小波神经网络在原油含水率预测中的应用研究

通过对原油含水率测量方法,以及原油含水率与影响其因素之间存在的非线性映射关系的研究,提出基于小波分解与神经网络相结合的小波神经网络原油含水率预测模型,给出具体的网络学习算法,并结合算法对原油含水率进行预测.实例分析表明,小波神经网络模型比传统的BP神经网络模型收敛速度快、预测精度高,且具有较强的学习能力和推广能力.     

煤炭发热量的二次回归方程预测分析

以灰分、全水分为煤样指标建模了煤炭发热量的非线性二次回归预测模型,通过测试及对比,该模型具有较高的预测精度,预测结果能够满足工程需要,预测效果优于线性回归模型及神经网络模型等。另外,该预测模型还具有容易程序实现、操作简便等特点     

基于灰色BP神经网络的水泥强度预测模型研究

水泥强度的预测具有多变量、非线性和大时滞特性,因此传统线性回归方法的结果不准确。除此之外,传统的神经网络预测可能对少量样本不够精确。本文建立灰色BP模型,以此来预测水泥的强度。建立一个多因素灰色模型GM(1,N)用于水泥化学成分的样本数据进行预处理,得到新的数据来作为建立预测模型的样本数据,通过BP神经网络建立预测模型。最终通过建立的灰色BP神经网络预测模型来预测28天水泥强度。仿真结果表明:灰色BP预测模型的效果比BP预测的要准确。     

基于广义回归神经网络的碳铵塔预测模型

采用改进的遗传算法优化广义回归神经网络(GRNN)的平滑参数,并运用GRNN的非线性映射能力,建立了碳铵塔出口碳化度和氨滴度的预测模型.检验结果表明,该模型具有良好的预测性能.     

基于贝叶斯理论的再生混凝土强度预测模型优势分析

碳中和背景下，再生混凝土的研究逐渐成为众多学者的研究焦点。贝叶斯理论以概率统计为基础，综合考虑主客观不确定性影响和先验信息的作用，适用于处理混凝土强度预测中的参数估计。针对再生混凝土强度的统计学特性，基于贝叶斯理论建立再生混凝土强度预测模型，通过与最小二乘回归模型和BP神经网络模型的对比，探究贝叶斯回归预测模型在再生混凝土强度预测上的优势。试验结果表明：线性与非线性回归模型的拟合优度均大于0.85，满足实际应用需求；线性与非线性贝叶斯回归模型的拟合优度，均高出最小二乘回归模型0.1～0.3，具有相对更好的预测精度和预测效果；贝叶斯回归模型克服了BP神经网络模型可解释性差、样本需求量大的缺陷，并且具有良好的鲁棒性和自适应性。     

基于Hammerstein-Wiener模型的连续搅拌反应釜神经网络预测控制

针对化工过程中广泛使用的连续搅拌反应釜(CSTR),提出一种基于神经网络的模型预测控制策略,采用分段最小二乘支持向量机辨识Hammerstein-Wiener模型系数的方法,在此基础上建立线性自回归模式〖DK〗(ARX)结构和高斯径向基神经网络串联的非线性预测控制器。利用BP神经网络训练预测控制输入序列和拟牛顿算法求解非线性预测控制律,从而实现一种基于支持向量机Hammerstein-Wiener辨识模型的非线性神经网络预测控制算法。对CSTR的仿真结果表明,该方法能够更有效地跟踪控制反应物浓度。     

活性炭吸附甲基橙模拟废水预测模型研究

分别采用BP人工神经网络算法及多元线性回归法,以实验所得的36组数据为样本,建立了以吸附时间、活性炭投加量及甲基橙废水浓度为输入变量,以活性炭吸附处理后甲基橙溶液的吸光度为输出变量的吸附预测模型,并进行了两模型预测效果的对比。结果表明,BP神经网络模型获得了比多元线性回归更好的拟合预测效果。使用BP神经网络模型可以实现同时考虑三个操作因素条件下活性炭吸附特性的预测,而且预测结果与实验数据吻合度较高,其预测样本最大和最小相对偏差分别为2.92%和0.029%,残差绝对值小于0.050 5。     

