Genetic Algorithm Neural Network Research Articles

Treatment of carpet and textile industry effluents using Diplosphaera mucosa VSPA: A multiple input optimisation study using artificial neural network-genetic algorithms

The wastewater treatment efficiency of Diplosphaera mucosa VSPA was enhanced by optimising five input parameters and increasing the biomass yield. pH, temperature, light intensity, wastewater percentage (pollutant concentration), and N/P ratio were optimised, and their effects were studied. Two competitive techniques, response surface methodology (RSM) and artificial neural network (ANN), were applied for constructing predictive models using experimental data generated according to central composite design. Both MATLAB and Python were used for constructing ANN models. ANN models predicted the experimental data with high accuracy and less error than RSM models. Generated models were hybridised with a genetic algorithm (GA) to determine the optimised values of input parameters leading to high biomass productivity. ANN-GA hybridisation approach performed in Python presented optimisation results with less error (0.45%), which were 7.8 pH, 28.8 °C temperature, 105.20 μmol m−2 s−1 light intensity, 93.10 wastewater % (COD) and 23.5 N/P ratio.

Production of diesel-range oil through pyrolysis of polyolefins recovered from municipal solid waste.

Pyrolysis is an effective method to valorize plastic waste and obtain value-added fuels. This study adopted the ANN-GA (artificial neural network-genetic algorithm) coupled with a central composition factorial design to optimize the oil production from the pyrolysis of waste polyolefins (WP). The interactive effects of PE mass fraction (20-80 wt%), residence time (20-60 min), and carrier gas flow rate (0-100 mL/min) on the yields of WP pyrolysis products were investigated extensively by ANN. Moreover, the highest WP pyrolysis oil production of 78.87 wt%, optimized by GA, was obtained under 80 wt% PE, 60 min, and 0 mL/min. It was found that the different conditions of PE mass fraction, residence time, and carrier gas flow rate did not change the types of oil's main functional groups (-CH2-, -C=C-, -C=CH2, -CH3, and =C-H). The conditions affected the WP pyrolysis oil fractions significantly. The highest diesel selectivity of 91.42% was obtained under 20 wt% PE, 20 min, and 0 mL/min. Additionally, according to the interactive effects of different conditions on the productions of WP pyrolysis products, the pyrolysis pathways were proposed to understand the pyrolysis mechanism of WP better.

Hydroisomerisation and Hydrocracking of n-Heptane: Modelling and Optimisation Using a Hybrid Artificial Neural Network–Genetic Algorithm (ANN–GA)

In this paper, the focus is on upgrading the value of naphtha compounds represented by n-heptane (n-C7H16) with zero octane number using a commercial zeolite catalyst consisting of a mixture of 75% HY and 25% HZSM-5 loaded with different amounts, 0.25 to 1 wt.%, of platinum metal. Hydrocracking and hydroisomerisation processes are experimentally and theoretically studied in the temperature range of 300–400 °C and under various contact times. A feedforward artificial neural network (FFANN) based on two hidden layers was used for the purpose of process modelling. A total of 80% of the experimental results was used to train the artificial neural network, with the remaining results being used for evaluation and testing of the network. Tan-sigmoid and log-sigmoid transfer functions were used in the first and second hidden layers, respectively. The optimum number of neurons in hidden layers was determined depending on minimising the mean absolute error (MAE). The best ANN model, represented by the multilayer FFANN, had a 4–24–24–12 topology. The ANN model accurately simulates the process in which the correlation coefficient (R2) was found to be 0.9918, 0.9492, and 0.9426 for training, validation, and testing, respectively, and an average of 0.9767 for all data. In addition, the operating conditions of the process were optimised using the genetic algorithm (GA) towards increasing the octane number of the products. MATLAB® Version 2020a was utilised to complete all required computations and predictions. Optimal operating conditions were found through the theoretical study: 0.85 wt.% Pt-metal loaded, 359.36 °C, 6.562 H2/n-heptane feed ratio, and 3.409 h−1 weight-hourly space velocity (WHSV), through which the maximum octane number (RON) of 106.84 was obtained. Finally, those operating conditions largely matched what was calculated from the results of the experimental study, where the highest percentage of the resulting isomers was found with about 78.7 mol% on the surface of the catalyst loaded with 0.75 wt.% Pt-metal at 350 °C using a feed ratio of 6.5 H2/n-C7 and WHSV of 2.98 h−1.

Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA

Abstract This study aims at evaluating the performance of the Elman Neural Network (ENN), Elman Neural Network-Genetic Algorithm (ENN–GA), and Support Vector Machine-simulated annealing (SVM–SA) in determining the discharge of a newly proposed rotary gate for the inclusive data range from free flow to highly submerged conditions. For individual free and submerged flows, the models performed as well as that of the traditional relationships. However, the superiority of the intelligent models comes when dealing with the inclusive data set of both flow conditions, where no deterministic approach is available for discharge evaluation prior to specifying the threshold condition. In such complex flow conditions, the ENN–GA hybrid model with a proper structure determined the discharge with rather a high accuracy, i.e., SE of 6.12%. Also, in defining the threshold state, the ENN and ENN–GA models achieved superior results compared to the currently available relationship, i.e., it accurately recognized the threshold condition in almost 100% of the cases while the traditional relationship results were limited to 93% of the cases. Such accuracy of the employed model in assessing the discharge of the structure and its high ability in recognizing the flow state could be of great advantage for irrigation network structure automation.

Research on the Reliability Test and Life Assessment Methods of Relays Used in Circuit Breaker Operating Mechanism

As one of the key components of the circuit breaker operating mechanism, the relay can experience performance degradation due to harsh environmental factors, such as salt spray, during operation, which can ultimately affect the normal operation of the circuit breaker and threaten the safe and stable operation of the power system. To effectively evaluate the condition of the relay operating in salt spray environments over the long term, this study conducted accelerated aging experiments on a certain model of auxiliary switch under salt spray conditions using an existing test platform. The relay’s pull-in voltage, release voltage, pull-in time, and release time were analyzed as characteristic parameters affected by salt spray. The results showed that the intrusion of salt spray caused a decline in the coil performance of the relay, which required higher voltage to provide the electromagnetic force needed for operation, leading to an increase in operating voltage. On the other hand, coil degradation also caused a decrease in the electromagnetic force generated by the step voltage, resulting in a slower operating time. Subsequently, a Genetic Algorithm_Back Propagation Neural Network GA_BP) algorithm model suitable for identifying the relay status in this study was established. After optimization by a Genetic Algorithm Neural Network (GA), the recognition accuracy increased from 78.6% to 91.8%, which showed significant improvement. By clarifying the changes in the characteristic parameters of the relay in a salt spray environment and relying on experimental data, this study established a relevant state recognition model. The research results have important engineering application value for understanding the relay operating status of the circuit breaker operating mechanism in salt spray environments, conducting circuit breaker operating mechanism relay life prediction, and preventing circuit breaker operating mechanism failures.

Modeling, optimization and efficient use of MMT K10 nanoclay for Pb (II) removal using RSM, ANN and GA

Regarding the long-term toxic effects of Pb (II) ions on human health and its bioaccumulation property, taking measures for its reduction in the environment is necessary. The MMT-K10 (montmorillonite-k10) nanoclay was characterized by XRD, XRF, BET, FESEM, and FTIR. The effects of pH, initial concentrations, reaction time, and adsorbent dosage were studied. The experimental design study was carried out with RSM-BBD method. Results prediction and optimization were investigated with RSM and artificial neural network (ANN)-genetic algorithm (GA) respectively. The RSM results showed that the experimental data followed the quadratic model with the highest regression coefficient value (R2 = 0.9903) and insignificant lack of fit (0.2426) showing the validity of the Quadratic model. The optimal adsorption conditions were obtained at pH 5.44, adsorbent = 0.98 g/l, concentration of Pb (II) ions = 25 mg/L, and reaction time = 68 min. Similar optimization results were observed by RSM and artificial neural network-genetic algorithm methods. The experimental data revealed that the process followed the Langmuir isotherm and the maximum adsorption capacity was 40.86 mg/g. Besides, the kinetic data indicated that the results fitted with the pseudo-second-order model. Hence, the MMT-K10 nanoclay can be a suitable adsorbent due to having a natural source, simple and inexpensive preparation, and high adsorption capacity.

Probabilistic modelling of surface drift prediction in marine disasters based on the NN–GA and ARMA model

This paper studies the issue of target drift prediction based on ocean current from high-frequency (HF) ground-wave radar. A large number of unpowered drift observation experiments were carried out with a typical South China Sea offshore fishing vessel in the range of radar radiation. A target drift prediction model was proposed based on the neural network-genetic algorithm (NN–GA) and Auto-regressive Moving Average (ARMA) model. Replacing the traditional AP98 model, the NN–GA was used to establish the target wind-induced drift characteristic model, while the ARMA model replaced the traditional sub-grid model to refine the time series characteristics of sub-grid velocity. Firstly, trajectory simulation based on the Runge-Kutta method was used to preliminarily verify the improvement of target drift prediction accuracy by the ARMA model. In addition, the MonteCarlo method was used to calculate and compare the prediction range of different sub-grid velocity models. The study also investigated the NN–GA and ARMA model with different continuous prediction time steps. Compared with the traditional AP98 and sub-grid velocity model, the proposed method does not need to pay attention to the detailed force analysis of the drifting target and can significantly improve the performance of the target drift prediction model in terms of mean prediction error and separation. However, the ARMA model is also limited by its continuous prediction step size to a large extent. This study provides a new approach for trajectory analysis of drifting targets and verifies that the NN–GA and ARMA model with short-term continuous prediction can improve the performance of the target drift prediction model.

Multi-characteristic optimization and modeling analysis of Cu2+ removal from wastewater using activated coke/MnFe2O4 magnetic composite

This study used pyrolytic coke (PC) as a viable source for producing an adsorbent. The surface magnetization of activated coke (AC) with MnFe2O4 nanoparticles was carried out to remove Cu2+ from aqueous media after PC activation via a chemical activation method. The structural and surface properties of the adsorbents were examined using a variety of analyses, including thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FT-IR), Burner-Emmett-Teller (BET), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) mapping, Raman, vibrating-sample magnetometry (VSM), atomic force microscopy (AFM), and dynamic light scattering-zeta potential (DLS-ZP). Several parameters were investigated to determine the efficacy of the adsorption process: initial Cu2+ concentration (10–50 mg/L), pH (2−10), adsorbent dosage (0.75–3.75 g/L), temperature (25–65 °C), and contact time (30–150 min). Response surface methodology-central composite design (RSM-CCD) and artificial neural network-genetic algorithm (ANN-GA) methods were used to optimize the process and have correlation coefficients of 0.9890 and 0.9943, respectively. Optimum conditions were determined at an initial Cu2+ concentration of 40 mg/L, a pH of 7.61, an adsorbent dosage of 0.75 g/L, a temperature of 55 °C, and a contact time of 150 min using the RSM-CCD approach. The adsorption process efficiency for AC and AC/MnFe2O4 magnetic composite was determined to be 90.24 and 99.06 %, respectively. According to the results, the adsorption capacity of AC/MnFe2O4 was 72.71 mg/g. The Cu2+ adsorption using magnetic composites followed the Elovich kinetic model. Thermodynamic studies revealed that the process is endothermic and spontaneous. Adsorption efficiency decreased for all adsorbents when Ca2+ and Na+ ion concentrations in aqueous solutions rose. As the number of steps in the adsorption/desorption process increases, the adsorption marginally reduces. Adsorbents' ability to adsorb Cu2+ ions in multi-metallic solutions compared to single-metallic solutions has diminished. The results showed that the AC/MnFe2O4 adsorbent has a high capacity to adsorb-desorb Cu2+ and remove polluted effluent. Additionally, actual wastewater treatment outcomes demonstrated the AC/MnFe2O4 magnetic composite's high efficacy in removing Cu2+ ions.

Development of a flat conical chamber-based non-dispersive infrared CO2 gas sensor with temperature compensation.

