Gholamreza Zahedi

Waste cooking oil is a potential substitution of refined vegetable oil for the production of biodiesel due to the low cost of raw material and for solving their disposal problem. In this study, optimization of esterification process of free fatty acids in artificially acidified soybean oil with oleic acid has been carried out using methanol as an agent and ion exchange resin as a heterogeneous catalyst. The esterification reaction has been investigated based on the mass balance of the developed model. The model has been validated against experimental data and effects of temperature and catalyst weight have been analyzed. Thereafter, optimization process has been fulfilled for two different objective functions as conversion of acid oil and benefit. Optimization results indicated that the maximum conversion of acid is 95.95%, which is achievable at 4.48-g catalyst loading and reaction temperature of 120°C. Maximum benefit was obtained as US$0.057 per batch of reaction at a catalyst amount of 1 g and temperature of 120°C.

Seawater is described by a number of physical and chemical parameters that are useful in measurement and analysis of materiel effects. The material dissolved in seawater will not only affect its specific gravity, but also its optical properties, or rather, the degree to which light is refracted as it passes through the sample of water. The specific gravity and refractive index of seawater are related directly to salinity and temperature. In this work, an attempt has been made to develop simple predictive tools to estimate specific gravity and refractive index of seawater as a function of salinity and temperature. Estimations are found to be in excellent agreement with reported data in the literature with average absolute deviation being less than 0.2%.The predictive tool developed in this study can be of immense practical value for engineers to have a quick estimate on the specific gravity and refractive index of seawater without opting for any experimental trials. In particular, process and water treatment engineers would find the proposed method to be user-friendly with transparent calculations involving no complex expressions.

To calculate the radiant intensity at a point distant from a radiator, a geometrical or configuration factor must be used. If the temperature is known, then the emissive power can be calculated using the mean equivalent beam length, but before the radiant heat flux at a distance can be estimated, a configuration factor must be calculated. In this article a simple predictive tool is developed to estimate the configuration factor for a geometry including a receiving element lying on the perpendicular to one corner of a radiant rectangle. The results are found to be in excellent agreement with reported data in the literature with average absolute deviation being around 2%. The results can be used in follow up calculations to estimate heat fluxes on surfaces exposed to radiation. The tool developed in this study can be of immense practical value for engineers as a quick check on the configuration factor for a geometry including a receiving element lying on the perpendicular to one corner of a radiant rectangle without opting for any experimental trials. In particular, engineers would find the approach to be user-friendly with transparent calculations involving no complex expressions.

Notwithstanding the plentiful published work on the production of ethanol from lignocellulosic materials this comprehensive review relates how the basic research of a commercially viable industrial production strategy is still lacking. The objective of this review is to compile information on the different strategies and to consolidate the biologically mediated events involved in the conversion of pre-treated lignocellulose to ethanol and associated expenses so that some generalized information can be developed that could help policy makers and other stakeholders in designing a policy framework to promote second generation biofuels. The review also discusses the potential of process integration, its cost competitiveness and role in establishing commercial facilities for the production of ethanol from lignocellulosic biomass.

This paper presents the prediction of ozone pollution as a function of meteorological parameters including wind speed and direction, relative humidity, temperature, solar intensity, concentration of primary pollutants consisting of methane, carbon monoxide, carbon dioxide, nitrogen oxide, nitrogen dioxide, sulfur dioxide, non‐methane hydrocarbons, and dust around the Shuaiba industrial area in Kuwait by a fuzzy neural network (FNN) modeling approach. A subtractive clustering analysis was performed for the input data to produce a concise representation of the system's behavior leading to the minimum number of rules. In addition, Sugeno–Takagi–Gang fuzzy inference and hybrid algorithm were used to prepare the FNN system. It is perceived that the FNN model is more accurate and reliable than artificial neural network model to forecast the pre‐mentioned concentration. Finally, sensitivity analysis was applied. It was found that temperature, solar radiation, and relative humidity are the dominant parameters affecting the ozone level.