S1000-11型SO2鼓风机提高参数的分析与应用

介绍了S1 0 0 0 1 1型SO2 鼓风机改型增容的分析与应用。通过综合改变该风机转子的结构参数 ,实现了扩容增产的目的 ,并在大型硫酸厂试用成功。     

国内外振动技术在塑料成型工艺中的应用

介绍国内外振动技术在塑料成型工艺中的应用。通过振动保压、多点动态进料保压及推—拉注塑技术在注塑成型中的应用，有效地消除了制件中的缩孔、疏松和表面沉陷，控制了制件中增强纤维和分子链的取向，消除熔合痕，从而提高了产品质量。所述振动保压技术的基本工作机理是在保压过程中，使熔体产生剪切，得到内热，从而延缓了薄壁部位的冷却时间，使厚壁部分冷却收缩时能从浇口得到足够的熔料补充；其余两种技术是振动保压技术的进一步发展，它们能使熔融塑料受到更大的剪切，从而效果更好。上述技术成功的应用实例有：３ｍｍ厚的ＰＰ玻纤增强塑料采用多点动态进料保压成型，其熔接线强度提高了５０％～８５％；ＡＢＳ采用动态保压注塑成型，制品的拉伸强度提高了１７％；玻纤增强ＬＣＰ进行推—拉注塑，拉伸强度和弯曲模量分别提高了４２０％和２７％。     

茶多酚制品中有机溶剂多残留检测方法研究进展

现有的茶多酚分离制备工艺大多采用有机溶剂或大孔树脂,给产品带来了有机溶剂残留的安全隐患。本文概述了茶多酚的生产工艺及其残留溶剂的检测方法,主要介绍了直接进样气相色谱法、顶空气相色谱法和固相微萃取气相色谱法这三种方法在残留溶剂测定中的应用进展;提出有必要研究有机溶剂多残留快速检测技术,制定限量标准,提升加工工艺。     

智能仪器中数字校准技术的研究

嵌入了微处理器的智能仪器的数字校准,是采用软件方法实现仪器的零值误差和幅值系数的校准。零值误差的校准,应根据A/D转换器不同的正负值输出方式采取不同的处理方法。幅值系数k的取值范围一般在0.97~1.03之间可调,且k的字长应保证其小数部分改变一个LSB时,仪器显示值的变化不超过±1个字。另外,校准参数的保护、校准用的人机交互界面的形成,都是不容忽视的环节。     

猪肉脯制作工艺的改进

本文系统地研究了新型猪肉脯的加工工艺以及提高肉糜肉脯色泽和风味的方法,结果表明:肉糜中添加适量的亚硝酸盐、2%食盐,3%蔗糖、0.02%抗坏血酸、在0～4℃下腌制6h,肉糜肉脯成色效果较好.若同时添加0.5%复合磷酸盐、5%淀粉、肥瘦比定为1/4、在60℃下烘干4h,在120℃下烘5min,即可获得色、香、味俱佳的肉糜肉脯.     

拉曼光谱在磷矿加工过程中的应用

综述拉曼光谱分析技术特点和应用现状,国内外磷矿加工过程的分析现状与拉曼光谱在磷加工过程的应用。通过采用便携式拉曼光谱仪对磷矿选矿过程中的浮选药剂、相关原料及产品进行摄谱和谱图分析,并将获取的拉曼光谱数据存放入自建的磷化工拉曼光谱专项数据库,使拉曼光谱分析技术有望取代传统的磷矿加工过程分析检测方法,成为磷矿加工过程分析检测的重要手段。     

Identification of the thermal properties of concrete for the temperature calculation of concrete slabs and columns subjected to a standard fire—Methodology and proposal for simplified formulations

The fire resistance of concrete members is controlled by the temperature distribution of the considered cross section. The thermal analysis can be performed with the advanced temperature dependent physical properties provided by EN 1992-1-2. But the recalculation of laboratory tests on columns from TU Braunschweig shows, that there are deviations between the calculated and measured temperatures. Therefore it can be assumed, that the mathematical formulation of these thermal properties could be improved. A sensitivity analysis is performed to identify the governing parameters of the temperature calculation and a nonlinear optimization method is used to enhance the formulation of the thermal properties. The proposed simplified properties are partly validated by the recalculation of measured temperatures of concrete columns. These first results show, that the scatter of the differences from the calculated to the measured temperatures can be reduced by the proposed simple model for the thermal analysis of concrete.     

Parameter identification for state estimation—application to bioprocess software sensors

Together with some on-line measurements, a reliable process model is the key ingredient of a successful state observer design. In common practice, the model parameters are inferred from experimental data so as to minimize a model prediction error, e.g. so as to minimize an output least-squares criterion. In this procedure, no care is actually exercised to ensure that the unmeasured model states are sensitive to the measured states. In turn, if sensitivity is too low, the resulting state observer will probably generate poor estimates of the unmeasured states. To alleviate these problems, a new parameter identification procedure is proposed in this study, which is based on a cost function combining a conventional prediction error criterion with a state estimation sensitivity measure. Minimization of this combined cost function produces a model dedicated to state estimation purposes. A thorough analysis of the procedure is presented in the context of bioreactor modeling, including parameter identification, model validation and design of extended Kalman filters and full horizon observers.     

Optimal hybrid modeling approach for polymerization reactors using parameter estimation techniques

The dynamics of polymerization catalytic reactors have been investigated by many researchers during the past five decades; however, the emphasis of these studies was directed towards correlating process model parameters using empirical investigation based on small scale experimental setup and not on real process conditions. The resulting correlations are of limited practical use for industrial scale operations. A statistical study for the relative correlation of each of the effective process parameters revealed the best combination of parameters that could be used for optimizing the process model performance. Parameter estimation techniques are then utilized to find the values of these parameters that minimize a predefined objective function. Published real industrial scale data for the process was used as a basis for validating the process model. To generalize the model, an artificial neural network approach is used to capture the functional relationship of the selected parameters with the process operating conditions. The developed ANN-based correlation was used in a conventional fluidized catalytic bed reactor (FCR) model and simulated under industrial operating conditions. The new hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. The suggested parameter estimation and modeling approach can be used for process analysis and possible control system design and optimization investigations.