Artificial Neural Network Genetic Algorithm Research Articles

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.

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.

Fabrication, characterization, and photocatalytic degradation potential of chitosan-conjugated manganese magnetic nano-biocomposite for emerging dye pollutants

The release of toxic dyes from different industries through industrial effluents cause hazardous effects to human and aquatic life. Therefore, the detoxification of such toxic pollutants is very important for a clean environment. In this regard, chitosan conjugated Mn-magnetic nano-biocomposite (Mn-CCMN) was synthesized, characterized, and applied for the photocatalytic decontamination of crystal violet (CV) dye in the contaminated aqueous system and industrial wastewater. The characterization was performed using SEM, XRD, EDX and FTIR spectroscopic techniques and determine PZC. The data shows the successfully synthesis of the resultant nano-biocomposite with amorphous nature and Fe and Mn were present therein. The SEM study revealed the flat and irregular shaped structure with porous surface and 87 ± 4 nm as mean particle size. The application of as-synthesized nano-biocomposite was determined as a photocatalyst for the eradication of CV in the aqueous environment under UV light illumination. The different photocatalysis conditions were studied for maximum degradation of CV and under the best experimental factors, tremendous CV degradation was attained in the real samples and industrial wastewater. The synthesized Mn-CCMN sustain excellent photocatalytic performance for four successive batches. The photocatalytic data was fitted to the pseudo-first-order kinetic model (R2 = 0.992) having 0.007672 min−1 and 0.0549 ppm/min as k and reaction rate, respectively. The statistical models such as response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) were also applied for confirmation of the experimental data and the results showed a good agreement. It is deduced that the synthesized Mn-CCMN could be an excellent alternative to the present photocatalysts for the decontamination of toxic organic dyes from contaminated water and industrial wastewater.

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.

Two integrated conceptual–wavelet-based data-driven model approaches for daily rainfall–runoff modelling

Abstract Rainfall–runoff modelling is crucial for enhancing the effectiveness and sustainability of water resources. Conceptual models can have difficulties, such as coping with nonlinearity and needing more data, whereas data-driven models can be deprived of reflecting the physical process of the basin. In this regard, two hybrid model approaches, namely Génie Rural à 4 paramètres Journalier (GR4 J)–wavelet-based data-driven models (i.e., wavelet-based genetic algorithm–artificial neural network (WGANN); GR4 J–WGANN1 and GR4 J–WGANN2), were implemented to improve daily rainfall–runoff modelling. The novel GR4 J–WGANN1 hybrid model includes the outflow (QR) and direct flow (QD) obtained from the GR4 J model, and the GR4 J–WGANN2 hybrid model includes the soil moisture index (SMI) obtained from the GR4 J model as input data. In hybrid models, wavelet analysis and the Boruta algorithm were implemented to decompose input data and select wavelet components. Four gauging stations in the Eastern Black Sea and Kızılırmak basins in Turkey were used to observe modelling performance. The GR4 J model exhibited poor performance for extreme flow forecasting. The novel GR4 J–WGANN1 approach performed better than the GR4 J–WGANN2 model, and the hybrid models improved modelling performance up to 40% compared to the GR4 J model. In this regard, integrated conceptual–wavelet-based data-driven models can be useful for improving the conceptual model performance, especially regarding extreme flow forecasting.

Data-driven recognition and modelling of deterioration patterns in the US National Bridge Inventory: A genetic algorithm-artificial neural network framework

Preserving infrastructures in a functioning and safe state requires maintenance or rehabilitation actions for the structures’ future needs. Thus, reliable estimations of deterioration rates are necessary for prioritizing structural interventions and assisting decision-makers in optimally allocating budgets. Among various approaches employed to model structural deterioration, statistical modelling using infrastructure databases is an increasingly utilized practice. Information contained within infrastructure databases usually concerns structural and operational attributes of the inventoried structures. Incorporating additional deterioration factors (e.g. environmental conditions) requires extracting content from other data sources. This process may result in large databases that entail complex nonlinear relationships among the included factors. The use of expert judgment or performing a subjective selection of deterioration factors from a small pre-specified set of factors has led to the generation of more manageable data collections, facilitating information processing. This study proposes an unbiased data-driven framework that combines Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) to simultaneously: (a) optimize the architecture of a suitable ANN model predicting infrastructure condition status and (b) perform a feature selection of essential deterioration factors among explanatory variables contained in the data collection used. The combined GA-ANN framework is applied to a dataset for 223,369 bridge structures located across the conterminous US region, which includes 53 variables with data from the National Bridge Inventory (NBI) and other reliable data sources. The results of the framework's application are assessed for their accuracy, efficiency and relevance, showing that artificial intelligence-based pattern recognition renders promising prospects for objectively capturing essential bridge deterioration information.

Process optimization of cypermethrin biodegradation by regression analysis and parametric modeling along with biochemical degradation pathway.

Pyrethroid pesticides are of great environmental and health concern with regard to neurotoxicity and ubiquitous occurrence. Here, we reported a new bacterial strain identified as Bacillus cereus AKAD 3-1 that degraded 88.1% of 50mg/l of cypermethrin in an aqueous medium. The biodegradation of cypermethrin was optimized by CCD (central composite design) and validated by ANN-GA (artificial neural network-genetic algorithm). Both the approaches proved to possess good performance in modeling and optimizing the growth conditions. Results indicated that the process variables have a significant (< 0.0001) impact on cypermethrin biodegradation. Moreover, the predicted CCD model had a "lack of fit p-value" of "0.9975." The optimum CCD and ANN model had an R2 value of 0.9703 and 0.9907, indicating that the two models' experimental and predicted values are closely fitted. The isolate successfully converted cypermethrin to CO2 and phenol without producing any toxic metabolite. Finally, a degradation pathway was proposed with the intermediate compounds identified by GC-MS. The present study highlights an important potential application of strain AKAD 3-1 for the in situ bioremediation of cypermethrin-contaminated environments.

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.

Macromolecules assessment from spent biomass during phycoremediation of pollutants from coke-oven wastewater: A prospective approach for production of value added products

Three cyanobacterial and microalgal cultures such as consortium of Leptolyngbya sp. and Planktothrix sp. (Type I), Tetraspora sp. NITD 18 (Type II), and consortium of Chlorella sp. and Synechococcus sp. (type III) were chosen for the growth in both BG-11 and synthetic coke-oven wastewater (STCW). Maximum productivities of protein (75.63 (mg/L)/day, lipid (13.96 (mg/L)/day) were obtained in BG-11 medium for culture type II and that for carbohydrate (86.25 (mg/L)/day) was obtained with Type I. However, in STCW, maximum productivities were obtained as carbohydrate: 75.55 (mg/L)/day (Type III), protein: 57.67 (mg/L)/day (Type III), and lipid: 12.51 (mg/L)/day (Type I). Maximum yield was obtained as follows: carbohydrate - 146.47 mg/g and 122.26 mg/g (Type II), protein - 435.24 mg/g and 537.05 mg/g (Type II), and lipid - 111.45 mg/g and 217.47 mg/g (Type I) in BG-11 and STCW solutions, respectively. Artificial Neural Network-Genetic algorithm (ANN-GA) was used for modeling and optimization to get maximum outputs. The aim is selection of the suitable culture for the production of macromolecules under naïve and stressed environment.

