多因素多目标国Ⅴ排放生物柴油混合燃料发动机性能优化

在一台国Ⅴ排放高压共轨柴油机上燃用20%生物柴油-柴油混合燃料,通过调节共轨压力、主喷定时、预喷定时、预喷油量、后喷定时、后喷油量6个喷油控制参数研究生物柴油发动机的多因素多目标优化方法。采用混合试验设计方法分别针对低、中、高转速下的低、中、高负荷9个工况点设计试验方案,并进行台架试验采集发动机性能及排放数据。根据试验结果用混合径向基函数神经网络模型(Hybrid RBF)拟合发动机各项性能指标数学模型,设计优化方程并利用组合优化算法求解以比油耗(BSFC)最小和氮氧化物(NO_x)排放最小为目标的全局多目标最优解,弥补了传统发动机优化标定方法单因素逐一优化的局限性。优化结果使得所选取的9个工况点的BSFC平均降幅为1.31%,同时NOx排放平均降幅高达24.59%。     

基于机器学习的车用发动机性能预测及优化方法研究

随着人工智能技术的不断进步,基于机器学习的研究方法逐渐被应用于解决车用发动机性能优化问题。本文提出了一种基于机器学习的车用发动机性能预测及优化方法,并进行了案例研究:通过利用台架试验数据,建立了遗传算法-反向传播神经网络(GA-BPNN)预测模型,对发动机功率和有效燃油消耗率(BSFC)实现了较为准确的预测,误差率仅分别为1.58%和1.72%。此外,采用交叉遗传-粒子群(CMPSO)算法对功率和BSFC进行了多目标优化,将最优控制参数输入到台架试验中,得到的功率和BSFC的实际运行值与优化值基本一致。研究结果证明了本文提出的方法的有效性。该方法在保证一定精度的前提下,大幅减少了时间和经济成本的投入,为发动机性能优化研究提供了一种新的工作思路。     

增程器发动机的喷油策略优化

为了研究轨道压力、主喷正时、预喷正时及预喷油量这4个主要喷油参数对增程式电动汽车增程器(APU)发动机有效燃油消耗率和NOx排放特性的影响,根据APU发动机不同工况下的运行特点,结合整车功率需求及所匹配发电机的最佳工作区域,采用多工况点模式的基础控制策略,选取3个稳态工况点为APU发动机的运行工况。之后基于空间填充+V最优设计方法采用混合试验方案,利用二阶多项式回归模型+径向基函数(RBF)神经网络模型建立发动机性能和排放拟合模型。给出APU发动机的性能优化约束条件和优化目标,利用法线-边界交集优化算法(NBI)进行油耗最低和NOx排放最低的双目标优化,确定了3个稳态工况点的最佳喷油策略。结果表明各工况点在优化后的有效燃油消耗率平均降低了4.03%,NOx排放平均降低了30.51%。     

PSO RBF神经网络在城市需水量预测中的应用

为克服径向基函数（RBF）神经网络由于参数选取不当而对其收敛性能的干扰,利用粒子群优化算法（PSO）的全局搜索能力对RBF神经网络的三个参数进行寻优,建立了基于PSO RBF神经网络算法的城市需水量预测模型。结果显示,PSO RBF神经网络算法拟合某市1998～2007年需水量数据的平均相对误差为0.18%,预测2008～2010年需水量数据的平均相对误差为3.84%,耗时1.2 s;通过RBF神经网络算法拟合的误差平均值为0.28%,预测的平均相对误差为5.62%,耗时2.1 s,表明PSO RBF神经网络算法具有更高的收敛速度与精度。     

基于自适应粒子群优化BP神经网络的氨法烟气脱硫效率预测

针对氨法烟气脱硫效率的预测问题,建立了以脱硫系统运行中8个主要参数作为输入变量的BP神经网络模型,采用粒子群优化算法(PSO)对建立的BP神经网络模型的权值进行优化,提出基于粒子群优化算法的BP神经网络(PSO-BP)预测新模型,并利用某电厂脱硫系统20组运行数据对该模型进行了验证.结果表明:采用PSO算法对BP神经网络的权值和阈值进行寻优,避免了网络局部极小值的出现,提高了网络的泛化能力,采用PSO-BP预测模型可以对氨法烟气脱硫效率进行较高精度的预测.     

