genetic algorithm
در نشریات گروه شیمی-
The application of exergy analysis in thermodynamic systems is rapidly expanding alongside energy analysis, providing valuable insights into processes causing exergy destruction and losses. Environmental concerns have driven increased investigation on heat recovery, with the Organic Rankine Cycle (ORC) emerging as an effective solution for converting low-temperature waste heat into useful work. This research performs a comparative and optimization assessment of various carbon dioxide Rankine cycles—namely simple, cascade, and split configurations—specifically for recovering waste heat from gas turbines. This research employs a multi-objective optimization strategy, validated by simulation outcomes, that integrates a Genetic Algorithm with various machine learning techniques—such as Random Forest, XGBoost, Artificial Neural Networks, Ridge Regression, and K-Nearest Neighbors—to forecast the performance of the cycle. Results highlight the split cycle's superior power generation, achieving optimized performance metrics of 7.99 MW net power output, 76.17% heat recovery, 26.38% system efficiency, and 57.96% exergy efficiency, positioning it as a promising solution for waste heat recovery applications.
Keywords: Organic Rankine Cycle, Optimization, Genetic Algorithm, Exergy Analysis, Waste Heat Recovery -
In this research, a new design approach for shell and tube heat exchanger optimization design based on NEPCM nanofluid and applying adoptive genetic algorithm has been developed. Nano Encapsulated Phase Change Material (NEPCM) was used as base fluid inside the Shell and Tube Heat Exchanger (STHE). A systematic optimization design approach has not been introduced for designing these nanofluid-based STHE. The exergy efficiency and cost are two important parameters in heat exchanger design. The total cost includes the capital investment for equipment (heat exchanger surface area) and operating cost (for energy expenditures related to pumping). Tube. diameter, tube pitch ratio, tube number, baffle spacing ratio, nanofluid concentration as well as baffle cut ratio were considered as seven design parameters. For optimal design of a shell and tube heat exchanger, it was first thermally modeled using eeNTU method while BelleDelaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. Fast and elitist non-dominated sorting. genetic algorithm (.GA) with continuous and discrete variables were applied to obtain the maximum exergy efficiency and the minimum total cost as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called ‘Pareto optimal solutions. The sensitivity analysis of change in optimum effectiveness and total cost with change in design parameters of the shell and tube heat exchanger was also performed and the results are reported. The results showed that using NEPCM concentration and tube number enhanced exergy efficiency around 9%, while increasing cost about 22%.Keywords: Shell, Tube, Optimization, Economic, Phase Change Material, Genetic Algorithm
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The Autothermal Thermophilic Aerobic Digestion (ATAD) process stands as a modern wastewater treatment method used for sludge digestion. This process uses thermophilic microbes, such as Thermotogaceae and Clostridiaceae, to break down organic waste naturally without needing external energy sources. This makes it a good way to stabilize the waste, by degrading it into simpler biochemicals through endogenous respiration. Despite its effectiveness, this process is complex, involving microbiological, thermodynamic, biochemical, and kinetic intricacies. This study delves into the kinetics aspect, acknowledging the potential operational setbacks arising from inadequate comprehension. The investigation emphasizes the intricate nature of the biokinetics, encompassing diverse bioreactions related to microorganism growth and sludge digestion. Recognizing the challenges of classical modeling approaches, the paper advocates for artificial neural networks (ANNs) as a promising alternative, citing their ability to handle complex and non-linear data structures. The study used kinetic data to construct an optimized ANN model predicting the kinetic rate constant (KATAD). The model was further tuned with the genetic algorithm (GA), which is a well-known nature-inspired optimizer, to demonstrate exceptional accuracy (more than 99%). Model evaluation using causal index (CI) showed that temperature (TATAD) was the most influential parameter (CI = 1.23), followed by the primary to secondary sludge ratio (P/S) parameter (CI = -0.47), and secondary sludge concentration (Cs) with the least impact on KATAD (CI = 0.19). This research presents a novel study exploring the kinetics of the recently developed ATAD technology. Moreover, it used a cutting-edge approach to tackle the complexities of ATAD. This work advances our understanding of ATAD kinetics and lays the groundwork for improved wastewater treatment strategies. The successful application of the ANN-GA model paves the way for more accurate and effective treatment processes.Keywords: Artificial Neural Network, Autothermal Thermophilic Aerobic Digestion, Genetic Algorithm, Hybrid Modeling, Kinetic Rate Constant, Wastewater Treatment