High-Efficiency Mitigation of Nonlinear Distortion in Microwave Photonics Link Assisted by Artificial Neural Network

A microwave photonics radio over fiber link can deliver the radio frequency (RF) signal to realize the antenna remote. When the signal is a broadband and multicarrier RF signal, there are some linear distortions in the link, such as the crossmodulation distortion (XMD) and third-order intermodulation distortion (IMD3). It will destroy the wide-bandwidth performance of the link. Traditional postdigital compensation methods for XMD and IMD3 mitigation extract the baseband signal and reconstruct the compensation signal with calculated compensation factor. Here, a kind of artificial neural network genetic algorithm (ANN-GA) distortion compensation technique is proposed to seek the compensation factor instead of calculations. The neural network algorithm is used to fit the mapping function between the compensation factor and the distortion and give a set of predicted data as the original individual fitness value for the genetic algorithm. Based on the original value, the minimal distortion, the corresponding optimal compensation factors γ and α are found with optimization iterations. Taking advantage of the optimal compensation factors, based on the traditional postdigital compensation method, the distortion is mitigated with a suppression ratio of -84.4 dB. In our paper, the mitigation technology of the XMD and IMD3 can be applied for any kinds of link instead of mathematically modelling the link and calculating the compensation factor. The technology can improve the intelligence and flexibility of microwave photonic link linearization design.

New hybrid GR6J-wavelet-based genetic algorithm-artificial neural network (GR6J-WGANN) conceptual-data-driven model approaches for daily rainfall–runoff modelling

New hybrid GR6J-wavelet-based genetic algorithm-artificial neural network (GR6J-WGANN) conceptual-data-driven model approaches for daily rainfall–runoff modelling

Intelligent identification of soil and operation parameters in mechanised tunnelling by a hybrid model of artificial neural network-genetic algorithm (case study: Tabriz Metro Line 2)

ABSTRACT In this article, the ability of the artificial neural network-genetic algorithm (ANN-GA) to perform back analysis and predict maximum surface settlement in mechanised tunnelling is investigated. The required data of the ANN meta-model was generated using 150 three-dimensional finite-difference simulations. The global sensitivity analysis was performed on 19 parameters, including 17 geotechnical parameters of soil layers and 2 operational parameters of face pressure and grouting pressure. The predicted results using ANN were in good agreement with the numerical simulations so that R = 99% and rRMSE = 1.5% are obtained. Then, back analysis was performed using the ANN-GA hybrid algorithm and the geotechnical data of the monitoring point were updated using the maximum surface settlement monitored at this point. Also, for the geotechnical parameters considered in the design phase, using the same algorithm, the number of operational parameters required for optimal settlement was predicted.

Optimal conditions of paint wastewater coagulation with gastropod shell conchiolin using response surface design and artificial neural network-genetic algorithm

The potential of gastropod shell conchiolin (GSC) (a waste product of the deprotenization stage of chitosan production) as one of the alternatives to chemical coagulants has been explored for treatment of paint industrial wastewater (PW). The accuracy of response surface design (RSD) and the precision of artificial intelligence in predicting and optimizing the process conditions were harnessed in raising experimental design matrix and response optimization, respectively for the bench scale jar test coagulation experiment. PW was characterized using American Public Health Association standard methods. Extraction of conchiolin was done via alkaline extraction method. PW contains 2098 mg/l total suspended solid above discharge limit (1905 mg/l). Fourier transform infrared (FTIR) spectrum of GSC revealed a broad N–H wagging band at 750–650 cm−1 indicating the presence of secondary amine linked to the presence of protein. Turbidity removal from PW via one factor at a time was found to be a function of pH, GSC dosage, temperature and time. Artificial neural network response prediction shows 92% correlation with the RSD experimental result. The optimal conditions obtained via genetic algorithm for the response optimization at the best pH of 4 indicate optimal turbidity removal of 98% at GSC dosage, time and temperature of 4 g, 20 min and 45 °C, respectively.

