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.     

Exploring artificial neural networks to model interatomic and intermolecular potential energy surfaces

We demonstrate how a back-propagation artificial neural network can be trained to represent a potential energy surface (PES) in a formless manner with limited data points and exploited to predict interaction energies for configurations not included in the training set. A similar exercise is undertaken for predicting the eigenvalues and eigenvectors of a model Hamiltonian matrix that delicately depends on parameters and exhibits crossing of eigen values.     

Three-Dimensional Diabatic Potential Energy Surfaces of Thiophenol with Neural Networks

Three-dimensional (3D) diabatic potential energy surfaces (PESs) of thiophenol involving the S\begin{document}$_0$\end{document}, and coupled \begin{document}$^1$\end{document}\begin{document}$\pi\pi^*$\end{document} and \begin{document}$^1$\end{document}\begin{document}$\pi\sigma^*$\end{document} states were constructed by a neural network approach. Specifically, the diabatization of the PESs for the \begin{document}$^1$\end{document}\begin{document}$\pi\pi^*$\end{document} and \begin{document}$^1\pi\sigma^*$\end{document} states was achieved by the fitting approach with neural networks, which was merely based on adiabatic energies but with the correct symmetry constraint on the off-diagonal term in the diabatic potential energy matrix. The root mean square errors (RMSEs) of the neural network fitting for all three states were found to be quite small (\begin{document}$<$\end{document}4 meV), which suggests the high accuracy of the neural network method. The computed low-lying energy levels of the S\begin{document}$_0$\end{document} state and lifetime of the 0\begin{document}$^0$\end{document} state of S\begin{document}$_1$\end{document} on the neural network PESs are found to be in good agreement with those from the earlier diabatic PESs, which validates the accuracy and reliability of the PESs fitted by the neural network approach.     

人工神经元网络辅助丙烷氨氧化催化剂设计

报道了利用人工神经元网络辅助丙烷氨氧化制丙烯腈催化剂设计的研究进展结果．通过筛选得到了可以拟合该反应体系的神经元网络模型，从而使丙烷转化率和丙烯腈选择性可以表示为催化剂组成的函数，利用培训后的神经元网络模型所得到的权值文件，结合作者自编的优化程序，该网络可用于预测最优的催化剂组成．实验得到丙烯腈收率和选择性可分别达到４３．０％和５６．２４％．     

Modelling and prediction of retention in high-performance liquid chromatography by using neural networks

Summary Multi-layer feed-forward neural networks trained with an error back-propagation algorithm have been used to model retention behaviour of liquid chromatography as a function of the composition of the mobile phases. Conventional hydro-organic and micellar mobile phases were considered. Accurate retention modelling and prediction have been achieved using mobile phases defined by two, three and four parameters. With micellar mobile phases, the parameters involved included the concentrations of surfactant and organic modifier, pH and temperature. It is shown that neural networks provide a competitive tool to model varied inherent nonlinear relationships of retention behaviour with respect to the mobile phase parameters. The soft models defined by the weights of the networks are capable of accommodating all types of linear and nonlinear relationships, neural networks being specially useful when the relationships between retention behaviour and the mobile phase parameters are unknown. However, to train neural networks more experimental points than with hard-modelling methods are required, hence the use of the networks is recommended only for those cases where adequate theoretical or empirical models do not exist.     

自组织神经树用于判别氟化物非晶态形成条件

1人工神经网络──自组织神经树模型人工神经网络（ANN）是八十年代中期迅速兴起的一门非线性科学．它力图模拟人脑的一些基本特性，如自适应性、自组织性、容错性等，已在模式识别、数据处理及自动化控制等领域得到了初步应用，取得了相当好的效果［1，2］．1993年，Tao－Li等提出了自组织神经树网络．它是一个多层树状网络（见图1），每个输入节点与所有神经树的节点（神经元）通过权W相联系，实现对输入信号的非线性降维映射．映射中保持拓扑不变性，即把拓扑意义下相似的输入（即在高维空间中距离较近的输入向量）映射到相同子树的节…     

Coordination Polymers. 9. Chelate Polymers Derived from Bisphenolic Complexes and Propylenediamine

Chelate polymers derived from bis(2,4-dihydroxybenzaldehyde)propyl-enediimine M and bis(2,4-dihydroxyacetophenone)propylenediimine M (M = Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, Zn²⁺) with aromatic acid chlorides were prepared by interfacial polycondensation. Also, chelate polysiloxanes were obtained from the same monomers and α,ω-dichloropolydimethyl-siloxane. The spectral, thermal, magnetic, and electrical properties of the polychelates were studied.     

A novel algorithm for chromatogram matching in qualitative analysis

The matching of the pattern of peaks produced during gas chromatography is of importance to many applications. At present, this task is generally performed manually, but this generates the usual problems associated with human inspection, such as a lack of objectivity and reproducibility, proneness to errors, and practical restriction of the volume of data which can reasonably be processed. This paper explores the use of a novel algorithm for automation of this task. The performance of the method on well controlled simulated data sets and real chromatograms is used to show not only how problems of manual inspection can be circumvented, but also how the existence of such a powerful method should open up the possibility of many new analyses for quality control, discrimination of varieties of sample, and the identification of specific components within a sample.