Artificial Neural Network-genetic Algorithm Approach Research Articles

Ultrasonic-Assisted Extraction of Flavonoids from Juglans mandshurica Maxim.: Artificial Intelligence-Based Optimization, Kinetics Estimation, and Antioxidant Potential.

Ultrasonic-assisted extraction (UAE) of flavonoids (JMBF) from Juglans mandshurica Maxim., an important industrial crop in China, was investigated in the present study. To improve the extraction efficiency of JMBF, suitable UAE was proposed after optimization using a hybrid response surface methodology–artificial neural network–genetic algorithm approach (RSM–ANN–GA). The maximum extraction yield (6.28 mg·g−1) of JMBF was achieved using the following optimum UAE conditions: ethanol concentration, 62%; solid–liquid ratio, 1:20 g·mL−1; ultrasonic power, 228 W; extraction temperature, 60 °C; extraction time, 40 min; total number of extractions, 1. Through the investigation of extraction kinetics, UAE offered a higher saturated concentration (Cs) for JMBF in comparison to traditional solvent extraction (TSE). Scanning electron microscopy (SEM) images showed that deeper holes were generated in J. mandshurica powder under the action of ultrasound, indicating that ultrasound significantly changed the structure of the plant materials to facilitate the dissolution of active substances. Extracts obtained using UAE and TSE were compared by Fourier-transform infrared spectroscopy analysis, the results of which revealed that the functional group of bioactive compounds in the extract was unaffected by the ultrasonication process. Moreover, JMBF was further shown to exhibit significant antioxidant properties in vitro. This study provides a basis for the application of JMBF as a natural antioxidant.

Study on Optimization of BP-GA Method Applied to Shaft Laser Cladding Repairing Technology

Laser cladding technique, which has the characteristics of refining microstructure, minimum deformation and high safety during processing, could enhance the strength of work-piece, shorten the repairing cycle and reduce the restoration cost by roughly sixty percent on shaft repairing. Process parameters optimization plays a key role in repairing quality and cost in laser cladding technique. This paper studied the application of YAG laser cladding technique on shaft repairing and which had important sense to popularize laser cladding technology. Through the experimental research and Artificial Neural Network-Genetic Algorithm approach, this technological process for laser cladding showed the capability to improve efficiency and quality of shaft repairing and reduce repairing cost with guarantee of enough bonding strength and no cracks in restoration area by optimization on key parameters like work-piece rotation speed, defocusing amount, powder stream rate.

Evaluation of material properties using planetary ball milling for modeling the change of particle size distribution in a gas-solid fluidized bed using a hybrid artificial neural network-genetic algorithm approach

This study aims to use planetary ball-milling as an evaluation tool of material properties and the result is subsequently used to develop a model for the change in particle size distribution (PSD) during fluidization for a range of materials using artificial neural network (ANN) method. It is believed that material properties such as hardness, density, brittleness, structure, etc play a crucial role in the particle attrition behavior. Unfortunately, little information on material properties is available, considering the wide variety of materials present. As a result, planetary ball-milling is proposed as a fast assessment technique to identify the properties of different materials. Planetary ball milling devices are readily available in most laboratories and the reduction in PSD can resemble the particle attrition process during fluidization. A Rosin-Rammler (RR) distribution was used to describe the PSD for both fluidization and ball milling processes.

Multi-fold enhancement in sustainable production of biomass, lipids and biodiesel from oleaginous yeast: an artificial neural network-genetic algorithm approach

Implementation of an artificial neural network-genetic algorithm technique for improvement in sustainable production of biomass, lipids and biodiesel from oleaginous yeast.

Hybrid neural network–particle swarm optimization algorithm and neural network–genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy

Aerospace and automobile industries employ aluminium alloys due to its lightweight and excellent resistance to corrosion. As aluminium is highly reflective and highly thermally conductive, it is difficult-to-cut material by laser processing. The quality characteristics of the cut predominantly depend upon the combination of laser processing parameters. The main quality indices for evaluating CO2 laser cutting were surface roughness, kerf width and kerf taper; and the machining parameters considered were laser power, speed and gas pressure. This work suggests hybrid artificial neural network (ANN)–particle swarm optimization (PSO) algorithm and artificial neural network (ANN)–genetic algorithm (GA) to optimize the associated multi-response characteristics during CO2 laser cutting of aluminium 6061 alloys. The results illustrate that the hybrid ANN–GA and ANN–PSO model is an efficient tool for the optimization of process parameters in CO2 laser cutting of difficult-to-cut material—aluminium. From the optimization results, it can be concluded that the proposed ANN–GA approach can be efficiently utilized to optimize the parameters for obtaining minimum roughness, kerf width and kerf taper.

Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach.

Multiplex quantitative polymerase chain reaction (qPCR) has found an increasing range of applications. The construction of a reliable and dynamic mathematical model for multiplex qPCR that analyzes the effects of interactions between variables is therefore especially important. This work aimed to analyze the effects of interactions between variables through response surface method (RSM) for uni- and multiplex qPCR, and further optimize the parameters by constructing two mathematical models via RSM and back-propagation neural network-genetic algorithm (BPNN-GA) respectively. The statistical analysis showed that Mg2+ was the most important factor for both uni- and multiplex qPCR. Dynamic models of uni- and multiplex qPCR could be constructed using both RSM and BPNN-GA methods. But RSM was better than BPNN-GA on prediction performance in terms of the mean absolute error (MAE), the mean square error (MSE) and the Coefficient of Determination (R2). Ultimately, optimal parameters of uni- and multiplex qPCR were determined by RSM.

Photocatalytic activity of g-C3N4: An empirical kinetic model, optimization by neuro-genetic approach and identification of intermediates

The polymeric graphitic carbon nitride (g-C3N4) was synthesized via direct heating of melamine precursor. The obtained solid was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV–vis diffuse reflection spectra (DRS) and Fourier transform infrared (FT-IR). The photocatalytic performance of the synthesized g-C3N4 was investigated by photodegradation of Reactive Black 5 as a model organic pollutant. The removal efficiency of dye over g-C3N4 was yielded 95% after 120min. The effect of operational parameters including pH, catalyst dosage and dye initial concentration was investigated. An artificial neural network-genetic algorithm approach was utilized to find the optimal conditions for achieving maximum degradation efficiency. A nonlinear empirical kinetic model was also developed to predict the first order rate constant (kapp). The photocatalytic degradation intermediates were identified using GC-MS and a probable degradation pathway was proposed.

Modeling the change in particle size distribution in a gas-solid fluidized bed due to particle attrition using a hybrid artificial neural network-genetic algorithm approach

Particle size distribution (PSD) is an important parameter in gas-solid fluidized bed. The change in PSD due to particle attrition can affect the long-term performance of fluidized bed. In this study, artificial neural network (ANN) with genetic algorithm (GA) as a meta-modeling tool was employed to model the change in PSD during fluidization. Experiments were conducted using incineration bottom ash (IBA) as the fluidizing particles and different mass percentage of large and small glass beads were used as the grinding medium. Rosin–Rammler (RR) distribution was used to describe the IBA PSD. The ANN-GA models developed were subsequently used to study the effect of fluidization time, mass percentage of glass beads and size of glass beads used on the IBA particle attrition during fluidization.

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box–Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand–metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model–genetic algorithm (RSM–GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47μg/g) is facilitated at 30.22mg C/L of EtOH with initial As(III) concentration of 196.77μg/L at pH5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM.

Batch growth of Kluyveromyces lactis cells from deproteinized whey: Response surface methodology versus Artificial neural network—Genetic algorithm approach

Deproteinized cheese making whey (CMW) was investigated as an alternative medium for the production of Kluyveromyces lactis as single-cell protein. Batch runs were performed according to a Full Factorial Design (FFD) on CMW supplemented with yeast extract, magnesium sulfate and ammonium sulfate in different concentrations. These independent variables were tested in duplicate at three levels, while dry biomass productivity was used as the response. The results were used to construct two models, one based on Response surface methodology (RSM) and another on Artificial neural network (ANN). Two different training methods (10-fold cross validation and training/testing) were utilized to obtain two different network architectures, while a Genetic algorithm was utilized to obtain optimal concentrations of the above medium components. A quadratic regression by RSM (R2=0.840) was the best modeling and optimization tool under the specific conditions selected here. The highest biomass productivity (approximately 2.14gDW/Lh) was ensured by the following optimal levels: 7.04–9.99% (w/v) yeast extract, 0.430–0.503% (w/v) magnesium sulfate and 4.0% (w/v) ammonium sulfate. These results demonstrate the feasibility of using CMW as an interesting alternative to produce single-cell protein.

Application of a hybrid artificial neural network–genetic algorithm approach to optimize the lead ions removal from aqueous solutions using intercalated tartrate-Mg–Al layered double hydroxides

The aim of this work was to develop an eco-friendly adsorbent to remove the lead ions from aqueous solutions. The present study uses the artificial neural network (ANN) and genetic algorithm (GA) as tools for simulation and optimization of the lead ions removal from aqueous solutions using intercalated tartrate-Mg–Al layered double hydroxides as an adsorbent. A multi-layer ANN model trained with Levenberg–Marquardt (LM) algorithm was incorporated for developing a predictive model. The input variables of the model were time, solution pH, adsorbent dosage, and lead ion concentration, while the percentage of lead ions removal was the output. The performance of the ANN model was determined and showed the efficiency of the model to predict the percentage of lead ions removal accurately with determination coefficient (R2) of 0.99986. The developed ANN model was then used with GA to optimize the percentage of lead ions removal from aqueous solution. The optimized conditions were obtained as follows: 11.8h, 4.5, 0.054g and 10.0mg/L for the time, solution pH, adsorbent dosage, and lead ion concentration, respectively. Under these optimum conditions, the predicted and actual percentages of lead ions removal were 101.2 and 98.7%, respectively.

ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler

An artificial neural network (ANN) and genetic algorithm (GA) approach to predict NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas, is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature, and nozzle tilt were studied. The data collected from parametric field experiments was used to build a feed-forward back-propagation neural net. The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load conditions and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in GA. The genetic search was used to find the optimum level of input operating conditions corresponding to low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.

On-capillary derivatization using a hybrid artificial neural network-genetic algorithm approach

The first reported hybrid artificial neural network-genetic algorithm (ANN-GA) approach for the optimization of on-capillary dipeptide derivatization is presented. More specifically, genetic optimization proved valuable in the determination of effective network structure with three defined parameter inputs: (i) phthalic anhydride injection volume, (ii) time of injection, and (iii) voltage, for the maximum conversion of the dipeptide D-Ala-D-Ala by phthalic anhydride. Results obtained from the hybrid approach proved superior to an ANN model without GA optimization in terms of training data and predictive ability. The model developed will likely prove useful for the analysis of other organic-based reaction systems.

Artificial Neural Network-Genetic Algorithm Approach to Optimize Media Constituents for Enhancing Lipase Production by a Soil Microorganism

Results of lipase production by a soil microorganism, expressed in terms of lipolytic activities of the culture were modeled and optimized using artificial neural network (ANN) and genetic algorithm (GA) techniques, respectively. ANN model, developed based on back propagation algorithm, were highly accurate in predicting the system with coefficient of determination (R2) value being close to 0.99. Optimization using GA, based on the ANN model developed, resulted in the following values of the media constituents: 9.991 ml/l oil, 0.100 g/l MgSO4 and 0.009 g/l FeSO4. And a maximum value of 7.69 U/ml of lipolytic activity at 72 h of culture was obtained using the ANN-GA method, which was found to be 8.8% higher than the maximum values predicted by a statistical regression-based optimization technique-response surface methodology.