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Considering the limitations of energy consumption and the increasing problems of pollution from low-quality fossil fuels and the need to increase the quality of these fuels by using oxygen additives such as Ethyl tert-butyl ether (ETBE) for their combustion, reviewing and optimizing the production processes of oxygen additives is of great importance. The goal of this study is to simulate-optimize the ETBE production which is used as an oxygenate gasoline additive in the production of gasoline from crude oil. The feed for the ETBE unit comprised two flows of hydrocarbon and ethanol. The Soave-Redlich-Kwong (SRK) equation of state for the vapor phase and the UNIQUAC activity coefficient model for the liquid phase have been used. In this work, at first, the reactive distillation process was simulated by HYSYS software for producing ETBE. Then, the coding was written using MATLAB software and the Genetic algorithm (GA). Both software have been linked simultaneously and the later optimization data was transferred and compared with HYSYS data. The objective function was reflected as the total annual income of ETBE production. The parameters of the objective function were optimized by GA. Optimization was made on decision variables of the objective function which included the output stream temperature of the heater (Tho), input stream temperature of the reactive distillation tower (Tti), output stream temperature of the cooler (Tco), and input stream feed pressure of the distillation tower (Pti). The results of GA optimization show that reboiler duty decreases by 10% as well as total annual profit increases by 15%. Additionally, the comparison of the present work with the findings of researchers reveals a good agreement.Keywords: Annual Profit, ETBE, Genetic Algorithm, Optimization, Reactive Distillation
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In the recent decade, low salinity water flooding attained remarkable attention as a pioneering method for recovering oil. However, this method has several risks. To decrease the potential risk of conflict between injected water and rock /brine/crude oil system, the ion concentrations in injection water should be optimized. In this work, injection water composition during low salinity water flooding in a core model of carbonate reservoirs was simulated based on maximizing wettability change and minimizing compatibility. The optimum ion concentrations in injection water were obtained from geochemical software (PHREEQC) which was coupled with ECLIPSE software and a genetic algorithm. The optimum conditions of injection water were provided in low concentration of sodium ion (186 ppm) and high concentration of sulfate ion (7064 ppm). The optimization process based on the genetic algorithm was stopped after 764 times running of scenarios. The average recovery factor was obtained near 0.141 and the results presented that the average recovery factor was not dependent on calcium and chloride ions concentration ranges. Hence, these ions were not effective in the optimization process. However, the average recovery factor had a strong relation to sodium and sulfate ion concentrations.Keywords: Geochemical Modeling, Coupled PHREEQC- ECLIPSE, Genetic Algorithm, Recovery Factor, Wettability, Injected Water Ion
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This study presents a pioneering application of a novel quantitative structure-property relationship (QSPR) model to predict the selectivity coefficients of a cation-selective electrode. Specifically, the selectivity coefficients of a Lanthanum (La(III)) membrane sensor utilizing 8-amino-N-(2-hydroxybenzylidene) naphthylamine (AIP) as the sensing ligand were efficiently estimated and predicted. To establish the QSPR model, calculated molecular descriptors were employed, considering the limitation of cation descriptors. A new strategy was introduced for descriptor calculation by optimizing the structure of Mn+-AIP and utilizing density functional theory (DFT) with the B3LYP functional and SBKJC basis set. Genetic algorithm (GA) and stepwise techniques were employed for descriptor selection, with the most significant descriptors identified. Following variable selection, multiple linear regression (MLR) was employed to construct linear QSPR models. Comparative analysis revealed that the GA-MLR modeling approach exhibited superior performance compared to the stepwise-MLR method. Furthermore, the predictions generated by the GA-MLR model demonstrated excellent agreement with the experimental values. The proposed strategy outlined in this study has the potential to be extended to other QSPR investigations involving cation-selective electrodes. These findings contribute to the advancement of predictive modeling in the field of cation-selective sensors and offer valuable insights for future research in this area.Keywords: Selectivity coefficient, Multiple Linear Regression (MLR), genetic algorithm, molecular descriptors, QSPR, Chemometrics
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In this work, the oil treatment plant of the Rumaila oil field in Iraq was simulated using Aspen HYSYS. Industrial data from the plant was applied to validate the simulation results. The process was optimized in single-objective and multi-objective modes using a genetic algorithm. The process was optimized for reducing CO2, H2S, and CH4 in the outlet oil flow and the energy of the heater simultaneously by changing the molar flow and temperature of dry crude oil and water. The result shows that by decreasing the temperature of the dry crude oil and water, the amount of the consumed energy will decrease to a large extent, but the amount of H2S, and CH4 in the outlet oil will decrease. Also, it can be concluded that by separating more CO2, H2S, and CH4 in the outlet oil, the temperature should be increased and as a result, the consumption of the energy will be increased. The single-objective optimization results showed that the amount of CO2, H2S, and CH4 was decreased by 46.52%, 43.,94%, and 27.8%, respectively. On the other hand, the results from multi-objective optimizations illustrated a lower reduction in the amounts of CO2, H2S, and CH4. Consequently, it was concluded that single-objective optimization results were better than multi-objective optimizations.
