A Novel Hybrid Framework for Co-Optimization of Power and Natural Gas Networks Integrated With Emerging Technologies

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions

Multi-factor optimization of bio-methanol production through gasification process via statistical methodology coupled with genetic algorithm

This work innovatively explores the bio-methanol production process, conducts comprehensive analyses, develops statistical models, and optimizes operational conditions, contributing valuable insights to the field of sustainable energy production from biomass. Accordingly, bio-methanol production from biomass through gasification route was investigated and simulated using Aspen Plus software. The effects of operational parameters on energy duty of gasification reactor and the methanol production rate in syngas to methanol reactor were investigated. The parameters affecting the process performance including temperature, pressure, and steam/feed ratio were examined using the response surface methodology (RSM) by central composite design (CCD) technique. Analysis of variance (ANOVA) was performed, and two quadratic models were derived. The predicted R2 values of these models for methanol mass flowrate and energy duty were 0.9394 and 0.9363, respectively. The optimal operational conditions were identified using genetic algorithm (GA). The optimum values of temperature, pressure, and steam/feed ratio in gasification reactor were 900 ◦C, 4 bar, and 0.675, respectively. This condition leads to methanol mass flowrate and energy duty of 4.254 kg/s and 40736.355 kw, respectively. In addition, sensitivity analysis was performed on syngas to methanol reactor performance

Chemical Constituents of Dichloromethane Extract of Cultivated Satureja khuzistanica

Four compounds β-sitosterol, β-sitosterol-3-O-β-d-glucopyranoside, ursolic acid and 4′,5,6-trihydroxy-3′, 7-dimethoxyflavone were characterized from the dichloromethane extract of the aerial parts of Satureja khuzistanica (Lamiaceae), a native medicinal plant growing in Iran, on the basis of spectral analysis and comparing with the data in literature. The natural occurrence of these compounds can be conclusive for the chemotaxonomic characterization of the genus Satureja

COX inhibition: Catalepsy and Striatum Dopaminergic-GABAergic-Glutamatergic Neurotransmission

Selective COX-2 and COX-1 inhibitors were administered (i.p. acutely) to normal and parkinsonian rats, followed by the analysis of the striatal dopamine, GABA and glutamate concentrations using the microdialysis technique, simultaneously, the catalepsy of animals was evaluated. Selective COX-2 inhibition showed improving effects on the catalepsy followed by decreasing the striatum glutamatergic-GABAergic and enhancing the dopaminergic neurotransmission. Nonetheless COX inhibition had no significant improving effects on damaged Substantia Nigra Pars Compacta (SNc) neurons

Comparison of Flexural Strength of Mineral Trioxide Aggregate, Calcium-enriched Mixture and BioAggregate

Introduction: The aim of this study was to compare the flexural strength of mineral trioxide aggregate (MTA), calcium-enriched mixture (CEM), and BioAggregate (BA). Methods and Materials: In this study, the flexural strength of materials was measured using a 3-point bend test. After being prepared, MTA, CEM, and BA were inserted into the intra-putty molds using amalgam plugger. The specimens were covered with a sponge wetted with synthetic tissue fluid (STF) and incubated for 96 h. They were then subjected to a 3-point bend test using Universal Testing Machine. The Kruskal-Wallis and Mann-Whitney U tests were used to compare flexural strength in groups. In this study, P<0.05 was considered as the significant level. Results: There were significant differences between the three groups in terms of the flexural strength (P<0.001). The mean flexural strength in the BA, CEM, and MTA groups were 27.32±2, 9.09±1.16, and 10.25±1.6, respectively. Pairwise comparison showed significant differences between the three groups. Conclusion: This in vitro study showed that BA has the highest and CEM has the lowest flexural strength.Keywords: BioAggregate; CEM Cement; Flexural Strength; Mineral Trioxide Aggregate