Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models.

Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent.

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization.

Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10-4, 7.814 × 10-5, and 1.664 × 10-4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10-4, 6.197 × 10-5, and 8.777 × 10-5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10-3) vector.

Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug.

These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

Angioregulatory microRNAs in breast cancer: Molecular mechanistic basis and implications for therapeutic strategies.

Background: Cancer-associated angiogenesis is a fundamental process in tumor growth and metastasis. A variety of signaling regulators and pathways contribute to establish neovascularization, among them as small endogenous non-coding RNAs, microRNAs (miRNAs) play prominent dual regulatory function in breast cancer (BC) angiogenesis. Aim of Review: This review aims at describing the current state-of-the-art in BC angiogenesis-mediated by angioregulatory miRNAs, and an overview of miRNAs dysregulation association with the anti-angiogenic response in addition to potential clinical application of miRNAs-based therapeutics. Key Scientific Concepts of Review: Angioregulatory miRNA-target gene interaction is not only involved in sprouting vessels of breast tumors but also, trans-differentiation of BC cells to endothelial cells (ECs) in a process termed vasculogenic mimicry. Using canonical and non-canonical angiogenesis pathways, the tumor cell employs the oncogenic characteristics such as miRNAs dysregulation to increase survival, proliferation, oxygen and nutrient supply, and treatment resistance. Angioregulatory miRNAs in BC cells and their microenvironment have therapeutic potential in cancer treatment. Although, miRNAs dysregulation can serve as tumor biomarker nevertheless, due to the association of miRNAs dysregulation with anti-angiogenic resistant phenotype, clinical benefits of anti-angiogenic therapy might be challenging in BC. Hence, unveiling the molecular mechanism underlying angioregulatory miRNAs sparked a booming interest in finding new treatment strategies such as miRNA-based therapies in BC.

Wood-Derived Hydrogels as a Platform for Drug-Release Systems.

Wood (cellulose and lignin)-based hydrogels were successfully produced as platforms for drug-release systems. Viscoelastic and cross-linking behaviors of precursor solutions were tuned to produce highly porous hydrogel architectures via freeze-drying. Pore sizes in the range of 100-160 µm were obtained. Varying lignin molecular structure played a key role in tailoring swelling and mechanical performance of these gels with organosolv-type lignin showing optimum properties due to its propensity for intermolecular cross-linking, achieving a compressive modulus around 11 kPa. Paracetamol was selected as a standard drug for release tests and its release rate was improved with the presence of lignin (50% more compared to pure cellulose hydrogels). This was attributed to a reduction in molecular interactions between paracetamol and cellulose. These results highlight the potential for the valorization of lignin as a platform for drug-release systems.

Facile Tailoring of Structures for Controlled Release of Paracetamol from Sustainable Lignin Derived Platforms.

Nowadays, sustainable materials are receiving significant attention due to the fact that they will be crucial for the development of the next generation of products and devices. In the present work, hydrogels have been successfully synthesized using lignin which is non-valorized biopolymer from the paper industry. Hydrogels were prepared via crosslinking with Poly(ethylene) glycol diglycidyl ether (PEGDGE). Different crosslinker ratios were used to determine their influence on the structural and chemical properties of the resulting hydrogels. It has been found that pore size was reduced by increasing crosslinker amount. The greater crosslinking density increased the swelling capacity of the hydrogels due to the presence of more hydrophilic groups in the hydrogel network. Paracetamol release test showed higher drug diffusion for hydrogels produced with a ratio lignin:PEGDGE 1:1. The obtained results demonstrate that the proposed approach is a promising route to utilize lignocellulose waste for producing porous materials for advanced biomedical applications in the pharmacy industry.

Synthesis of multi-organo-functionalized fibrous silica KCC-1 for highly efficient adsorption of acid fuchsine and acid orange II from aqueous solution.

