Computational System for Simulation and Forecasting in Waste Management Incomplete Data Problems

Abstract

Nowadays, shift towards an effective waste management is a big issue as well as a necessity for many countries. Infrastructural projects in waste management (e.g. new waste-to-energy plants, WTE) have long-term implementation phase (up to 10 y). WTE, processing residual waste, represent keystone of sustainable concepts. In this context, information on waste availability and incinerated waste properties, especially calorific value, are vital for successful design and operation of these plants. However, hardly predictable future changes in these parameters make the design process challenging. In this contribution, we present a tool for verification and forecasting of current and future production of residual municipal waste and its lower heating value (LHV) to support WTE design process. Newly developed tool JUSTINE is introduced. From the principal point of view it is a wait-and-see optimization model recursively applied to region divided into several sub regions and also their parts. It processes variety of spatially distributed statistical data bound together through equations and constraints. This data is supposed to be incomplete (some local information might be unavailable) and uncertain. The wait-and-see optimization model is used to obtain point estimates of desired parameters that can be used for waste production and LHV forecasts. In addition, for randomly simulated input data reflecting real-world uncertainties empirical confidence intervals of input and output values can be computed. The practical contribution of this tool is presented through a case study.