柴油机燃用柴油醇的性能与排放特性的研究

研究了两种15％柴油醇在柴油机上的性能与排放特性。在ZS1100柴油机上，使用两种15％的柴油醇(加与不加十六烷值改进剂)，在保持发动机动力性不变的条件下，测试了发动机在主要工况下的油耗、排气温度、烟度和一氧化碳(CO)、氮氧化物(NOx)、总碳氢(THC)等有害气体排放物，且与原机进行了比较。分析结果发现：在柴油中加入一定比例的乙醇，发动机的比油耗增加，但有效热效率可以提高1％～2％；发动机的烟度在各种工况下都能够显著降低；CO在低负荷下增加，在高负荷下减少；而NOx在低负荷下降低，却在大负荷下增加；THC在各种工况下都增加。在柴油醇中加入十六烷值改进剂后，发动机的经济性和热效率没有明显变化，但是CO和NOx在各种工况下都低于不加十六烷值的混合燃料，THC却进一步升高。     

基于粒子群优化算法的RBF神经网络在泾惠渠灌区地下水位埋深预测中的应用

针对不恰当地选取RBF神经网络的网络结构和参数会使网络收敛慢的问题,采用粒子群优化算法对RBF神经网络参数进行优化,建立了基于粒子群优化算法的RBF神经网络模型(PSO-RBF模型),对泾惠渠灌区地下水位埋深进行了模拟和预测。结果表明,与单一的RBF神经网络相比,PSO-RBF模型具有较高的预测精度。再根据时间序列预测法预测的降水量、径流量、蒸发量、渠灌引水量、地下水开采量、气温等模型的输入变量,用训练好的PSO-RBF模型预测了泾惠渠灌区2009~2020年地下水位埋深,发现该灌区地下水位埋深呈下降趋势。     

基于PSO优化的BP神经网络在电动机绝缘剩余寿命预测中的应用

针对普通的电动机绝缘剩余寿命预测模型收敛速度慢、结果偏差大的缺陷,提出了一种基于粒子群算法(PSO)优化BP神经网络的电动机绝缘剩余寿命预测模型。首先,利用PSO算法全局随机最优解搜索的特性,对传统BP神经网络模型的权值和阈值进行优化设计。其次,为便于预测模型的运算处理,对采集的三相异步电动机的数据进行归一化处理。最后,结合经PSO算法优化的BP神经网络模型对三相异步电动机的绝缘剩余寿命进行试验预测。结果表明,基于PSO优化的BP神经网络比传统BP神经网络有更为精准的预测能力以及更快的收敛速度。     

基于改进RBF代理模型的海上风机单桩基础优化设计北大核心CSCD

文章提出了一种基于径向基函数(RBF)神经网络结合粒子群算法(PSO)的海上风机单桩基础优化设计方法。该方法考虑局部冲刷的影响,提高了优化计算效率和结构安全性。首先,基于6.45 MW海上风机单桩基础工程实际问题,以泥面以下部分的桩径、壁厚和桩长为设计变量,采用拉丁超立方抽样法选取样本点;然后,建立考虑局部冲刷的单桩基础有限元模型并计算响应值,构建RBF代理模型,结合PSO算法进行全局寻优。寻优结果表明:考虑局部冲刷影响的海上风机单桩基础优化设计结果更趋于安全;适当增加桩径和壁厚比增加桩长更加经济;使用RBF代理模型能显著提高优化效率。     

基于支持向量机和果蝇优化算法的循环流化床锅炉NO_x排放特性研究

为了控制循环流化床(CFB)锅炉的NOx排放量,以某热电厂300MW CFB锅炉测试数据为样本,应用支持向量机(SVM)建立NOx排放特性预测模型.针对SVM回归预测需要人为确定相关参数的不足,应用果蝇优化算法(FOA)优化SVM参数,采用不同工况下的样本数据检验FOA-SVM模型的预测性能,并将该模型的预测结果与粒子群算法(PSO)、遗传算法(GA)和万有引力搜索算法(GSA)优化的SVM模型预测结果进行了比较.结果表明:FOA-SVM模型的泛化能力较强,预测精度较高,训练时间较短,可以相对快速、准确地预测NOx排放质量浓度.     

Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends

This study deals with artificial neural network (ANN) modeling of a spark ignition engine to predict the engine brake power, output torque and exhaust emissions (CO, CO₂, NO_x and HC) of the engine. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke test engine was fuelled with ethanol-gasoline blended fuels with various percentages of ethanol (0, 5, 10,15 and 20%), and operated at different engine speeds and loads. An ANN model based on standard back-propagation algorithm for the engine was developed using some of the experimental data for training. The performance of the ANN was validated by comparing the prediction dataset with the experimental results. Results showed that the ANN provided the best accuracy in modeling the emission indices with correlation coefficient equal to 0.98, 0.96, 0.90 and 0.71 for CO, CO₂, HC and NO_x, and 0.99 and 0.96 for torque and brake power respectively. Generally, the artificial neural network offers the advantage of being fast, accurate and reliable in the prediction or approximation affairs, especially when numerical and mathematical methods fail.     

An investigation of optimum control of a spark ignition engine fueled by NG and hydrogen mixtures

In this study statistical analysis methods were used for optimizing a spark ignition engine fueled by NG and hydrogen mixtures. Firstly designs of experiment and range analysis of the results have been carried out in order to improve the efficiency of experiments and reduce the workload. And then, a flexible model of this kind of engine that is catered to multidimensional optimization has been built. After that, the genetic algorithm is used to optimize the model. Finally the optimum control parameters of this operated point are determined to be hydrogen fraction 30–40%, excess air ratio 1.45–1.6 and ignition timing 20–22° BTDC at 1200 r/min, 0.4 MPa. The comparison of the optimized results and the original CNG performance showed that CH₄, CO, NO_x, and BSFC decrease by 70%, 83.57%, 93%, and 5%, respectively. This proved that the combination of artificial neural network and genetic algorithm is an effective way to optimize the hydrogen blend natural gas engine.     

Experimental study of hydrogen enriched compressed natural gas (HCNG) engine and application of support vector machine (SVM) on prediction of engine performance at specific condition

The effect of excess air ratio (λ) and ignition advance angle (θ_ig) on the combustion and emission characteristics of hydrogen enriched compressed natural gas (HCNG) on a 6-cylinder compressed natural gas (CNG) engine has been experimental studied in an engine test bench, aiming at enriching the sophisticated calibration of HCNG fueled engine and increasing the prediction accuracy of the SVM method on automobile engines. Three different fuel blends were selected for the experiment: 0%, 20% and 40% volumetric hydrogen blend ratios. It is noted that combustion intensity varies with the excess air ratio and the ignition advance angle, so are the emissions. The optimal value of λ or θ_ig has been explored in the specific engine condition. Results show that blending hydrogen can enhance and advance the combustion and stability of CNG engine, and it also has some benefic influence on the emissions such as reducing the CO and CH₄. Meanwhile, a simulation research on forecasting the engine performance by using the support vector machine (SVM) method was conducted in detail. The torque, brake specific fuel consumption and NO_x emission have been selected to characterize the power, economic and emissions of the engine with various HCNG fuels, respectively. It can be seen that the optimal model built by the SVM method can highly describe the relationship of the engine properties and condition parameters, since the value of the complex correlation coefficient is larger than 0.97. Secondly, the prediction performance of the optimal model for torque or BSFC is much better than the case of NO_x. Besides, the optimal model built by the PSO optimization method has the best prediction accuracy, and the accuracy of the model obtained based on the training group with 20% hydrogen blend ratio is the best compared with those of others. The upshots in this article provide experimental support and theoretical basis for the adoption of HCNG fuel on internal combustion engines as well as the application of intelligent algorithmic in the engine calibration technology field.     

CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network

This study investigates the use of artificial neural network (ANN) modelling to predict brake power, torque, break specific fuel consumption (BSFC), and exhaust emissions of a diesel engine modified to operate with a combination of both compressed natural gas CNG and diesel fuels. A single cylinder, four-stroke diesel engine was modified for the present work and was operated at different engine loads and speeds. The experimental results reveal that the mixtures of CNG and diesel fuel provided better engine performance and improved the emission characteristics compared with the pure diesel fuel. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception network was used for non-linear mapping between the input and output parameters. It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.9884, 0.9838, 0.95707, and 0.9934 for the engine torque, BSFC, NO_x and exhaust temperature, respectively.     

