Abstract Research Subject: Global energy demand is increasing, so enhanced oil recovery techniques have incorporated in production processes. Water flooding is a common technique in oil recovery processes. One of the major challenges in this technique is heterogeneity of the reservoir structure which results in increased water production and reducing the oil recovery factor. Moreover, long-term water or chemical injection might lead to the increased horizontal and vertical heterogeneities in the reservoir. Selective blockage of high permeability areas and consequently improved production from low permeability regions is important for increasing the oil recovery factor. In recent years, using hydrogels in injection processes, has been associated with various field successes, indicating the ability of these materials for selectively blocking the areas of high permeability. Hydrogels are injected after water or polymer flooding to conduct the injected fluid to low permeability areas.
Research Approach: In this paper, hydrogel injection process was simulated in glass micromodels using Comsol Multiphysics software. Hydrogel functionality was studied in low permeability areas in porous media. Moreover, the optimized conditions for water flooding process was studies. For this purpose, after model validation, sensitivity analysis was performed on effective parameters on oil recovery factor and a mathematical model was presented to predict the oil recovery factor.
Main Results: Oil recovery factors obtained from experimental and simulation studies, were in good agreement with each other with absolute error values of 2.29% and 4.06%, for water and hydrogel flooding, respectively.
Four parameters of injection rate, contact angle, oil viscosity, and injection fluid viscosity were considered as effective parameters on oil recovery factor. Among them, contact angle was the most important parameter. In water flooding, the most important interacting parameters are viscosity and contact angle and the least important parameters are injection temperature and rate. In water flooding simulation studies, the thickness of the contact surface was obtained hmax/5, where is 230 micrometers. For hydrogel injection, the contact surface thickness was obtained terpf.ep_default / 5.65. Terpf.ep_default is the thickness of contact surface, equal to 631 micrometers

Abstract Research subject: Low solubility of pharmaceutical compounds leads to increasing the required drug dosage and their side effects as well as reducing their therapeutic efficiency. Producing pharmaceutical micro/nanoparticles with homogenous morphology and narrow size distribution is one of the confirmed approaches for their solubility enhancement. So, selection and designing an appropriate method for this purpose is one of the most important research fields of pharmaceutical industries. Over the past three decades, supercritical carbon dioxide (sc-CO₂) based methods as a clean and green technologies have been received much attention in various fields of pharmaceutical industries. However, in order to design and development of these methods for producing micro/nanoparticles, determination of the compounds solubility in sc-CO₂ is essential.
Research approach: In this research, well known empirical models (Adachi and Lu, Ch and Madras, Hozahzbr et al., Bian et al., Mendez-Santiago-Teja), as well as the artificial neural network model were applied for prediction the solubility of six anticancer drugs (Aprepitant, 5-Fluorouracil, Imatinib mesylate, Capecitabine, Letrozole, Docetaxel) in sc-CO₂.
In order to evaluate the accuracy of these models, a comparison was made between the calculated solubility values and the available experimental data, based on several statistical criteria, such as the average absolute relative deviation (AARD%), adjusted correlation coefficient (R_adj) and F-value.
Main results: According to obtained results, Adachi and Lu model with AARD% value of 12.12% and R_adj value of 0.97 provided acceptable results for solubility of mentioned drugs in sc-CO₂. Also, in comparison between empirical and artificial neural network models, the latter one with AARD% value of 1.65% and R_adj value of 0.9960 was appointed as the most appropriate model for correlation of drugs solubility data.

Abstract Research subject: Solvents are compounds that are used in the chemical, pharmacy, oil and gas industries, including in separation processes. These solvents include alkanolamines and ionic liquids (ILs). ionic liquids with a melting point below 100^oC are a particular class of chemical compounds that have unique properties and characteristics. Design and optimization of acid gases removal systems and separated CO₂ from the gas stream requires experimental data of physical properties, However, performing an experiment is time consuming and costly. Therefore, thermodynamic models are used to predict the properties of pure and mixture materials.
Research approach: In this study viscosities of 3 alkanolamines (Monoethanolamide (MEA), N-Methyldiethanolamine (MDEA), Diethanolamine (DEA)) and 12 ionic liquids based on imidazolium (imidazolium based families of tetrafluoroborate, hexafluorophosphate and bis[(trifluoromethyl)sulfonyl]imide) were investigated by the well-known friction theory (FT) based on friction concepts of classical mechanics was coupled with two simple cubic equation of state (EoS) of the Soave-Redlich-Kwong (SRK) and Peng-Robinson(PR) at over wide ranges of temperatures and pressure and in different mole fraction (for mixture) for prediction of viscosity. The models presented in this work are based on the viscosity behavior of pure alkanolamines and ionic liquids.
Main results: The result shows friction theory has good operation in prediction of viscosity. The average absolute Relative deviation (AARD) is 4.71% and 1.66% for pure ILs and alkanolamine respectively when PR equation state is used and when SRK equation of state is used these values is 4.70% and 1.99% about IL-IL mixture, experimental and predicted values were well matched and for IL-alkanolamine mixture FT5- and FT6- have best result.

