• 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.

Abstract In Europe, most of the district heat is produced from fossil energy sources such as coal or natural gas. Together with the intention to divert municipal waste from landfill, which is still the main way of treatment in approximately half of the EU countries, there is a potential for the construction of waste-to-energy plants (WtEP) that can partially replace fossil fuels. An important factor in planning the construction of new WtE plants is the assessment of the project’s economy. However, the ecological aspect of the project, namely the greenhouse-gas (GHG) savings, is becoming a topic of discussion nowadays. In terms of GHG savings, WtE plant integration into an existing district heating system (DHS) has a positive impact, but its level is often only roughly estimated. The paper presents a comprehensive mathematical optimization tool working on a daily time interval that is able to evaluate the impact of changing current technology or WtE plant construction on global warming potential. The technical parameters such as boiler output range, energy efficiency, etc. are considered. The tool also allows to put in the context the GHG savings and economic benefits of the project. These two factors are very difficult to compare. The study answers the question of how to deal with this issue and presents the possibility of comparison, which is generally transferable to any two parameters. Due to two poorly comparable criteria, the result does not include a single optimal solution, but clearly illustrates the view of the overall issue and gives a good basis for the final decision. The method used are illustrated in real-world DHS with various parameters where the relationship between GHG savings and the economic benefits of the integration of a WtE plant is evaluated. The whole problem is the task of linear integer programming and is implemented in the GAMS programming environment.

Abstract In many cases, WtE (waste-to-energy) plants are CHP (combined heat and power) producers. They are often integrated into a central heating system and they also export electricity to the grid. Therefore, they have to plan their operation on a long-term basis (months, years) as well as on a short-term basis (hours, days). Simulation models can effectively support decision making in CHP production planning. In general, CHP production planning on a short-term basis is a challenging task for WtE plants. This article presents a simulation based support. It is demonstrated on an example involving a real WtE plant. Most of the models of relevant WtE sub-systems (boilers, steam turbine) are developed using operational data and applying linear regression and artificial neural network technique. The process randomness given mainly by fluctuating heating value of waste leads to uncertainty in a calculation of CHP production and a stochastic approach is appropriate. The models of the sub-systems are, therefore, extended of a stochastic part and Monte-Carlo simulation is applied. Compared to the current planning strategy in the involved WtE plant, the stochastic simulation based planning provides increased CHP production resulting in better net thermal efficiency and increased revenue. This is demonstrated through a comparison using real operational data.

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.

Changing the fuel mix used in the heating industry, i.e., switching to greener fuels, is one of the possible solutions to prevent rising costs for final consumers in the context of rising emission allowance prices. This paper presents a methodology that offers the possibility to perform a comprehensive technical and economic assessment of a theoretical solution—changing the fuel mix of centralized heating sources—and other strategic decisions within a district’s heating systems. Emphasis is placed on fuels with a negative price, such as municipal waste. The presented approach can also be used to assess the effect of other significant changes related to the configuration of district heating systems on the economy of the plant, such as the impact of a decrease in heat demand and implementation of a steam turbine. The key benefit of this paper is an approach based on mathematical modelling of the operation of individual boilers with different operating parameters in terms of their start-up, shutdown, and mode of operation. A unique approach of optimizing an operation’s schedule using dynamic programming is presented, which enables the selection of a suitable solution for the configuration of binary variables in consecutive time steps. In this way, it is possible to achieve a more accurate estimate of the economics of the facility at the strategic planning stage that will consider the real operational capabilities of the heat source given its technical limitations. Using this approach, up to a 4% reduction in variable operating costs was achieved in the model case, when compared to static time interval planning.

Abstract The paper deals with the issue of fluctuations in heat demand and how they are dealt with when planning investments in the field of energy recovery of waste. The lifetime of Waste to Energy plants (WtEP) is typically 20–30 years. Their construction is also a time and investment-intensive business and requires a robust design with low sensitivity to future changes in key parameters. The paper analyses the effect of fluctuations in heat demand on the accuracy of techno-economic models and their outputs. The aim is to determine the sensitivity of optimisation models to the applied time step. The sensitivity of two different models is compared – a simple model of a WtEP cooperating with a gas boiler and a complex model of a WtEP integrated with a combined heat and power plant. Optimisation models, commonly used to design these facilities, perform calculations in certain time steps, typically on an annual or monthly basis. The simplification from hour to month time step may cause inaccuracies. The paper offers alternative solutions to modelling methods of WtEP, using so-called correction coefficients. They help to increase the accuracy of the models while maintaining acceptable calculation time.