Multi-functionalized fibrous silica KCC-1 (MF-KCC-1) bearing amine, tetrasulfide, and thiol groups was synthesized via a post-functionalization method and fully characterized by several methods such as FTIR, FESEM, EDX-Mapping, TEM, and N2 adsorption-desorption techniques. Due to abundant surface functional groups, accessible active adsorption sites, high surface area (572 m2 g-1), large pore volume (0.98 cm3 g-1), and unique fibrous structure, mesoporous MF-KCC-1 was used as a potential adsorbent for the uptake of acid fuchsine (AF) and acid orange II (AO) from water. Different adsorption factors such as pH of the dye solution, the amount of adsorbent, initial dye concentration, and contact time, affecting the uptake process were optimized and isotherm and kinetic studies were conducted to find the possible mechanism involved in the process. For both AF and AO dyes, the Langmuir isotherm model and the PFO kinetic model show the most agreement with the experimental data. According to the Langmuir isotherm, the calculated maximum adsorption capacity for AF and AO were found to be 574.5 mg g-1 and 605.9 mg g-1, respectively, surpassing most adsorption capacities reported until now which is indicative of the high potential of mesoporous MF-KCC-1 as an adsorbent for removal applications.

A novel and facile green synthesis method to prepare LDH/MOF nanocomposite for removal of Cd(II) and Pb(II).

To date, many nanoadsorbents have been developed and used to eliminate heavy metal contamination, however, one of the challenges ahead is the preparation of adsorbents from processes in which toxic organic solvents are used in the least possible amount. Herein, we have developed a new carboxylic acid-functionalized layered double hydroxide/metal-organic framework nanocomposite (LDH/MOF NC) using a simple, effective, and green in situ method. UiO-66-(Zr)-(COOH)2 MOF nanocrystals were grown uniformly over the whole surface of COOH-functionalized Ni50Co50-LDH ultrathin nanosheets in a green water system under a normal solvothermal condition at 100 °C. The synthesized LDH/MOF NC was used as a potential adsorbent for removal of toxic Cd(II) and Pb(II) from water and the influence of important factors on the adsorption process was monitored. Various non-linear isotherm and kinetic models were used to find plausible mechanisms involved in the adsorption, and it was found that the Langmuir and pseudo-first-order models show the best agreement with isotherm and kinetic data, respectively. The calculated maximum adsorption capacities of Cd(II) and Pb(II) by the LDH/MOF NC were found to be 415.3 and 301.4 mg g-1, respectively, based on the Langmuir model (pH = 5.0, adsorbent dose = 0.02 g, solution volume = 20 mL, contact time = 120 min, temperature = 25 â„ƒ, shaking speed 200 rpm).

Experimental and thermodynamic modeling decitabine anti cancer drug solubility in supercritical carbon dioxide.

Design and development of efficient processes for continuous manufacturing of solid dosage oral formulations is of crucial importance for pharmaceutical industry in order to implement the Quality-by-Design paradigm. Supercritical solvent-based manufacturing can be utilized in pharmaceutical processing owing to its inherent operational advantages. However, in order to evaluate the possibility of supercritical processing for a particular medicine, solubility measurement needs to be carried out prior to process design. The current work reports a systematic solubility analysis on decitabine as an anti-cancer medicine. The solvent is supercritical carbon dioxide at different conditions (temperatures and pressures), while gravimetric technique is used to obtain the solubility data for decitabine. The results indicated that the solubility of decitabine varies between 2.84 × 10-05 and 1.07 × 10-03 mol fraction depending on the temperature and pressure. In the experiments, temperature and pressure varied between 308-338 K and 12-40 MPa, respectively. The solubility of decitabine was plotted against temperature and pressure, and it turned out that the solubility had direct relation with the pressure due to the effect of pressure on solvating power of solvent. The effect of temperature on solubility was shown to be dependent on the cross-over pressure. Below the cross-over pressure, there is a reverse relation between temperature and solubility, while a direct relation was observed above the cross-over pressure (16 MPa). Theoretical study was carried out to correlate the solubility data using several thermodynamic-based models. The fitting and model calibration indicated that the examined models were of linear nature and capable to predict the measured decitabine solubilities with the highest average absolute relative deviation percent (AARD %) of 8.9%.

Controlled release evaluation of paracetamol loaded amine functionalized mesoporous silica KCC1 compared to microcrystalline cellulose based tablets.