Keywords: Oil treatment plant, Rumaila oil field, Simulation, Aspen HYSYS, Genetic Algorithm -
QSAR investigations of Caspofungin derivatives were conducted using Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Monte Carlo Methods. The obtained results were compared and GA-ANN and ICA-MLR combinations showed the best performance according to its correlation coefficient (R2) and Root Mean Sum Square Errors (RMSE). The most important physicochemical and structural descriptors were presented and discussed. Monte Carlo method revealed that the presence of a double bond with branching, a six-member cycle, the absence of halogens, the presence of sp2 carbon connected to branching, the presence of Nitrogen and Oxygen atoms, absence of Sulphur and Phosphorus are the most important molecular features. The best Caspofungin derivative was exposed to reaction with Cu, Zn, Fe using B3lyp/6-311g/lanl2dz to investigate the stability of the formed complexes, from which the Zn complex was perceived to be the most stable one. It was concluded that QSAR study and the Monte Carlo method can lead to a more comprehensive understanding of the relation between physicochemical, structural, or theoretical molecular descriptors of drugs to their biological activities and Lipophilicity.Keywords: Caspofungin Drugs, QSAR, genetic algorithm, Monte Carlo method
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This paper aims to investigate the robust control problem of Continuously Stirred Tank Reactors (CSTR). A CSTR is one of the most essential pieces of equipment in chemical processes, whose effects of highly nonlinear dynamic and external disturbances make it very difficult to be controlled. Firstly, a novel finite-time sliding mode control is introduced that eliminates disturbance effects and ensures finite-time tracking. Secondly, to better compensate for disturbances and to improve controller performance, a finite-time disturbance observer is developed. Finally, an adaptive robust control method is introduced based on the proposed sliding mode control and the disturbance observer. Stability analysis is performed to investigate the finite-time tracking of the closed-loop system under the proposed controllers. Besides, to enhance the performance of the proposed controllers, the design parameters are tuned by the genetic optimization algorithm. Simulation results are produced to confirm the efficiency of the proposed methods in terms of tracking errors and convergence rates. The proposed finite-time sliding mode control and the adaptive finite-time sliding mode control with settling times of 1.73s and 1.71s as well as IAE of 0.509 and 0.4843, respectively, showed more desirable performance than other controllers.Keywords: CSTR, Disturbance estimation, Robust control, Finite-time convergence, genetic algorithm
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In this work, the heat transfer coefficient in the pool boiling process was investigated for different alcoholic solutions. To exact evaluation, the bubble dynamic including bubble departure diameter, bubble departure frequency, and active nucleation sites’ density were studied. The results showed that with increasing isopropanol concentration (20 V.% - 80 V.%), bubble departure frequency and active nucleation sites increased while bubble departure diameter decreased. The bubble dynamic cannot be effective in any amount and must be optimized to reach an optimum heat transfer coefficient. Isopropanol concentration of 20 V.% was reported as an optimum state and lower decrease versus deionized water (11.892%). This result confirmed that the bubble departure diameter played a significant role in promoting the heat transfer coefficient. Finally, to predict the experimental data, a Genetic Algorithm (GA) based correlation (power-law function) was developed. The optimization procedure revealed that the GA model had a good agreement with the experimental data (R2=0.968, AAD= 0.0288). In addition, this approach was compared with conventional models (Palen, Stephan, Unal, Fujita, and Inoue). The GA and the Stephan models presented the best and worst performance, respectively.Keywords: Bubble dynamic, Heat transfer coefficient, Pool boiling, Optimization, genetic algorithm
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The quantitative structure-property relationship (QSPR) method has been used for the prediction of carbonate potentiometric selectivity of plasticized polymeric membrane sensors. The variable selection tools of genetic algorithm (GA) combined with the multiple linear regressions (MLR) as linear and support vector machine (SVM) as nonlinear regression methods have been used. The K-means clustering method has been used for dividing the data set into the training set and test set. The validation of the models was done by the internal cross-validation and external test set. The results showed that the GA-SVM was a very accurate method in predicting of carbonate potentiometric selectivity with high correlation coefficients of 0.983 and 0.965 for the training and test sets. The results of this study and the interpretation of entered descriptors in the model can help to design new selective ligands.
