Artificial Neural Network-genetic Algorithm Model Research Articles

Modeling and Optimization of the Culture Medium for Efficient 4′-N-Demethyl-Vicenistatin Production by Streptomyces parvus Using Response Surface Methodology and Artificial-Neural-Network-Genetic-Algorithm

4′-N-demethyl-vicenistatin is a vicenistatin analogue that has better antitumor activity with promising applications in the pharmaceuticals industry. The harnessing of the complete potential of this compound necessitates a systematic optimization of the culture medium to enable the cost-effective production of 4′-N-demethyl-vicenistatin by Streptomyces parvus SCSIO Mla-L010/ΔvicG. Therefore, in this study, a sequential approach was employed to screen the significant medium compositions, as follows: one-factor-at-a-time (OFAT) and Plackett–Burman designs (PBD) were initially utilized. Cassava starch, glycerol, and seawater salt were identified as the pivotal components influencing 4′-N-demethyl-vicenistatin production. To further investigate the direct and interactive effects of these key components, a three-factor, five-level central composite design (CCD) was implemented. Finally, response surface methodology (RSM) and an artificial-neural-network-genetic-algorithm (ANN-GA) were employed for the modeling and optimization of the medium components to enhance efficient 4′-N-demethyl-vicenistatin production. The ANN-GA model showed superior reliability, achieving the most 4′-N-demethyl-vicenistatin, at 0.1921 g/L, which was 17% and 283% higher than the RSM-optimized and initial medium approaches, respectively. This study represents pioneering work on statistically guided optimization strategies for enhancing 4′-N-demethyl-vicenistatin production through medium optimization.

A Comparative Study on the Estimation of Wind Speed and Wind Power Density Using Statistical Distribution Approaches and Artificial Neural Network-Based Hybrid Techniques in Çanakkale, Türkiye

In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in Çanakkale Province, located in the northwest of Türkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network–genetic algorithm (ANN-GA) and ANN–particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model.

Comparison of an Ultrasound-Assisted Aqueous Two-Phase System Extraction of Anthocyanins from Pomegranate Pomaces by Utilizing the Artificial Neural Network-Genetic Algorithm and Response Surface Methodology Models.

As a by-product of pomegranate processing, the recycling and reuse of pomegranate pomaces (PPs) were crucial to environmentally sustainable development. Ultrasound-assisted aqueous two-phase extraction (UA-ATPE) was applied to extract the anthocyanins (ACNs) from PPs in this study, and the central composite design response surface methodology (CCD-RSM) and artificial neural network-genetic algorithm (ANN-GA) models were utilized to optimize the extraction parameters and achieve the best yield. The results indicated that the ANN-GA model built for the ACN yield had a greater degree of fit and accuracy than the RSM model. The ideal model process parameters were optimized to have a liquid-solid ratio of 49.0 mL/g, an ethanol concentration of 28 g/100 g, an ultrasonic time of 27 min, and an ultrasonic power of 330 W, with a maximum value of 86.98% for the anticipated ACN yield. The experimental maximum value was 87.82%, which was within the 95% confidence interval. A total of six ACNs from PPs were identified by utilizing UHPLC-ESI-HRMS/MS, with the maximum content of cyanidin-3-O-glucoside being 57.01 ± 1.36 mg/g DW. Therefore, this study has positive significance for exploring the potential value of more by-products and obtaining good ecological and economic benefits in the future.

Artificial intelligence model for prediction of cardiovascular disease: An empirical study

Cardiovascular disease (CVD) is a disease related to the heart and blood vessels. Prediction of CVD is essential for early detection and diagnosis, which is however compounded by the complex interplay between medical history, physical examination outcomes, and imaging results. While the existing automated systems are fraught with the usage of irrelevant and redundant attributes, artificial intelligence (AI) helps in the identification of potential CVD populations by prediction models. This work aims at developing an AI model for predicting CVD using different classifications of machine learning techniques. The CVD dataset was obtained from the UCI repository containing about 76 cardiac attributes for training in various machine learning models, which include a hybrid of artificial neural network-genetic algorithm (ANN-GA), artificial neural network, support vector machine (SVM), K-means, K-nearest neighbor (KNN), and decision tree (DT). The performance of the models was measured in terms of accuracy, means square error, sensitivity, specificity, and precision. The results showed that the hybrid model of ANN-GA performs better with an accuracy of 86.4%, compared to the SVM, K-means, KNN, and DT measured at 84.0%, 59.6%, 79.0%, and 77.8%, respectively. It was observed that the system performs better as the number of datasets increases in the database, with a fewer selection of attributes using genetic algorithm for selection. Thus, the ANN-GA model is recommended for CVD prediction and diagnosis.

Process optimization of microwave-assisted aqueous extraction of tannins and saponins from Malabar Spinach (Basella alba) leaves using ANN-GA and RSM methodology

The effect of microwave-assisted aqueous extraction (MAE) conditions on the recovery and quantification of tannins and saponins from Basella alba leaves has been explored. The experiment was carried out according to the Box-Behnken experimental design of response surface methodology (RSM) with 17 runs. The independent variables of the study were microwave power (100 to 300 W), treatment time (5 to 15 min), and feed/solvent ratio (0.025 to 0.05). The effect of extraction conditions on the response variables, total condensed tannin, total hydrolyzed tannin, and total saponin content, were predicted using RSM and artificial neural network-genetic algorithm (ANN-GA) methodology. The extracted values of total condensed tannin, total hydrolyzed tannin, and total saponin content, using RSM and ANN-GA were compared and evaluated for goodness of fit. The predicted values using RSM and ANN-GA models for total condensed tannin were found to be 0.18 and 0.26 mg TAE g−1, total hydrolyzed tannin were 0.14 and 0.15 mg TAE g−1, and total saponin content were 1.66 and 1.49 mg DE g−1, respectively. When compared to RSM, ANN-GA demonstrated higher value of R2 and lower values of other statistical parameters. Additionally, the predicted value of ANN-GA was more closely aligned with the experimental value.

Tertiary treatment using ultrafiltration in an existing sewage treatment plant for industrial reuse: a modelling approach using artificial neural network with uncertainty estimation

Abstract Navi Mumbai Municipal Corporation of Maharashtra state, India, unified a tertiary treatment plant (TTP) of 20 × 103 m3/day capacity with ultrafiltration technology in an existing Koparkhairane sewage treatment plant (STP) for producing effluent quality usable for industrial purposes. As prior art, an artificial neural network-genetic algorithm (ANN-GA) along with uncertainty estimation using prediction interval is employed to model secondary treated effluent (STE) flow rate (QT) and other quality parameters such as biochemical oxygen demand, chemical oxygen demand, and total suspended solids (TSS) to conclude the reliability of the range in which the input available to TTP. ANN-GA model provides a coefficient of determination above 0.90 for all STE parameters modelled other than TSS. Inferring that a good quantity and quality of 20 × 103 m3/day STP treated water is currently available, where a decreasing trend of QT is also noticed and highlighted. Further, the Wilcoxon signed-rank test on the quality parameter of effluent TTP for industrial reuse standard infers TSS shows infringement during the initial period but started adhering to standards over time. The research delineates at the outset of exploring water reuse policy in India, emphasizing Maharashtra state, modelling STE using ANN-GA and performance evaluation of TTP.

Modeling and optimization of photo-fermentation biohydrogen production from co-substrates basing on response surface methodology and artificial neural network integrated genetic algorithm

The main aim of the present study was to establish a relationship model between bio-hydrogen yield and the key operating parameters affecting photo-fermentation hydrogen production (PFHP) from co-substrates. Central composite design-response surface methodology (CCD-RSM) and artificial neural network-genetic algorithm (ANN-GA) models were used to optimize the hydrogen production performance from co-substrates. Compared to CCD-RSM, the ANN-GA had higher determination coefficient (R2 = 0.9785) and lower mean square error (MSE = 9.87), average percentage deviation (APD = 2.72) and error (4.3%), indicating the ANN-GA was more suitable, reliable and accurate in predicting biohydrogen yield from co-substrates by PFHP. The highest biohydrogen yield (99.09 mL/g) predicted by the ANN-GA model at substrate concentration 35.62 g/L, temperature 30.94 °C, initial pH 7.49 and inoculation ratio 32.98 %(v/v), which was 4.20 % higher than the CCD-RSM model (95.10 mL/g).

