Search Results (44)

Accurate prediction of dust concentration is essential for effectively preventing and controlling mine dust. The environment of opencast mines is intricate, with numerous factors influencing dust concentration, making accurate predictions challenging. To enhance the prediction accuracy of dust concentration in these mines, a combined prediction algorithm utilizing RF-GA-LSSVM is developed. Initially, the random forest (RF) algorithm is employed to identify key features from the meteorological and dust concentration data collected on site, ultimately selecting five indicators—temperature, humidity, stripping amount, wind direction, and wind speed—as the input variables for the prediction model. Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. Additionally, model evaluation metrics and testing methods are established. Compared with LSSVM, PSO-LSSVM, ISSA-LSSVM, GWO-LSSVM, and other prediction models, the GA-LSSVM model demonstrates a final fitting degree of 0.872 for PM2.5 concentration data and 0.913 for PM10 concentration data. The GA-LSSVM model clearly demonstrates a strong predictive performance with low error and high fitting. The research results can serve as a foundation for developing dust control measures in opencast mines. Full article

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues in steel pipes, the demand for glass fiber-reinforced plastic (GFRP) pipes is increasing in oil and gas gathering and transmission systems. However, the medium that is transported through these pipelines contains multiple acid gases such as CO₂ and H₂S, as well as ions including Cl⁻, Ca²⁺, Mg²⁺, SO₄²⁻, CO₃²⁻, and HCO₃⁻. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped defect GFRP pipes with depths of 2 mm and 5 mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage. Full article

Blasting prediction and parameter optimization can effectively improve blasting effectiveness and control production energy consumption. However, the presence of multiple factors and diverse effects in open-pit blasting increases the difficulty of effective prediction and optimization. Therefore, this study takes blasting fragmentation as the prediction indicator and proposes a hybrid intelligent model based on multiple parameters. The model employs a least squares support vector machine (LSSVM) optimized by a genetic algorithm (GA) for prediction. Additionally, the performance of GA-LSSVM was compared with LSSVM optimized by rime optimization algorithms (RIME-LSSVM) and by particle swarm optimization algorithms (PSO-LSSVM), unoptimized LSSVM, and the Kuz–Ram empirical model. Furthermore, considering both blasting fragmentation and blasting cost, a multi-objective particle swarm optimization (MOPSO) algorithm was used for blasting parameter optimization, followed by field validation. The results indicated that the GA-LSSVM model provided the best prediction of blasting fragmentation, achieving optimal evaluation metrics: a root mean square error (RMSE) of 1.947, a mean absolute error (MAE) of 1.688, and a correlation coefficient (r) of 0.962. Moreover, the MOPSO optimization model yielded the optimal blasting parameter combination: a burden of 5.5 m, spacing of 4.3 m, specific charge of 0.51 kg/m³, and subdrilling of 2.0 m. Field blasting tests confirmed the reliability of these parameters. This study can provide scientific recommendations for open-pit mine blasting design and cost control. Full article

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications. Full article

Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing. Full article

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas’s oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm–long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an $R^2$ value of 0.98664. The whale optimization algorithm–long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization–least squares support vector machine (PSO-LSSVM), and particle swarm optimization–long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants. Full article

In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wavelet transformation resulting in wavelet time–frequency representations that combine temporal and spectral information. Subsequently, these time–frequency representations are fed into the deep belief networks, which perform semi-supervised dimensionality reduction and feature extraction, thereby uncovering distinct fault characteristics in the track circuit. Finally, the genetic algorithms are employed to improve the kernel function and penalty factor parameters of the least-squares support vector machine, thus establishing an optimal DBN-GA-LSSVM diagnostic model. Experimental validation demonstrates the effectiveness of the proposed time–frequency intelligent network model by leveraging the advantages of deep belief networks in hierarchical feature extraction and the superior performance of the least-squares support vector machine in addressing high-dimensional pattern recognition problems with limited samples. The achieved accuracy rate on the testing dataset reaches an impressive 99.6%. Consequently, this comprehensive approach provides a viable solution for data-driven track circuit fault diagnosis. Full article

Due to the dark red surface of ripe fresh peaches, their internal injury defects cannot be detected using the naked eye and conventional images. The rapid and accurate detection of fresh peach defects can improve the efficiency of fresh peach classification. The goal of this paper was to develop a nondestructive approach to simultaneously detecting internal injury defects and external injuries in fresh peaches. First, we collected spectral data from 347 Kubo peach samples using hyperspectral imaging technology (900–1700 nm) and carried out pretreatment. Four methods (the competitive adaptive reweighting algorithm (CARS), the combination of CARS and the average influence value algorithm (CARS-MIV), the combination of CARS and the successive projections algorithm (CARS-SPA), and the combination of CARS and uninformative variable elimination (CARS-UVE)) were used to extract the characteristic wavelength. Based on the characteristic wavelength extracted using the above methods, a genetic algorithm optimization support vector machine (GA-SVM) model and a least-squares support vector machine (LS-SVM) model were used to establish classification models. The results show that the combination of CARS and other feature wavelength extraction methods can effectively improve the prediction accuracy of the model when the number of wavelengths is small. Among them, the discriminant accuracy of the CARS-MIV-GA-SVM model reaches 93.15%. In summary, hyperspectral imaging technology can accomplish the accurate detection of Kubo peaches defects, and provides feasible ideas for the automatic classification of Kubo peaches. Full article

Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and r of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control. Full article

Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass flow, water content in the PEM and stack voltage of the PEMFC are deeply studied. Furthermore, the dynamic output characteristics of the PEMFC under the conditions of flooding and drying membrane are reported, and the influence of water content in PEM on output performance of the PEMFC is analyzed. To effectively diagnose membrane drying and flooding faults, prolong their lifespan and thus to improve operation performance, this paper proposes the state assessment of water content in the PEM based on BP neural network optimized by genetic algorithm (GA). Simulation results show that compared with LS-SVM, GA-BP neural network has higher estimation accuracy, which lays a foundation for the fault diagnosis, life extension and control scheme design of the PEMFC. Full article

The valve is a key control component in the oil and gas transportation system, which, due to the environment, transmission medium, and other factors, is susceptible to internal leakage, resulting in valve failure. Conventional testing methods cannot judge the service life of valves. Therefore, it is important to carry out valve life prediction research for oil and gas transmission safety. In this work, a valve service life prediction method based on the PCA-PSO-LSSVM algorithm is proposed. The main factors affecting valve service life are obtained by principal component analysis (PCA), the least squares support vector machine (LSSVM) is used to predict the valve service life, the parameters are optimized by using particle swarm optimization (PSO), and the valve service life prediction model is established. The results show that the predicted valve service life based on the PCA-PSO-LSSVM algorithm is closer to the actual value, with an average relative error (MRE) of 16.57% and a root mean square error (RMSE) of 1.2636. Valve life prediction accuracy is improved, which provides scientific and technical support for the maintenance and replacement of valves. Full article

As one of the core pieces of equipment of the thermal power generation system, the economic and environmental performance of a boiler determines the energy efficiency of the thermal power generation unit. The oxygen content in boiler flue gas is an important parameter reflecting the combustion status of the furnace, and accurate prediction of flue gas oxygen content is of great significance for online boiler optimization. In order to solve the online prediction problem of the oxygen content in boiler flue gas, a CNN is applied to build a time series prediction model, which takes the time series samples within a fixed time window as the input of the model and uses several feature extraction modules containing convolutional, activation, and pooling layers for feature extraction and compression, and the model output is the oxygen content in boiler flue gas. Since the oxygen content in boiler flue gas is not only correlated with other variables but also influenced by its own historical trend, the input of the CNN model is improved, and an oxygen content in boiler flue gas time series prediction model (TS-CNN) is established, which takes the historical values of the boiler flue gas oxygen content as the input of the model. The comparison test results show that the $R^2$ and $RMSE$ of the TS-CNN model are 0.8929 and 0.1684, respectively. The prediction accuracy is higher than the CNN model, LSSVM model, and BPNN model by 18.6%, 31.2%, and 54.6%, respectively. Full article

This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional gas emission prediction models are neither accurate nor universally applicable. Through analysis, this paper identified 12 factors that affected gas emissions. A total of 30 groups of typical data for gas outflow were standardized, after which a full subset regression feature selection method was used to categorize 12 influencing factors into different regular patterns and select 18 feature parameter sets. Meanwhile, based on nuclear principal component analysis (KPCA), an optimized gas emission prediction model was constructed where the dimensionality of the original data was reduced. An optimized algorithm set was constructed based on the hybrid kernel extreme learning machine (HKELM) and the least squares support vector machine (LSSVM). The performance of feature parameters adopted in the prediction algorithm was evaluated according to certain metrics. By comparing the results of different sets, the final prediction sequence could be obtained, and a model that was composed of the optimal feature parameters was applied to the optimal machine learning algorithm. The results showed that the HKELM outperformed LSSVM in prediction accuracy, running speed, and stability. The root meant square error (RMSE) for the final prediction sequence was 0.22865, the determination coefficient (R2) was 0.99395, the mean absolute error (MAE) was 0.20306, and the mean absolute percentage error (MAPE) was 1.0595%. Every index of accuracy evaluation performed well and the constructed prediction model had high-prediction accuracy and a wide application. Full article

The stability of the slope is a very important topic in the construction of hydropower projects, especially the slope engineering in the dam area, as its stability will directly affect the safety of engineering. Taking the inlet slope of the flood discharge structure of Lianghekou Hydropower Project as the research object, based on the analysis and exploration of the geological condition of the slope and the field monitoring data, GA-LSSVM is used to establish the non-linear mapping relationship, and the BP neural network is used to establish the mechanical parameters back analysis of the slope at different water impoundment stages. A numerical simulation model is also established to set up different reservoir impoundments to study the stability and sensibility of the slope and provide guidance for slope operation. This case study shows that there is a hysteresis in the response of slope deformation to reservoir impoundment. At the same time, the mechanical parameters of the slope will be weakened by the seepage. In the process of water level changes, the stability of the slope decreases due to the decrease in mechanical parameters. The study will be practically useful for engineering applications. Full article

High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder. Full article