ANN-GA, ANFIS-GA and Thermodynamics base modeling of crude oil removal from surface water using organic acid grafted banana pseudo stem fiber

Banana pseudo stem fiber adsorbent (BPSF) with well-ordered framework of porosity was fabricated via esterification reaction between organic acid and the biomass. The resulting adsorbent (BPSF) was applied for the removal of crude oil from water surface. Optimization and modeling of the process parameters was done using response surface methodology (RSM), artificial neural network-genetic algorithm (ANN-GA), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA). Result shows that the optimum removal of oil occurred at oil water ratio of 0.4 g /100cm3 with 94.8% oil removal. Meanwhile, BPSF exhibited high adsorptive potential at a very low pH of 4 with 95.12% oil removal. Thermodynamic studies revealed activation energy, change in enthalpy and change in entropy of the process as (19.55, 24.68, -0.438 KJ/mols) and (53.82,31.66, -0.186 KJ/mols) indicating non-spontaneous process. Equilibrium modeling revealed that the composite material was highly matched to Langmuir isotherm model with regression coefficient > 98% with maximum adsorption capacity of 53.26 mg/g. Optimization of the process shows the optimum conditions as 100 °C, 0.4 g/100cm3, 2.1 g, 6 and 75 mins for temperature, oil concentration, adsorbent dosage, pH and time. RSM, ANFIS, and ANN models adequately predicted the oil removal with correlation coefficient > 0.97 but statistically, ANN was the best model followed by ANFIS and RSM models. Results obtained from this investigation has shown that esterified banana pseudo stem fiber composite is an efficient, economic viable and sustainable adsorbent since the properties obtained is competing favorably with commercial adsorbents.

Experimental investigation and modeling of drying process of balangu seeds gum using infrared dryer by genetic algorithm-artificial neural network method

Experimental investigation and modeling of drying process of balangu seeds gum using infrared dryer by genetic algorithm-artificial neural network method

Oil production from waste polyethylene and polystyrene co-pyrolysis: Interactions of temperature and carrier gas flow rate

This study investigated the co-pyrolysis of waste polyethylene (WPE) and polystyrene (WPS) for oil production under different operating conditions (temperature and carrier gas flow rate). The ratio of the real-life WPE and WPS is different in different regions. Therefore, the interactions of operating conditions were investigated under different WPE/WPS mixture compositions. A hybrid model of artificial neural network-genetic algorithm (ANN-GA) was adopted to predict and optimize the co-pyrolysis oil yield due to the complex interactions of the WPE/WPS mixture composition and operating conditions. Consequently, the highest oil yield was 82.33 wt% under 525°C, 10 wt% PS, and a non-sweeping atmosphere (0 mL/min). The results indicated that high temperature, low PS mass fraction, and low carrier gas flow rate led to a higher oil yield. The WPE/WPS co-pyrolysis oils were composed of alkanes, alkenes, and aromatics. Meanwhile, styrene accounted for the highest proportion of aromatics in oils. ANN-GA was also adopted to predict and optimize the oil components and fractions. The results revealed that low temperature, high PS mass fraction, and low carrier gas flow rate were conducive to a light WPE/WPS co-pyrolysis oil production. The findings could guide the industrial process of waste plastic pyrolysis in different regions. Moreover, ANN-GA coupled with central-composite design can be used to regulate different target products under more complex conditions due to its good robustness.

Prediction of transportation energy demand by novel hybrid meta-heuristic ANN

Road automobiles are deemed one of the major resources of energy consumption throughout cities. To realize and design sustainable urban transport, it is essential to comprehend as well as evaluate interactions among a set of elements, which form transport impacts and behaviors. The goal of the current research was to propose a hybrid algorithm, Artificial Neural Network (ANN)-Genetic Algorithm (ANN-GA), ANN-Simulated Annealing (ANN-SA), and Particle Swarm Optimization (ANN-PSO) to better optimize the coefficients for predicting the energy demand based on the several predictor variables (1975–2019) i.e., GDP, year, vehicle-km, population, oil price, passenger-km, and ton-km in Turkey. Eleven combinations of all predictor variables were selected and then compared with real data. The outcomes exposed that the proposed ANN-PSO technique based on the GDP, population, ton-km outperforms the other two models. It is anticipated that this research can be useful for developing extremely productive and applicable planning regarding transportation energy policies.

