**3. Description of case study**

Solid waste management is developing into a complex task. New or modified treatment technologies are appearing. During the past two decades, thermal wastes management followed heavily disparate trends. In the 1980s, the focus was on new market players, and then in the 1990s on new technologies, especially pyrolysis and melting processes (Bieda & Tadeusiewicz, 2008).

Novel processes utilizing pyrolysis and gasification have attracted publicity as a potential alternative to incineration. Such systems offer some benefits in terms of recycling and public acceptance. However, because they are new, they are less proven in operation than conventional technologies-and may therefore be more risky. The main advantage that gasification has over incineration is its ability to conserve the chemical energy of the waste in the produced syngas rather than convert it to heat energy in hot flue gas (Klein et al, 2004).

The new Polish environmental strategy emphasizes the principle of sustainable development and it encourages the government of Konin to develop a waste management

Risk Analysis of the Waste to Energy Pyrolysis Facility Designs for City of Konin, in Poland, Using SimLab®

In SimLab®, the assumptions or input range for each parameter was defined by choosing a probability distribution that describes the uncertainty of the data. Input distribution may be normal, uniform, triangular, skewed, or any shape that reflects the nature of the

At the start Simlab® displays the main panel (Figure 1); this panel is divided in three frames

2. The *Model Execution module*: executes the model for each point in the sample of input

The deterministic project approach traditionally requires that the values for all input data be known exactly. But data in many real manufacturing projects cannot be precisely given. The stochastic approach is based on the replacement deterministic data with random variables. Important studies to stochastic variables incorporated in the data envelopment analysis can be found in (Sengupta, 1982, 1987, 1990, 1997, 1998, 2000, Cooper et al, 1998; Huang & Li,

1996; Morita & Seiford, 1999; Sueyoshi, 2000, as cited in Azadi & Saen, 2011).

3. The *Statistical Post Processor module*: performs the uncertainty and sensitivity analysis.

1. The *Statistical Pre Processor module*: generates a sample in the space of the input.

report results and make decisions.

measurement being assessed.

Fig. 1. Simlab® main panel.

**5. Results and discussion** 

(Saltelli et al., 2004):

factors.

Toolpack 249

plan for their communities based on the use the technology for a gasification with waste to energy system. One scenario has been chosen: American Gasification System (design at 200 T/D). The Capital Budget – Project Costs of the American Scenario is given in Tables 1.

The revenues were based on the Proposal to Design, Develop and Construct a Waste-to-Energy Facility for the City of Konin. The revenues include:


The selling prices of the marketable material, and the tipping fee for each ton of waste that is delivered to the landfill are coming from the Waste Program Revenue from the city or others. The general operating parameters of the Konin's Waste-to-Energy Facility are as follows:


Municipality has been entered into a contract to supply an average of 200/250 tons of municipal waste per day with options for increased volume as the demand increases.


Table 1. Capital Budget – Project Costs of the proposed American Gasification System.
