Investigation of Mixing Behavior of Hydrogen Blended to Natural Gas in Gas Network

Hydrogen is of great significance for replacing fossil fuels and reducing carbon dioxide emissions. The application of hydrogen mixing with natural gas in gas network transportation not only improves the utilization rate of hydrogen energy, but also reduces the cost of large-scale updating household or commercial appliance. This paper investigates the necessity of a gas mixing device for adding hydrogen to existing natural gas pipelines in the industrial gas network. A three-dimensional helical static mixer model is developed to simulate the mixing behavior of the gas mixture. In addition, the model is validated with experimental results. Parametric studies are performed to investigate the effect of mixer on the mixing performance including the coefficient of variation (COV) and pressure loss. The research results show that, based on the, the optimum number of mixing units is three. The arrangement of the torsion angle of the mixing unit has a greater impact on the COV. When the torsion angle θ = 120°, the COV has a minimum value of 0.66%, and when the torsion angle θ = 60°, the COV has a maximum value of 8.54%. The distance of the mixing unit has little effect on the pressure loss of the mixed gas but has a greater impact on the COV. Consecutive arrangement of the mixing units (Case A) is the best solution. Increasing the distance of the mixing unit is not effective for the gas mixing effect. Last but not least, the gas mixer is optimized to improve the mixing performance

Efficiency Enhancement of an Ammonia-Based Solar Thermochemical Energy Storage System Implemented with Hydrogen Permeation Membrane

The ammonia-based solar thermochemical energy storage (TCES) is one of the most promising solar TCESs. However, the solar-to-electric efficiency is still not high enough for further commercialization. The efficiency is limited by the high ammonia decomposition reaction temperature, which does not only increase the exergy loss through the heat recuperation but also causes a large re-radiation loss. Nonetheless, lowering the reaction temperature would impact the conversion and the energy storage capacity. Thanks to the recent development of the membrane technology, the hydrogen permeation membrane has the potential to enhance the conversion of ammonia decomposition under the moderate operating temperature. In this paper, an ammonia-based solar thermochemical energy storage system implemented with hydrogen permeation membrane is proposed for the first time. The system model has been developed using the Aspen Plus software implemented with user-defined Fortran subroutines. The model is validated by comparing model-generated reactor temperatures and conversions profiles with data from references. With the validated model, an exergy analysis is performed to investigate the main exergy losses of the system. Furthermore, the effects of the membrane on system efficiency improvement are studied. The results show that exergy loss in the charging loop is dominant, among which the exergy losses of Heat Exchanger Eh,A, together with that of the re-radiation Er, play important roles. Compared with the conventional system, i.e., the system without the membrane, the Eh,A and Er of the proposed system are more than 30% lower because the hydrogen permeation membrane can improve ammonia conversion at a lower endothermic reaction outlet temperature. Consequently, the proposed system, presumably realized by the parabolic trough collector at ~400 °C, has a theoretical solar-to-electric efficiency of ηste, which is 4.4% higher than the conventional ammonia-based solar thermochemical energy storage system. Last but not least, the efficiency is 3.7% higher than that of a typical parabolic trough solar power plant, which verifies the thermodynamic feasibility of further commercialization

EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm

In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, “small sample size problem”, diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer’s disease dataset. The results indicated that EpiMOGA was obviously superior to other related and competitive methods in both detection efficiency and accuracy, especially for small-sample-size datasets, and the performance of EpiMOGA remained stable across datasets of different disease models. At the same time, a number of SNP loci and 2-order epistasis associated with Alzheimer’s disease were identified by the EpiMOGA method, indicating that this method is capable of identifying high-order epistasis from genome-wide data and can be applied in the study of complex diseases

Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data

Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways