Prediction of Bending Force in the Hot Strip Rolling Process Using Multilayer Extreme Learning Machine

Traffic flow modeling has a pivotal role within Intelligent Transportation Systems (ITSs), holding vital importance in alleviating traffic congestion and decreasing carbon emissions. Due to the presence of variability and nonlinear attributes in traffic flow, developing an effective and resilient model for predicting traffic flow poses a significant challenge. Precisely predicting traffic flow is not merely a feasible issue; it also poses significant difficulties to the researchers involved in this field. This study proposes a hybrid predictive model to forecast traffic flow. The proposed model effectively merges the strengths of the Sparrow Search algorithm (SSA) and Multi-layer Extreme Learning Machine (ML-ELM) model, enhancing prediction accuracy. SSA optimization technique is applied to optimize the initial weights and bias parameters for ML-ELM model. ELM approach is a machine learning approach that employs a single hidden layer to address various tasks. However, in situations where more complex problems are encountered, ML-ELM extends this concept by incorporating multiple hidden layers to enhance its capabilities and address challenges more effectively. Finally, SSA technique is utilized to achieve the optimal tuning of hyperparameters in the context of ML-ELM model to improve the prediction accuracy. Compared to the other selected models, the proposed model outperforms them in terms of performance metrics, including Root Mean Square Errors (RMSE), Mean Absolute Errors (MAE), Mean Absolute Percentage Errors (MAPE) and Correlation Coefficients (r), indicating that it is appropriate for this prediction task.

In the hot strip rolling (HSR) process, accurate prediction of bending force can improve the control accuracy of the strip crown and flatness, and further improve the strip shape quality. In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection operator (LASSO), and Gaussian Process Regression (GPR), were applied to predict the bending force in the HSR process. A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. The prediction performance of the six models was assessed using three evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results show that the GPR model is considered as the optimal model for bending force prediction with the best prediction accuracy, better stability, and acceptable computational cost. The prediction accuracy and stability of CART and ANN are slightly lower than that of GPR. Although BRT also shows a good combination of prediction accuracy and computational cost, the stability of BRT is the worst in the six models. SVM not only has poor prediction accuracy, but also has the highest computational cost while LASSO showed the worst prediction accuracy.

The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan’an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling‐Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI‐1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI‐1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting.

The influence of lubrication on profile and flatness control during asymmetry self-compensating work roll (ASR) hot strip rolling process is studied in order to meet the requirement of schedule-free rolling (SFR) campaigns for non-oriented electrical steel production with identical strip width. The effects of lubrication on rolling force, surface topography, and work roll wear are analyzed through experimental data. A work roll wear model for a wide non-oriented electrical steel production considered lubrication during the hot rolling process is built, which can accurately reflect the work roll wear during the hot rolling process with lubrication. A 3D finite element model for roll stack considering lubrication is built. The effects of shifting strategy on ASR work roll wear contour, loaded roll gap crown, and edge drop are analyzed based on the models. The optimal shifting rhythm is 1, and the optimal shifting step is 2 ~ 3 mm. By combining the ASR technology with lubrication, the life of work rolls expands from 70 to 100 ~ 150 by different shifting step, the loaded roll gap crown can be efficiently controlled by bending force and the edge drop control ability can be improved efficiently within an entire rolling campaign.

Aiming at the problem of low precision of hot-rolled strip head thickness, a prediction model based on deep neural network (DNN) was presented. Considering the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Pearson correlation coefficient (R), the data of 2,005 coils collected from a hot rolling production line were used to establish the model. Hybrid particle swarm optimization and genetic algorithm (HPSO-GA) was used to optimize the prediction performance of DNN model. Parameters setting of DNN model and HPSO-GA, including learning rate, hidden neurons, selection of optimizer, population size and crossover probability were investigated to obtain the optimal model. Prediction performance comparisons of support vector regression (SVR), random forest (RF), DNN and HPSO-GA-DNN were presented. HPSO-GA-DNN had the smallest error and the highest R value, in which RMSE, MAE, MAPE and R value were 0.0197mm, 0.0151mm, 0.499% and 0.989, respectively. And 96.74% of the prediction data had an absolute error of less than 0.05 mm. The results showed that HPSO-GA-DNN had strong learning ability and good generalization performance, and could be well applied to hot rolling production.

Sewage treatment process has the following characteristics: nonlinear, delay etc, and is very complicated to establish the model for its control process. A reasonable model is set up for elaborate prediction effluent quality, which can satisfy the standard of the effluent water and requirements of energy saving simultaneously. Extreme learning machine (ELM), i.e the machine learning method that new lately developed has high accuracy, reliability and outstanding performance in prediction. To get higher prediction effect, in this paper, there are two ways are proposed to improve the ELM, (1) Optimizing the parameters. The ELM whose input weights and bias threshold are optimized by particle swarm optimization algorithm (PSO) and genetic algorithm (GA), respectively; (2) Changing learning mode. To develop an online sequential learning algorithm (OS) for the ELM with additive or radial basis function (RBF) hidden nodes in a unified framework. Therefore, the several comparison approaches refer to optimize the ELM, e.g., PSO-ELM, GA-ELM, OS-ELM are applied to effluent quality prediction, and chemical oxygen demand (COD) is taken as examples in this paper. The results show that PSO-ELM model has remarkably superior performance on effluent quality prediction than peer models in terms of mean absolute error, mean absolute percentage error, root mean square error, and coefficient of determination.

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

It is necessary to know the heat transfer intensity for predicting temperature distribution in the hot strip rolling process. The HTC (heat transfer coefficient) was usually obtained by the experiments and mathematical model. In this paper the HTC prediction was discussed based on the measured or target temperature by the proposed finite element method (FEM). The temperature evolution and HTC in the hot strip rolling process according to a certain plant were analyzed by the iteration calculation. The result shows that the HTC between strip and work roll was much more than the value in the air cooling and water cooling process. Furthermore, the HTC value is lower in the air cooling process compared with that of water cooling. The maximum and minimum value of HTC were about 1.5×105 (W/m2.K) and 80(W/m2.K) respectively. The temperature in the rough rolling according to the predicted HTC has been solved and the calculated results have a good agreement to the meausred value. Therefore, the research could be used to control the temperature distribution accurately and optimize the parameters.

Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. Monthly climatical parameters have been used as an input variable for simulating the evaporation rate. Several statistical measures (e.g., mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), and modified index of agreement (Md)), as well as graphical inspection, were used to compare the performances of the applied models. The results showed that the GBM model has much better performance in predicting monthly evaporation over two stations compared to other applied models. For the first case study which was in Diyala, the results showed a prediction enhancement in terms of MAE and RMSE by 7.17%, 21.01%; 16.51%, 15.74%; and 23.14%, 26.64%; using GBM compared to ELM, GPR, and QRF, respectively. However, for the second case study (in Erbil), the prediction enhancement was improved in terms of reduction of MAE and RMSE by 10.88%, 9.24%; 15.24%, 5%; and 16.06%, 15.76%; respectively, compared to ELM, GPR, and QRF models. The results of the proposed GMBM model can therefore assist local stakeholders in the management of water resources.

Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture

Experiments have proven that traditional prediction research methods have limitations in practice. Proposing countermeasures for environmental changes is the key to optimal control of the cold chain environment and reducing the lag of control effects. In this paper, a cold chain transportation environment prediction method, combining k-means++ and a long short-term memory (LSTM) neural network, is proposed according to the characteristics of the cold chain transportation environment of agricultural products. The proposed prediction model can predict the trend of cold chain environment changes in the next ten minutes, which allows cold chain vehicle managers to issue control instructions to the environmental control equipment in advance. The fusion process for temperature and humidity data measured by multiple data sensors is performed with the k-means++ algorithm, and then the fused data are fed into an LSTM neural network for prediction based on time series. The prediction error of the prediction model proposed in this paper is very satisfactory, with a root-mean-square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) and R-squared of 0.5707, 0.2484, 0.3258, 0.0312 and 0.9660, respectively, for temperature prediction, and with an RMSE, MAE, MSE, mean absolute percentage error and R-squared of 1.6015, 1.1770, 2.5648, 0.2736 and 0.9702, respectively, for humidity prediction. Finally, the LSTM neural network and back propagation (BP) neural network are compared in order to enhance the reliability of the results. In terms of the prediction effect of the temperature and humidity in cold chain vehicles transporting agricultural products, the proposed model has a higher prediction accuracy than that of existing models and can provide strategic support for the fine management and regulation of the cold chain transportation environment.

Recently, customers are demanding for hot rolled strip products to have tight oxide scales on the surfaces. Therefore, high finishing rolling temperature, low coiling temperature and fast finishing rolling speed have to be used to obtain tight oxide scale, which is different from conventional controlled rolling. In order to ensure the mechanical properties at the same time, a framework consisting of the Bayesian neural network and multi-objective particle swarm optimization has been established to determine the optimal hot strip rolling parameters. Due to excellent generalization ability, the Bayesian neural network was employed to develop the model for the prediction of mechanical properties of hot rolled automotive beam steels. The accuracy between the measured and predicted values was within ±30 MPa and ±4% for strength and elongation, respectively, providing a reliable model for the optimal process design. By applying multi-objective particle swarm optimization, the optimized hot rolling process was obtained for the production of hot rolled automotive beam steel with “Tight Oxide Scale”. Industrial trials have been carried out, which showed good agreement with the optimized hot strip rolling processes. It has been theoretically and practically proven that the optimal process design framework can effectively locate the optimal processing window for hot strip rolling.

In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit of the Yermarus Thermal Power Station (YTPS), India. Calculation of the accurate electrical output of a boiler in an operating system requires the knowledge of hundreds of operating parameters. The dimensionality of the input dataset is reduced by applying principal component analysis using IBM@SPSS Software. In the process of principal component analysis, a dataset of 232 parameters is standardized into 16 principal components. The total dataset collected is divided into training and testing datasets. The extreme learning machine is designed for various activation functions and the number of neurons. Sigmoid and hyperbolic tangent activation functions are studied here. Its generalization performance is examined in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square (RMSE), and Mean Absolute Percentage Error (MAPE). ELM and PCA–ELM are compared. In both the ELM and PCA–ELM models, when the extreme learning machine was designed with a sigmoid activation function with 100 nodes in the hidden layer, RMSE was 5.026 and 4.730, respectively. Therefore, the developed combined approach of PCA–ELM proved as a promising technique in forecasting with reduced errors and reduced time.

This article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models’ errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3).