Bark as feedstock for dual fluidized bed gasifiers—Operability,efficiency, and economics

The demand for biofuels and biochemicals is expected to increase in the future, which will in turn increase the demand for biomass feedstock. Large gasification plants fueled with biomass feedstock are likely to be a key enabling technology in a resource‐efficient, bio‐based economy. Furthermore, the costs for producing biofuels and biochemicals in such plants could potentially be decreased by utilizing inexpensive low‐grade residual biomass as feedstock. This study investigates the usage of shredded tree bark as a feedstock for the production of biomethane in the GoBiGas demonstration plant in Gothenburg, Sweden, based on a 32 MW_th industrial dual fluidized bed gasification unit. The plant was operated with bark feedstock for 12 000 hours during the period 2014 to 2018. Data from the measurement campaign were processed using a stochastic approach to establish the plant's mass and energy balances, which were then compared with operation of the plant with wood pellets. For this comparison, an extrapolation algorithm was developed to predict plant performance using bark dried to the same moisture content as wood pellets, ie, 8%_w.b. Plant operation with bark feedstock was evaluated for operability, efficiency, and feedstock‐related cost. The gas quality achieved during the test period was similar to that obtained for operation with wood pellets. Furthermore, no significant ash sintering or agglomeration problems were observed more than 750 hours of operation. The calculated biomass‐to‐biomethane efficiency is 43% to 47% (lower heating value basis) for operation with wet bark. However, the predicted biomass‐to‐biomethane efficiency can be increased to 55%–65% for operation with bark feedstock dried to 8% moisture content, with corresponding feedstock costs in the range of 24.2 to 32.7 EUR/MWh; ie, a cost reduction of about 40% compared with wood pellets.     

Peaks Over Thresholds Modeling With Multivariate Generalized Pareto Distributions

When assessing the impact of extreme events, it is often not just a single component, but the combined behavior of several components which is important. Statistical modeling using multivariate generalized Pareto (GP) distributions constitutes the multivariate analogue of univariate peaks over thresholds modeling, which is widely used in finance and engineering. We develop general methods for construction of multivariate GP distributions and use them to create a variety of new statistical models. A censored likelihood procedure is proposed to make inference on these models, together with a threshold selection procedure, goodness-of-fit diagnostics, and a computationally tractable strategy for model selection. The models are fitted to returns of stock prices of four UK-based banks and to rainfall data in the context of landslide risk estimation. Supplementary materials and codes are available online.     

Application of kernel principal component analysis for single-lead-ECG-derived respiration

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter σ(2) that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both p<0.0001), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.