Keywords: Ion-selective electrode, Carbonate sensing, QSPR, genetic algorithm, Support Vector Machine -
In this paper, we optimize the electrical characteristics of Nano-scale symmetric double-gate metal oxide semiconductor field effect transistors (DG-MOSFETs) for digital applications using a genetic algorithm. We use a single-objective genetic algorithm to optimize the threshold voltage (Vth</sub>) with a distinct analytical relationship. The optimization of the threshold voltage is accomplished for three cases with considering two structural variables of the oxide thickness (tox</sub>), the channel thickness (tsi</sub>), and the channel doping density (Na</sub>). The fourth case of optimization is done with considering these three variables. Comparison of these four cases illustrates that the best threshold voltage is 0.15 V for a channel doping concentration of 1.2×10 10 </sup>cm-3</sup> and an oxide thickness of 1.49 nm. In addition, we optimize the OFF-current criterion based on the gate oxide thickness, the channel thickness, the channel doping concentration and the channel length and width. The optimization processes of the device are validated by simulating in SILVACO software. Furthermore, we use the two-objective genetic algorithm with the threshold voltage and the OFF-current objects for four structural variables including the gate oxide thickness, the channel layer thickness, and the channel length and width. This process is applicable to digital circuit design. To evaluate the accuracy of the proposed device optimization, the optimized device and other situations are simulated in SILVACO simulator. The optimized device illustrates the best treatment.
Keywords: Genetic Algorithm, Nano-Scale Double-Gate MOSFET, OFF-Current, Scaling, Threshold Voltage -
The growing energy demand and depletion of conventional energy resources presented a need for an alternative reliable source of energy that can readily replace conventional fuels like diesel and petrol. In the current work, biodiesel is synthesized from Karanja oil by using transesterification. The yield is obtained at varying KOH concentrations (1 wt %, 1.5 wt %, 2 wt %), varying molar ratios of methanol: oil (3:1, 4.5:1, 6:1), and varying times (15 min, 30 min, 45 min, 60 min). The optimal conditions from the experiment are obtained as a temperature of 50° C, a reaction time of 45 minutes, a methanol-oil ratio of 4.5:1, and a catalyst concentration of 1.5 %. The viscosity of biodiesel is found to be between 0.036 - 0.038 stokes. The optimum conditions obtained were compared with the statistics available in the literature. The produced biodiesel from Karanja oil conforms to the ASTM D6751 standards. The produced biodiesel is characterized using Fourier Transform Infra Red (FT-IR) Analysis and Gas Chromatography-Mass Spectrometry (GC-MS). Further Artificial Intelligence techniques namely Support Vector Machine, Genetic Algorithm, and Particle Swarm Optimization have been used for predicting the optimum conditions of biodiesel production. The predicted yield with the Support Vector Machine is compared with the yield obtained from experiments. The SVM accurately predicted the experimental results with R2 = 0.999. PSO and GA can effectively be used as a tool for predicting the optimum parameters for biodiesel production.
