Thermodynamic Analysis of Hydrogen Production from Bio-Oil Steam Reforming Utilizing Waste Heat of Steel Slag

(1) Background: The discharged temperature of steel slag is up to 1450 °C, representing heat having a high calorific value. (2) Motivation: A novel technology, integrating bio-oil steam reforming with waste heat recovery from steel slag for hydrogen production, is proposed, and it is demonstrated to be an outstanding method via thermodynamic calculation. (3) Methods: The equilibrium productions of bio-oil steam reforming in steel slag under different temperatures and S/C ratios (the mole ratio of steam to carbon) are obtained by the method of minimizing the Gibbs free energy using HSC 6.0. (4) Conclusions: The hydrogen yield increases first and then decreases with the increasing temperature, but it increases with the increasing S/C. Considering equilibrium calculation and actual application, the optimal temperature and S/C are 706 °C and 6, respectively. The hydrogen yield and hydrogen component are 109.13 mol/kg and 70.21%, respectively, and the carbon yield is only 0.08 mol/kg under optimal conditions. Compared with CaO in steel slag, iron oxides have less effect on hydrogen production from bio-oil steam reforming in steel slag. The higher the basicity of steel slag, the higher the obtained hydrogen yield and hydrogen component of bio-oil steam reforming in steel slag. It is demonstrated that appropriately decreasing iron oxides and increasing basicity could promote the hydrogen yield and hydrogen component of bio-oil steam reforming that utilizes steel slag as a heat carrier during the industrial application

The Thermodynamic Characterizations of Hydrogen Production from Catalyst-Enhanced Steam Reforming of Bio-Oil over Granulated Blast Furnace Slag as Heat Carrier

To promote the efficiency of waste heat recovery from granulated blast furnace (BF) slag, a novel method of catalyst-enhanced steam reforming of bio-oil to recover heat from slag is proposed. CaO is utilized as a superior catalyst for the process of catalyst-enhanced steam reforming. The thermodynamic production of the catalyst-enhanced steam reforming of bio-oil in granulated BF slag is obtained using HSC 6.0 software. The optimal conditions are mainly assessed according to the hydrogen yield, hydrogen concentration and carbon production. Through the thermodynamic production and industrial application, the temperature of 608 °C, S/C of eight and pressure of 1 bar are found as the optimal conditions. At the optimal conditions, the hydrogen yield, hydrogen concentration and carbon production are 95.25%, 76.89% and 0.28 mol/kg, respectively. Taking the temperature of 625 °C, S/C of eight and pressure of 1 bar as an example, the catalyst could improve the hydrogen yield and hydrogen concentration from 93.99% and 70.31% to 95.15% and 76.49%, respectively. It is implied that utilizing the catalyst could promote the hydrogen yield and hydrogen concentration of steam reforming of bio-oil to recover waste heat from granulated BF slag. The mechanism of catalyst-enhanced steam reforming of bio-oil to recover waste heat from granulated BF slag is obtained to guide the subsequent industry application

Essential insights into decision mechanism of landslide susceptibility mapping based on different machine learning models

This work aims to discuss and compare the inherent essence of different machine learning algorithms for landslide susceptibility models (LSMs), which is of great significance for accurate prevention and detection of landslides. A geospatial database was established in GIS based on various factors of topography, geological conditions, environmental conditions and human activities, including 22 conditioning factors and 866 historical landslides. As for model algorithms, ANN is an operation model composed of a large number of interconnected nodes, and RF refers to an ensemble method of separately trained binary decision trees. Two algorithms were adopted in this paper for landslide susceptibility models. Meantime, an interpretable algorithm SHAP was used to gain insight into essential decision mechanism of LSMs. The result showed that RF model exhibits better stability and robustness. The global interpretation shows that the same landslide moderators play different roles in different models. The local interpretation shows that for the same evaluation unit different models give different decision mechanisms, and the local interpretation can be combined with the field survey, which can provide a comprehensive framework for assessing the assigned landslide

Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3. 0

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods.status: publishe

[Scheda bibliografica]: Popoli dell'Africa mediterranea in et\ue0 romana

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.This study was funded by the National Centre of Excellence in Research (NCER) on Parkinson’s disease, the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant no. DE-SC0010429. This project also received funding from the European Union’s HORIZON 2020 Research and Innovation Programme under grant agreement no. 668738 and the Luxembourg National Research Fund (FNR) ATTRACT program (FNR/A12/01) and OPEN (FNR/O16/11402054) grants. N.E.L. was supported by NIGMS (R35 GM119850) and the Novo Nordisk Foundation (NNF10CC1016517). M.A.P.O. was supported by the Luxembourg National Research Fund (FNR) grant AFR/6669348. A.R. was supported by the Lilly Innovation Fellows Award. F.J.P. was supported by the Minister of Economy and Competitiveness of Spain (BIO2016-77998-R) and the ELKARTEK Programme of the Basque Government (KK-2016/00026). I.A. was supported by a Basque Government predoctoral grant (PRE_2016_2_0044). B.Ø.P. was supported by the Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517)

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods