Temperature Prediction Model Research Articles

Statistical-empirical pavement temperature prediction models based on data from road weather stations in Estonia

Asphalt pavement properties are greatly affected by seasonal temperature variation. Binder grade selection needs to be aligned with forecasted operational pavement temperatures to maximise pavement longevity. Different widely adopted models for determining binder Superpave Performance Grading have been shown to provide inaccurate results for the Nordic-Baltic region, leading to under- or overestimation of operational pavement temperatures and erroneous selection of a suitable binder grade. This study proposes empirically formulated pavement temperature prediction models for Estonia based on temperature data collected from six different road weather stations in Estonia from 2019 to 2023. Models were derived using measurements from daily maximum and minimum air temperatures combined with pavement temperature profiles with depth.

PSO-FDM (Particle Swarm Optimization-Finite Difference Method)-Based Simulation Model of Temperature and Velocity of Full-Scale Continuous Annealing Furnace

Improving the accuracy of the temperature field prediction model for continuous annealing line strips and enhancing the model’s adaptability to full-size strips are key technical challenges in continuous annealing lines. This paper developed a continuous annealing temperature prediction model based on a variable step-size strategy for the heating section, even-heat section, slow-cooling section, and fast-cooling section of the continuous annealing line. To improve the prediction accuracy for different strip sizes, the PSO optimization algorithm was employed to determine the optimal heat transfer coefficient for each strip size. Additionally, due to the limited production of certain strip gauges, providing insufficient data for optimization, this study introduces a combined file approach to address gauge vacancies. The experimental results indicate that the optimized model with variable step size can control the absolute prediction error to less than 4 °C, improving prediction accuracy by 61.9% and prediction speed by 26.8% compared to the traditional equal-step prediction model. This study verified that the merger method is effective for addressing side gauge vacancies, while the proposed method is suitable for resolving middle gauge vacancies. The main technical contribution of this study is the establishment of a high-precision prediction model for continuous annealing temperature of variable step length strips, ensuring high temperature control accuracy for full-gauge strips when passing through the continuous annealing production line.

Grain storage temperature prediction based on chaos and enhanced RBF neural network

Grain storage has very strict temperature requirements. Aiming at the problems of nonlinear characteristics and poor prediction accuracy of temperature parameters in grain storage, a combination of chaos theory and enhanced radial basis neural network is proposed as a temperature prediction model for grain storage (C-ERBF). The model first determines the embedding dimension and time delay of the grain storage temperature sequence using chaos theory. It then calculates the Lyapunov exponent to confirm its chaotic properties and reconstructs the sequence in the phase space to extract the hidden dynamic information and structure behind the sequence. Furthermore, the q-Normalized Least Mean Square Fourth (qXE-NLMF) algorithm is designed to enhance the radial basis function (RBF) neural network model for weight updating, to improve its prediction accuracy, and to accelerate the training speed of the model. As verified by the simulation experiments of Mackey–Glass chaotic time series prediction, the enhanced RBF (ERBF) network has faster convergence speed and lower steady-state error compared to the traditional RBF network. Finally, the optimized dataset from chaos theory is input into the model to achieve accurate predictions of grain storage temperature series. The experimental results show that the proposed C-ERBF model has higher prediction accuracy compared to other time series prediction methods. It can realize the grain pile temperature in advance, and take control measures in advance. This proactive approach significantly reduces the consumption of stored grain and prevents issues before they arise.

Junction Temperature Prediction and Lifetime Assessment in a PV Inverter Using a 10-year Mission profile

The expansion of great-scale photovoltaic (PV) power plants indicates the need for an accurate lifetime assessment of inverters to maintain energy supply availability. In this context, the study contributes in two ways. First, we use machine-learning (ML) models for junction temperature prediction. Second, we perform reliability assessments using a 10-year mission profile in three Brazilian cities. The thermal loadings are obtained through a look-up table approach. Although the ML models exhibit different performances in regression, other factors must be considered, such as easy-to-apply, interpretability, and generalization capability. The reliability assessment is typically based on an annual mission profile, assuming damage repeats until failure. However, only the historical series can confirm whether this choice was acceptable, pessimistic, or optimistic. For instance, in Campos do Jordão-SP, if the chosen mission profile is 2014, the expected failure of 10\% of inverter samples occurs three years earlier than suggested by the historical series. Regardless of the methodology used to estimate thermal loading or accumulated damage, the mission profile significantly influences photovoltaic inverter reliability, indicating that if more data is available, the chosen mission profile should align with the historical series.

Simulation of Friction Stir Welding of AZ31 Mg Alloys.

Friction stir welding has been extensively applied for the high-quality bonding of Mg alloys. The welding temperature caused by friction and plastic deformation is essential for determining the joint characteristics, especially the residual stress and weld microstructure. In this work, a modified moving heat source model was proposed by considering the variations in heat generation caused by friction shear stress at both the side and bottom surfaces of the tool. The application of this model was further extended to the entire welding process, especially in the plunging stage. The relative errors between the experimental and simulated peak temperatures at characteristic points were small, with a maximum of 10%, thereby validating the model for accurate temperature prediction. Furthermore, the influence of welding and rotational speed on temperature fields was systematically investigated. At relatively low welding and rotational speeds, the welding temperature increased significantly with either an increase in rotational speed or a decrease in welding speed. However, this effect gradually diminished at higher welding and rotational speeds. These results provide some valuable guidelines for controlling heat generation to improve the quality of Mg alloy welds.

A Predictive Model for Sintering Ignition Temperature Based on a CNN-LSTM Neural Network with an Attention Mechanism

The sintering ignition process parameters fluctuate frequently and significantly, resulting in large variations in ignition temperature, which in severe cases can exceed 200 °C. This not only increases gas consumption but also affects the quality of the sinter. Because the intelligent control model based on feedback mechanisms struggles to deal with high-frequency fluctuation conditions over time, the prediction of sintering ignition temperature using feedforward regulation is attracting increasing attention. Given the multi-variable, time-sequential and strongly coupled characteristics of the sintering ignition process, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are deeply integrated, with an attention mechanism incorporated to develop the sintering ignition temperature prediction model, enabling the accurate prediction of the ignition temperature. The research demonstrates that the combination of a CNN and the attention mechanism effectively addresses the challenges posed by the multi-variable and strongly coupled nature of sintering ignition data to predictive modeling. The LSTM network resolves the sequential data issues through its gating mechanism. As a result, the coefficient of determination (R2 ) of the CNN_LSTM-Attention model in predicting the sintering ignition temperature can reach 0.97, with a mean absolute error (MAE) as low as 10.23 °C. The predicted values closely match the actual values, achieving a hit rate of 93% within the acceptable error range. These performance metrics are significantly superior to those of the CNN-Attention and LSTM-Attention models, greatly enhancing the control accuracy of the ignition temperature.

Accurate Oil Temperature Prediction Model and Oil Refilling Parameters Optimization for Hydraulic Closed-Circuit System

Oil temperature plays a crucial role in hydraulic closed-circuit systems (HCS), and conventional thermal equilibrium models and coupled simulation models face challenges in terms of accuracy, efficiency, and cost when calculating oil temperature. This study introduces an innovative HCS oil temperature precise prediction model and oil refilling parameter optimization method. The initial sample space was determined through a Sobol sensitivity analysis and improved Latin hypercube sampling, leading to the development of a combinatorial agent model (CAM) suitable for oil temperature prediction with superior accuracy and stability compared to other methods. Based on CAM, the optimal oil refilling flow rates under various operational conditions are computed. To validate the efficacy of the theoretical analysis, an HCS experiment platform was established. The data indicates that the temperature prediction error range of the CAM model falls between 0.30 °C and 1.05 °C, and optimizing the oil refilling flow rate can effectively enhance system efficiency while ensuring that the oil temperature remains within permissible limits. The research methodology and findings are applicable in engineering practice and can be extended to optimize the design of other hydraulic systems.

Prediction of Evaporation Temperature in Air-Water Heat Source Heat Pump Based on Artificial Neural Network

To improve the low-temperature heating stability and performance of the air-source heat pump (ASHP), a heat transfer model was established for an air-water heat source heat pump(A-WSHP) in this study. Key factors affecting A-WSHP evaporating temperature were analyzed through simulation. An artificial neural network (ANN) prediction model for refrigerant evaporating temperature was developed using fin pitch, air velocity, air temperature, spray flow rate, spray water temperature, and droplet diameter as inputs. In the winter climate of Xiangtan City, this model predicts the refrigerant evaporating temperature and regulates it by adjusting various input parameters. Results indicate that fin pitch and droplet size significantly impact the refrigerant evaporating temperature, more so than fin thickness. The air temperature (34.78%) and spray water temperature (38.02%) have the highest weights, while fin thickness has the least influence, with a weight of 0.75%. The R2 value of the ANN refrigerant evaporating temperature prediction model is 0.974, indicating a good fit. By properly adjusting the heat pump fan and spray valve, changes in air velocity and spray flow rate can enhance the refrigerant evaporating temperature, leading to stable and efficient heat pump operation. This research not only provides a theoretical basis for the operational strategy of air-water heat pumps but also offers guidance for frost-free operation in low-temperature environments.

Optimized BP-LSTM neural network based on IGA for heat exchanger outlet temperature prediction

Abstract In this paper, an improved Genetic Algorithm (IGA) is proposed to optimize the BP-LSTM neural network for predicting heat exchanger temperature. The study introduces an enhanced genetic algorithm designed to optimize both short- and long-term memory networks (LSTM) in conjunction with the BP neural network temperature prediction model and the BP-LSTM neural network model. Simulation and comparison results demonstrate that the proposed prediction method can effectively forecast heat exchanger temperatures.

Study on the influencing factors of asphalt mixture compaction quality test method based on simulation and experimentation

The compaction quality and compaction temperature of asphalt pavement during construction are crucial to the durability of pavement. However, the current methods for testing the compaction quality and compaction temperature of pavement have the drawbacks of low test efficiency and inability to truly reflect the compaction state. Therefore, this study investigated the factors affecting the test method of asphalt mixture compaction quality and compaction temperature based on simulation and experimentation. The effect of environmental factors on the surface and internal temperature of mixture was revealed based on temperature measurement equipment. Meanwhile, the dynamic analysis and finite element simulation were used to investigate the effect of temperature, compactness and base modulus on the compaction quality test index. Subsequently, the compaction quality prediction model was constructed based on support vector machine. Based on that, the effect of temperature, compactness and base modulus on compaction quality was verified based on indoor vibration compaction experiment. The results clearly showed that the function with average relative deviation of 1.7 % including environmental factors was established as compaction temperature prediction model based on support vector machine. The vibration acceleration amplitude was chosen as the compaction quality test index based on dynamic analysis and numerical simulation. The effect of temperature on the relationship between compactness and vibration acceleration amplitude was more obvious when the compactness was larger and the temperature was lower. Compared with the flexible base modulus, the semi-rigid base modulus had more influence on compaction quality. The results of vibration compaction experiment verified the feasibility of compaction quality prediction model constructed based on numerical simulation, and the average relative deviation of prediction model was only 0.44 %.

Research on Temperature Prediction based on BP Neural Network Approach

Weather changes have an important impact on human production and life as well as socio-economic activities. In recent years, people have also gradually paid attention to the study of weather, especially the study of temperature, and it is of great practical significance to find an accurate temperature prediction model. At the same time, the use of machine learning methods to construct a temperature model can predict temperature changes more accurately and quickly, and provide a more reliable basis for weather forecasting. Therefore, in response to the unsatisfactory accuracy of temperature prediction by traditional prediction models, this paper uses the time series of daily average temperature of Hangzhou City, China, from 2013 to 2022 as the sample data, and combines the traditional ARMA model with the BP neural network model, which enables the BP neural network model to better fit and predict the temperature changes. The study shows that the prediction accuracy of the BP neural network model is significantly improved compared with the ARMA model.

Study on microwave ablation temperature prediction model based on grayscale ultrasound texture and machine learning.

Temperature prediction is crucial in the clinical ablation treatment of liver cancer, as it can be used to estimate the coagulation zone of microwave ablation. Experiments were conducted on 83 fresh ex vivo porcine liver tissues at two ablation powers of 15 W and 20 W. Ultrasound grayscale images and temperature data from multiple sampling points were collected. The machine learning method of random forests was used to train the selected texture features, obtaining temperature prediction models for sampling points and the entire ultrasound imaging area. The accuracy of the algorithm was assessed by measuring the area of the hyperechoic area in the porcine liver tissue cross-section and ultrasound grayscale images. The model exhibited a high degree of accuracy in temperature prediction and the identification of coagulation zone. Within the test sets for the 15 W and 20 W power groups, the average absolute error for temperature prediction was 1.14°C and 4.73°C, respectively. Notably, the model's accuracy in measuring the area of coagulation was higher than that of traditional ultrasonic grey-scale imaging, with error ratios of 0.402 and 0.182 for the respective power groups. Additionally, the model can filter out texture features with a high correlation to temperature, providing a certain degree of interpretability. The temperature prediction model proposed in this study can be applied to temperature monitoring and coagulation zone range assessment in microwave ablation.

