A new approach to characterization of asphaltene

Asphaltene is the fraction of crude oil which is soluble in toluene but insoluble in light hydrocarbon solvents such as pentane or heptanes. In this work, a new approach has been proposed to account critical properties of asphaltene, and the proposed approach was verified using experimental data reported in the literature. The results show that the proposed approach correlates more accurately the critical properties of asphaltene data and is in agreement with data reported in the literature with an average absolute deviation of 6.7%. Therefore, the correlations obtained from this proposed approach resulted in a good agreement with data which show a significant improvement in comparison with the previous models in the available literature.     

Floodplain Inundation Analysis Combined with Contingent Valuation: Implications for Sustainable Flood Risk Management

This study presents the results of open-ended contingent valuation method (CVM) to estimate the residents’ maximum willingness to pay (WTP) for flood insurance and structural flood control measures in the Neka River Basin in Northern Iran. Flood inundation analysis and floodplain risk mapping were conducted by applying the HEC-RAS model combined with GIS analysis. A calibrated 100-year flood risk inundation map was considered as a basis for this research. This paper demonstrates applicability of CVM combined with flood inundation analysis to understand public participation for flood risk management, and their perception of flooding, considering associated socioeconomic and environmental factors. The results have shown that stated WTPs significantly varies with household income, distance people live from the river and the land use type of properties. Findings of this study suggest that the majority of respondents view flood hazard as the most important natural disaster. Furthermore, WTPs are significantly higher for those who have high level of flood risk perception. Three policy options for flood risk management are discussed, which include flood zoning and land use regulation, flood insurance program, and structural measure of levee construction. The advantages and disadvantages of each option are explored. It was concluded that a combination of possible mitigation options should be considered in order to achieve sustainable flood risk management in the Neka River Floodplain.     

Synthesis and characterization of salep sulfate and its utilization in preparation of heavy metal ion adsorbent

A multicomponent polysaccharide obtained from dried tubers of certain natural terrestrial orchids was chemically modified by sulfonation using chlorosulfonic acid–dimethylformamide (HClSO_3–DMF) complex as a reagent. For a structural characterization of salep sulfate ¹H nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectra, and Thermogravimetric analysis (TGA) curves were recorded. The sulfate content of modified salep was determined using elemental analysis. This modified biopolymer was used to prepare a new environment‐friendly heavy metal ion adsorbent, salep sulfate‐graft‐polyacrylic acid hydrogel (SS‐g‐PAA). Swelling rate and equilibrium water absorbency in various pH and saline solutions were investigated to study the effect of salep sulfate on swelling behavior of the hydrogel. In addition, the effect of sulfate content on heavy metal ion adsorption from aqueous solution was investigated. The results show that SS‐g‐PAA can effectively remove heavy metal ions (Co²⁺, Zn²⁺, Cu²⁺) from aqueous solution and swelling behavior of the hydrogels highly dependent on the amount of sulfate group on corresponding modified polysaccharide. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 3001–3008, 2013     

An adaptive neuro–fuzzy inference model for prediction of coal char conversion in CO2 gasification

Today, in industry, the gasification of coal as an important energy source is an interesting perspective. In this study, the application of adaptive neuro–fuzzy inference system (ANFIS) technique for estimation of char conversion in CO₂ gasification is investigated. The main variables affecting char conversion are particle size, reaction time, and reaction temperature, which are chosen as input variables of the proposed model. Experimental data which are gathered from the literature are applied for training, testing, and validation of developed ANFIS model. The results reveal the exact estimation of char conversions with the corresponding experimental values with the regression coefficients (R²) greater than 0.99.     

Desulfurization of high sulfur petroleum coke by molten caustic leaching

Desulfurization by molten caustic leaching (MCL) at 400–500 °C has been investigated in order to reduce the sulfur content of petroleum coke. Effective parameters on desulfurization of petroleum coke, other than temperature, include alkali to feed (petroleum coke) mass ratio, time and mesh size in the ranges of 0.5–1.5, 1–3 h and 200–600 µm, respectively. In this work, petroleum coke desulfurization conditions using solid KOH have been studied. Maximum petroleum coke desulfurization by MCL method has been obtained by Taguchi L9 design using alkali to feed mass ratio of 1, temperature of 600 °C, time of 2 h and mesh size of 200 µm. The changes in the main groups on the coke surface have been determined using FTIR spectroscopy. In addition, SEM-EDX, TGA and XRD analyses have been used to investigate the changes in coke physical and chemical properties.     

The control of MSF desalination plants based on inverse model control by neural network

In this paper, a nonlinear inverse model control strategy based on neural network is proposed for MSF desalination plant. Artificial neural networks (ANNs) can handle complex and nonlinear process relationships, and are robust to noisy data. The designed neural networks consist of three layers identified from input–output data and trained with a descent gradient algorithm. The set point tracking performance of the proposed method was studied when the disturbance is present in the MSF system. Three controllers are designed for controlling the top brine temperature, the level of last stage and salinity. These results show that a neural network inverse model control strategy (NNINVMC) is robust and highly promising to be implemented in such nonlinear systems. Also the comparison between the top brine temperature of the proposed model and NN predicted data from the literature supports the accuracy of the model.