Abstract Research subject: In recent years, several studies have been performed for improving the adhesion properties of polyurethane and acrylic pressure-sensitive adhesives (PSAs). Generally, polyurethane PSAs are of higher shear strength, while acrylic PSAs have higher tack. This research is a feasibility study of exploiting the properties of both of these adhesives through a simple blending method, and the adhesion properties were evaluated.
Research approach: First, acrylic copolymer (Ac) consisting of 82 vol. % butyl acrylate and 18 vol. % methyl methacrylate was solution polymerized. On the other hand, a thermoplastic polyurethane (TPU) containing 17.5 wt. % hard segment was prepared by bulk polymerization. Blending of these two polymers was performed by solution mixing. Solutions of the pure polymers and their blends at different contents were cast on polyethylene terephthalate backing and dried at room temperature. Fourier transform infrared spectroscopy, gel permeation chromatography, and differential scanning calorimetry were used to identify TPU and Ac. Loop tack, static shear strength, dynamic mechanical behavior, contact angle of sessile drop, morphology, and haze of the PSAs were evaluated.
Main results: Tack of the acrylic PSA was higher than TPU PSA. Tack of the blend PSAs containing 20, 40, and 60 wt. % TPU was higher than the pure components and that of the blend containing 40 wt. % TPU was maximum. This blend demonstrated the lowest water contact angle compared to the other blends and the shortest relaxation time compared to the pure polymers, which resulted in better wetting and higher tack. The shear strength of the PSAs increased with increase in the content of TPU to higher than 40 wt. % in the blends compared to the acrylic PSA; so that the pure TPU showed the highest modulus at various frequencies and hence exhibited high-shear PSA characteristics in the Chang’s viscoelastic window and the highest adhesion strength. The immiscibility of the blends was confirmed by measuring the haze and calculating the Hansen solubility parameter.

Abstract Research subject: In recent decades, hybrid optimizations methods based on natural phenomenon have placed special position according to their capabilities in finding optimal solutions without expensive computational loads and disassociation on choosing initial points. Artificial Neural Network is used as one of the powerful tools of Artificial Intelligence for process simulation. The employment of the neural network in the modeling of m-Cresol alkylation process of with isopropanol as well as meta-heuristic methods in obtaining the optimal conditions for the catalyst and the reaction can prepare an effective step towards a high efficiency process.
Research approach: In the present study, the artificial neural network is applied to model alkylation of m‐Cresol with isopropanol process. In addition, the bee colony is employed in order to optimize the process yield. To verify its performance, the proposed method is used in prediction of the m‐Cresol conversion and Thymol selectivity of the alkylation process with isopropanol 120 data. In this process, the input variables are Weight Hourly Space Velocity (WHSV), pressure and temperature; m-cresol conversion and thymol selectivity are considered as the output variables of the neural network. Five hidden neurons are considered for the proposed neural network. 120 data is used to train the neural network. The meta-heuristic approach based on bee colony (BC) is applied to maximize the yield of the process.
Main results: The results confirm that the proposed method develops the accurate model with an R² value of greater than 97.5%. The maximum yield is obtained 28.9% by bee colony algorithm with adjustable variables that are WHSV of 0.062 hr^-1, the pressure of 1.5 bar and the temperature of 300^oC. In addition, in order to achieve the better performance of the optimization algorithm, the appropriate values of acceleration coefficient and population size are chosen 100 and 10 during the trial-and-error phase.

