Activated Carbons Based on Shea Nut Shells (<i>Vitellaria paradoxa</i>): Optimization of Preparation by Chemical Means Using Response Surface Methodology and Physicochemical Characterization

In this study, shea residues (Vitellaria paradoxa) dumped in the wild by the units processing almonds into butter were used in the production of activated carbons. Shea nut shells harvested in the locality of Baktchoro, West Tandjile Division of Chad were used as a precursor for the preparation of activated carbons by chemical activation with phosphoric acid (H3PO4) and sulphuric acid (H2SO4). Central Composite Design (CCD) was used to optimize the preparation conditions, and the factors used were concentration of activating agent (1 - 5 M), carbonization temperature (400°C - 700°C) and residence time (30 - 120 min). The studies showed that at optimal conditions the yield was 51.45% and 42.35%, while the iodine number (IN) was 709.45 and 817.36 mg/g for CAK-P (phosphoric acid activated carbon) and CAK-S (sulphuric acid activated carbon) respectively. These two activated carbons (ACs) which were distinguished by their considerable iodine number, were variously characterized by elementary analysis, pH at the point of zero charge (pHpzc), bulk density, moisture content, Boehm titration, Fourier transform infrared spectroscopy, BET adsorption and scanning electron microscopy. These analyses revealed the acidic and microporous nature of CAK-P and CAK-S carbons, which have a specific microporous surface area of 522.55 and 570.65 m2·g−1 respectively.

基于小波网络模型的EPI过程的控制和优化

针对具有强非线性、大纯滞后特性的EPI反应分馏生产过程的废液DOC体积分数控制这一控制难题,应用小波神经网络模型和结合机理分析模型及小波神经网络模型的混合模型结构进行实际EPI反应分馏生产过程的建模控制和优化,提出了基于稳态小波神经网络模型的优化和基于动态小波神经网络模型的非线性预测控制两层结构的整体解决方案,在实际生产过程中获得了成功的应用,不仅使生产过程平稳运行,而且显著地降低排放废液中的DOC体积分数,达到了环保的标准。所提出的技术适合于许多连续反应系统。

Optimized synthesis and photovoltaic performance of TiO_2 nanoparticles for dye-sensitized solar cell

This paper presents response surface methodology （RSM） as an efficient approach for modeling and optimizing TiO2 nanoparticles preparation via co-precipitation for dye-sensitized solar cell （DSSC） perfor- mance. Titanium （IV） bis-（acetylacetonate） di-isopropoxide （DIPBAT）, isopropanol and water were used as precursor, solvent and co-solvent, respectively. Molar ratio of water, aging temperature and calcina- tion temperature as preparation factors with main and interaction effects on particle characteristics and performances were investigated, Particle characteristics in terms of primary and secondary sizes, crys- tal orientation and morphology were determined by X-ray diffractometry （XRD） and scanning electron microscopy （SEM）. Band gap energy and power conversion efficiency of DSSCs were used for perfor- mance studies. According to analysis of variance （ANOVA） in response surface methodology （RSM）, all three independent parameters were statistically significant and the final model was accurate. The model predicted maximum power conversion efficiency （0.14%） under the optimal condition of molar ratio of DIPBAT-to-isopropanol-to-water of 1 ： 10：500, aging temperature of 36 C and calcination temperature of 400 ℃. A second set of data was adopted to validate the model at optimal conditions and was found to be 0.14 ± 0.015%, which was very close to the predicted value. This study proves the reliability of the model in identi（ving the optimal condition for maximum performance.

基于小波网络的非线性多变量约束预测控制

为了解决非线性多变量系统的建模、控制和优化问题 ,论文扩展基于小波神经网络的单变量系统辨识到多变量系统辨识 ,并用它实现非线性预测控制。对开环稳定过程 ,引入一个具有输入约束的基于小波神经网络模型的区域预测控制方案 ,它的闭环稳定性能够通过适当选择它的预测水平来保证。基于上述动态控制方案 ,提出了一个稳态状态优化方案。通过对一个聚酯生产过程的仿真研究 ,证实了上述方法的有效性。由于能够通过线性最小二乘 (L S)估计方法来辨识 ,该模型易于实现并可用作通用模型。仿真研究的结果表明了该模型的通用性、辨识和控制方法的简单性 。