In the last two decades, nanotechnology has flourished due to its vast number of applications in many fields such as drug delivery, oil and gas, and thermal applications, like cooling and air-conditioning. This study focuses on the applications of nanoparticles/nanofluids in the Enhanced Oil Recovery (EOR) process to increase oil recovery efficiency. To understand the nanoparticle-assisted EOR process, the first step is to understand the flow characteristics of nanoparticles in porous media, including entrapment and release in the pores and the behavior of nanoparticles under high temperatures, pressures, and salinity levels and in the presence of external electric and magnetic fields. Also, the process looks at the roles of various pore distributions during their application as EOR agents. The experimental approaches are not only time consuming, but they are also cumbersome and expensive. Hence, the mathematical models could help to facilitate the understanding of the transport and interaction of nanofluids in a reservoir and how such processes can be optimized to get maximum oil recovery and, in turn, reduce the production cost. This paper reviews and critically analyzes the latest developments in mathematical modeling and simulation techniques that have been reported for nanofluid-assisted EOR. One section is dedicated to discussing the challenges ahead, as well as the research gaps in the modeling approach to help the readers to also contribute to further enlightening the modeling nanofluid-assisted EOR process.

Subjective analysis of thermal comfort of occupants relates to the recording of the level of satisfaction or dissatisfaction of occupants with regard to indoor environmental conditions on a scale which ranges from -5 to +5. This requires recruitment of subjects and matching for gender, age etc. In this study, we have tried to predict the thermal comfort of occupants by observing their real behavior inside the test room fitted with a novel thermoelectric air duct (TE-AD) cooling system rather than a conventional air conditioning system. Firstly, real experimental data were collected for more than two months from the test room equipped with the TE-AD cooling system operated at an input power supply of 6 A and 5 V. After that, the ANN model was developed based on the Levenberg-Marquardt algorithm by taking experimental parameters such as air temperature, relative humidity, globe temperature, wind speed, metabolic rate, and clothing value as model input. The ANN model is optimized by developing different models with different data points as a starting input in the training and validation process. The neuron optimization has been carried out in these models to minimize the mean square error (MSE) for the ANN model. The result shows that among the three models M1, M2, and M3, the optimum predictive mean value (PMV) was obtained from M1 at 10 neurons with MSE of 0.07956, while for predicted percentage dissatisfied (PPD), M3 gives optimum accuracy at 10 neurons with MSE value of 5.1789. The ANN model is then generalized to predict thermal comfort for one week and then for one month. Finally, all the model results were validated with the experimental data.

To impede nitrogen loss due to leaching and NH₃ volatilization, pristine urea particles are coated with synthetic polymers such as poly(acrylic acid) and polyethylene for the controlled release of nitrogen. However, due to nonbiodegradability and environmental and economic issues, these synthetic polymers are replaced with cheap, biodegradable, and green coating materials. In this study, borax-modified starch biopolymer is used to produce controlled release urea (CRU) in a tumbling fluidized bed. Central composite rotatable design is used to study the interactive effect of process parameters on time and kinetics of nitrogen release. Nitrogen release in distilled water is monitored by the UV–Vis spectrophotometer and soil burial test is used to observe release in soil. The optimum values of process parameters and response objectives generated by the analysis of variance are validated by triplicates of confirmation runs and the % error is reported as a precision indicator. A mathematical model is used to determine the diffusion coefficient of nitrogen release. Biopolymer-coated urea results in much better release performance when compared with starch-based CRU reported in the literature. The most influential process parameter both for time and kinetics of nitrogen release appears to be the coating time. Release time increases linearly with coating time and the diffusion coefficient decreases with increase in coating time. The kinetic study reveals that some of the samples followed non-Fickian diffusion and others followed Case-II transport during the release of nitrogen.