Modification of ash flow properties of coal rich in calcium and iron by coal gangue addition

Flow property of coal ash and slag is an important parameter for slag tapping of entrained flow gasifier.The viscosity of slag with high contents of calcium and iron exhibits the behavior of a crystalline slag,of which viscosity sharply increases when temperature is lowered than temperature of critical viscosity(TCV).The fluctuation in temperature near the TCVcan cause an accumulation of slag inside the gasifier.In order to prevent slag blockage,it is necessary to adjust the ash composition by additive to modify the flow property of coal rich in calcium and iron.Main components of coal gangue are Al_(2)O_(3) and SiO_(2),which is a potential additive to modify the ash flow properties of these coals.In this work,we investigated the ash flow properties of a typical coal rich in calcium and iron by adding coal gangue with different SiO_(2)/Al_(2)O_(3)ratio.The results showed that the ash fusion temperatures(AFTs)firstly decreased,and then increased with increasing amount of coal gangue addition.Chemical composition of coal ash rich in calcium and iron moved from gehlenite primary phase to anorthite,quartz and corundum primary phases.The slags with coal gangue addition behaved as a glassy slag,of which the viscosity gradually increased as temperature decreased.Besides,a high SiO_(2)/Al_(2)O_(3)ratio of coal gangue was beneficial to modify the slag viscosity behavior.Addition of coal gangue with a high SiO_(2)/Al_(2)O_(3)ratio impeded formation of crystalline phases during cooling.This work demonstrated that coal gangue addition was an effective way to improve the ash flow properties of the coal rich in calcium and iron for the entrained flow gasifier.

Aging-elevated inflammation promotes DNMT3A R878H-driven clonal hematopoiesis

Aging-elevated DNMT3A R882H-driven clonal hematopoiesis(CH)is a risk factor for myeloid malignancies remission and overall survival.Although some studies were conducted to investigate this phenomenon,the exact mechanism is still under debate.In this study,we observed that DNMT3 A R878 H bone marrow cells(human allele:DNMT3 A R882 H)displayed enhanced reconstitution capacity in aged bone marrow milieu and upon inflammatory insult.DNMT3 A R878 H protects hematopoietic stem and progenitor cells from the damage induced by chronic inflammation,especially TNFa insults.Mechanistically,we identified that RIPK1-RIPK3-MLKL-mediated necroptosis signaling was compromised in R878 H cells in response to proliferation stress and TNFa insults.Briefly,we elucidated the molecular mechanism driving DNMT3 A R878 H-based clonal hematopoiesis,which raises clinical value for treating DNMT3 A R882 H-driven clonal hematopoiesis and myeloid malignancies with aging.

Sparse Deep Nonnegative Matrix Factorization

Nonnegative Matrix Factorization(NMF)is a powerful technique to perform dimension reduction and pattern recognition through single-layer data representation learning.However,deep learning networks,with their carefully designed hierarchical structure,can combine hidden features to form more representative features for pattern recognition.In this paper,we proposed sparse deep NMF models to analyze complex data for more accurate classification and better feature interpretation.Such models are designed to learn localized features or generate more discriminative representations for samples in distinct classes by imposing L1-norm penalty on the columns of certain factors.By extending a one-layer model into a multilayer model with sparsity,we provided a hierarchical way to analyze big data and intuitively extract hidden features due to nonnegativity.We adopted the Nesterov’s accelerated gradient algorithm to accelerate the computing process.We also analyzed the computing complexity of our frameworks to demonstrate their efficiency.To improve the performance of dealing with linearly inseparable data,we also considered to incorporate popular nonlinear functions into these frameworks and explored their performance.We applied our models using two benchmarking image datasets,and the results showed that our models can achieve competitive or better classification performance and produce intuitive interpretations compared with the typical NMF and competing multilayer models.