Summary
Due to the increasing need for drinking water in the world, various membrane processes have been developing significantly over the past several years. The process of membrane distillation is ...one of the non‐isothermal processes that cause steam to pass from the membrane due to the difference in temperature and the resulting difference in vapor pressure. Due to the lack of precision in the amount of flux and thermal efficiency in various operating conditions the membrane distillation process still needs development. This article attempted to examine the effects of flowrate, temperature, and module length on the performance of the membrane distillation process using computational fluid dynamics simulation and experiments design method. Since each of these parameters interacts with other parameters, the design of experiments method was used for numerical modeling of flux and thermal efficiency. The results showed that increasing flowrate, inlet feed temperature, and decreasing length of the membrane module increase the water flux and thermal efficiency. Numerical flux modeling results show that temperature (T), module length (L), and interaction module length‐temperature (L‐T) parameters have the most influence on flux and module length (L), temperature (T) and quadratic relation of module length (L2) parameters have the greatest effect on thermal efficiency, respectively.
CFD simulation of Direct Contact Membrane Distillation (DCMD) process and numerical modeling the effect of flowrate, feed temperature, and module length parameters on flux and thermal efficiency by design of experiments method.
Biogas production among other renewable energy sources is an economical and environmentally friendly way. Since high percentage of human food in the world is converted into food waste, one of the ...most suitable sources used as a feed for bio-gas production is food waste. One of the most important methods for producing biogas from organic waste is the use of anaerobic digesters. The factors affecting the production of biogas in anaerobic digestion machines are two general types of feed and its characteristics and the effect of process parameters on the amount of biogas production. In this paper, an anaerobic digestion machine was constructed and the effect of the parameters of the input feed type and operating parameters such as temperature and retention time on the operation of this process are examined by applying ‘‘Response Surface Methodology’’ (RSM) technique. Experimental results in this paper show that with increasing temperature and (C/N) and retention time, the amount of gas produced in anaerobic digesters increases. By increasing the temperature parameter to 45 °C and C/N to 40 in the remaining time of one month, the amount of biogas produced reaches 265 mL.. RSM Model shows that (C/N) parameters has the greatest impact on the amount of biogas and T-square (T2) term has the least impact on the amount of gas produced. In the following article, the function of the amount of gas produced based on the independent temperature and C/N parameters is expressed as a mathematical relation.
Extracellular vesicles (EVs) are nano‐sized vesicles, released from many cell types including cardiac cells, have recently emerged as intercellular communication tools in cell dynamics. EVs are an ...important mediator of signaling within cells that influencing the functional behavior of the target cells. In heart complex, cardiac cells can easily use EVs to transport bioactive molecules such as proteins, lipids, and RNAs to the regulation of neighboring cell function. Cross‐talk between intracardiac cells plays pivotal roles in the heart homeostasis and in adaptive responses of the heart to stress. EVs were released by cardiomyocytes under baseline conditions, but stress condition such as hypoxia intensifies secretome capacity. EVs secreted by cardiac progenitor cells and cardiosphere‐derived cells could be pinpointed as important mediators of cardioprotection and cardiogenesis. Furthermore, EVs from many different types of stem cells could potentially exert a therapeutic effect on the damaged heart. Recent evidence shows that cardiac‐derived EVs are rich in microRNAs, suggesting a key role in the controlling of cellular processes. EVs harboring exosomes may be clinically useful in cell‐free therapy approaches and potentially act as prognosis and diagnosis biomarkers of cardiovascular diseases.
Recent evidence shows that cardiac‐derived EVs are rich in microRNAs, suggesting a key role in the controlling of cellular processes. EVs harboring exosomes may be clinically useful in cell‐free therapy approaches and potentially act as prognosis and diagnosis biomarkers of cardiovascular diseases (CVDs).
•Intelligent model for prediction of pure and impure CO2–Oil MMP is introduced.•Results show the superiority of LSSVM model to empirical methods.•Validity of the model to the most important input ...variables is studied.
Multiple contact miscible floods such as injection of relatively inexpensive gases into oil reservoirs are considered as well-established enhanced oil recovery (EOR) techniques for conventional reservoirs. A fundamental factor in the design of gas injection project is the minimum miscibility pressure (MMP), whereas local sweep efficiency from gas injection is very much dependent on the MMP. Slim tube displacements, and rising bubble apparatus (RBA) are two main tests that are used for experimentally determination of MMP but these tests are both costly and time consuming. Hence, searching for quick and accurate mathematical determination of gas–oil MMP is inevitable. The objective of this study is to present a reliable, and predictive model namely, Least-Squares Support Vector Machine (LSSVM) to predict pure and impure CO2 MMP. To this end, about 147 data sets belonging to experimental CO2 MMP values from the literature and corresponding gas/oil compositional information was used to construct and evaluate the reliability of the model. The results show that the proposed model significantly outperforms all the existing methods and provide predictions in acceptable agreement with experimental data. Moreover, it is shown that the proposed model is capable of simulating the actual physical trend of CO2 MMP versus five most important input parameters: reservoir temperature, molecular weight of pentane plus, hydrogen sulfide and nitrogen concentration. Finally, for detection of the probable doubtful CO2 MMP data, outlier diagnosis was performed on the data sets.
•The gene expression programming technique is used to develop a model for the minimum miscibility pressure (MMP).•An extensive set of experimental data was used to develop and validate the model.•The ...model is more accurate, extensive, and efficient compared with the empirical correlations.
