Band 12, Heft 3 -

In the PUREX (Plutonium Uranium Recovery by Extraction Process) process, the extraction of uranyl ion from dissolver solution to the organic phase is influenced by co extraction of the other species, such as water and nitric acid and it is assumed that the presence of water or acid droplets in the organic phase intensifies the coordination mechanism of TBP. The present study illustrates the uranyl extraction from the aqueous phase to the organic phase using molecular dynamics (MD) simulation. Here, we consider the biphasic systems to gain insights into the characteristics of the interface and humidity of the organic phase under different acidic and neutral conditions. MD being a force field method, can’t satisfactorily model the bond making and breaking process therefore a priori choice has been made concerning the different status of proton for the acidic phase. Further, the importance of charge species transferability during uranyl-TBP complexation have been investigated considering two different models of uranyl nitrate; united UO 2 (NO 3 ) 2 complex and separate UO 2 2+ and NO 3 – ions. From the results, it is recommended to use the ionic uranyl model with separate UO 2 2+ and NO 3 – to study the structural and dynamical properties of extracted uranyl ions in the organic phase. Also, it was noticed that extracted uranyl ions in the organic phase are not completely dehydrated but are surrounded by water molecules. In other words the results show co extraction of other species such as water and acid molecules to the organic phase. Most remarkably, the present study evident that the neutral HNO 3 effectively represents the acidity effect for the receiving phase in terms of acid/water extraction and their aggregation to form water droplet, especially when ionic model of uranyl nitrate is considered.

Thin-layer drying kinetics of pistachio nuts were examined experimentally as a function of drying conditions in a fluidized bed dryer with microwave pretreatment. Four drying specifications of diffusivity, shrinkage, specific energy consumption and total color change were calculated and the effects of parameters were studied. Numerous experimentations were conducted at three levels of air temperature (40, 55, 70 °C), air velocity (1.2, 2.93, 4.01 m/s), and microwave power (270, 450, 630 W). The variation ranges of diffusivity, shrinkage, energy consumption and color change were recorded from 5.01×10 –10 to 5.07×10 –9 m 2 /s, from 26.95 % to 13.13 %, from 1.04 to 9.23 kWh and from 10.44 to 17.17, respectively. According to response surface methodology, optimum condition of drying process occurred at microwave power of 630, air temperature of 70 ˚C, and air velocity of 1.2 m/s. In this optimum point, the values of diffusivity, shrinkage, specific energy consumption and total color change were 4.865×10 –9 , 14.22 %, 2.164 kWh and 12.312, respectively.

A multi-period planning approach for water reuse and regeneration networks in Eco-Industrial Parks (EIPs) is presented. The objective of the optimization problem is to determine the lowest network cost design for such systems, by taking into account an entire planning horizon. A source-to-sink mapping approach has been proposed to formulate the multi-period planning problem. Water sources can either be allocated to water sinks, treatment units or discharged to environment. Freshwater streams and treated water are made available to mix with water sinks to enable reuse between plants. Waste water is allowed to be discharged into environment at threshold contaminant levels. The problem has been illustrated initially with two-stage centralized treatment unit, then by considering a hybrid treatment setup consisting of both centralized and decentralized options. The results obtained indicate considerable cost reductions, when compared to those developed separately for each individual period. Moreover, a decrease in the complexity of the water networks has also been observed, when simultaneously considering the entire planning horizon.

Studying the pressure drop in venturi scrubbers had been the subject of many types of researches due to its importance for removing pollutants from polluted gas. In this study, two new approaches based on Multi-Gene Genetic Programming (MGGP) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to predict the pressure drop in venturi scrubbers. The main parameters studied were the throat gas velocity of venturi scrubbers (Vgth), the liquid to gas flow rate ratio (L/G), and the axial distance of the venturi scrubbers (z) as the inputs to the network, while the pressure drop was as the output. One set of experimental data, which was gathered from five different venturi scrubbers including a circular and an adjustable prismatic venturi scrubber with a wetted wall irrigation, a rectangular venturi scrubber and two ejector venturi scrubbers with different throat diameters were applied for this study. The results of ANFIS and MGGP were compared with experimental data and those values from Artificial Neural Networks (ANNs) from our previous work. In this work, the coefficient of the determination (i. e. R 2 value) was used to show the prediction ability of these new approaches. Results showed that MGGP and ANFIS can accurately predict the pressure drop in venturi scrubbers with R 2 values of 0.9972 and 0.9734, respectively. The results also showed that MGGP has more precision than ANFIS and ANNs. Therefore, based on MGGP, two correlations were generated for two clusters of data. The comparison results between one of these correlations (i. e. correlation 1 with R 2 value equal to 0.9937) and other models showed that our correlation has a very good precision and can predict the pressure drop in a more agreement with the experimental data.