In order to accurately monitor CO2 concentration based on the non-dispersive infrared technique, a novel flat conical chamber CO2 gas sensor is proposed and investigated by simulation analysis and experimental verification. First, the optical design software and computational fluid dynamics method are utilized to theoretically investigate the relationship between the energy distribution, absorption efficiency of infrared radiation, and chamber size. The simulation results show that the chamber length has an optimal value of 8cm when the cone angle is 5° and the diameter of the detection surface is 1cm, which makes infrared absorption efficiency optimal. Then, the flat conical chamber CO2 gas sensor system is developed, calibrated, and tested. The experimental results indicate that the sensor can accurately detect CO2 gas concentrations in the range of 0-2000 ppm at 25 °C. It is found that the absolute error of calibration is within 10 ppm, and the maximum repeatability and stability errors are 5.5 and 3.5%, respectively. Finally, the genetic neural network algorithm is presented to compensate for the output concentration of the sensor to solve the problem of temperature drift. Experimental results demonstrate that the relative error of the compensated CO2 concentration is varied from -0.85 to 2.32%, which is significantly reduced. The study has reference significance for the structural optimization of the infrared CO2 gas sensor and the improvement of the measurement accuracy.

MMC-based PV-Fed STATCOM with Hybrid GA-RBFNN for PQ Enhancement

Power quality (PQ) is a consequential factor, which is highly influenced by the functioning of the transmission and distribution network. In the distribution systems, voltage swells, sags, harmonics and flickers are regarded as major power quality issues. The Static Synchronous Compensator (STATCOM) is one among the specialized power devices, which has gained considerable interest for its capacity of enhancing the performance of power systems. This paper analyses the power quality issues of a photovoltaic (PV) system adopting a STATCOM-based five-level Modular Multilevel Converter (MMC). An interleaved quadratic boost converter (QBC) is employed in this work, which escalates the PV output in a wider range. An Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed for obtaining the fast, efficient and flexible control of the DC-DC converter. The MMC plays a crucial part in mitigating power quality issues as it has the beneficial impacts such as scalability, power quality and modularity. In this approach, a five-level MMC is implemented, which transfers active power to the load by maintaining the power factor constant. The reference current generation of MMC is carried out by a hybrid Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFNN) algorithm and the pulses of MMC are generated through a hysteresis controller. It provides improved training speed and convergence along with enhanced operating efficiency of the network. The output of five-level MMC is fed to a transformer-coupled LC filter. The simulations are carried out in Matlab and a minimized Total Harmonic Distortion (THD) of 1.12% is obtained.

Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework

Supercapacitors are high power electrochemical energy storage systems that are attractive candidates for use in high power applications. Yet, their widespread adoption has been restricted due to their relatively low energy density. Improving the energy storage performance of supercapacitors is linked to rationally optimizing the key descriptors that affect capacitance. This work presents a systematic approach based on a hybrid artificial neural network (ANN) and genetic algorithm (GA) integrated with the Big M method to efficiently and rationally design carbon-based supercapacitors with improved energy storage performance. By performing structural and electrochemical characterization on systems we fabricate, we experimentally validate the robustness and generalizability of the developed ANN-GA framework. This study takes a step towards the rational design of supercapacitors by implementing the hybrid ANN-GA framework as an optimization tool to provide guidelines for rationally tuning material properties and operational conditions for improved capacitance.

Artificial Neural Network and ESPRIT-TLS Combination-Based Approach for Fault Bearing Recognition in Induction Machines

Several studies have been conducted in recent decades on the detection of bearing faults to develop methods for real-time monitoring. Faults are found in almost all electromechanical systems and the major cause of damage to machinery, resulting in serious accidents to humans and material environments. While methods such as MCSA (Motor Current Signature Analysis) and PCA (Principal Component Analysis) have been widely used for obtaining and analyzing the machine's stator current and reducing its dimensions and noise, they do not allow for easier detection and parameter estimation when a defect appears, and sometimes help from an expert is needed to explain the signals. In this paper, the use of ANN-GA (Artificial Neural Networks-Genetic Algorithm) is investigated in combination with the best variant of the ESPRIT (Estimation of Signal Parameters via Rotational Invariant Techniques) method applied on stator current signals for efficient real-time bearing fault detection, to avoid any help being required from experts. MATLAB simulations of these variants revealed that the TLS variant highly discriminates the fault. By using this variant to prepare the data in the search of the best ANN model, in combination with genetic algorithms (used for optimizing the search of ANN parameters), two architectures capable of discriminating this bearing fault in real time in time and frequency domain were obtained.

