Prediction of the water content of biodiesel using ANN-MLP: An environmental application

Fuel quality, especially biodiesel, is highly dependent on its water content, and the major sources of water in the fuel relate to the transportation, production, and storage processes. In this present contribution, the multilayer perceptron artificial neural network (MLP-ANN) was applied to predict the water content of biodiesel and diesel blend in terms of temperature and composition. The proposed algorithm was trained and tested by utilizing 400 experimental data points which were extracted from the literature. Based on the results, the MLP-ANN model has great ability to estimate the water content of biodiesel and diesel blend. The R-squared (R²), root mean square error, average absolute relative deviation, and a?bsolute deviation parameters for the total data set are obtained, respectively, as 0.99784, 123919.1172, 3.3632, and 1.17%, which indicate the effective performance suggested by ANN. As the computational study is cheaper and easier than the experimental study, the developed software could be considered as an alternative for laboratory study, and the environmental effect of biodiesel and produced undesired product after biodiesel combustion which is directly related to the water content of biodiesel is estimable with the information released in this study.     

A novel approach based on artificial neural network for calibration of multi-hole pressure probes

Imperfections in the manufacturing process of flow measuring probes affect their measuring behavior. Nevertheless, in order to provide the highest possible accuracy, each individual multi-hole pressure probe has to be calibrated before using them in turbomachinery. This paper presents a novel method based on artificial neural networks (ANN) to predict the flow parameters of multi-hole pressure probes. A two-stage ANN approach using multilayer perceptron (MLP) is proposed in this study. The two-stage prediction approach involves two MLP networks, which represent the calibration data and the prediction error. For a given set of inputs, outputs from both networks are combined to estimate the measured value. The calibration data of a 5-hole probe at RWTH Aachen was used to develop and validate the proposed ANN models and two-stage prediction approach. The results showed that the ANN can predict the flow parameters with high accuracy. Using the two-stage approach, the prediction accuracy was further improved compared to polynomial functions, i.e. a commonly used method in probe calibration. Furthermore, the proposed approach offers high interpolation capabilities while preventing overfitting (i.e. failure to fit new data). Unlike polynomials, it is shown that the ANN based method can provide accurate predictions at intermediate points without large oscillations.     

基于高斯混合隐马尔科夫模型与人工神经网络的紧急换道行为预测方法

为了有效降低因驾驶员紧急换道行为而诱发的交通事故，提高道路交通事故链阻断效率，提出一种基于高斯混合隐马尔科夫模型（GMM-HMM）和人工神经网络（ANN）的紧急换道行为预测方法。首先利用GMM-HMM对车辆行驶状态以及驾驶行为连续观察序列进行换道意图辨识，采用ANN预测下一时段的驾驶行为，再预测换道过程中的横向加速度变化率，从而判断紧急换道的危险程度。驾驶员在环仿真实验及实车实验结果表明，该方法预测避险成功率达92.83%，实验避险成功率达90.32%。该方法能有效地对紧急换道行为进行提前警告与干预。     

A new method of predicting the saturation pressure of oil reservoir and its application

Saturation pressure is a vital parameter of oil reservoir which can reflect the oilfield characteristics and determine the oilfield development process, and it is determined by experiments in the laboratory in general. However, there was only one well with saturation pressure test in this target reservoir, and it is necessary to determine whether this parameter is right or not.In this work, we present a new method for quickly determining saturation pressure using machine learning algorithms, including random forest regressor (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN or NN). Using these approaches, saturation pressure was obtained by using the initial solution gas-oil ratio (GOR), temperature, API gravity and other reservoir-fluid data available in the oilfields. Compared with the empirical formula for saturation pressure calculation, the calculated result shows that the accuracy given from machine learning is higher than that from other formulas at home and abroad, and has a good match with the lab test. On the basis of the calculated saturation pressure, it can determine whether the reservoir enters into the stage of dissolved gas drive or not, which also provides the basis for maintaining the reservoir pressure by water injection in advance, rational development decision-making and work over measures.This approach above can provide technical guidance for predicting the saturation pressure in the development of different kinds of reservoirs, including the sandstone reservoirs and carbonate reservoirs.     

Thermal management in PEMFCs: The respective effects of porous media in the gas flow channel

This study aims to evaluate the convective heat transfer enhancement of the proton exchange membrane fuel cells (PEMFC) numerically. As the higher heat transfer surfaces lead to higher heat transfer rates, a flat plate porous layer is utilized in the gas flow channel (GFC). This enhancement in heat transfer stems from the corresponding modification in the temperature and velocity profiles. The influencing parameters on these profiles are the thickness, permeability, and porosity of the GFC porous layer. After performing the simulations, the results indicate that convective heat transfer has a direct relationship with GFC porous layer's thickness and permeability. However, lower values of porosity lead to the higher Nusselt numbers. Previous investigations have also mentioned the positive impact of the microporous layer (MPL) on the water management of these fuel cells. Therefore, six different sizes of MPL and the gas diffusion layer (GDL) are utilized to evaluate their impacts on the thermal management. Results indicate that although these sizes have negligible effects on the heat transfer, Nu increases by enhancing the total size of MPL and GDL. The results also show that thicker MPLs lead to higher heat transfer rates. The evaluation of the friction factor also indicates the adverse effect of the GFC porous layer, although this undesirable effect is negligible. Finally, all the simulated values are utilized to train an artificial neural network (ANN) model with high precision. This ANN model can produce more data for sensitivity analysis and presenting respective 3D diagrams of the influencing parameters on heat transfer.     

Air-steam gasification of municipal solid wastes (MSWs) for hydrogen production

Gasification process can be considered as a partial thermal oxidation, which results in the production of a mixture of useful gases (CO, H₂, CH₄, and other gaseous hydrocarbons), little quantities of carbon black (char), ash, and several organic impurities (tar). In this study, we introduced an artificial neural network (ANN) model to simulate the influence of operating conditions on the concentration of products during the gasification process of municipal solid wastes (MSW). Results showed when increasing the residence time, more char is gasified, leading to an increase in the greenhouse gas emissions. It is also found that a further increase in the residence time results in a constant rate of products due to the heat and mass transfer limitations.     

Comparison of modeling methods for wind power prediction: a critical study

Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.     

利用卡尔曼滤波和人工神经网络相结合的油藏井间连通性研究

油藏连通性的认识对于制定合理的开发调整方案和提高水驱油藏采收率具有重要意义。基于注采井的生产动态数据,建立一种卡尔曼滤波和人工神经网络相结合的分析方法,对油藏井间动态连通性进行定量表征研究。考虑到注入数据的噪声污染和注入信号在地层传播过程中的时滞影响,分别利用卡尔曼滤波算法和非线性扩散滤波器对注采数据进行预处理,从而减少注采数据对机器学习模型的干扰,提高连通性分析的准确性。基于预处理后的历史注采数据,对以生产井产液量为响应,注水井的注水量为输入的人工神经网络进行训练和参数优化,模拟和挖掘注采系统中的井间连通关系。通过对训练好的模型进行参数敏感性分析,量化油藏井间连通程度。应用所建模型和方法分析了均质、各向异性、包含封闭断层、具有高渗透带的4种典型特征油藏和实际非均质油藏的井间连通性。计算结果与油藏地质特征高度吻合,验证了该方法的实用性,可作为量化注采系统连通状况的有效方法。     

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.