Design and Decomposition of Waste Prognostic Model with Hierarchical Structures

The waste management is a dynamically progressive area, with the current trend leading to circular economy scheme. The development in this area requires quality prognosis reflecting the analysed timeframe. The forecast of the waste production and composition of waste is an important aspect with regards to the planning in waste management. However, the regular prognostic methods are not appropriate for these purposes due to short time series of historical data and unavailability of socio-economic data. The paper proposes a general approach via mathematical model for forecasting of future waste-related parameters based on spatially distributed data with hierarchical structure. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The decomposition of the model into subtasks is performed in order to simpler implementation and reasonable time solvability. The individual algorithm steps are applied to municipal waste production data in the Czech Republic

greenhousegasemissionsfromthermaltreatmentofnonrecyclablemunicipalwaste

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

greenhousegasemissionsfromthermaltreatmentofnonrecyclablemunicipalwaste

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

Imaging Modalities Used for Frameless and Fiducial-Less Deep Brain Stimulation: A Single Centre Exploratory Study among Parkinson’s Disease Cases

Deep brain stimulation (DBS) is a beneficial procedure for treating idiopathic Parkinson’s disease (PD), essential tremor, and dystonia. The authors describe their set of imaging modalities used for a frameless and fiducial-less method of DBS. CT and MRI scans are obtained preoperatively, and STN parcellation is done based on diffusion tractography. During the surgery, an intraoperative cone-beam computed tomography scan is obtained and merged with the preoperatively-acquired images to place electrodes using a frameless and fiducial-less system. Accuracy is evaluated prospectively. The described sequence of imaging methods shows excellent accuracy compared to the frame-based techniques