Artificial neural network-Genetic algorithm optimized graded metal foam

The effective utilization of renewable energy directly correlates to efficient performance of integrated energy storage system. The efficiency of Thermal Energy Storage (TES) systems strongly correlates with the heat transfer rate during phase change processes. Heterogeneous metal foams which allow augmenting heat transfer without compromising the thermal capacity suffers from high manufacturing cost. A series of homogeneous metal foams stacked is employable to reap advantages associated with heterogeneous metal foam at a similar manufacturing cost as homogeneous metal foam. The present study focuses on optimizing the porosity distribution of graded metal foam to augment the performance of the TES system. The unidirectional phase change process, observed particularly during solidification, acts as a bottleneck. Therefore, the present study focuses on augmenting unidirectional phase-change processes. The present study discusses the influence of PCM and metal foam thermophysical properties and operational characteristics of the TES system on heat transfer augmentation in different graded metal foams. A numerical model is developed to quantify the influence of graded metal foam on heat transfer rate, which is used to train an Artificial Neural Network (ANN) model. The porosity distribution has been optimized using a Genetic algorithm, which employs the ANN model to ascertain heat transfer rate for different graded metal foams. The optimal distribution has been found to be 93.7% and 96.3% for two-layer graded metal foam and 92.8%, 95%, and 97.2% for three-layer graded metal foam, which augments heat transfer by 1160% and 1185%, respectively.

Pressure‐driven crossflow microfiltration coupled with centrifugation for tannin reduction and clarification of cashew apple juice: Modeling of permeate flux decline and optimization of process parameters

The process parameters such as rotational speed, time, membrane pore size and transmembrane pressure were chosen for centrifugation and microfiltration, respectively. The artificial neural network-genetic algorithm technique was followed to achieve the optimized process parameters for centrifugation (7532 rpm rotational speed and 52.6 min time). Likewise, the optimum process conditions for microfiltration were obtained as 0.2 µm membrane pore size and 138 kPa transmembrane pressure. At this optimum condition, 88% tannin content, 97% turbidity, and 95% color has been removed, and 87% ascorbic acid content and 97% TSS have been preserved in the clarified juice. Besides, a high steady-state permeate flux of 74.87 l/m2.h was also achieved at this optimum condition. Furthermore, a resistance-in-series model was developed which can accurately represent the flux behavior of the microfiltration system. Hence, microfiltration coupled with centrifugation can be effectively utilized to remove tannin from cashew apple juice, the primary reason behind consumer acceptability.

Upper confidence bound integrated genetic algorithm‐optimized long short‐term memory network for click‐through rate prediction

AbstractData on online advertising is rising rapidly due to the fast development of science and technology. Click‐through rate (CTR) prediction has become a critical task regarding the digital advertising industry and a key element in increasing advertising profits and user experience. Therefore, this article describes the problem of CTR prediction as a function of sequence classification tasks. Then, we proposed a novel optimization strategy to solve the high‐dimensional problem and find a subset of relevant variables to ensure high performance of our model and maximize the number of clicks. Here, we introduced a feature selection and hyper‐parameter optimization approach using genetic algorithms (GA) and the upper confidence bound (UCB) model to optimize micro‐targeting technology, along with the long short‐term memory (LSTM) network‐based CTR prediction model. The efficiency of the proposed UCB‐LSTM‐GA model and two hybrid models, namely LSTM‐GA and LSTM‐PSO, is evaluated by comparing them to each other and to other machine‐learning‐based classification methods, including LSTM using a UCB algorithm (UCB‐LSTM), High‐order Attentive Factorization Machine (HoAFM), genetic algorithm‐artificial neural network (GA‐ANN), and a feature interaction graph neural network model (Fi‐GNN). Our solution achieved as high as 87%, 89%, and 92% for respectively accuracy, precision, and recall, using the popular python tools with real Avazu datasets.