Genetic Algorithm-back Propagation Neural Network Research Articles

Performance prediction and operating parameters optimization for proton exchange membrane fuel cell based on data-driven surrogate model and particle swarm optimization

The operating parameters of proton exchange membrane fuel cell (PEMFC) are critical to its performance and working life. This study presents a data-driven modeling approach combining a surrogate model with the particle swarm optimization algorithm to optimize operating parameters for PEMFC and obtain the maximum power density. The results show that the operating parameters significantly influence power density under high current densities, with inlet temperature having the most significant effect. Lower inlet temperature, relative humidity in the cathode and anode, along with higher operating pressure yield improved output performance. The Genetic Algorithm-Backpropagation Neural Network based surrogate model exhibits excellent predictive performance with correlation coefficients of 0.99896 and 0.99815 for the training and test sets, respectively. Optimized conditions achieve a 3.3% increase in power density compared to initial settings, with only a 0.15% error in simulation calculations. This data-driven approach provides valuable insights for maximizing PEMFC efficiency and performance.

Conditions optimization and mechanistic study of chlortetracycline hydrochloride adsorption by TBAOH-MXene: Prediction results based on backpropagation neural network-genetic algorithm and response surface methodology

Conditions optimization and mechanistic study of chlortetracycline hydrochloride adsorption by TBAOH-MXene: Prediction results based on backpropagation neural network-genetic algorithm and response surface methodology

A Tightly Coupled GNSS RTK/IMU Integration with GA-BP Neural Network for Challenging Urban Navigation

Abstract Intelligent Transportation System (ITS) is increasing the importance of real-time acquisition of Positioning, Navigation, and Timing (PNT) information from high-accuracy Global Navigation Satellite Systems (GNSS) based on carrier phase observations. The complexity of urban environments, however, means that GNSS signals are prone to reflection, diffraction and blockage by tall buildings, causing a degraded positioning accuracy. To address this issue, we have proposed a tightly coupled single-frequency multi-system single-epoch Real-time Kinematic (RTK) GNSS/Inertial Measurement Unit (IMU) integration algorithm with the assistance of Genetic Algorithm Back Propagation (GA-BP) based on low-cost IMU equipment for challenging urban navigation. Unlike the existing methods, which only use IMU corrections predicted by machine learning as a direct replacement of filtering corrections during GNSS outages, this algorithm introduces a more accurate and efficient IMU corrections prediction model, and it is underpinned by a dual-check GNSS assessment where the weights of GNSS measurements and neural network predictions are adaptively adjusted based on duration of the integrated system GNSS failure, assisting RTK/IMU integration in GNSS outages or malfunction conditions. Field tests demonstrate that the proposed prediction model results in a 68.69% and 69.03% improvement in the Root Mean Square Error (RMSE) in the 2D and 3D component when the training and testing data are collected under 150s GNSS signal-blocked conditions. This corresponds to 52.43% and 51.27% for the signal discontinuously blocked with 500s.

Enhancing urban blue-green landscape quality assessment through hybrid genetic algorithm-back propagation (GA-BP) neural network approach: a case study in Fucheng, China

This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network’s approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.

Multi-uncertainty river health assessment in the Loess Plateau based on a novel comprehensive similarity cloud model

With the development of related research on river health assessment, the traditional assessment methods cannot solve the uncertainty problems caused by diversified indicators and ambiguous weights. In order to comprehensively understand the river health status in the Loess Plateau and solve the uncertainty of assessment results, this study takes Wuding River as the research object, and constructs an assessment indicator system incorporating the ecological environment and social functions of the river. The genetic algorithm-BP neural network was used to establish a weight learning mechanism for assessment indicators, and the system weight was calculated by combining various weighting preferences. Based on the cloud model theory, a sample data cloud and a health level cloud were constructed, and a comprehensive similarity model considering the shape and distance of the cloud diagram was developed to identify the health level of Wuding River. The spatial and temporal variability of the river health status and the sources of pressure causing unhealthy problems were analyzed. It is shown that Wuding River is in basic health status, which gradually decreases from the upstream reaches to the downstream reaches. The health status of social service functions is better than that of the ecological environment attributes such as water resources and water quality. The sources of health pressure in the upstream and downstream reaches are different. In the upstream reaches, the pressure mainly comes from natural attributes such as successive declines in water production, slow governance of water and soil erosion, and the poor stability of the river. The health pressure in the midstream and dowmstream reaches comes from the interference of human economic activities, which include deterioration of water quality caused by the discharge of fossil energy industry, destruction of aquatic habitats, and flood safety hazards caused by insufficient standards for flood control project design. In addition, SDSCM provides river managers with accurate and comprehensive health risk information to help them manage rivers more effectively.

Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment

The multi-field coupling of grain piles in grain silos is a focal point of research in the field of grain storage. The porosity of grain piles is a critical parameter that affects the heat and moisture transfer in grain piles. To investigate the distribution law of the bulk grain pile porosity in grain silos, machine learning algorithms were incorporated into the prediction model for grain porosity. Firstly, this study acquired the database by conducting compression experiments on grain specimens and collecting data from the literature. The back propagation neural network (BPNN) algorithm was optimized using three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA)). Five machine learning models (GA–BPNN, PSO–BPNN, WOA–BPNN, BPNN, and random forest (RF)) were developed to predict the grain porosity using three input parameters (vertical pressure, grain type, and moisture content). The five models were assessed using four evaluation metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), to determine the best porosity prediction model. Finally, the generalization ability of the best prediction model was verified using the results of the grain cell box experiment on wheat piles. The results indicated that the WOA–BPNN model was the best prediction model with an R2 value of 0.9542, an RMSE value of 0.0079, an MAE value of 0.0044, and an MAPE value of 1.1467%. The WOA–BPNN model demonstrated strong generalization ability, confirming the feasibility of using this model to predict grain porosity. It also established an expression for the relationship between wheat porosity and the vertical pressure of the grain pile. This study presents a machine learning prediction method for determining the porosity of grain piles. The obtained porosity distribution law serves as a crucial basis for conducting comprehensive multi-field coupling analysis of grain piles and offers theoretical support for safe grain storage.

Synthesis of HNTs-Ca/Zn catalyst for biodiesel production from acidulated palm oil: Optimized by GA-BP

The development of heterogeneous base catalyst with acid resistance has become a major topic in the current research on the biodiesel production process. Halloysite nanotubes (HNTs) is supported with bimetallic of calcium and zinc for modification and labeled as HNTs-Ca/Zn to show both the acid and base characteristics. Further, this supported catalyst is used to produce biodiesel from the acidulated palm oil through catalyzing transesterification in one-step in this study. The HNTs-Ca/Zn catalyst, synthesized by the impregnation method, is characterized by TG-DTG, XRD, XPS, TEM, SEM-EDX, nitrogen adsorption-desorption, CO2/NH3-TPD, FTIR and Raman. The capability of the HNTs-Ca/Zn catalyst in catalyzing transesterification is investigated, where the maximum yield of 95.22% is achieved with the molar ratio of Zn to Ca of 1.5. The advantage of HNTs-Ca/Zn1.5 catalyst is prominent when the transesterification source oil is acidulated, where the biodiesel yield of 92.66% is obtained even at the acid value of 26.02 mg KOH g−1. Besides, reusability of the catalyst is acceptable, where the yield of 77.91% is maintained after the fourth reused cycle. Further, the Genetic Algorithm-Back Propagation (GA-BP) neural network is used to train and predict the transesterification parameters of the reaction temperature, catalyst amount and methanol to oil molar ratio. The prediction of the biodiesel yield is reasonable. At the predicted reaction parameters of 158 °C, catalyst amount of 7.1 wt.% and methanol to oil molar ratio of 15.5, the predicted biodiesel yield is 98.71%, where the experimental verification value is 98.60%. Therefore, the HNTs-Ca/Zn catalyst is considered to demonstrate exceptional catalytic performance and recyclability, offering the potential for biodiesel production.

Study on the quality of Corydalis Rhizoma in Zhejiang based on multidimensional evaluation method

Ethnopharmacological relevanceThe quality requirements of Corydalis Rhizoma (CR) in different producing areas are uniform, resulting in uneven efficacy. As a genuine producing area, the effective quality control of CR in Zhejiang Province (ZJ) could provide a theoretical basis for the rational application of medicinal materials. Aim of the studyThe purpose of this study was to effectively distinguish the CR inside and outside ZJ, and provided a theoretical basis for the quality control and material basis research of ZJ CR. Materials and methodsThe core components of ZJ CR could be identified by HPLC combined with chemometrics screening, and the quality of CR from different producing areas was evaluated by a genetic algorithm-back propagation (GA-BP) neural network. Chromaticity and near-infrared (NIR) spectroscopy were used to identify CR inside and outside ZJ, and rapid content prediction was realized. The analgesic effect of CR in different regions was compared by a zebrafish analgesic experiment. Analgesic experiments in rats and analysis of the research status of quality components were used to screen the quality control components of ZJ CR. ResultsThe contents of palmatine hydrochloride (YSBMT), dehydrocorydaline (TQZJJ), tetrahydropalmatine (YHSYS), tetrahydroberberine (SQXBJ), corydaline (YHSJS), stylopine (SQHLJ), and isoimperatorin (YOQHS) in ZJ CR were higher than those in CR from outside ZJ, but the content of protopine (YAPJ) and berberine hydrochloride (YSXBJ) was lower than that in CR from outside ZJ. YHSJS and SQHLJ could be used as the core components to identify ZJ CR. The GA-BP neural network showed that the relative importance of ZJ CR was the strongest. Chroma-content correlation analysis and the NIR qualitative model could effectively distinguish CR from inside and outside of ZJ, and the NIR quantitative model could quickly predict the content of CR from inside and outside of ZJ. Zebrafish experiments showed that ZJ, Shaanxi (SX), Henan (HN), and Sichuan (SC) CR had significant analgesic effects, while Hebei (HB) CR had no significant analgesic effect. Overall comparison, the analgesic effect of ZJ CR was better than that of CR outside ZJ. The comprehensive score of the grey correlation degree between YAPJ, YSBMT, YSXBJ, TQZJJ, YHSYS, YHSJS, SQXBJ, and SQHLJ were higher than 0.9, and the research frequency were extremely high. ConclusionsThe relative importance of the content and origin of most components of ZJ CR was higher than that of CR outside ZJ. The holistic analgesic effect of ZJ CR was better than that of CR outside ZJ, but slightly lower than that of SX CR. YHSJS and SQHLJ could be used as the core components to identify ZJ CR. YAPJ, YSBMT, YSXBJ, TQZJJ, YHSYS, SQXBJ, YHSJS, and SQHLJ could be used as the quality control components of ZJ CR. The multidimensional evaluation method used in this study provided a reference for the quality control and material basis research of ZJ CR.

Dynamic modeling and vibration control optimization of a rotating hollow beam with ESACLD treatment

As an active and passive hybrid control technology, enhanced segmented active constrained layer damping (ESACLD) is an intelligent damping structure for suppressing adverse vibrations in flexible structures. In this study, the three-dimensional spatial dynamic model of a rotating hollow beam with improved ESACLD treatment is established based on the finite element method in a floating frame of reference. The damping effects of the ESACLD with both edge elements and incisions are considered in the dynamic model for the first time. Simulation results show the damping performance of the ESACLD is better than traditional active constrained layer damping with or without incisions (SACLD or ACLD). The Genetic Algorithm-Backpropagation (GA-BP) neural network is used to optimize the physical parameters of ESACLD, and the optimized model can effectively suppress the transverse and flapping bending vibration. It is also found that flapping and transverse bending vibrations exhibit differential dependence on nutation and precession angular velocities; higher nutation and lower precession velocities suppress flapping bending. The existing commercial finite element software has some problems, such as low efficiency and inaccuracy. The new finite element proposed in this paper uses a new form function to ensure accuracy in the simulation of rotation and vibration and has obvious advantages in dealing with rotational structures and complex boundary conditions. Research in this work can provide a design framework for vibration prediction and control of flexible beam-like structures.

Prediction modeling of cigarette ventilation rate based on genetic algorithm backpropagation (GABP) neural network

AbstractThe ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventilation rate prediction model, guide the design of unit parameters in advance, to achieve the goal of stabilizing unit ventilation rate, improve the stability of cigarette ventilation rate, and enhance the quality of cigarettes. This paper used multiple linear regression networks (MLR), backpropagation neural networks (BPNN), and genetic algorithm-optimized backpropagation (GABP) to construct a model for the prediction of cigarette ventilation rate. The model results indicated that the total ventilation rate was significantly positively correlated with weight (P < 0.01), circumference, hardness, filter air permeability, and open resistance. The results showed that the MLR models' (RMSE = 0.652, R2 = 0.841) and the BPNN models’ (RMSE = 0.640, R2 = 0.847) prediction ability were limited. Optimization by genetic algorithm, GABP models were generated and exhibited a little better prediction performance (RMSE = 0.606, R2 = 0.873). The results indicated that the GABP model has the highest accuracy in the prediction of predicting ventilation rate and can accurately predict cigarette ventilation rate. This method can provide theoretical guidance and technical support for the stability study of the ventilation rate of the unit, improve the design and manufacturing capabilities and product quality of short cigarette products, and help to improve the quality of cigarettes.

Bed density prediction of gas–solid separation fluidized bed based on genetic algorithm-back propagation neural network

The fluctuation of bed density in the process of mineral separation in gas–solid fluidized bed is particularly critical, and accurate prediction of bed density will play a crucial role in industrial production. In this paper, the axial and radial distribution of bed density in gas–solid fluidized bed under different ratios of dense medium and gas velocities was investigated. Based on the data of this study and other scholars, the corresponding data set was constructed. A 13–14-1 bed density prediction model was established by using BP neural network algorithm. The overall correlation of the model is 0.9771. The model is more accurate to predict the bed density of single dense medium. The mean absolute percentage error is 4.17 % and the root mean square error is 0.0298. The optimized GA-BP (Genetic Algorithm-Back Propagation) neural network by genetic algorithm results are closer to the actual experimental values. The overall correlation of the model is increased to 0.9793, and the prediction accuracy can reach more than 90 %. The GA-BP neural network prediction model is more accurate in predicting fluidized beds with density fluctuation range of 1.6–2.2 g/cm3, which can meet the needs of industrial applications.

GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation

Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.

Model for Technology Risk Assessment in Commercial Banks

As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.

Research on dynamic splitting tensile damage characteristics of steel fiber reinforced concrete under freeze-thaw cycle environment based on AE

This paper aims to investigate the dynamic splitting tensile damage characteristics of steel fiber reinforced concrete (SFRC) and achieve early warning of the dangerous stage under freeze-thaw cycle environment through acoustic emission (AE) technology, Mel-frequency cepstral coefficients (MFCCs) and genetic algorithm-back propagation (GA-BP) neural network. The effects of fiber content and loading rate on mechanical properties, wavelet energy spectrum coefficient and wavelet time-frequency-energy maximum, as well as MFCCs of AE signals were studied herein. Then a GA-BP neural network was established to realize the classification of the damage degree of SFRC under freeze-thaw cycling with MFCCs as the signal characteristic. The results indicate that the increasing loading rate leads to more serious brittle failure, but the appropriate steel fiber content can make the cracks generated develop fully during the dynamic splitting tensile failure process by analyzing the wavelet energy spectrum coefficient and wavelet time-frequency-energy maximum. It is also found that the damage degree of SFRC can be identified by analyzing the MFCCs. Furthermore, the GA-BP neural network performs well in the classification of the damage degree of SFRC with the recognition over 90%, which is more suitable for concrete conditions with a proper loading rate and fiber content.

Quantitative research on stress failure risk assessment for girth welds with unequal wall thickness of the X80 pipeline under lateral load

The cumulative length of the X80 steel pipeline extends to 17,000 km, with a predominant portion traversing mountainous terrains characterized by intricate geological formations. Recurrent geological hazards significantly impede the safe operation of oil and gas pipelines. Landslides can induce substantial axial forces on steel pipelines, significantly increasing the risk of pipeline failure. Moreover, as a result of sudden changes in mechanical structure, the pipeline's unequal wall thickness leads to local stress concentration, making it more vulnerable to external loads than the steel pipeline itself. Therefore, to analyze the mechanical response of steel pipelines under different working conditions, we establish a smooth particle hydrodynamics, finite element transverse landslide pipeline model for girth welds with unequal wall thickness and investigate a tearing accident caused by a lateral landslide in a section of the large-diameter natural gas pipeline. The landslide volume is found to be the main controlling factor for the failure of pipeline circumferential welds. According to the genetic algorithm backpropagation neural network, the von Mises stress of the most dangerous section of the pipeline is calculated based on pipeline and landslide parameters, and the error is 11.67 %. According to the API RP 581–2016 Risk-Based Inspection Methodology, a failure risk assessment matrix is established using the pipeline statute as an evaluation indicator to evaluate the landslide risk to pipelines in different regions. A variable-wall-thickness girth weld on the ZG pipeline was selected for application. This method has good applicability for evaluating the failure risk of circumferential welds with wall thicknesses of 12.8/15.3 mm under the action of small landslides.

Intelligent Prediction of FSW Physical Quantity and Joint Mechanical Properties

Friction stir welding (FSW) process parameters influence welding temperature field and axial force, which affect welding strength. At present, how the FSW process parameters of aluminum alloy 2219-T8 thick plates influence process physical quantity and how the process physical quantity changes the tensile strength about the welded joint are unknown. We focus on the intelligent prediction of FSW temperature, axial force, and mechanical properties, to provide a basis for FSW process control of aluminum alloy 2219-T8 thick plate. Firstly, we conducted the FSW experiment of aluminum alloy 2219-T8 thick plate. Then, we input the welding process parameters, set up a prediction model by particle swarm optimization-back propagation (PSO-BP) neural network to predict the peak temperature and axial force. Finally, we input the peak temperature and axial force, use genetic algorithm-back propagation (GA-BP) neural network to establish a weld tensile strength estimation model, and comply with the prediction of tensile strength.

Improvement of an Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method: A Case Study of Office Building Cooling Load Prediction

Artificial intelligence algorithms (AIAs) have gained widespread adoption in air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. To improve the prediction accuracy of air conditioning load, this study presents an AIA prediction model based on the method of similarity sample screening. Initially, the comprehensive similarity coefficient between samples was obtained by using the gray correlation method improved with information entropy. Subsequently, a subset of closely related samples was extracted from the original dataset and employed to train the artificial intelligence prediction model. Finally, the trained AIA prediction model was used to predict the air conditioning load. The results illustrate that the method of similarity sample screening effectively improved the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is essential to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

Application of fund analysis model to identify key factors in construction projects

This study constructs a funnel analysis model by collecting relevant data to achieve fault monitoring. Back-propagation (BP) neural networks are also used to identify structural damage in construction projects, and a genetic algorithm (GA) is used to optimise BP to improve issues such as slow convergence and long time consumption. The results indicate that the difference between the third-order frequency and the first-order curvature mode is the most suitable indicator for damage warning and identification. The difference in the first-order curvature mode of adjacent measurement points of the damaged component increases with the increase in the degree of damage. Comparing the GA–BP neural network and BP neural network, the former has a smaller error in identification and better performance. The maximum and minimum relative errors of GA–BP in identifying the damage degree of the structure are 8.06 and 1.61%, respectively, meeting the accuracy requirements of the project. The identification of the key factors in construction projects based on the funnel analysis model is beneficial for identifying structural damage and ensuring the safety of engineering projects.

Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants

Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants

Research on the Heat Extraction Performance Optimization of Spiral Fin Coaxial Borehole Heat Exchanger Based on GA–BPNN–QLMPA

Geothermal energy, a renewable energy source with enormous reserves independent of the external environment, is essential for reducing carbon emissions. Spiral fin coaxial borehole heat exchanger (SFCBHE) is vital for geothermal energy extraction. Its heat extraction performance requires further improvements for efficient performance that consider the structural sizes and installation positions of the SFCBHE and the nonlinear coupling with respect to several factors. The heat extraction performance of SFCBHE is optimized using a combination of genetic algorithm–back-propagation neural network (GA–BPNN) and the Q-learning-based marine predator algorithm (QLMPA). This study analyzes and compares the effects of geothermal energy extraction of smooth pipe TY-1, structure before optimization TY-2, and optimized structure TY-3. Following optimization with GA–BPNN–QLMPA, the heat extraction performance of TY-3 is enhanced by 30.8% and 23.6%, respectively. The temperature of maximum extraction is improved by 26.8 K and 24.0 K, respectively. The power of maximum heat extraction is increased by 148.2% and 109.5%, respectively. The optimization method can quickly and accurately determine the heat extraction performance for different structural sizes and installation positions of the SFCBHE. These findings are crucial for developing high-performance SFCBHE and efficiently using geothermal energy.