The TEABPP (Techno-Economic Assessment of Biomass Pre-Processing) project aim is to deliver process modelling for a carefully selected set of bioenergy value chains, comparing the costs, performance and emissions of supply chain configurations with/without pre-processing, and with/without conversion plant improvements. Optimal system designs is derived for different scales, feedstock types and end uses, highlighting areas of the supply chain with greatest potential for improvement, and circumstances when pre-processing choices are most beneficial. Uncertainties and sensitivities are explicitly analysed throughout. The core process models has been developed in the gPROMS modelling platform. Conducting an exhaustive techno-economic analysis of the relative impacts of the selected biomass technology combinations presents a formidable computational challenge. A way to alleviate this is to develop and employ a fast-response, High Dimensional Model Representation (HDMR)-based computational surrogate, which faithfully and efficiently simulates the physical processes. This is greatly beneficial in a sensitivity analysis because it readily allows global sensitivities for each of the inputs to be calculated. Global sensitivities can then be used to assess which input parameters (both in feedstock type and quality but also in pre-processing techniques) are most influential on the variance of each of the output parameters, i.e. system performance, emissions, wastes and costs.




The creation of the surrogate models and the sensitivity analyses were carried out using CMCL’s Model Development Suite (MoDS). One of the output from MoDS is the percentage sensitivity for each of the key inputs. Spider diagrams were also produced to provide a qualitative understanding of the local sensitivities. The most uncertain outputs were identified using normalised error bars. MoDS was the main user interface for visualising the results of the analysis.



The TEABPP project is funded by ETI (Energy Technologies Institute). The consortium is led by E4Tech and is composed of PSE (Process Systems Engineering), Imperial College London, Black & Veatch, the University of Sheffield and CMCL Innovations.