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Keywords = PSO-GA-BPNN

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20 pages, 2734 KiB  
Article
An Intelligent Optimization System Using Neural Networks and Soft Computing for the FMM Etching Process
by Wen-Chin Chen, An-Xuan Ngo and Jun-Fu Zhong
Mathematics 2025, 13(13), 2050; https://doi.org/10.3390/math13132050 - 20 Jun 2025
Viewed by 261
Abstract
The rapid rise of flexible AMOLED displays has prompted manufacturers to advance technologies to meet growing global demand. However, high costs and quality inconsistencies hinder industry competitiveness and sustainability. This study addresses these challenges by developing an intelligent optimization system for the fine [...] Read more.
The rapid rise of flexible AMOLED displays has prompted manufacturers to advance technologies to meet growing global demand. However, high costs and quality inconsistencies hinder industry competitiveness and sustainability. This study addresses these challenges by developing an intelligent optimization system for the fine metal mask (FMM) etching process, a critical step in producing high-resolution AMOLED panels. The system integrates advanced optimization techniques, including the Taguchi method, analysis of variance (ANOVA), back-propagation neural network (BPNN), and a hybrid particle swarm optimization–genetic algorithm (PSO-GA) approach to identify optimal process parameters. Experimental results demonstrate a marked improvement in product yield and process stability while reducing manufacturing costs. By ensuring consistent quality and efficiency, this system overcomes limitations of traditional process control; strengthens the AMOLED industry’s global competitiveness; and provides a scalable, sustainable solution for smart manufacturing in next-generation display technologies. Full article
(This article belongs to the Section E2: Control Theory and Mechanics)
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22 pages, 4890 KiB  
Article
Machine Learning-Based Cost Estimation Models for Office Buildings
by Guolong Chen, Simin Zheng, Xiaorui He, Xian Liang and Xiaohui Liao
Buildings 2025, 15(11), 1802; https://doi.org/10.3390/buildings15111802 - 24 May 2025
Viewed by 506
Abstract
With the increasing trend of office buildings towards high-rise, multifunctional, and structurally complex architecture, the difficulty of engineering cost management has increased. Accurately estimating costs during the decision-making stage is crucial for ensuring the overall project’s financial viability. Therefore, finding straightforward and efficient [...] Read more.
With the increasing trend of office buildings towards high-rise, multifunctional, and structurally complex architecture, the difficulty of engineering cost management has increased. Accurately estimating costs during the decision-making stage is crucial for ensuring the overall project’s financial viability. Therefore, finding straightforward and efficient methods for cost estimation is essential. This paper explores the application of algorithm-optimized back propagation neural networks and support vector machines in predicting the costs of office buildings. By employing grey relational analysis and principal component analysis to simplify indicators, six prediction models are developed: BPNN, GA-BPNN, PSO-BPNN, GA-SVM, PSO-SVM, and GSA-SVM models. After considering accuracy, stability, and computation time, the PCA-GSA-SVM model is identified as the most suitable for office building cost prediction. It achieves stable and rapid results, with an average mean square error of 0.024, a squared correlation coefficient of 0.927, and an average percentage error of 5.52% in experiments. Thus, the model proposed in this paper is both practical and reliable, offering valuable insights for decision-making in office building projects. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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20 pages, 7280 KiB  
Article
Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning
by Yong Xu, Xuewei Zhang, Wenlong Xie, Shihong Zhang, Yaqiang Tian and Liansheng Chen
Appl. Sci. 2025, 15(9), 5045; https://doi.org/10.3390/app15095045 - 1 May 2025
Viewed by 443
Abstract
To predict the forming behaviour of aluminium alloy variable diameter tubes during hydroforming, a genetic algorithm-enhanced particle swarm optimisation (GA-PSO) is used to optimise a backpropagation neural network (BP-NN). A fast prediction model based on the GA-PSO-BP neural network for the hydroforming of [...] Read more.
