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Research subject: In situ synthesis of nanoparticles due to greater impact on production mechanisms (such as reducing oil viscosity), uniform distribution of nanoparticles in reservoir fluids, no reduction in formation permeability due to no injection of nanofluids into the reservoir and also economic efficiency is more importance than other nanoparticle synthesis methods which are used in enhanced oil recovery (EOR) processes.
Research approach: In this study, the effect of in-situ synthesized of cerium oxide (CeO₂) nanoparticles at low temperature on the oil recovery factor was investigated. For this purpose, water was considered as the based fluid for dispersion of synthesized nanoparticles. Also, in order to study the effect of nanoparticles concentration in the base fluid on the final oil recovery factor, several nanofluids were prepared at different concentrations of 0.01, 0.1, 0.25 and 0.5 wt.%. Finally, the prepared nanofluids were injected at the injection rate of 0.07 ml/h up to 1 PV into the micromodel and the amount of produced oil and the movement of the injected fluid in the porous medium were analyzed.
Main results: The results showed that the synthesized CeO₂ nanoparticles in this study have appropriate performance to improve the oil recovery factor. The presence of small amounts of these nanoparticles (concentration of 0.01 wt. %), causes a significant increment in oil recovery factor (about 7%) compared to water injection alone. Also, the oil extraction coefficient increased by increasing the concentration of nanoparticles in the base fluid. So that for nanofluids with concentrations of 0.01, 0.1, 0.25 and 0.5 wt.%, the oil recovery factor were 25%, 38%, 43% and 45%, respectively. However, by increasing the concentration of nanoparticles in the base fluid, from an optimal amount onwards, the probability of particle deposition in the micromodel increased, the effect of nanoparticles on changing the hydrodynamic properties of the injected fluid and oil production mechanisms decreased.
 

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The robot has to adapt its movement with the various condition of surfaces and ensuring the stability with the proper motion of its bust and legs in order to be able to move on different surfaces. A lot of basic parameters of the gait can be expressed by the planned seven-linked bipedal robot. One of the issues that have always attracted the attention of researchers, in this field, is to predict the motion path that guarantees the stability and minimizes the energy. In this study, parameter optimization being used which means that at the first, joint angles are defined as a parameter functions and then with respect to kinematics constraints that define maximum and minimum of joint angles, the problem of motion obtained in the way that maximizing stability of robot and minimizing energy and puts the robot in the permitted stable region. Also, we tried to have zero moment point scale used to calculate the stability of robot. Experimental tests in order to motion analysis for walking healthy were performed and the results were validated. Finally, due to the presented model and predicted path, the robot can move like a person. Comparison of experimental results and the result of presented model were used in each step to validate the accuracy of the proposed method.