基于模糊非线性回归的煤炭发热量预测研究

针对经典线性回归模型无法反映变量间的非线性关系,不适宜预测有模糊数的煤炭发热量的问题,提出了一种基于三角模糊数的多元非线性回归的煤炭发热量预测模型。以我国新疆伊犁地区煤炭工业分析为建模数据和模型检验数据,将计算模糊中心值和模糊幅度值的问题转化为约束非线性优化问题,采用MATLAB优化工具箱求解。最后对比分析了模糊非线性回归、经典线性回归、BP(Back Propagation)神经网络及支持向量机回归4种模型对测试煤样发热量的预测结果。结果表明,模糊非线性回归模型的线性拟合优度值为0.9997,调整后的非线性拟合优度值为0.9838,均方误差为0.4473;测试煤样的平均相对误差为0.0203,80%的测试煤样模糊隶属度大于0.5。模糊非线性回归模型具有很高的精确度和可靠性,可用来预测预报煤炭发热量。     

基于改进灰色-多元回归组合预测模型的燃煤电厂智慧水务研究

以某燃煤电厂水务系统为研究对象,对机组运行参数和水量历史数据进行筛选和关联性分析,根据前期水平衡测试结果,结合响应面分析验证,发现机组负荷、蒸发损失系数、浓缩倍率和循环水温升这四个因素能够对全厂供水量产生关键性影响。基于灰色理论和多元非线性回归分析,分别建立各因素的灰色预测模型GM(1, 1),再将灰色模型预测值作为自变量输入到多元非线性回归方程中,得到了改进灰色-多元非线性回归组合的供水量预测模型,其模型拟合度R²为0.913且与真实值的平均相对误差为6.9%左右,实现了灰色模型和回归模型优势互补,有效地预测该电厂供水量未来变化;而供水量预测是智慧水务建设的关键所在,是水务管理和智能调度的主要依据,也是实现供水管网漏损和仪表故障报警的重要途径。     

航空发动机磨损趋势预测模型研究

文章讨论了神经网络的BP算法和遗传算法,提出用遗传算法来优化BP神经网络,应用遗传算法训练神经网络权重,实现网络结构的优化,用优化后的BP人工神经网络建立了航空发动机磨损故障趋势预测模型,利用发动机的光谱监测数据作为预测磨损趋势的特征参数,进行了模型的训练和预测试验,并将该模型预测结果与BP算法和多元线性回归法的预测结果进行了比较,证明了基于遗传算法的人工神经网络是航空发动机磨损故障趋势预测的一种理想方法。     

辽河含水超稠油粘度预测模型

研究了不同含水量时稠油的粘温性质,建立了辽河超稠油粘度与含水量的预测模型,并采用非线性回归与三次样条插值相结合的方法求解该模型,与直接用实验数据进行非线性回归得到的模型相比,该模型计算值与实测值更为接近。实验检验发现该模型能够较好的描述不同温度下超稠油粘度随含水量变化的规律。该模型为准确计算含水超稠油的粘度奠定了基础,为含水超稠油的存运提供了快速获得粘度数据的方法。     

Density measurements of coal samples by different probe gases and their interrelation

Density is useful in deducing the spatial structure of coals. In this paper, nitrogen has been used instead of the commonly employed helium, for the gas displacement pycnometer based density determination of a number of coals of Indian origin. The results show that the nitrogen-based densities are always higher than the helium-based ones. Also, empirical relationships between the helium-based and nitrogen-based coal densities have been developed by two modeling methods, namely, multi-variable regression and artificial neural networks. Although the two models have fared well, the neural network model exhibits a relatively better prediction accuracy and generalization performance than the regression model. This study thus demonstrates that nitrogen, which is cheaper and easily available, can be used gainfully as the probe gas for estimating the true density of coals.     

Dynamic crude oil fouling prediction in industrial preheaters using optimized ANN based moving window technique

The objective of this paper is to develop and validate a reliable, efficient and robust artificial neural network (ANN) model for online monitoring and prediction of crude oil fouling behavior for industrial shell and tube heat exchangers. To explore the complex dynamics of fouling, a new modeling strategy based on moving-window neural network approach is proposed. The essential character of this modeling approach is online updating of the ANN model whenever a new data block is available, so that it can effectively capture the slowly changing of process dynamics. The results of these models have been compared with appropriate sets of experimental data. The mean relative errors (MRE) of training and prediction subsets were about 6.61% and 8.06%, respectively. Since the data extraction in the refinery was performed every 2 h, the modeling approach led to an MRE of about 8% for fouling rate prediction of the next 50 h.     

Data-driven prediction model of indoor air quality in an underground space

Several data-driven prediction methods based on multiple linear regression (MLR), neural network (NN), and recurrent neural network (RNN) for the indoor air quality in a subway station are developed and compared. The RNN model can predict the air pollutant concentrations at a platform of a subway station by adding the previous temporal information of the pollutants on yesterday to the model. To optimize the prediction model, the variable importance in the projection (VIP) of the partial least squares (PLS) is used to select key input variables as a preprocessing step. The prediction models are applied to a real indoor air quality dataset from telemonitoring systems data (TMS), which exhibits some nonlinear dynamic behaviors show that the selected key variables have strong influence on the prediction performances of the models. It demonstrates that the RNN model has the ability to model the nonlinear and dynamic system, and the predicted result of the RNN model gives better modeling performance and higher interpretability than other data-driven prediction models.     

Maximum solid concentrations of coal water slurries predicted by neural network models

The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg-Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation.     

Artificial neural network-based prediction of hydrogen content of coal in power station boilers

Artificial neural networks (ANN) are powerful tools that can be used to model and investigate various highly complex and non-linear phenomena. This paper describes the development and training of a feed-forward back-propagation artificial neural network (BPNN), which is used to predict the hydrogen content in coal from proximate analysis. The ultimate objective is to enhance the performance of the combustion control system with the aid of regularly obtained knowledge of the elemental content of coal.In the present work, network modelling was performed using MATLAB with the Levenberg–Marquardt algorithm. Nine-hundred and three sets of data from a diverse range of coals have been used to develop the neural network architecture and topology. Trials were performed using one or two hidden layers with the number of neurons varied from 4 to 30. Validation data has been adopted to evaluate each trial and better model structure is determined to combat the over-fitting problem. As a result, it was found that a 4-12-1 or 4-8-4-1 network could give the most accurate prediction for this particular study. The regression analysis of the model tested gave a 0.937 correlation coefficient and the mean squared error of 0.0087. The average relative error is 5.46%. This has demonstrated that artificial neural networks have good potential for predicting elemental content of coal from frequently available proximate analysis data in power utilities.     

Physical refining of coconut oil: Effect of crude oil quality and deodorization conditions on neutral oil loss

In the present study, neutral oil loss (distillative and mechanical carry-over) during physical refining of coconut oil was quantified. Neutral oil loss seems to depend on both the crude oil quality and the process conditions during deodorization. The distillation of volatile glyceridic components (monoand diglycerides), originally present in the crude oil, was confirmed as the major cause for the neutral oil loss. The amount of these volatile components in crude coconut oils cannot be derived as such from the initial free fatty acid content. A lower deodorization pressure with less sparge steam resulted in a larger neutral oil loss than a higher pressure with more steam. A “deodorizability” test on a laboratory scale under standardized conditions (temperature=230°C, pressure=3 mbar, time=60 min, sparge steam=1%), to evaluate crude oil quality and to obtain a more accurate prediction of the expected neutral oil loss and free fatty acid content in the fatty acid distillate, is described.     

基于神经网络的常压塔柴油凝点软测量的研究与应用

研究某炼油厂常压塔三线柴油凝点的软测量建模问题,分析过程变量对柴油凝点的影响。基于在线分析仪6min采样数据,利用前向网络和时延前向网络(TDNN)分别建立了三线柴油凝点的静态软测量模型和动态软测量模型,并结合在线分析仪对模型实现了在线修正。通过两种模型的仿真和在线实施效果,表明基于神经网络的软测量模型取得了较好的应用效果,而且动态模型的实施效果优于静态模型。     

重力热管振荡传热特性RBF神经网络动态建模

The work address the problem of modeling the dynamical oscillating behavior during both unstable and stable operations, of an experimental thermosyphon. A standard RBF artificial neural network-based prediction model was developed for predicting the oscillating heat transfer of thermosyphon by means of input-output experimental measurements with the characteristics of time series. A comparison of prediction values between the RBF network and the MLP network was giving. The precision of RBF network was higher than that of the other neural networks such as BP-MLP network etc. The dynamical model of RBF network could be used to describe, predict and control the heat transfer process of a thermosyphon or a heat pipe system.