In the pharmaceutical manufacturing, drug release behavior development is remained as one of the main challenges to improve the drug effectiveness. Recently, more focus has been done on using mesoporous silica materials as drug carriers for prolonged and superior control of drug release in human body. In this study, release behavior of paracetamol is developed using drug-loaded KCC-1-NH2 mesoporous silica, based on direct compaction method for preparation of tablets. The purpose of this study is to investigate the utilizing of pure KCC-1 mesoporous silica (KCC-1) and amino functionalized KCC-1 (KCC-1-NH2) as drug carriers in oral solid dosage formulations compared to common excipient, microcrystalline cellulose (MCC), to improve the control of drug release rate by manipulating surface chemistry of the carrier. Different formulations of KCC-1 and KCC-NH2 are designed to investigate the effect of functionalized mesoporous silica as carrier on drug controlled-release rate. The results displayed the remarkable effect of KCC-1-NH2 on drug controlled-release in comparison with the formulation containing pure KCC-1 and formulation including MCC as reference materials. The pure KCC-1 and KCC-1-NH2 are characterized using different evaluation methods such as FTIR, SEM, TEM and N2 adsorption analysis.

Intensification of CO2 absorption using MDEA-based nanofluid in a hollow fibre membrane contactor.

Porous hollow fibres made of polyvinylidene fluoride were employed as membrane contactor for carbon dioxide (CO2) absorption in a gas-liquid mode with methyldiethanolamine (MDEA) based nanofluid absorbent. Both theoretical and experimental works were carried out in which a mechanistic model was developed that considers the mass transfer of components in all subdomains of the contactor module. Also, the model considers convectional mass transfer in shell and tube subdomains with the chemical reaction as well as Grazing and Brownian motion of nanoparticles effects. The predicted outputs of the developed model and simulations showed that the dispersion of CNT nanoparticles to MDEA-based solvent improves CO2 capture percentage compared to the pure solvent. In addition, the efficiency of CO2 capture for MDEA-based nanofluid was increased with rising MDEA content, liquid flow rate and membrane porosity. On the other hand, the enhancement of gas velocity and the membrane tortuosity led to reduced CO2 capture efficiency in the module. Moreover, it was revealed that the CNT nanoparticles effect on CO2 removal is higher in the presence of lower MDEA concentration (5%) in the solvent. The model was validated by comparing with the experimental data, and great agreement was obtained.

Chloroquine (antimalaria medication with anti SARS-CoV activity) solubility in supercritical carbon dioxide.

Unfortunately, malaria still remains a major problem in tropical areas, and it takes thousands of lives each year and causes millions of infected cases. Besides, on December 2019, a new virus known as coronavirus appeared, that its rapid prevalence caused the World Health Organization (WHO) to consider it a pandemic. As a potential drug for controlling or treating these two undesired diseases at the cellular level, chloroquine and its derivatives are being investigated, although they possess side effects, which must be reduced for effective and safe treatments. With respect to the importance of this medicine, the current research aimed to calculate the solubility of chloroquine in supercritical carbon dioxide, and evaluated effect of pressure and temperature on the solubility. The pressure varied between 120 and 400 bar, and temperatures between 308 and 338 K were set for the measurements. The experimental results revealed that the solubility of chloroquine lies between 1.64 × 10-5 to 8.92 × 10-4 (mole fraction) with different functionality to temperature and pressure. Although the solubility was indicated to be strong function of pressure and temperature, the effect of temperature was more profound and complicated. A crossover pressure point was found in the solubility measurements, which indicated similar behaviour to an inflection point. For the pressures higher than the crossover point, the temperature indicated direct effect on the solubility of chloroquine. On the other hand, for pressures less than the crossover point, temperature enhancement led to a reduction in the solubility of chloroquine. Moreover, the obtained solubility results were correlated via semi-empirical density-based thermodynamic correlations. Five correlations were studied including: Kumar & Johnston, Mendez-Santiago-Teja, Chrastil, Bartle et al., and Garlapati & Madras. The best performance was obtained for Mendez-Santiago-Teja's correlation in terms of average absolute relative deviation percent (12.0%), while the other examined models showed almost the same performance for prediction of chloroquine solubility.

