GHG) emissions. The objective is to identify the environmental burdens and credits measured by GWP for the three fundamental methods for treatment of residual waste unsuitable for material recovery. The three methods are waste-to-energy (WTE), landfilling and mechanicalbiological treatment (MBT) with subsequent utilization of refuse-derived fuel. The composition of the waste itself and content of fossil-derived carbon and biogenic carbon are important parameters to identify amounts of GHG. In case of WTE, subsequent use of the energy, e.g., in district heating systems in case of heat, is another important parameter to be considered. GWP function dependant on WTE capacity is introduced. The conclusion of this paper provides an assessment of the potential benefits of the results in optimisation tasks for the planning of overall strategy in waste management.

Abstract This paper introduces a novel approach to forecasting future commodity production in hundreds of nodes, which represents a key input for many applications of supply-chain models. A mathematical model was proposed to handle the problem of forecasting with spatially distributed and uncertain data. It is derived from the principle of regression analysis and extended by a data reconciliation technique. Additional areal constraints guarantee mass conservation in a tree-like structure, which reflects the organisational arrangement of an investigated region. The proposed model was tested through a case study, where future production of hazardous waste suitable for thermal treatment was forecasted in 206 base-nodes, 14 superior nodes and one apex. Based on an extensive investigation of historical data, it was revealed that extrapolations carried out at different levels of the hierarchical organisational structure lead to inconsistent forecasts. The differences between forecasts reached up to 50%. In addition to this, mass conservation was violated. Significant corrections were performed by computations utilizing the formulated model. The corrections ranged from between 0% and 12% for 90% of nodes. There were 17 nodes, where massive adjustments of up to 30% were inevitable.

Strategic plans for waste management require information on the current and future waste generation as a primary data source. Over the years, various approaches and methods for waste generation modeling have been presented and applied. This review provides a summary of the tasks that require information on waste generation that are most frequently handled in waste management. It is hypothesized that there is not currently a modeling approach universally suitable for forecasting any fraction of waste. It is also hypothesized that most models do not allow for modeling different scenarios of future development. Almost 360 publications were examined in detail, and all of the tracked attributes are included in the supplementary. A general step-by-step guide to waste generation forecasting, comprising data preparation, pre-processing, processing, and post-processing, was proposed. The problems that occurred in the individual steps were specified, and the authors’ recommendations for their solution were provided. A forecasting approach based on a short time series is presented, due to insufficient options of approaches for this problem. An approach is presented for creating projections of waste generation depending on the expected system changes. Researchers and stakeholders can use this document as a supporting material when deciding on a suitable approach to waste generation modeling or waste management plans.

Abstract The paper deals with the development and presentation of the use of an advanced computational optimization tool for the conceptual planning of facilities in the field of waste-to-energy. The determination of the suitable capacity and sizing of an appropriate heat recovery system, according to adopted heat utilization strategy (i.e. either only electricity production or combined heat and power if feasible), represent crucial decisions about each individual incineration plant in its early project stage. The economic feasibility of the project should be guaranteed at the same time. The feasibility is measured by internal rate of return. An optimization model supporting such decisions was built and is introduced. Building a new incinerator, from the initial considerations to its full operation, is a long-term process with duration at a minimum of 5–7 years. The erection is then followed by an operational phase exceeding 20 years. The unclear future development of important parameters affecting the project sustainability is reflected by implementing principal concepts of stochastic programming. In the article, a brief overview of principal ideas related to decision making under uncertainty (wait-and-see and/or here-and-now approaches, specification, and use of scenarios) is given first, followed by the description of a mathematical model. Then, the selected approach is demonstrated through a case study involving a municipal solid waste incinerator.

Abstract The data regarding the current potential of unrecyclable waste and its spatial distribution play an essential role in the planning process. As per the circular economy strategies, especially the bulky waste streams suitable for energy recovery are to be identified. The public databases, however, collect data from a variety of sources (production and handling reports), which implies the presence of errors. This paper therefore proposes a multi-objective approach to identification and elimination of such errors to improve the accuracy of the assessment of potential energy recovery. The discussed model tracks the flow of waste from producers to processing nodes and minimises the deviation from the original data. Economic aspects are considered as well by preferring the shortest transport distance. The combination of data reconciliation and network flow enhances performance, as objective functions are solved separately, and only then the normalised individual optima are used in the multi-objective function. The model was tested using a Czech Republic regional-level dataset from 2015. A new perspective on the current state of waste management was provided, and valuable information for future planning was revealed, which can be useful for modelling of flows of other commodities.

Abstract Waste-to-Energy facility location with practical insights into its economic sustainability is assessed by two mathematical models. The first model minimising transportation and investment costs leads to a mixed-integer linear problem, for which commercial solvers perform very well. However, economic performance, which is needed for long-term projects requiring large investments, is not met when the capacity of the plant is not fully utilised. This can be resolved by a revenue model defining gate fees for potential plant capacities. Therefore, a second model including penalty co st functions associated with reduced energy sales and unutilised capacity of plants is developed. This leads to a non-linear model where solvers perform well for small and medium-size instances and so a modified meta-heuristic algorithm is proposed. Both models are applied to data from the Czech Republic. Insights into performance of the models and their economical sustainability using demand influence on the energy sales are provided. While the solution of the linear model proposes a higher number of facilities with less total capacity repletion, the non-linear model suggests a smaller number of facilities with higher total repletion presenting a reasonable sustainable solution. The strategy supports the decision-making of authorities for the sustainable planning of new projects.

The aim of this paper is to introduce a novel approach which supports facility planning in the field of waste management. Only 23 % of municipal solid waste (MSW) was thermally treated in the EU 27 in 2011. The increased exploitation of its potential for energy recovery must be accompanied by massive investments into highly efficient and reliable incineration technologies. Therefore, the challenge is to be efficient and use the technology to its optimal level. Feasibility studies of all plants providing a service for a region create a large and complex task. Gate fee (the charge for waste processing in the facility) represents one of the most crucial input parameters for the assessment. The gate fee is driven by configuration of the technology, competition, market development, environmental taxation and costs of waste transport to satisfy the plant’s capacity. Valid prediction of the gate fee thus presents a demanding task. In this paper, first, an advanced tool called NERUDA is introduced, which addresses logistic optimization and capacity sizing. The key idea is to focus on the problem of competition modelling among waste-to-energy plants, landfill sites, and mechanical–biological treatment plants producing refuse-derived fuel. Then, the main theoretical concepts are discussed, followed by the development of a suitable mathematical model. The goal is to obtain a minimized cost of MSW treatment for waste producers (municipalities). The application of the developed tool is demonstrated through a case study, where uncertain parameters entering the calculation are handled by a repetitive Monte Carlo simulation based on real-world data.