Simulation-optimization framework for synthesis and design of natural gas downstream utilization networks

Many potential diversification and conversion options are available for utilization of natural gas resources, and several design configurations and technology choices exist for conversion of natural gas to value-added products. Therefore, a detailed mathematical model is desirable for selection of optimal configuration and operating mode among the various options available. In this study, we present a simulation-optimization framework for the optimal selection of economic and environmentally sustainable pathways for natural gas downstream utilization networks by optimizing process design and operational decisions. The main processes (e.g., LNG, GTL, and methanol production), along with different design alternatives in terms of flow-sheeting for each main processing unit (namely syngas preparation, liquefaction, N2 rejection, hydrogen, FT synthesis, methanol synthesis, FT upgrade, and methanol upgrade units), are used for superstructure development. These processes are simulated using ASPEN Plus V7.3 to determine the yields of different processing units under various operating modes. The model has been applied to maximize total profit of the natural gas utilization system with penalties for environmental impact, represented by CO2eq emission obtained using ASPEN Plus for each flowsheet configuration and operating mode options. The performance of the proposed modeling framework is demonstrated using a case study. 2018 by the authors.The authors would like to acknowledge the financial support from NSERC and from Qatar University to conduct this research. A.E. and M.A.S. would also like to acknowledge the Gas Research Center (GRC) at the Petroleum Institute during the later stages of this research.Scopu

Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Characterization of potassium carbonate salt hydrate for thermochemical energy storage in buildings

Thermochemical heat storage in salt hydrates is a promising method to improve the solar fraction in the built environment. One of the most promising salt hydrates to be used as thermochemical material is potassium carbonate. In this study, the use of potassium carbonate in heat storage applications is investigated experimentally. The most important objective is to form a kinetic model for the de/re-hydration reaction of the material. In order to do so, it is crucial to understand the behavior of the salt when it reacts with water vapor. Reaction kinetics and mechanism are investigated for K2CO3, as one of the most promising materials. Characterization of the materials is carried out with combined Thermo-Gravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC) methods. By employing the experimental results, kinetics models are developed for the hydration and dehydration reactions of the material. The kinetics model can be further used to predict the performance of a heat storage system working with K2CO3. In addition, cyclability and reaction enthalpy are investigated

Integrating Simulation in Optimal Synthesis and Design of Natural Gas Upstream Processing Networks

A natural gas upstream processing network consists of several main processing units. Many process configurations are available for selection, and the choice of technologies can be vast. There is no single technology or process configuration that is superior in all aspects. Thus, there is a need for a mathematical model that considers different flowsheet configurations and operating mode options and selects optimally among them. In this paper, a comprehensive design and operational mixed integer programming model is presented for superstructure optimization to optimally select the most cost-effective pathway in natural gas upstream processing networks. The key processing units of the considered processing network include stabilization, acid gas removal, dehydration, sulfur recovery, natural gas liquid (NGL) recovery, and NGL fractionation. The developed optimization model considers a superstructure with all available technologies for each processing step as well as mode of operation, such as variations in temperature and pressure which impacts the product yields. These units have been simulated using ASPEN Plus to determine the yields of different units for each design alternative under different operating modes. The bilinear terms in the resulting mixed integer nonlinear programming (MINLP) model are linearized based on either input or output streams, whichever are less in number. The model has been applied to design and operate optimally the natural gas upstream processing network. Two illustrative case studies are presented to show the applicability of the overall framework and formulated models. 2017 American Chemical Society.The authors would like to acknowledge the financial support from NSERC and from Qatar University to conduct this research. M.A.S. and A.E. would also like to acknowledge the Gas Research Center (GRC) at the Petroleum Institute during the later stages of this research. Many thanks to Professor Mahmoud El-Halwagi from Texas A&M University, Chemical Engineering Department, for the valuable inputs and comments about the model formulation and manuscript

Integrating Simulation in Optimal Synthesis and Design of Natural Gas Upstream Processing Networks

A natural gas upstream processing network consists of several main processing units. Many process configurations are available for selection, and the choice of technologies can be vast. There is no single technology or process configuration that is superior in all aspects. Thus, there is a need for a mathematical model that considers different flowsheet configurations and operating mode options and selects optimally among them. In this paper, a comprehensive design and operational mixed integer programming model is presented for superstructure optimization to optimally select the most cost-effective pathway in natural gas upstream processing networks. The key processing units of the considered processing network include stabilization, acid gas removal, dehydration, sulfur recovery, natural gas liquid (NGL) recovery, and NGL fractionation. The developed optimization model considers a superstructure with all available technologies for each processing step as well as mode of operation, such as variations in temperature and pressure which impacts the product yields. These units have been simulated using ASPEN Plus to determine the yields of different units for each design alternative under different operating modes. The bilinear terms in the resulting mixed integer nonlinear programming (MINLP) model are linearized based on either input or output streams, whichever are less in number. The model has been applied to design and operate optimally the natural gas upstream processing network. Two illustrative case studies are presented to show the applicability of the overall framework and formulated models. 2017 American Chemical Society.The authors would like to acknowledge the financial support from NSERC and from Qatar University to conduct this research. M.A.S. and A.E. would also like to acknowledge the Gas Research Center (GRC) at the Petroleum Institute during the later stages of this research. Many thanks to Professor Mahmoud El-Halwagi from Texas A&M University, Chemical Engineering Department, for the valuable inputs and comments about the model formulation and manuscript.Scopu