Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

基于LIBS技术的水泥粉末在线成分分析

在工业现场生产水泥的过程中，各种成分的含量直接影响着水泥的质量，因此如何快速准确地监测水泥中各个成分的含量意义重大。采用的实验方式为，将不经过任何预处理的水泥粉末直接放入位于二维移动平台上的物料盒中，通过激光诱导击穿光谱(LIBS)直接对水泥粉末表面的不同位置进行激发检测，对得到的光谱数据首先进行归一化和主成分分析等预处理操作，然后针对水泥中Ca, Si, Al, Fe, Mg五种元素，分别建立偏最小二乘(PLS)和支持向量回归(SVR)两种定量分析模型进行方法比较。此外，对比了粉末状水泥与压片式水泥两种测量方式的结果。实验结果表明，采用粉末状水泥直接测量的方式下，针对水泥样品元素浓度与所得到的光谱中特征线强度的关系，SVR方法比PLS方法更具优势，粉末状水泥直接测量的精度接近压片式测量的精度，说明LIBS技术对水泥粉末状样品直接在线测量具有可行性。     

Summability in of a vector measure

We show a picture of the relations among different types of summability of series in the space  of integrable functions with respect to a vector measure m of relatively norm compact range. In order to do that, we study the class of the so‐called m‐1‐summing operators. We give several applications regarding the existence of copies of c ₀ in , as well as on m‐1‐summing operators which are weakly compact, Asplund or weakly precompact.     

模拟退火优化SVM参数的变压器故障诊断

利用群智能算法优化支持向量机(SVM)参数往往需要引入额外的变量,使变压器故障诊断问题更加复杂,对此提出一种自适应的模拟退火算法优化(ASA)支持向量机参数。通过设计自适应的冷却进度表,使得寻参的过程仅仅依赖于退火速率以及网格搜索粒度,保持了较少的参数设置。在相关数据集上的实验表明,与已提出的粒子群算法(PSO)、遗传算法(GA)相比,ASA算法具有更快的收敛速度以及较好的诊断精度。利用自适应的模拟退火算法能够较好的优化SVM参数并提高变压器故障诊断的精度。     

基于功率谱包络能量和SVM的舰用发动机故障诊断

发动机是军舰上的重要部件之一,其稳定性对军舰的正常航行具有重要影响。以舰用发动机关键部件(主泵轴承)为具体研究对象,提出了基于功率谱包络能量和支持向量机相结合的故障诊断方法。首先获取了大量可表征舰用发动机主泵轴承健康状态的振动加速度信息,对其进行功率谱分析,获得其功率谱的包络能量;以获取的舰用发动机主泵轴承功率谱的包络能量构建特征向量,并设计基于SVM的舰用发动机主泵轴承故障诊断模型,对主泵轴承的故障进行诊断研究。研究结果表明,采用基于功率谱包络能量和SVM相结合的舰用发动机关键部件故障诊断方法,可以很好实现主泵轴承的故障诊断效能,为舰用发动机主泵轴承故障诊断的工程应用奠定了基础。     

Structure formation in turbulence as an instability of effective quantum plasma

Structure formation in turbulence can be understood as an instability of “plasma” formed by fluctuations serving as effective particles. These “particles” are quantumlike in the sense that their wavelengths are non-negligible compared to the sizes of background coherent structures. The corresponding “kinetic equation” describes the Wigner matrix of the turbulent field, and the coherent structures serve as collective fields. This formalism is usually applied to manifestly quantumlike or scalar waves. Here, we show how to systematically extend it to more complex systems using compressible Navier–Stokes turbulence as an example. In this case, the fluctuation Hamiltonian is a five-dimensional matrix operator and diverse modulational modes are present. As an illustration, we calculate these modes for a sinusoidal shear flow and find two modulational instabilities. One of them is specific to supersonic flows, and the other one is a Kelvin–Helmholtz-type instability that is a generalization of the known zonostrophic instability. Our calculations are readily extendable to other types of turbulence, for example, magnetohydrodynamic turbulence in plasma.