Sb (III) Removal from Aqueous Solutions by the Mesoporous Fe3O4/GO Nanocomposites: Modeling and Optimization Using Artificial Intelligence

The mesoporous graphene oxide-supported ferroferric oxide (Fe 3 O 4 /GO) nanocomposites (the average size of 30.08 nm) were controllably synthesized in the present study. The successful in situ growth of Fe 3 O 4 nanoparticles on GO surface was ascribed to the oxygen-containing groups on GO. The magnetic separation was employed for Sb(III) removal from aqueous solutions and artificial intelligence techniques were adopted to reduce the number and cost of experiments, in order to render these nanocomposites of a practical value. The three methods, including response surface methodology (RSM), artificial neural network-genetic algorithm (ANN-GA) and artificial neural network-particle swarm optimization (ANN-PSO), were used to model and optimize the removal of Sb(III) from aqueous solutions. These three models were evaluated based on correlation coefficient (R 2 ) and mean squared error (MSE). The higher R 2 value and lower MSE of ANN-GA demonstrated the superiority of ANN-GA model over ANN-PSO and RSM models. Analysis of variance, gradient boosted regression trees (GBRT) and Garson method exhibited that contact time was the most influential variable for the Sb(III) removal. Fitting of isotherm data showed that the removal process was controlled by the monolayer adsorption on a homogeneous surface based on the values of R 2 , x 2 , sum of absolute errors (SAE) and average percentage errors (APE). The adsorption process followed the pseudo-second-order model, which was spontaneous and entropy-driven. It was observed that the adsorption process was accompanied with the redox reaction based on the XPS analysis. The regeneration experiments showed that the mesoporous Fe 3 O 4 /GO nanocomposites are an effective and reusable adsorbent within four regeneration cycles.

Modeling and optimization of the hydrolysis and acidification via liquid fraction of digestate from corn straw by response surface methodology and artificial neural network

This study investigated liquid fraction of digestate (LFD) assisted hydrolysis and acidification (HA) of corn straw (CS) for enhanced biogas production. Response surface methodology-central composite design (RSM-CCD) and artificial neural network-genetic algorithm (ANN-GA) tools were used to optimize the acidogenic performance. Optimized performance was obtained at the hydraulic retention time of 8 d, temperature of 41 °C, the ratio of substrate and inoculum of 4, and pH 10. As a result of combining LFD and HA modification, performance in anaerobic digestion was improved. The highest observed methane yield under the optimal condition was 305.6 mL g−1·TS−1, which was 92.3% higher than the control group. In comparison to the RSM model (R2 = 0.921, RMSE = 20.3), the ANN-GA model indicated a higher R2 and a lower RSME value (R2 = 0.965, RMSE = 13.4), which demonstrated its advantages in determining the non-linear behavior of corn straw anaerobic digestion. The supplementation of LFD along with HA can be useful for enhancing the VFAs yield and bioenergy recovery from CS.

Production of Blending Quality Bioethanol from Broken Rice: Optimization of Process Parameters and Kinetic Modeling.

The enzymatic and bio-enzymatic saccharification of waste broken rice (79.8% of starch) was successfully carried out to produce a reducible sugar solution (160g/L). Bioethanol of concentration 71.2g/L (9.0% v/v) was prepared by fermentation of reducing sugar solution, using the commercially available waste brewer's yeast (Saccharomyces cerevisiae). The fermentation process parameters were optimized through response surface methodology (RSM) and hybrid artificial neural network-genetic algorithm (ANN-GA) for optimizing the ethanol concentration. The hybrid ANN-GA model predicted a maximum concentration of 71.9g/L with a deviation of only 0.97% from the experimental value (71.2g/L). Four different kinetic models were attempted to fit the experimental time evolution of concentrations with the kinetic parameters estimated by the Levenberg-Marquardt optimization technique. The 4th order Runge-Kutta algorithm was implemented through a C program module. The accuracy of each model was checked against coefficient of determination R2, adjusted R2, the absolute mean deviation (AMD), and root mean square deviation (RMSD). The Andrew-Levenspiel kinetics produced the best performance criteria at two initial substrate concentrations of 160 and 170g/L. Finally, the FTIR analysis of 781.2g/L (98.5% v/v) bioethanol (concentrated by two-stage vacuum distillation followed by treatment with 3A molecular sieve) showed a favorable blending possibility with the commercial gasoline (petrol) as a green fuel.

Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions

This work proposes a new method for estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions based on a parameterization method and a genetic algorithm–artificial neural network (GA-ANN) algorithm. The new method establishes a GA-ANN model based on simulated data, and then combines MODIS satellite data and ERA5 reanalysis data to estimate the DSLR. According to the validation results of the field sites, the bias and RMSE are –9.18 and 34.88 W/m2, respectively. Compared with the existing research, the new method can achieve reasonable accuracy. Parameter analysis using independently simulated data shows that the near-surface air temperature (Ta) and cloud base height (CBH) have an important influence on DSLR estimation under cloudy-sky conditions. With an increase in CBH, DSLR gradually decreases; however, with an increase in Ta, DSLR shows a trend of gradual increase. When estimating DSLR under cloudy-sky conditions, under the influence of clouds, except for cirrus, the change in DSLRs with CBH and Ta is greater than 20 W/m2.

Microwave vacuum drying of pomegranate peel: Evaluation of specific energy consumption and quality attributes by response surface methodology and artificial neural network

Microwave vacuum drying of pomegranate peel was studied through this work and the drying process was modeled with the aid of response surface methodology (RSM) and artificial neural network (ANN) method. The drying experimental runs were performed on varied ranges of microwave power (175, 330, and 485 W) and vacuum pressure (10, 15, and 20 kPa) using a face-centered composite design. The influence of process parameters on five target responses, namely: total hydrolysable tannin (THT), color change (ΔE), maximum temperature evolved (Tmax), effective moisture diffusivity (Deff), and specific energy consumption (SEC) were analyzed. For microwave power and vacuum, the optimal conditions attained with aid of RSM and ANN were 260 W, 15.59 kPa, and 287 W, 19 kPa, respectively. The THT retention, ΔE, Tmax, Deff, SEC determined with RSM and ANN optimized values were 78.51 mg TAE/g of DW, 13.48, 67.02°C, 3.38 × 10−6 m2/s, 20.02 kWh/kg and 90.32 ± 0.35 mg TAE/g of DW, 12.488, 64.83°C, 5.64 × 10−6 m2/s, 20.01 ± 0.33 kWh/kg, respectively. The influence analysis explained that pomegranate peel dried at optimized ANN conditions of microwave power 287 W and vacuum 19 kPa possessed improved physicochemical properties and was efficient in terms of energy consumption compared to peel dried using RSM suggested conditions. Furthermore, the major criterion for evaluation between RSM and ANN tools in this study was the goodness of fit. The value for R2 produced via RSM varied between 0.8354 and 0.9870, whereas subsequent R2 in the case of ANN was considerably greater, varying between 0.9213 and 0.9956. The mean square error value displayed the minimum value in the case of ANN compared to the RSM model. These signified rather greater exactness and predictive capability of the ANN tool. Fourier transform infrared analysis established the existence of peaks between 3,308.92 and 908.89 cm−1 and high-performance liquid chromatography showed that pomegranate peel produced using microwave vacuum drying contains a good amount of gallic acid and tannic acid derivative compounds. Scanning electron microscopy elucidated that the combined microwave vacuum effect resulted in the formation of bigger pores on the peel surface ensuing in elevated extraction of THT at reduced energy consumption. Practical applications Shifting into a bio-economy is unrealistic with no proficient use of agricultural by-products and food waste. Microwave vacuum drying is a novel technique that would be applied to preserve the product bioactive components while drying. This study has established that drying of waste pomegranate peel using microwave vacuum dryer aided in excellent recovery rate of bioactive compounds particularly hydrolysable tannin (strong antioxidant and antimicrobial agent). This promises pomegranate peel powder obtained using microwave vacuum drying to be utilized in functional and nutraceutical food processing industries as a prospective resource of antimicrobial and antioxidant compounds. The findings also identified that the ANN-GA (artificial neural network–genetic algorithm) model would be effective for modeling the microwave vacuum drying method of pomegranate peel with preservation of peel quality. The optimal process values attained using coupled ANN-GA tool could be applied with respect to microwave vacuum-based industrial dryers to achieve the objective of drying at a considerably quicker rate.

Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique

Background and objectiveCement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed (CDE) subjects using quantitative computed tomography-based airway structural and functional features. MethodsWe obtained the airway features in five central and five sub-grouped segmental regions and the lung features in five lobar regions and one total lung region from 311 CDE and 298 non-CDE (NCDE) subjects. The five-fold cross-validation method was adopted to train the following classification models:ANN, support vector machine (SVM), logistic regression (LR), and decision tree (DT). For all the classification models, linear discriminant analysis (LDA) and genetic algorithm (GA) were applied for dimensional reduction and hyperparameterization, respectively. The ANN model without LDA was also optimized by the GA method to observe the effect of the dimensional reduction. ResultsThe genetically optimized ANN model without the LDA method was the best in terms of the classification accuracy. The accuracy, sensitivity, and specificity of the GA–ANN model with four layers were greater than those of the other classification models (i.e., ANN, SVM, LR, and DT using LDA and GA methods) in the five-fold cross-validation. The average values of accuracy, sensitivity, and specificity for the five-fold cross-validation were 97.0%, 98.7%, and 98.6%, respectively. ConclusionsWe demonstrated herein that a quantitative computed tomography-based ANN model could more effectively detect CDE subjects when compared to their counterpart models. By employing the model, the CDE subjects may be identified early for therapeutic intervention.

The Influence of Xanthan and Balangu Seed Gums Coats on the Kinetics of Infrared Drying of Apricot Slices: GA-ANN and ANFIS Modeling

ABSTRACT Polysaccharide-based edible coating can be useful as a pretreatment for drying since it prevents the oxidation of nutritional compounds, thereby improving the quality of dried products. In this study, the effects of polysaccharide coating (xanthan and balangu seed gums) on the drying kinetics of apricot slices were investigated. In addition, genetic algorithm–artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used for prediction of drying time (DT) and moisture content (MC) of coated apricot slices in an infrared (IR) dryer. The GA-ANN and ANFIS were fed with two inputs of IR radiation intensity (150, 250, and 375 W) and the distance of slices from lamp surface (5, 7.5, and 10 cm) for prediction of average DT. Also, to predict the MC, these models were fed with three inputs of IR power, lamp distance, and treatment time. The developed GA-ANN, which included seven hidden neurons, predicted the DT of apricot slices with a correlation coefficient (r) of 0.970. Also, the GA-ANN model with nine neurons in one hidden layer predicts the MC with a high r-value (r = 0.999). The calculated r-values for prediction of DT and MC using the ANFIS-based subtractive clustering algorithm were 0.986 and 0.999, respectively, which showed a high correlation between predicted and experimental values. Sensitivity analysis results showed that IR intensity and treatment time were the most sensitive factors for prediction of DT and MC of coated apricot slices, respectively. Both GA-ANN and ANFIS models’ predictions agreed well with testing data sets, and they could be useful for understanding and controlling the factors affecting on drying kinetics of apricot slices in an IR dryer.

Optimization of process parameters for bio-enzymatic and enzymatic saccharification of waste broken rice for ethanol production using response surface methodology and artificial neural network-genetic algorithm.

Reducible sugar solution has been produced from waste broken rice by a novel saccharification process using a combination of bio-enzyme (bakhar) and commercial enzyme (α-amylase). The reducible sugar solution thus produced is a promising raw material for the production of bioethanol using the fermentation process. Response surface methodology (RSM) and Artificial neural network-genetic algorithm (ANN-GA) have been used separately to optimize the multivariable process parameters for maximum yield of the total reducing sugar (TRS) in saccharification process. The maximum yield (0.704g/g) of TRS is predicted by the ANN-GA model at a temperature of 93°C, saccharification time of 250min, 6.5 pH and 1.25mL/kg of enzyme dosages, while the RSM predicts the maximum yield of 0.7025g/g at a little different process conditions. The fresh experimental validation of the said model predictions by ANN-GA and RSM is found to be satisfactory with the relative mean error of 2.4% and 3.8% and coefficients of determination of 0.997 and 0.996.

Process optimization of catalytic steam reforming of toluene to hydrogen using response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA)

Catalytic steam reforming of toluene (SRT) over nickel-cobalt supported on modified activated carbon for hydrogen production has been investigated. The center composite design of experiment in response surface methodology (RSM) was initially applied to optimize the catalytic SRT for hydrogen production before being utilized in the model building of the hybrid artificial neural network-genetic algorithm (ANN-GA). The genetic algorithm was carried out over the ANN model to achieve the maximum target response. The process optimization modeling using the best fitness function gave an insight of the optimal operating condition in SRT over the prepared catalyst. The results conferred that maximum hydrogen yield could be obtained at the optimal conditions of 700 °C temperature, 0.034 ml/min feed flow rate, 0.1 g catalyst loading and S/C ratio of 1 by ANN-GA model, and 762 °C temperature, 0.022 ml/min feed flow rate, 0.3 g catalyst loading and S/C ratio of 5.6 by the RSM model. Predicted results from ANN model were in higher agreement with the experimental data at R2=0.95 compared with the RSM model.

Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships

Linear and non-linear quantitative structure–property relationship (QSPR) models were developed to predict corrosion inhibition efficiency for a series of 41 pyridine and quinoline N-heterocycles. Out of 20 physicochemical and quantum chemical variables related to the surface adsorption behaviour of the inhibitors, consensus models were constructed using the genetic algorithm-partial least squares (GA-PLS) and genetic algorithm-artificial neural network (GA-ANN) methods. The consensus GA-PLS model comprised of eight variables (exponential of the calculated adsorption energy, LUMO, van der Waals surface area and volume, polarizability, electron affinity, electrophilicity, electron donor capacity) exhibited an %RMSECV of 16.5% and mean %RMSE of 14.9%. Such a model moderately captured the complex relationships between inhibition efficiency and the quantum chemical variables. The consensus GA-ANN model comprised of nine input variables (exponential of the calculated adsorption energy, HOMO, LUMO, HOMO-LUMO Gap, electronegativity, softness, electrophilicity, electron donor capacity and N atomic charge) exhibited an %RMSECV of 16.7 ± 2.3% and mean %RMSE (training/testing/validation) of 8.8%, performing better than its linear counterpart in terms of predictive ability. Both models revealed the importance of adsorption to the metal surface, and electronic parameters quantifying electron acceptance/donation to/from the iron surface, suggesting key corrosion inhibition design principles.

Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm)

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

Experimental and modeling investigation of adsorption equilibrium of CH4, CO2, and N2 on activated carbon and prediction of multi-component adsorption equilibrium

In this study, the equilibrium adsorption isotherms of N2, CH4 and CO2 on two types of activated carbon adsorbents were measured. The experimental data of the equilibrium adsorption were fitted to the Sips isotherm model at different temperatures. It was found that the Sips model is appropriate to predict the behavior of gas adsorption. Moreover, a method based on the Gibbs energy of desorption of the solid adsorbent was used to estimate the amount of separation factor of binary mixtures at different temperatures. Also, the amount of gas adsorption on activated carbon was predicted by using the artificial neural network-genetic algorithm (ANN-GA) model. The results of the ANN-GA model indicated that the performance of the model for predicting the behavior of gas adsorption is acceptable. Finally, the extended Sips model was used to simulate the multi-component adsorption of three binary mixtures CO2/N2, CH4/N2 and CO2/CH4, and the results of this model were validated by experimental data.

A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

This study aims to identify the suitability of hybridizing the firefly algorithm (FA), genetic algorithm (GA), and particle swarm optimization (PSO) with two well-known data-driven models of support vector regression (SVR) and artificial neural network (ANN) to predict blast-induced ground vibration. Here, these combinations are abbreviated using FA–SVR, PSO–SVR, GA–SVR, FA–ANN, PSO–ANN, and GA–ANN models. In addition, a modified FA (MFA) combined with SVR model is also proposed in this study, namely, MFA–SVR. The feasibility of the proposed models is examined using a case study, located in Johor, Malaysia. Then, to provide an objective assessment of performances of the predictive models, their results were compared based on several well known and popular statistical criteria. According to the results, the MFA–SVR with the coefficient of determination (R2) of 0.984 and root mean square error (RMSE) of 0.614 was more accurate model to predict PPV than the PSO–SVR with R2 = 0.977 and RMSE = 0.725, the FA–SVR with R2 = 0.964 and RMSE = 0.923, the GA–SVR with R2 = 0.957 and RMSE = 1.016, the GA–ANN with R2 = 0.936 and RMSE = 1.252, the FA–ANN with R2 = 0.925 and RMSE = 1.368, and the PSO–ANN with R2 = 0.924 and RMSE = 1.366. Consequently, the MFA–SVR model can be sufficiently employed in estimating the ground vibration, and has the capacity to generalize.