Pattern recognition of the fluid flow in a 3D domain by combination of Lattice Boltzmann and ANFIS methods

The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

Comprehensive review on the efficiency of ionic liquid materials for membrane separation and environmental applications

In the current twenty years, industrial applications of ionic liquids (ILs) have been of paramount attention due to their indisputable positive characteristics like negligible volatility and chemical/thermal stability. These brilliant advantages open new horizons towards environmentally friendly application of ILs in several industrial activities like membrane-based CO₂ separation, electrolyte, bioprocessing, targeted drug delivery and solar panels. The principal intention of this article is to prepare a comprehensive review on the potential efficiency of IL-based absorbents to separate CO₂ acidic contaminant from industrial gaseous streams compared to alkanolamine absorbents as the benchmark. For this purpose, a techno-economic evaluation is presented to compare the cost-effectiveness of ILs compared to alkanolamine absorbents. Finally, major environmental impacts of the ILs applications in industries are discussed and future perspectives towards solving the operational challenges are presented in detail.

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).

The Role of Tryptophan Metabolites in Neuropsychiatric Disorders

In recent decades, neuropsychiatric disorders such as major depressive disorder, schizophrenia, bipolar, etc., have become a global health concern, causing various detrimental influences on patients. Tryptophan is an important amino acid that plays an indisputable role in several physiological processes, including neuronal function and immunity. Tryptophan’s metabolism process in the human body occurs using different pathways, including the kynurenine and serotonin pathways. Furthermore, other biologically active components, such as serotonin, melatonin, and niacin, are by-products of Tryptophan pathways. Current evidence suggests that a functional imbalance in the synthesis of Tryptophan metabolites causes the appearance of pathophysiologic mechanisms that leads to various neuropsychiatric diseases. This review summarizes the pharmacological influences of tryptophan and its metabolites on the development of neuropsychiatric disorders. In addition, tryptophan and its metabolites quantification following the neurotransmitters precursor are highlighted. Eventually, the efficiency of various biomarkers such as inflammatory, protein, electrophysiological, genetic, and proteomic biomarkers in the diagnosis/treatment of neuropsychiatric disorders was discussed to understand the biomarker application in the detection/treatment of various diseases.

State-of-the-Art Review on the Application of Membrane Bioreactors for Molecular Micro-Contaminant Removal from Aquatic Environment

In recent years, the emergence of disparate micro-contaminants in aquatic environments such as water/wastewater sources has eventuated in serious concerns about humans’ health all over the world. Membrane bioreactor (MBR) is considered a noteworthy membrane-based technology, and has been recently of great interest for the removal micro-contaminants. The prominent objective of this review paper is to provide a state-of-the-art review on the potential utilization of MBRs in the field of wastewater treatment and micro-contaminant removal from aquatic/non-aquatic environments. Moreover, the operational advantages of MBRs compared to other traditional technologies in removing disparate sorts of micro-contaminants are discussed to study the ways to increase the sustainability of a clean water supplement. Additionally, common types of micro-contaminants in water/wastewater sources are introduced and their potential detriments on humans’ well-being are presented to inform expert readers about the necessity of micro-contaminant removal. Eventually, operational challenges towards the industrial application of MBRs are presented and the authors discuss feasible future perspectives and suitable solutions to overcome these challenges.

Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results

Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al₂O₃/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd's variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.

Pressure and temperature predictions of Al2O3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS

Artificial intelligence (AI) techniques have illustrated significant roles in finding general patterns of CFD (Computational fluid dynamics) results. This study is conducted to develop combination of the ant colony optimization (ACO) algorithm with the fuzzy inference system (ACOFIS) for learning the CFD results of a physical case study. This binary join of the ACOFIS and CFD was used for pressure and temperature predictions of Al₂O₃/water nanofluid flow in a heated porous pipe. The intelligence of ACOFIS is investigated for different input numbers and pheromone effects, as the ant colony tuning parameter. The results showed that the intelligence of the ACOFIS could be found for three inputs (x and y nodes coordinates and nanoparticles fraction) and the pheromone effect of 0.1. At the system intelligence, the ACOFIS could predict the pressure and temperature of the nanofluid on any values of the nanoparticles fraction between 0.5 and 2%. Comparing the ANFIS and the ACOFIS, it was shown that both methods could reach the same accuracy in predictions of the nanofluid pressure and temperature. The root mean square error (RMSE) of the ACOFIS (~ 1.3) was a little more than that of the ANFIS (~ 0.03), while the total process time of the ANFIS (~ 213 s) was a bit more than that of the ACOFIS (~ 198 s). The AI algorithms process time (less than 4 min) shows their ability in the reduction of CFD modeling calculations and expenses.

Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube

The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system’s boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.