Flooding fuel-generated CO2 into oil reservoirs will lead to two advantages in both enhanced oil recovery (EOR) and reduction in atmospheric emission of carbon dioxide. The main factor for determination of the possibilities to EOR by e.g. CO2 injection, in particular miscible case, into a specific oil reservoir is the CO2–oil minimum miscibility pressure (MMP). In this communication, we present the utilization of a soft-computing technique – gene expression programming (GEP) – for developing a new symbolic equation to pursue our objective. In other words, this work presents a new approach to predict both pure and impure CO2–oil MMP in live reservoir oil systems. The parameters of the new model involve the molecular weight of C5+ fraction in crude oil, reservoir temperature, the mole percentage ratio of volatile to intermediate components of oil and critical temperature. A comprehensive error investigation is done to discuss accuracy of the recently proposed MMP model. Additionally, the results obtained by the new GEP-based model are compared with most widely-used empirically derived correlations available in the literature to show the superiority of the model. The results obtained in this study are encouraging and can propose accurate and efficient solutions for the case of CO2–oil MMP of pure and impure components in live oil systems.
Bubble point pressure is a crucial PVT parameter of reservoir fluids, which has a significant effect on oil field development strategies, reservoir evaluation and production calculations. This ...communication presents a new mathematical model to calculate the saturation pressures of crude oils as a function of temperature, hydrocarbon and non-hydrocarbon reservoir fluid compositions, and characteristics of the heptane-plus fraction. The model was developed and tested using a total set of 130 experimentally measured compositions and saturation pressures of crude oil samples from different geographical locations covering wide ranges of crude oil properties and reservoir temperatures. In-depth comparative studies have been carried out between this new model and five well known predictive models for estimation of saturation pressure of crude oils. The results show that the developed model is more accurate and reliable with the average absolute relative deviation of 4.7% and correlation coefficient of 0.992. In addition, it is shown that the proposed model correctly captures the physical trend of changing the saturation pressure as a function of the input variables. Finally, the applicability domains of the proposed model and quality of the existing experimental data were examined by outlier diagnostics.
•An accurate model to calculate the saturation pressure of crude oil is introduced.•Least-square support vector machine (LSSVM) was applied for this purpose.•Comparative studies are carried out between proposed model and the other models.•The validity of the model to the experimental data is successfully verified.•Outlier diagnosis is performed for detection of the probable doubtful data.
Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, ...namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive models/correlations available in the literature. It has been observed that the proposed model correctly captures the physical trend of changing the natural gas viscosity as a function of the temperature and pressure. Finally, sensitivity analysis was performed to assess the effect of the gas viscosity uncertainty on the cumulative gas production for a synthetic natural gas reservoir, using a numerical reservoir simulation. Results revealed that applications of LSSVM modeling can lead to a more accurate and reliable estimation of the gas viscosity over a wide range of reservoir conditions.
Dew-point pressure is one of the most important quantities for characterizing and successful prediction of the future performance of gas condensate reservoirs. The objective of this study is to ...present a reliable, computer-based predictive model for prediction of dew-point pressure in gas condensate reservoirs. An intelligent approach based on least square support vector machine (LSSVM) modeling was developed for this purpose. To this end, the model was developed and tested using a total set of 562 experimental data points from different retrograde gas condensate fluids covering a wide range of variables. Coupled simulated annealing (CSA) was employed for optimization of hyper-parameters of the model. The results showed that the developed model significantly outperforms all the existing methods and provide predictions in acceptable agreement with experimental data. In addition, it is shown that the proposed model is capable of simulating the actual physical trend of the dew-point pressure versus temperature for a constant composition fluid on the phase envelope.
•Predictive model for dew-point pressure prediction in condensate systems is developed.•Comparative studies are carried out between developed model and the other models.•Validity of the developed model to temperature is evaluated.
In the current experiment, the combined regime of resveratrol and a Wnt-3a inhibitor, sulindac, were examined on the angiogenic potential of cancer stem cells from human colon adenocarcinoma cell ...line HT-29 during 7 days. Cancer stem cells were enriched via a magnetic-activated cell sorter technique and cultured in endothelial induction medium containing sulindac and resveratrol. Expression of endothelial markers such as the von Willebrand factor (vWF) and vascular endothelial cadherin (VE-cadherin) and genes participating in mesenchymal-to-epithelial transition was studied by real-time PCR assay. Protein levels of Wnt-3a and angiogenic factor YKL-40 were examined by western blotting. ELISA was used to determine the level of N-acetylgalactosaminyltransferase 11 (GALNT11) during mesenchymal-endothelial transition. Autophagy status was monitored by PCR array under treatment with the resveratrol plus sulindac. Results showed that resveratrol and sulindac had the potential to decrease the cell survival of HT-29 cancer cells and the clonogenic capacity of cancer stem cells compared with the control (
p
< 0.05). The expression of VE-cadherin and vWF was induced in cancer stem cells incubated with endothelial differentiation medium enriched with resveratrol (
p
< 0.05). Interestingly, the Wnt-3a level was increased in the presence of resveratrol and sulindac (
p
< 0.05). YKL-40 was reduced after cell exposure to sulindac and resveratrol. The intracellular content of resistance factor GALNT11 was diminished after treatment with resveratrol (
p
< 0.05). Resveratrol had the potential to induce the transcription of autophagy signaling genes in cancer stem cells during endothelial differentiation (
p
< 0.05). These data show that resveratrol could increase cancer stem cell trans-differentiation toward endothelial lineage while decrease cell resistance by modulation of autophagy signaling and GALNT11 synthesis.