Keywords: Biodiesel, Genetic Algorithm, Karanja oil, Particle Swarm Optimization, Support Vector Machine, Transesterification -
The stability constants of ion-ionophore complexes can determine the selectivity of ion-selective electrodes. In this study, the quantitative structure-property relationship (QSPR) model was employed to predict the complex stability constants of lead ions with different ionophores. The Genetic algorithm-multiple linear regression method (GA-MLR) developed models based on calculated molecular descriptors. Y-randomization testing, cross-validation, and test set compounds were applied to evaluate the predictive ability of the built model. This built model obtained high statistical quantities (R2train= 0.899, R2adj = 0.877, Q2LOO = 0.831, Q2LOO = 0.776, and Q2boot = 0.780) and showed that GA-MLR was a promising tool to predict the complexation stability of pb2+ with different ionophores. The current study introduces an efficient model for testing and assessing selectophores in lead-selective sensors based on complex stability constants. Additionally, this model could guide the design of highly selective ionophores for Pb (II) sensors.Keywords: Lead-selective electrode, Complex stability constant, QSPR, Genetic algorithm, Multiple linear regression
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Iranian Journal of Chemistry and Chemical Engineering, Volume:40 Issue: 6, Nov-Dec 2021, PP 1985 -1998PRICO process is a promising method for liquefaction of the natural gas which is sometimes used with some optional equipment. Although the PRICO process is widely used in natural gas liquefaction, the configuration leading to the most desirable performance has not been determined. The liquefaction rate and the energy consumption are two important factors to evaluate the performance of the PRICO process. In this study, the PRICO process with five different configurations was simulated and compared. By the means of the multi-objective optimization method, the liquefaction rate and the energy consumption were optimized, simultaneously, for each of the procedures. The five different simulated configurations are simple PRICO process, simple process with the third compressor, simple process with second heat exchanger, simple process with pre-cooling heat exchanger, and full-set process. The optimization results demonstrated that the three-compressor and the full-set processes achieved the maximum liquefaction rate (96.51) and the minimum energy consumption (1219.53 kW), respectively. The economic analysis has also presented and revealed that the three-compressor process had the highest net profit (730.9288 M$/25 years) among the configurations. In other words, the three-compressor process outperformed other configurations with respect to the operation and economics (maximum liquefaction rate of 96.51 and net profit of 730.9288 M$/25 years).Keywords: PRICO Process, Liquefaction, NSGA-II, genetic algorithm, Economic Analysis
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This research presents quantitative structure-activity relationship (QSAR) of half maximal inhibitory concentration (IC50 ) values of 31 different Methotrxate derivatives by employing Multiple linear regression (MLR) and artificial neural networks (ANN), simulated annealing algorithm (SA) and genetic algorithm(GA). Furthermore, CORAL software was used for multiple probability simulation of the studied derivatives. The obtained results from MLR-MLR, MLR-SA, SA-ANN, MLR-GA and GA-ANN approaches were compared and GA-ANN combination showed the best performance according to its correlation coefficient (R2) and mean sum square errors (RMSE). From Monte Carlo simulations, it was found that the presence of double bond, the presence of nitrogen and oxygen, the absence of sulphur and phosphorus and connected sp2 carbon to the ring, are the most important molecular features that affect the biological activity of the drug. It was concluded that the simultaneous application of GA-ANN and Monte Carlo methods can provide a more comprehensive understanding of the relationship between a drug's physicochemical, structural, or theoretical molecular descriptors and its biological activity, leading to accelerate the development of new drugs.
Keywords: QSAR, Methotrexate derivatives, Monte Carlo method, Genetic algorithm -
The activity of the 25 different Carfilzomib derivatives was estimated using multiple linear regression (MLR), artificial neural network (ANN), and genetic algorithm(GA) and simulated annealing algorithm (SA) and Imperialist Competitive Algorithm (ICA) as optimization methods. The obtained results from MLR-MLR, MLR-GA, SA-ANN and GA-ANN techniques were compared and for combinations of modelling-optimization methods observed root mean sum square errors (RMSE) of 0.290, 0.0482, 0.0294, 0.0098 in gas phase, respectively (N=25). A high predictive ability was observed for the MLR-ICA model with the best number of empires/ imperialists (nEmp=50 ) and nEmp=100 with root-mean-sum-squared error (RMSE) of 0.00996 in gas phase. From the MLR-ICA method, it was revealed that RDF 075m, MATS1m, F04[N-O], O-059, F09[C-O] and Mor21p are the most important descriptors. From Monte Carlo simulations, it was found that the presence of double, absence of halogens, oxygen connected to double bond, sp2 carbon connected to double bond, double bond with ring, branching, nitrogen are the most important molecular features affecting the biological activity of the drug. It was concluded that simultaneous utilization of MLR-ICA, GA-ANN and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.