Using static method to measure tolmetin solubility at different pressures and temperatures in supercritical carbon dioxide.

Tolmetin is a non-steroidal anti-inflammatory drug being used to decrease the level of hormones which are the reasons for pain, swelling, tiredness, and stiffness for osteoarthritis and rheumatoid arthritis cases. We evaluated its solubility in supercritical carbon dioxide (SC-CO2) with the aim of drug nanonization, considering temperature and pressure variations between 120 and 400 bar and 308-338 K, in the experiments. In this way, a PVT solubility cell based on static solubility approach coupled with a simple gravimetric procedure was utilized to evaluate the solubility of tolmetin. The solubility values between 5.00 × 10-5 and 2.59 × 10-3 mol fraction were obtained for tolmetin depending on the pressure and temperature of the cell. The measured data demonstrated a direct correlation between pressure and solubility of tolmetin, while the effect of temperature was a dual effect depending on the crossover pressure (160 bar). The calculated solubility data were modeled using several semi-empirical correlations, and the fitting parameters were calculated using the experimental data via appropriate optimization method. The correlated solubility data revealed that the KJ model was the most accurate one with an average absolute relative deviation percent (AARD%) of 6.9. Moreover, the carried out self-consistency analysis utilizing these correlations illustrated great potential of these models to extrapolate the solubility of tolmetin beyond the measured conditions.

Mechanistic modeling and numerical simulation of axial flow catalytic reactor for naphtha reforming unit.

Naphtha catalytic reforming (NCR) process has been of tremendous attention all over the world owing to the significant requirement for high-quality gasoline. Industrialized naphtha reforming unit at oil refineries applies a series of fixed bed reactors (FBRs) to improve the quality of the low-octane hydrocarbons and convert them to more valuable products. The prominent purpose of this research is to understand the catalytic reactor of naphtha reforming unit. For this aim, an appropriate mechanistic modeling and its related CFD-based computational simulation is presented to predict the behavior of the system when the reactors are of the axial flow type. Also, the triangular meshing technique (TMT) is performed in this paper due to its brilliant ability to analyze the results of model's predictions along with improving the computational accuracy. Additionally, mesh independence analysis is done to find the optimum number of meshes needed for reaching the results convergence. Moreover, suitable kinetic and thermodynamic equations are derived based on Smith model to describe the NCR process. The results proved that the proceeding of NCR process inside the reactor significantly increased the concentration amount of aromatic materials, lighter ends and hydrogen, while deteriorated the concentration amount of naphthene and paraffin. Moreover, the pressure drop along the reactor length was achieved very low, which can be considered as one of the momentous advantages of NCR process.

Computational modeling of drug separation from aqueous solutions using octanol organic solution in membranes.

Continuous membrane separation of pharmaceuticals from an aqueous feed was studied theoretically by development of high-performance mechanistic model. The model was developed based on mass and momentum transfer to predict separation and removal of ibuprofen (IP) and its metabolite compound, i.e. 4-isobutylacetophenone (4-IBAP) from aqueous solution. The modeling study was carried out for a membrane contactor considering mass transport of solute from feed to organic solvent (octanol solution). The solute experiences different mass transfer resistances during the removal in membrane system which were all taken into account in the modeling. The model's equations were solved using computational fluid dynamic technique, and the simulations were carried out to understand the effect of process parameters, flow pattern, and membrane properties on the removal of both solutes. The simulation results indicated that IP and 4-IBAP can be effectively removed from aqueous feed by adjusting the process parameters and flow pattern. More removal was obtained when the feed flows in the shell side of membrane system due to improving mass transfer. Also, feed flow rate was indicated to be the most affecting process parameter, and the highest solute removal was obtained at the lowest feed flow rate.

Molecular separation of ibuprofen and 4-isobutylacetophenone using octanol organic solution by porous polymeric membranes.