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).

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning.

ANALYSIS OF COVID-19 GROWTH TRENDS THROUGH DATA MINING APPROACH AS DECISION SUPPORT

This study aims to analyze the growth trend of covid-19 using prediction algorithms in data mining for covid-19 data throughout Indonesia. This can be used as a decision support to analyze several government policies towards regulatory intervention so far. The method used is the best prediction method in time series data, including Neural Network, SVM, Linear Regression, K-Neirest Neighborn and optimizes it with optimization algorithms. This research is focused on the application of these applications. It is hoped that this research will produce an analysis of the growth trend of Covid cases every day, in addition to its contribution so that it can assist the government in determining the best policy direction and also as an education to the public. in addition, the research will contribute to science in the field of predictive analysis by finding the best RMSE formulation. The results of this study show that Neural Network-Particle Swarm Optimization has the smallest Roort Mean Square Error which is 265,326, and the two Neural Network Genetic Algorithm are 266.801, Neural Network Forward Selection is 275,372 and Neural Network without optimization has the largest RMSE which is 297.204. These results can be used as a reference for the use of similar algorithms in time series data, both Covid-19 data and other data.

Trajectory Optimization based on Genetic-Neural Network Algorithm

With the development of science and technology, aspherical surface has become an indispensable component in optical system. However, the aspheric surface has more complex optical properties than the spherical surface, so it restricts the rapid development of aspheric surface. In this paper, the main problem of aspherical machining is analyzed, which is“Trajectory control”, and the unique optical properties of aspherical surface are analyzed, a trajectory control algorithm based on genetic-BP neural network is proposed to solve the trajectory control in aspheric forming. The method is proved to be feasible and effective.

Improved Supervised and Unsupervised Metaheuristic-Based Approaches to Detect Intrusion in Various Datasets

Due to the increasing number of cyber-attacks, the necessity to develop efficient intrusion detection systems (IDS) is more imperative than ever. In IDS research, the most effectively used methodology is based on supervised Neural Networks (NN) and unsupervised clustering, but there are few works dedicated to their hybridization with metaheuristic algorithms. As intrusion detection data usually contains several features, it is essential to select the best ones appropriately. Linear Discriminant Analysis (LDA) and t-statistic are considered as efficient conventional techniques to select the best features, but they have been little exploited in IDS design. Thus, the research proposed in this paper can be summarized as follows. a) The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset. Nevertheless, owing to the large size of the CICIDS2017 Dataset, only 25% of the data was used. b) As a feature selection method, the LDA performance measure is chosen and combined with the t-statistic. c) For intrusion detection, unsupervised Fuzzy C-means (FCM) clustering and supervised Back-propagation NN are adopted. d) In addition and in order to enhance the suggested classifiers, FCM and NN are hybridized with the seven most known metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Cultural Algorithm (CA), Harmony Search (HS), Ant-Lion Optimizer (ALO) and Black Hole (BH) Algorithm. Performance metrics extracted from confusion matrices, such as accuracy, precision, sensitivity and F₁-score are exploited. The experimental result for the proposed intrusion detection, based on training and test CICIDS2017 datasets, indicated that PSO, GA and ALO-based NNs can achieve promising results. PSO-NN produces a tested accuracy, global sensitivity and F₁-score of 99.97%, 99.95% and 99.96%, respectively, outperforming performance concluded in several related works. Furthermore, the best-proposed approaches are valued in the most recent intrusion detection datasets: CSE-CICIDS2018 and LUFlow2020. The evaluation fallouts consolidate the previous results and confirm their correctness.

Retracted: Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese-English Parallel Corpus Noise Processing.

[This retracts the article DOI: 10.1155/2021/7144635.].

Hybrid-biotaxonomy-like machine learning enables an anticipated surface plasmon resonance of Au/Ag nanoparticles assembled on ZnO nanorods

A genetic algorithm neural network model based on machine learning has been established for precise prediction of plasmonic resonance by considering the fabrication parameters of heterostructural noble metal nanoparticles.

Retracted: Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN).

[This retracts the article DOI: 10.1155/2021/4133892.].