The kinetics study, modeling, simulation and optimization of water gas shift reaction were performed in a catalytic fixed bed reactor. The renowned empirical power law rate model was used as rate equation and fitted to experimental data to estimate the kinetics parameters using gPROMS. A good fit between predicted and experimental CO conversion data was obtained. The validity of the kinetic model was then checked by simulation of plug flow reactor which shows a good agreement between experimental and predicted values of the reaction rate. Subsequently, considering axial dispersion, a homogeneous model was developed for simulation of the water-gas shift reactor. The simulation results were also validated by checking the pressure drop of the reactor as well as the mass concentration at equilibrium. Finally, a multi-objective optimization was conducted for water-gas shift reaction in order to maximize hydrogen formation and carbon monoxide conversion, whereas the reactor volume to be minimized. Implementation of optimal controls leads to increase in hydrogen formation at reactor outlet up to 25.55 %.

This paper presents a new control methodology for a class of integrating systems. A PID controller augmented with a first order lead/lag filter is proposed for improved response. The polynomial approach is employed to derive the controller parameters. The novelty of the proposed method lies in the selection of pole locations. Multiple pole locations are considered where one of the poles is placed for cancelling the zero introduced by controller. The selection of tuning parameter is based on maximum sensitivity (MS). Set point weighting is employed to reduce the overshoot and settling time in the servo response. Various bench marking examples are adopted to evaluate the proposed method in terms of various performance indices. The results are superior to the recently proposed works in terms of both set point tracking and disturbance rejection.

The kinetic of catalytic upgrading of anisole as a lignin−derived bio−oil component is investigated experimentally over Pt/γAl 2 O 3 at 573−673 K and 14 bar. According to experimental results, benzene, phenol, 2−methylphenol, 2,6−dimethylphenol, 2,4,6−trimethylphenol, and hexamethylbenzene are identified as the main products. The results indicated that the kinetically significant reaction classes are hydrogenolysis, hydrodeoxygenation ( HDO ), alkylation, and hydrogenation. The response surface methodology ( RSM ) is applied to optimize the experimental data which obtained at suggested conditions by design of experiment ( DOE ). Due to the complex nature of the system, artificial neural networks ( ANN s) were employed as an efficient tool to model the behavior of the system. RSM and ANN methods were constructed based upon the DOE ’s points and then utilized for generating extra−simulated data. Data simulated by the RSM/ANN method were used to fit power law kinetic rate expressions for the reactions. The coefficient of determination ( R 2 ) was obtained 0.998 and 0.973 for anisole conversion model and benzene selectivity model which represented the high accuracy of model predictions. The correlation coefficient ( R ) and mean square error ( MSE ) of ANN model equaled to 0.97 and 8.3 × 10 −12 respectively means high accuracy of the developed model. The results of kinetic modeling with simulated data from the ANN and RSM models revealed that the highest reaction order during the upgrading process of anisole belongs to hydrogenolysis of anisole to phenol. Also the activation energy of hydrogenolysis reaction was lower than HDO .

High density polyethylene (HDPE) and polypropylene (PP) solubility in several pure and blend non-polar organic solvents was measured at 365–430 K temperature at atmospheric pressure, with polymer concentration of 0.5–25   g in 100 ml of solvent. The activity coefficients were estimated depending on the experimental solubility results for all the polymer-solvent systems. A non-ideal equation combined with activity coefficient was developed based on the crystallinity. A new correlation equation was attained, which is based on the melting temperature and heat of fusion using SSPS software. These two equations were used to predict the solid-liquid experimental data for the binary system polymer-solvent. The distinction between the experimental and model data was assessed by using mean absolute deviation percentage (MAD %). The non-ideal equation based on crystallinity and the new correlations showed low MAD %, displaying a close match with the experimental data.