Molecular separation of pharmaceutical contaminants from water has been recently of great interest to alleviate their detrimental impacts on environment and human well-being. As the novelty, this investigation aims to develop a mechanistic modeling approach and consequently its related CFD-based simulations to evaluate the molecular separation efficiency of ibuprofen (IP) and its metabolite 4-isobutylacetophenone (4-IBAP) from water inside a porous membrane contactor (PMC). For this purpose, octanol has been applied as an organic phase to extract IP and 4-IBAP from the aqueous solution due to high solubility of solutes in octanol. Finite element (FE) technique is used as a promising tool to simultaneously solve continuity and Navier-Stokes equations and their associated boundary conditions in tube, shell and porous membrane compartments of the PMC. The results demonstrated that the application of PMC and liquid-liquid extraction process can be significantly effective due to separating 51 and 54% of inlet IP and 4-IBAP molecules from aqueous solution, respectively. Moreover, the impact of various operational / functional parameters such as packing density, the number of fibrous membrane, the module length, the membrane porosity / tortuosity, and ultimately the aqueous solution flow rate on the molecular separation efficiency of IP and 4-IBAP is studied in more details.

ANFIS grid partition framework with difference between two sigmoidal membership functions structure for validation of nanofluid flow.

In this study, a square cavity is modeled using Computational Fluid Dynamics (CFD) as well as artificial intelligence (AI) approach. In the square cavity, copper (Cu) nanoparticle is the nanofluid and the flow velocity characteristics in the x-direction and y-direction, and the fluid temperature inside the cavity at different times are considered as CFD outputs. CFD outputs have been assessed using one of the artificial intelligence algorithms, such as a combination of neural network and fuzzy logic (ANFIS). As in the ANFIS method, we have a non-dimension procedure in the learning step, and there is no issue in combining other characteristics of the flow and thermal distribution beside the x and y coordinates, we combine two coordinate parameters and one flow parameter. This ability of method can be considered as a meshless learning step that there is no instability of the numerical method or limitation of boundary conditions. The data were classified using the grid partition method and the MF (membership function) type was dsigmf (difference between two sigmoidal membership functions). By achieving the appropriate intelligence in the ANFIS method, output prediction was performed at the points of cavity which were not included in the learning process and were compared to the existing data (the results of the CFD method) and were validated by them. This new combination of CFD and the ANFIS method enables us to learn flow and temperature distribution throughout the domain thoroughly, and eventually predict the flow characteristics in short computational time. The results from AI in the ANFIS method were compared to the ant colony and fuzzy logic methods. The data from CFD results were inserted into the ant colony system for the training process, and we predicted the data in the fuzzy logic system. Then, we compare the data with the ANFIS method. The results indicate that the ANFIS method has a high potentiality compared to the ant colony method because the amount of R in the ANIFS system is higher than R in the ant colony method. In the ANFIS method, R is equal to 0.99, and in the ant colony method, R is equal to 0.91. This shows that the ant colony needs more time for both the prediction and training of the system. Also, comparing the pattern recognition in the two systems, we can obviously see that by using the ANFIS method, the predictions completely match the target points. But the other method cannot match the flow pattern and velocity distribution with the CFD method.

Process Design of Ammonia Separation for Nitrification Control in Aeration Basins at an IKORC's Oily Wastewater Treatment Unit.

In the current decades, water shortage is well understood as one of the main limiting factors for oil industry development all over the world. One of the available and reasonable solutions is reusing wastewater. The oily wastewater treatment unit of the IKORC oil refinery provides a portion of the makeup water for cooling towers, applying physical, biological, and chemical treatments. Ammonia shocks are the only crisis that disrupts the nitrification process. This condition eventuates in destroying the microorganisms of aeration basins and leads to a high ammonia containing effluent. In order to protect the aeration process, it is mandatory to apply a suitable system for removing excess ammonia. In this study, at first, ammonia removal history is reviewed. Then quantity and quality of the oily sewer are investigated. Because of high volatility of ammonia contamination and high TDS, a stripping tower with air is selected among diverse solutions. Taking into account the principles of project econometrics, operating parameters such as stripping factor, pressure drop, tower volumetric flow rate, and number of towers are determined. Then, the process is designed and its environmental survey is conducted. Finally, calculating indices proved that this project is economically profitable in addition to its environmental benefits.