Maximum temperature of steel bridges due to multiple under-bridge fire sources

In this study, an analytical model was developed and predictive equations were proposed to estimate the maximum temperature of steel bridges in the event of a fire originating from complex facilities beneath the bridge. Based on statistical data, a representative shape of steel bridges in South Korea was selected. A representative Heat Release Rate (HRR) curve for the complex facility was selected, and a parametric study was conducted, including variables such as Heat Release Rate Per Unit Area (HRRPUA), fully developed time, vertical clearance, and the area of the complex facility. Through correlation analysis, key parameters influencing the maximum temperature in steel bridges were examined. Based on the results, both linear and non-linear temperature prediction equations were suggested. The non-linear equation demonstrated higher predictive performance than the linear equation. This study provides foundational data for enhancing the accuracy of fire analysis methods and temperature prediction models for steel bridges.

Model Predictive Temperature Control for Retinal Laser Treatments.

Manual, individual adjustment of the laser power in retinal laser therapies is time-consuming, is inaccurate with respect to uniform effects, and can only prevent over- or undertreatment to a limited extent. Automatic closed-loop temperature control allows for similar temperatures at each irradiated spot despite varying absorption. This is of crucial importance for subdamaging hyperthermal treatments with no visible effects and the safety of photocoagulation with short irradiation times. The aim of this work is to perform extensive experiments on porcine eye explants to demonstrate the benefits of automatic control in retinal laser treatments. To ensure a safe and reliable temperature rise, we utilize a model predictive controller. For model predictive control, the current state and the spot-dependent absorption coefficients are estimated by an extended Kalman filter (EKF). Therein, optoacoustic measurements are used to determine the temperature rise at the irradiated areas in real time. We use fluorescence vitality stains to measure the lesion size and validate the proposed control strategy. By comparing the lesion size with temperature values for cell death, we found that the EKF accurately estimates the peak temperature. Furthermore, the proposed closed-loop control scheme works reliably with regard to similar lesion sizes despite varying absorption with a smaller spread in lesion size compared to open-loop control. Our closed-loop control approach enables a safe subdamaging treatment and lowers the risk for over- and undertreatment for mild coagulations in retinal laser therapies. We demonstrate that modern control strategies have the potential to improve retinal laser treatments for several diseases.

Practical Application of Model Predictive Zonal Temperature Estimation and Control for Vehicle Cabins

Practical Application of Model Predictive Zonal Temperature Estimation and Control for Vehicle Cabins

Computational and intelligence modeling analysis of pharmaceutical freeze drying for prediction of temperature in the process

Accurate temperature prediction is crucial for various scientific and engineering applications, yet it remains challenging due to the complex relationships between spatial coordinates and temperature variations. This study addresses this challenge by exploring advanced machine learning models to improve prediction accuracy, filling the research gap in optimizing predictive models for temperature estimation in freeze drying of pharmaceuticals. We employed Support Vector Regression (SVR), Poisson Regression (POR), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, each enhanced using the Cheetah Optimizer (CO), to estimate temperature based on spatial coordinates (X, Y, Z) in the domain of process. The methodology involved training and testing these models on a dataset comprising spatial and temperature data, with a focus on improving accuracy through optimization techniques. Validation of the models showed that the CO-SVR model outperformed the others, achieving an R2 score of 0.953, Mean Squared Error (MSE) of 9.893, and Mean Absolute Error (MAE) of 2.549. This represents a significant improvement over the CO-ANFIS model, which obtained an R2 of 0.863, MSE of 23.583, and MAE of 3.912. The CO-POR model showed the lowest predictive capability, with an R2 score of 0.753, MSE of 57.608, and MAE of 6.756. These findings underscore the effectiveness of the Cheetah Optimizer in enhancing the accuracy of SVR models for temperature prediction, suggesting its potential for broader applications in predictive modeling where accuracy is paramount.

Research on time characteristic sea surface temperature prediction based on decomposition

Abstract The effective prediction of sea surface temperature has become a hot topic of scientific research. The hidden characteristics and time characteristics of sea surface temperature are studied, and a new sea surface temperature prediction model SLA based on decomposition is proposed in this paper. Firstly, the trend term and periodic seasonal term of sea surface temperature were decomposed by STL decomposition algorithm, which improved the problem of single characteristics of sea surface. Secondly, on the basis of the calculation method of the improved LSTM model and the attention mechanism, the problem that the prediction of sea surface temperature cannot be remembered in the long term and the influence weight of historical information is ignored is solved. Finally, the sea surface temperature of the South China Sea (112.125E,10.125N) was taken as the research object, and the experimental verification was carried out in three data sets. The experimental results show that the SLA model proposed in this paper has good prediction accuracy.

Prediction of airflow temperature in coal mining face under the influence of multiple heat sources coupling: Numerical simulation and verification

With the development of coal resources to the deep, the mine heat damage has become increasingly serious. In this work, we proposed a comprehensive airflow temperature prediction model considering the coupling effect of multiple heat sources in coal mining face. The model was then discretized and a coupled solution simulator was developed independently. Finally, we conducted field test to verify the simulation results, and carried out a multi-factor sensitivity analysis affecting the thermal environment of coal mining face. The results show that:(i) The maximum relative error of prediction results is less than 4 %, which verifies the reliability of the prediction model. (ii) Heat dissipation from electrical equipment, surrounding rock and hot airflow leakage of gob area account for 43.8 %, 28.8 % and 17.4 % of the total heat load, which are the main causes of heat damage. (iii) The maximum airflow temperature in coal mining face increases in a parabolic trend with average advance speed. (iv) As the original rock temperature increases, the thermal environment of coal mining face deteriorates rapidly. (v) Cooling the inlet airflow or increasing the airflow volume can alleviate the high temperature problem. This research can provide guidance for the treatment of deep mine heat damage.

Temperature prediction of submerged arc furnace in ironmaking industry based on residual spatial-temporal convolutional neural network

The submerged arc furnace is widely regarded as one of the most promising ore smelting technologies. However, the real-time monitoring of the multiple physical fields including electric, thermal, and mas, through computational fluid dynamics demands significant computational resources. This paper introduces the spatial-temporal convolutional neural network algorithm to address this challenge. Initially, the influences of various working conditions on these physical fields are analyzed. Subsequently, a prediction model is developed based on the coupling of these multiple physical fields model. The spatial-temporal convolutional neural network algorithm is then employed to elucidate the main parameter distributions, enabling the automatic real-time detection of temperature variation trends and providing a theoretical foundation for intelligent furnace operation. The findings indicate that the electric field is the predominant factor causing non-uniform heat distribution, with localized overheating primarily occurring at the electrode ends. The application of the proposed model facilitates dynamic prediction of the temperature distribution, establishing relationships between historical and future time steps as well as local and global temperature variations. The reliability of the temperature prediction model is confirmed, with the model achieving an accuracy of 99.76 %, surpassing the 98.18 % accuracy of the traditional multi-layer perceptron model.

Improving Module Temperature Prediction Models for Floating Photovoltaic Systems: Analytical Insights from Operational Data

Floating photovoltaic (FPV) systems are gaining popularity as a valuable means of harnessing solar energy on unused water surfaces. However, a significant gap persists in our comprehension of their thermal dynamics and the purported cooling benefits they provide. The lack of comprehensive monitoring data across different climatic regions and topographies aggravates this uncertainty. This paper reviews the applicability of established module temperature prediction models, originally developed for land-based PV systems, to FPVs. It then details the refinement of these models using FPV-specific data and their subsequent validation through large-scale, ongoing FPV projects. The result is a significant improvement in the accuracy of temperature predictions, as evidenced by the reduced Mean Absolute Error (MAE) and improved R-squared (R2) after parameter optimisation. This reduction means that the tailored models better reflect the distinct environmental influences and cooling processes characteristic of FPV systems. The results not only confirm the success of the proposed method in refining the accuracy of current models, but also indicate significant post-tuning changes in the parameters representing wind and convective effects. These adjustments highlight the increased responsiveness of FPVs to convective actions, especially when compared to ground-based systems, possibly due to the evaporative cooling effect of bodies of water. Through this research, we address a critical gap in our understanding of heat transfer in FPV systems and aim to enrich the knowledge surrounding the acknowledged cooling effect of FPVs.