Assesing the Local Biowaste Potential of Rural and Developed Areas Using GIS-Data and Clustering Techniques: Towards a Decision Support Tool

As the chemical and energy producing industries are steadily transitioning towards more sustainable processing practices, renewable biomass resources are becoming increasingly more valuable. Recently, following the realisation that renewable resources for the chemical and energy industry should not compete with food supplies, the use of plant-based biowaste has significantly gained in interest. Due to its inherently variable composition, diffuse distribution, and seasonality, it is of the utmost importance that (potential) biorefinery exploiters are well informed of the biowaste resources that are available in the vicinity of their (planned) biorefinery. Designing a biorefinery in such a way that it can tailor for the locally available biowaste resources, exhibits several compelling advantages. Apart from significantly reduced logistics costs, the usage of local biowaste can be a reciprocal advantage for both the involved community and the biorefinery. In this paper, a GIS-based (Geo-Information System) bio-inventory toolbox is presented. The toolbox is developed to aid the biorefinery designers and decision makers, e.g., governmental bodies, to get an adequate overview of the locally available plant-based biowaste resources and, linked to this, the expected periodical amounts, their composition, and their seasonality. The toolbox presented in this contribution is the first part of a decision support tool for the development of a locally embedded flexi-feed and small-scale biorefinery, additionally consisting out of a process modelling tool, and an optimisation tool. Both of these additional tools will employ the information obtained from the bio-inventory toolbox to simulate and optimise several suitable biorefinery designs. The eventual goal of the decision support tool is to provide users with several optimised biorefinery designs that are tailored for their local setting. The additional toolboxes are detailed elsewhere.

A framework to predict the price of energy for the end-users with applications to monetary and energy policies

Energy affects every single individual and entity in the world. Therefore, it is crucial to precisely quantify the “price of energy” and study how it evolves through time, through major political and social events, and through changes in energy and monetary policies. Here, we develop a predictive framework, an index to calculate the average price of energy in the United States. The complex energy landscape is thoroughly analysed to accurately determine the two key factors of this framework: the total demand of the energy products directed to the end-use sectors, and the corresponding price of each product. A rolling horizon predictive methodology is introduced to estimate future energy demands, with excellent predictive capability, shown over a period of 174 months. The effectiveness of the framework is demonstrated by addressing two policy questions of significant public interest.

Global energy transformation requires quantifying the "price of energy" and studying its evolution. Here the authors present a predictive framework that calculates the average US price of energy, estimating future energy demands for up to four years with excellent accuracy, designing and optimizing energy and monetary policies.