Performance of Euro III common rail heavy duty diesel engine fueled with Gas to Liquid

The effect of synthetic diesel fuel made from natural gas (Gas to Liquid, GTL) on the engine performances (such as power, efficiency and emission) was carried out on one Euro III common rail (CR) heavy duty (HD) diesel engine without any modification. The results showed that the engine fueled with GTL had some variations compared with the one fueled with petroleum-based low sulfur diesel fuel (sulfur content less 50 ppm). The maximum torque and power were decreased by 1.3% and 1.9%, respectively. The specific fuel consumption increased in volume but had no change in mass. Under the load characteristics, the NO_x, CO and THC were reduced by 13%, 55% and 55%, respectively. During the ESC cycle test, the NO_x, CO, THC and PM were reduced by 5.2%, 19.3%, 19.8% and 33%, respectively.     

基于优化PSO-RBF的柴油机故障诊断方法

针对传统RBF算法收敛速度慢,易于陷入局部极值的问题,提出了一种经优化的粒子群算法PSO,对RBF神经网络粒子群的改进参数、权值线性递减参数和标准参数进行训练寻优,构建出最优PSO-RBF神经网络,并将其用于柴油机的故障诊断预报。对MAN B&W 6L23/30H柴油机三种不同工况下第一缸试验参数的训练表明:改进的PSO-RBF神经网络在柴油机故障诊断中判别率更高,故障诊断的准确性与可靠性得到提高。     

Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle

This paper presents an alternative tool for vehicle tuning applications by incorporating the use of artificial neural network (ANN) virtual sensors for a hydrogen-powered car. The objective of this study is to optimize simple engine process parameters to regulate the exhaust emissions. The engine process parameters (throttle position, lambda, ignition advance and injection angle) and the exhaust emission variables (CO, CO₂, HC and NO_x) form the basis of the virtual sensors. Experimental data were first obtained through a comprehensive experimental and tuning procedure for neural network training and validation. The optimization layer-by-layer neural network was used to construct two ANN virtual sensors; the engine and emissions models. The performance and accuracy of the proposed virtual sensors were found to be acceptable with the maximum predictive mean relative errors of 0.65%. With its accurate predictive capability, the virtual sensors were then employed and simulated as a measurement tool for vehicle tuning and optimization. Simulation results showed that the exhaust emissions can be regulated by optimizing simple engine process parameters. This study presents an alternative tool for vehicle tuning applications for a hydrogen-powered vehicle. In addition, this work also provided a tool to better understand the effects of various engine conditions on the exhaust emissions without the need for any vehicle modifications.     

Optimization of port injection of n-decanol in a PCCI engine using response surface methodology

This work aims to define the optimum n-decanol fraction in the inlet port and the corresponding engine load for the better emissions and performance characteristics of a partially premixed charged compression ignition (PCCI) engine by response surface methodology (RSM). The numerical model based on multi-linear regression was established using experimental data. For this, the influence of various proportions of n-decanol through intake port including 10%, 20%, 30%, and 40% were experimentally investigated besides the primary injection of neem oil biodiesel in the volumetric ratio of 80% diesel and 20% neem biodiesel, namely NB20. The optimization using RSM is exploited to capitalize the brake thermal efficiency (BTE) and diminish the emissions including oxides of nitrogen (NO_x), carbon monoxide (CO) emission, smoke opacity, and hydrocarbon (HC) emission. The n-decanol fraction in the port injection of 31.43% and the engine load in terms of brake power of 2.950 kW were found to be optimum parameters with the maximum desirability of 0.752. The optimal responses for brake-specific fuel consumption (BSFC), BTE, CO, HC, smoke, and NO_x under these operating conditions were found to be 0.305 kg/kWh, 28.8%, 0.145%, 19.61%, 54.85 ppm, and 837.7 ppm, respectively. Likewise, the correlation coefficient R² values for BSFC, BTE, CO, HC, smoke and NO_x have been found to be 99.85%, 99.95%, 93.58%, 90.32%, 99.97%, and 99.93%, respectively. According to the study's findings, the RSM is a realistic method for calculating and enhancing a diesel engine's emission and performance values operating in PCCI mode and using n-decanol and NB20 as fuels.