Abstract Research Subject: Among the burning rate catalysts (BRCs), ferrocene-based ones have shown better performance; but show volatility problem. Therefore, the use of ferrocene derivatives in order to make compatible with hydroxyl terminated polybutadiene (HTPB) prepolymer, is a novel trend which is recently interested in the related researches.
Research Approach: In this research, at first, vinyl ferrocene monomer (VFM) were in-situ homopolymerized to prepare poly (vinyl ferrocene) (PVF)in the presence of hydroxyl terminated polybutadiene (HTPB) prepolymer at three different conditions(with and without BPO as initiator and different amounts of VFM).Then, blend of PVF/HTPB were characterized using FT-IR, ¹HNMR and GPC analyzes. In the second part, energetic composites containing PVF/HTPB blend were prepared and thermal properties of prepared samples investigated and compared with energetic composites containing conventional catalysts using TGA.
Main Results: The GPC results showed that the main peak was larger and wider due to the increase in the average molecular weight of PVF/HTPB blend. Comparison of thermal analysis showed that energetic composites based on PVF/HTPB blend catalyst perform better than common catalysts and more reduces the AP decomposition temperature. PVF/HTPB blend act as a potential BRC in energetic composites in which migration problem reduce due to in-situ blending of VFM to HTPB.

Abstract Research subject: Bio-hydrogen is a renewable energy source with many economic and environmental benefits as a fuel. Controlling the concentration of the substrate in the reactor has a significant effect on the amount of hydrogen production. However, bio-hydrogen production is a nonlinear process that requires the implementation of nonlinear control methods. In this paper, substrate concentration in an anaerobic bio-reactor is controlled using the feedback linearization method.
Research approach: The model employed for the simulation is a well-known model consisting of three state variables. The proposed controller is a globally linearized controller (GLC) designed based on the feedback linearization technique. In this method, the nonlinear system is precisely linearized by a transformation of the coordinate system. As a result, the linearized system can be controlled using a linear controller. In order to linearize the system, a nonlinear compensator is designed using the design model and applying the concepts of differential geometry. Proportional-integral (PI) controller is adopted as a linear controller. GLC controller performance has been compared with a nonlinear controller (NC) and a PI controller. The performance of these controllers has been studied by numerical simulation based on the integral of time-square error (ITSE).
Main results: The simulation results show that substrate concentration control can contribute to the hydrogen production. The control method applied has better set-point tracking than the other two control approaches. The ITSE performance index for the feedback linearization method is lower than the other two methods. The nonlinear feedback controller fails if the kinetic parameters are changed by 25%, but the PI method and the feedback linearization are robust against model uncertainty. An efficient controller guarantees stable bio-hydrogen production. Comparing open-loop and closed-loop simulation results shows that controlling the substrate concentration increases hydrogen production by 90%.

Abstract Research subject: Nowadays, due to the prevalence of coronavirus and the increasing use of disinfectant solutions and gels, the use of glycerin has also increased dramatically. But the suggested processes in this field need to be optimized in terms of production and energy consumption.

Research approach: In this paper, the transesterification method has studied and simulated, during which vegetable oil is converted into biofuel, and glycerin is also produced as a by-product of this process. For this purpose, process simulation of a conventional unit with 5.5 m³/min feed has been done in Hysys. Also, due to the importance of equipping the transesterification reactor, by importing the necessary process information, this equipment has been simulated in COMSOL MultiPhysics and the effective parameters have been studied in order to optimize the of product conversion. After validation of model, to better understand the factors affecting the performance of the transesterification reactor, the effect of selected parameters first examined by one-variable at the time design of experiment approach.
Main result: Finally, it has been shown that the feed temperature and the flowrate both have significance impact on quantity and quality of product and while providing a model for calculating the amount of glycerol produced per unit of energy consumed, the effective parameters are optimized by the response surface method. In optimal conditions of the ratio of product production to energy consumption, the temperature value was 470.7 K and the feed flow rate was 0.586 m³/s. According to the gained results, it can be obtained by adjusting the flow rate in the optimal amount, using a preheater in the production processes of biofuels and glycerin can have a significant effect on the amount of products produced so that the optimal temperature for the output of this preheater is at least 470.7 K should be considered. In the current research an optimization scheme has been suggested which can be used for different Biodiesel-Glycerol production units with varies range of flowrate.