Keywords: Carfilzomib, Antitumor drugs, QSAR, Genetic Algorithm, Monte Carlo method -
Since the selectivity of an ion selective sensor is directly related to the stability constants of ion–ionophore complexes, we predicted the complexation stability (K) of cerium ions with different ionophores by the quantitative structure–property relationship model. Genetic algorithm (GA) feature selection approach was selected to choose the proper molecular descriptors which were then subjected to multiple linear regression (MLR) for prediction of the log K. The predictive ability of the built genetic algorithm-multiple linear regression (GA-MLR) model was evaluated using Leave-one-out cross-validation, Leave-group-out cross-validation, Y-randomization, and test set compounds. Statistical parameters of the model (R2train=0.852, Q2LOO= 0.813, and Q2LGO=0.777) indicated the ability of the GA-MLR model to predict the response of ionophores in cerium-selective sensors based on complex stability constants. Also, the applicability domain of the model was analyzed by the Williams plot. Based on this study, some key features are identifiable to appraise the selectivity of cerium sensors that can be used to design new selectophores.
Keywords: QSPR, Complex stability constant, Cerium-selective electrode, genetic algorithm, Multiple Linear Regression -
In this study, the heat transfer coefficient of the pool boiling is evaluated in the nuclear region for the fluid at different concentrations of water-ethanol solution on a horizontal cylinder at 1 atm. For this purpose is examined the diameter of the growing bubble of water-ethanol solution in a heat flux range of 1 to 60 kW.m-2 in different concentrations on the horizontal cylinder of stainless steel. The results show that by an increase in heat flux, bubble diameter increases. The diameter of the bubbles created in heat flux is examined and compared with different dynamic models that according to the calculated average error of the model. Hamzehkhani model has better consistency with the experimental data. Recently, optimization methods have been widely used in fuzzy equilibrium calculations. Among these methods, genetic algorithms can be used to calculate the binary interaction components of activity coefficient patterns in equilibrium systems. The equations and relations of previous for the solution have a high error in predicting the heat transfer coefficient, so using the obtained data and applying the genetic algorithm. A newer experimental equation is presented which has a good fitting with the experimental data.Keywords: Cylindrical, Genetic Algorithm, heat flux, Pool Boiling, Stainless steel cylinder, Water-ethanol solution
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Iranian Journal of Chemistry and Chemical Engineering, Volume:40 Issue: 3, May-Jun 2021, PP 945 -954
This study aims to estimate the solute transport parameters in saturated porous media using a hybrid algorithm. In this study, the Physical Non-Equilibrium (PNE) model was used to describe the transport of solutes in porous media. A numerical solution for the PNE model is obtained using the Finite Volume Method (FVM) based on the Ttri-Diagonal Matrix Algorithm (TDMA). The developed program, written in Matlab, is capable to solve the advection-dispersion (ADE) and the PNE equations for the mobile -immobile (MIM)model with linear sorption isotherm. The Solute transport parameters, (immobile water content, mass transfer coefficient, and dispersion coefficient), are estimated using different algorithms such as the Levenberg-Marquardt algorithm (LM), genetic algorithm (GA), simulated annealing algorithm (SA). To overcome the limitations of deterministic optimization models which are rather unstable and divergent around a local minimum, a hybrid algorithm (GA+LM, SA+LM) permits to estimate of the solute transport parameters. Numerical solutions are verified using the experiments conducted by Semra (2003) which are about the transport of toluene through a column composed of impregnated Chromosorb grains at ambient temperature (20 °C) for three flow rates (1, 2 and 5ml/min). The results show that the hybrid algorithm (GA+LM, SA+LM) is more accurate than others (GA, SA, and LM). Comparing to the ADE model, The PNE with linear isotherm model gives a better description to the BeakThrough Curves (BTCs) with higher values of determination coefficient (R2 ) and lower values of Root Mean Square Error (RMSE). Also, the solute transport parameters tended to vary with the flow rate.
Keywords: Genetic algorithm, Finite volume method, Levenberg-Marquardt algorithm, Numerical solution, Physical non-equilibrium
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