To predict the forming behaviour of aluminium alloy variable diameter tubes during hydroforming, a genetic algorithm-enhanced particle swarm optimisation (GA-PSO) is used to optimise a backpropagation neural network (BP-NN). A fast prediction model based on the GA-PSO-BP neural network for the hydroforming of aluminium alloy variable diameter tubes was established. The loading paths (internal pressure, axial feeds, and coefficient of friction) were randomly sampled using the Latin hypercube random sampling method. The minimum wall thickness, maximum wall thickness, and maximum expansion height of the formed tubes are included in the main evaluation factors of the forming results. A variety of machine learning algorithms are used to predict, and the prediction results are compared with the finite element model in terms of error. The maximum average absolute value error and mean square error of the proposed model are less than 0.2, which improves the accuracy by 20.4% compared to the unoptimised PSO-BP neural network algorithm. The maximum error between simulated and predicted results is within 4%. The model allows effective prediction of the hydroforming effect of aluminium alloy variable diameter tubes and improves the computational rate and model accuracy of the model. The same process parameters are experimentally verified, the minimum wall thickness of the formed part is 1.27 mm, the maximum wall thickness is 1.53 mm, and the maximum expansion height is 5.11 mm. The maximum thinning and the maximum thickening rate comply with the standard of hydroforming, and the tube has good formability without obvious defects. Full article
(This article belongs to the Special Issue AI-Enhanced Metal/Alloy Forming)
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24 pages, 11292 KiB  
Article
Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River
by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu and Xuesheng Zhang
Remote Sens. 2025, 17(1), 119; https://doi.org/10.3390/rs17010119 - 2 Jan 2025
Cited by 2 | Viewed by 1462
Abstract
The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity [...] Read more.
The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (R2) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers. Full article
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38 pages, 14572 KiB  
Article
Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model
by Xianghong Liu, Sital Kumar Dangi, Zixuan Yang, Yinxuan Song, Qing Sun and Jiantao Wang
Buildings 2024, 14(11), 3375; https://doi.org/10.3390/buildings14113375 - 24 Oct 2024
Cited by 3 | Viewed by 1033
Abstract
A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of [...] Read more.
A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model’s bearing capacity predictions and each input parameter, confirming the model’s broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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22 pages, 10074 KiB  
Article
Power Transformer Fault Diagnosis Based on Random Forest and Improved Particle Swarm Optimization–Backpropagation–AdaBoost
by Lei Zhou, Zhongjun Fu, Keyang Li, Yuhui Wang and Hang Rao
Electronics 2024, 13(21), 4149; https://doi.org/10.3390/electronics13214149 - 22 Oct 2024
Viewed by 1464
Abstract
This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem [...] Read more.
This paper proposes a novel fault diagnosis methodology for oil-immersed transformers to improve the diagnostic accuracy influenced by gas components in power transformer oil. Firstly, the Random Forest (RF) algorithm is utilized to evaluate and filter the raw data features, solving the problem of determining significant features in the dataset. Secondly, a multi-strategy Improved Particle Swarm Optimization (IPSO) is applied to optimize a double-hidden layer backpropagation neural network (BPNN), which overcomes the challenge of determining hyperparameters in the model. Four enhancement strategies, including SPM chaos mapping based on opposition-based learning, adaptive weight, spiral flight search, and crisscross strategies, are introduced based on traditional Particle Swarm Optimization (PSO) to enhance the model’s optimization capabilities. Lastly, AdaBoost is integrated to fortify the resilience of the IPSO-BP network. Ablation experiments demonstrate an enhanced convergence rate and model accuracy of IPSO. Case analysis using Dissolved Gas Analysis (DGA) samples compares the proposed IPSO–BP–AdaBoost model with other swarm intelligence optimization algorithms integrated with BPNN. The experimental findings highlight the superior diagnostic accuracy and classification performance of the IPSO–BP–AdaBoost model. Full article
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21 pages, 33254 KiB  
Article
Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning
by Haitao Zhang, Xingwang Bai, Honghui Dong and Haiou Zhang
Metals 2024, 14(5), 567; https://doi.org/10.3390/met14050567 - 11 May 2024
Cited by 8 | Viewed by 2759
Abstract
Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which [...] Read more.
Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which will directly affect the morphology and quality of the weld bead. However, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expenditures. This paper employed different machine learning models, including SVR, BPNN, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR model was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing algorithm as the training data. The K-fold cross-validation strategy was applied to train and validate machine learning models. The comparison results showed that PSO–SVR predicted process parameters with the highest precision, with the RMSE, R2, and MAE being 1.1670, 0.9879, and 0.8310, respectively. Based on the process parameters produced by PSO–SVR, an optimal process parameter combination was chosen by taking into comprehensive consideration the impacts of power consumption and efficiency. The effectiveness of the process parameter optimization method was proved through three groups of validation experiments, with the energy consumption of the first two groups decreasing by 10.68% and 11.47%, respectively. Full article
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19 pages, 7274 KiB  
Article
Study on the Effect of Post-Freezing Mechanical Properties of Polypropylene Fibre Concrete Based on BAS-BPNN
by Cundong Xu, Jun Cao, Jiahao Chen, Zhihang Wang and Wenhao Han
Buildings 2024, 14(5), 1289; https://doi.org/10.3390/buildings14051289 - 2 May 2024
Viewed by 1124
Abstract
An indoor accelerated freezing and thawing test of polypropylene fibre-reinforced concrete in chloride and sulphate environments was conducted using the “fast-freezing method” with the objective of investigating the damage law of the post-freezing mechanical properties of hydraulic concrete structures and studying the effects [...] Read more.
An indoor accelerated freezing and thawing test of polypropylene fibre-reinforced concrete in chloride and sulphate environments was conducted using the “fast-freezing method” with the objective of investigating the damage law of the post-freezing mechanical properties of hydraulic concrete structures and studying the effects of different mixing amounts of polypropylene fibres on the mechanical properties of concrete. Furthermore, in order to reduce the cost of concrete tests and shorten the time required for conducting concrete tests, a backpropagation neural network based on a Beetle Antenna Search algorithm (BAS-BPNN) was established to simulate and predict the mechanical properties of polypropylene fibre-reinforced concrete. The accuracy of the model was verified. The results indicate that the order of improvement in the macro-physical properties of concrete due to fibre doping is as follows: PPF1.2 exhibited the greatest improvement in macro-physical properties of concrete, followed by PPF0.9, PPF1.5, PPF0.6, and PC. When the freezing and thawing medium and the number of cycles are identical, all four assessment indexes (R2, RMSE, SI, MAPE) demonstrate that the four groups of polypropylene fibre concrete exhibit superior performance to the control group of ordinary concrete. This indicates that polypropylene fibre can enhance the mechanical properties and freezing resistance of the concrete matrix, delay the process of freezing and thawing damage to the matrix, and extend the lifespan of the matrix, yet cannot prevent the ultimate failure of the matrix. The application of intelligent algorithms to optimise the parameters of an artificial neural network model can enhance its capacity to generalise and predict the mechanical properties of concrete. In terms of the coefficient of determination (R2), the Beetle Antenna Search algorithm (0.9782) outperforms the Particle Swarm Optimization (PSO; 0.9676), the Genetic Algorithm (GA; 0.9645), and the backpropagation neural network (BPNN; 0.9460). The improved backpropagation neural network based on the Beetle Antenna Search algorithm not only avoids the trap of local optimality but also improves the model accuracy while further accelerating the convergence speed. This approach can address the complexity, non-linearity, and modelling difficulties encountered during the freezing process of concrete. Moreover, it offers relatively accurate prediction outcomes at a reduced cost in comparison to traditional experimental methodologies. Full article
(This article belongs to the Section Building Materials, and Repair & Renovation)
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24 pages, 6544 KiB  
Article
Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network
by Wei Wang, Xinchao Cui, Yun Qi, Kailong Xue, Ran Liang and Chenhao Bai
Sensors 2024, 24(9), 2873; https://doi.org/10.3390/s24092873 - 30 Apr 2024
Cited by 10 | Viewed by 1436
Abstract
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). [...] Read more.
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
(This article belongs to the Section Sensor Networks)
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20 pages, 12455 KiB  
Article
Study on the Oil Spill Transport Behavior and Multifactorial Effects of the Lancang River Crossing Pipeline
by Jingyang Lu, Liqiong Chen and Duo Xu
Appl. Sci. 2024, 14(8), 3455; https://doi.org/10.3390/app14083455 - 19 Apr 2024
Cited by 2 | Viewed by 2431
Abstract
As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. [...] Read more.
As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. This study established two-dimensional hydrodynamic and oil spill models for the Lancang River crossing pipeline basin and verified the model’s accuracy. The oil spill transport process under different scenarios was simulated, and the oil spill transport state data set was established. The effects of river flow, wind, and leakage mode on the transport behavior of oil spills were studied. The results show that an increase in flow rate accelerates the migration, diffusion, and longitudinal extension behavior of oil spills; Changes in wind speed have less effect on the transport behavior of oil spills under downwind and headwind conditions. The mode of leakage mainly affects the diffusion and longitudinal extension of the oil spill. The oil spill transport state prediction model was established using machine learning combination algorithms. The three combined machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and the length of the oil spill contamination zone, respectively, with the coefficient of determination (R2) and the 1-Mean Absolute Percentage of Error (1-MAPE) above 0.971, and the prediction model has excellent accuracy. This study can provide support for the rapid development of emergency response plans for river crossing pipeline oil spill accidents. Full article
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22 pages, 5806 KiB  
Article
Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment
by Jiahao Chen, Jiaxin Li, Deqian Zheng, Qianru Zheng, Jiayi Zhang, Meimei Wu and Chaosai Liu
Appl. Sci. 2024, 14(7), 2960; https://doi.org/10.3390/app14072960 - 31 Mar 2024
Cited by 4 | Viewed by 1510
Abstract
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 [...] Read more.
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. Full article
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14 pages, 9645 KiB  
Article
Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
by Jun Zhao, Rongyao Chen, Shikang Liu, Shanshan Zhou, Mingbiao Xu and Feixu Dai
Polymers 2023, 15(22), 4441; https://doi.org/10.3390/polym15224441 - 17 Nov 2023
Viewed by 1602
Abstract
Oil well cement is prone to corrosion and damage in carbon dioxide (CO2) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on [...] Read more.
Oil well cement is prone to corrosion and damage in carbon dioxide (CO2) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on the mechanical and corrosion properties of oil well cement was studied. The corrosion law of polymer anti-corrosion cement in an acidic gas environment was studied. The long-term corrosion degree of polymer anti-corrosion cement was evaluated using an improved neural network model. The cluster particle algorithm (PSO) was used to improve the accuracy of the neural network model. The results indicate that in acidic gas environments, the compressive strength of polymer anti-corrosion cement was reduced under the effect of CO2, and the corrosion depth was increased. The R2 of the prediction model PSO-BPNN3 is 0.9970, and the test error is 0.0136. When corroded for 365 days at 50 °C and 25 MPa pressure of CO2, the corrosion degree of the polymer anti-corrosion cement was 43.6%. The corrosion depth of uncorroded cement stone is 76.69%, which is relatively reduced by 33.09%. The corrosion resistance of cement can be effectively improved by using polymer resin. Using the PSO-BP neural network to evaluate the long-term corrosion changes of polymer anti-corrosion cement under complex acidic gas conditions guides the evaluation of its corrosion resistance. Full article
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15 pages, 2399 KiB  
Article
Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application
by Wei Wang, Ran Liang, Yun Qi, Xinchao Cui, Jiao Liu and Kailong Xue
Fire 2023, 6(10), 381; https://doi.org/10.3390/fire6100381 - 7 Oct 2023
Cited by 9 | Viewed by 1900
Abstract
The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can [...] Read more.
The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can be taken to avoid the occurrence of fires. In order to accurately predict the limit parameters of coal spontaneous combustion, the prediction model of coal spontaneous combustion limit parameters based on GA-SVM was constructed by coupling genetic algorithm (GA) and support vector machine (SVM). Meanwhile, the GA and particle swarm optimization algorithm (PSO) were used to optimize the back propagation neural network (BPNN) to construct the GA-BPNN and PSO-BPNN prediction models, respectively. To predict the intensity of air leakage of the upper limit of coal spontaneous combustion in the goaf, the prediction results of the models were compared and analyzed using MAE, MAPE, RMSE, and R2 as the prediction performance evaluation indexes. The results show that the MAE of the GA-SVM model, the PSO-BPNN model, and the GA-BPNN model are 0.0960, 0.1086, and 0.1309, respectively; the MAPE is 2.46%, 3.11%, and 3.69%, respectively; the RMSE is 0.1180, 0.1789, and 0.2212, respectively; and the R2 is 0.9921, 0.9818, and 0.9722. The prediction results of the GA-SVM model are the most optimal in four evaluation indexes, followed by the PSO-BPNN and the GA-BPNN models. Applying each model to the prediction of minimum residual coal thickness in the goaf of a coal mine in Shanxi, the GA-SVM model has higher accuracy, which further verifies the universality and stability of the model and its suitability for the prediction of coal spontaneous combustion limit parameters. Full article
(This article belongs to the Special Issue Advance in Fire Safety Science)
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17 pages, 4270 KiB  
Article
Establishment and Accuracy Evaluation of Cotton Leaf Chlorophyll Content Prediction Model Combined with Hyperspectral Image and Feature Variable Selection
by Siyao Yu, Haoran Bu, Xue Hu, Wancheng Dong and Lixin Zhang
Agronomy 2023, 13(8), 2120; https://doi.org/10.3390/agronomy13082120 - 13 Aug 2023
Cited by 6 | Viewed by 2015
Abstract
In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative detection research. Through correlation spectroscopy (COS), a total of [...] Read more.
In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative detection research. Through correlation spectroscopy (COS), a total of 882 representative samples from the seedling stage, bud stage, and flowering and boll stage were used for feature wavelength screening, resulting in 213 selected feature wavelengths. Based on all wavelengths and selected feature wavelengths, a backpropagation neural network (BPNN), a backpropagation neural network optimized by genetic algorithm (GA-BPNN), a backpropagation neural network optimized by particle swarm optimization (PSO-BPNN), and a backpropagation neural network optimized by sparrow search algorithm (SSA-BPNN) prediction models were established for cotton leaf chlorophyll content, and model performance comparisons were conducted. The research results indicate that the GA-BPNN, PSO-BPNN, and SSA-BPNN models established based on all wavelengths and selected feature wavelengths outperform the BPNN model in terms of performance. Among them, the SSA-BPNN model (referred to as COS-SSA-BPNN model) established using 213 feature wavelengths extracted through correlation analysis showed the best performance. Its determination coefficient and root-mean-square error for the prediction set were 0.920 and 3.26% respectively, with a relative analysis error of 3.524. In addition, the innovative introduction of orthogonal experiments validated the performance of the model, and the results indicated that the optimal solution for achieving the best model performance was the SSA-BPNN model built with 213 feature wavelengths extracted using the COS method. These findings indicate that the combination of hyperspectral data with the COS-SSA-BPNN model can effectively achieve quantitative detection of cotton leaf chlorophyll content. The results of this study provide technical support and reference for the development of low-cost cotton leaf chlorophyll content detection systems. Full article
(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture)
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25 pages, 5302 KiB  
Article
Monthly Runoff Forecasting Using Particle Swarm Optimization Coupled with Flower Pollination Algorithm-Based Deep Belief Networks: A Case Study in the Yalong River Basin
by Zhaoxin Yue, Huaizhi Liu and Hui Zhou
Water 2023, 15(15), 2704; https://doi.org/10.3390/w15152704 - 27 Jul 2023
Cited by 8 | Viewed by 1616
Abstract
Accuracy in monthly runoff forecasting is of great significance in the full utilization of flood and drought control and of water resources. Data-driven models have been proposed to improve monthly runoff forecasting in recent years. To effectively promote the prediction effect of monthly [...] Read more.
Accuracy in monthly runoff forecasting is of great significance in the full utilization of flood and drought control and of water resources. Data-driven models have been proposed to improve monthly runoff forecasting in recent years. To effectively promote the prediction effect of monthly runoff, a novel hybrid data-driven model using particle swarm optimization coupled with flower pollination algorithm-based deep belief networks (PSO-FPA-DBNs) was proposed, which selected the optimal network depth via PSO and searched for the optimum hyper parameters (the number of neurons in the hidden layer and the learning rate of the RBMs) in the DBN using FPA. The methodology was divided into three steps: (i) the Comprehensive Basin Response (COM) was constructed and calculated to characterize the hydrological state of the basin, (ii) the information entropy algorithm was adopted to select the key factors, and (iii) the novel model was proposed for monthly runoff forecasting. We systematically compared the PSO-FPA-DBN model with the traditional prediction models (i.e., the backpropagation neural network (BPNN), support vector machines (SVM), deep belief networks (DBN)), and other improved models (DBN-PLSR, PSO-GA-DBN, and PSO-ACO-DBN) for monthly runoff forecasting by using an original dataset. Experimental results demonstrated that our PSO-FPA-DBN model outperformed the peer models, with a mean absolute percentage error (MAPE) of 18.23%, root mean squared error (RMSE) of 230.45 m3/s, coefficient of determination (DC) of 0.9389, and qualified rate (QR) of 64.2% for the data from the Yalong River Basin. Also, the stability of our PSO-FPA-DBN model was evaluated. The proposed model might adapt effectively to the nonlinear characteristics of monthly runoff forecasting; therefore, it could obtain accurate and reliable runoff forecasting results. Full article
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