Multistage Inverse Modeling Method and Its Application in Gelatin Solution Production Process

针对一类串联型工业大系统,提出了多阶段逆模型建模方法:将串联大系统分为若干个阶段,以产品质量指标作为过程设计的起点,用逆向推理的方法,建立各个阶段的逆模型;根据产品质量指标的要求,直接求出各个阶段的控制变量设定值。将该方法应用于胶液生成过程的软测量建模,采用多阶段建模方法和整体建模方法分别建立了基于bP神经网络的胶液生成过程逆模型,并从误差平方和MSE和命中率等方面对两种建模方法的建模精度进行了比较。结果表明,多阶段建模方法可以获得更高的建模精度;同时,具有更大的灵活性;而且逆模型方法可以根据质量指标求出控制变量设定值,更便于实际应用。Aiming at a class of serially connected industrial system,a novel multistage inverse modeling method was presented.The large-scale system is divided into several stages.Using specified product qualities as a starting point for process design.By backward reasoning the required process conditions and the control variable set points of all stages for processing system were found.The inverse models of gelatin solution production process were established based on the BP neural network by using multistage modeling method and whole stage modeling method,and modeling accuracy comparison were made from error and hit rates.The simulation results indicate the model based on the proposed method has smaller error and higher hit rates.Meanwhile,the break down of the sub models increases the flexibility of model development and reduces the effort to change the model when the sub models change.And the required process conditions and the control variable set points of all stages for processing system were found according to specified product qualities.Thus,it is easy to be really applied.This method has been successfully applied on improving the gelatin solution production process and product quality control.厦门市科技计划项目(3502Z20083028);国家自然科学基金项目(50843059);福建省教育厅科技项目(JA08218

中国微重力科学研究回顾与展望

微重力科学主要研究微重力环境中物质运动的规律,以及不同重力环境中重力对物质运动的影响.中国微重力科学研究起步于20世纪60年代,兴起于80年代中后期,经过多年发展,目前已初具规模,在一些重要方向具有明显特色和一定优势.本文回顾了中国微重力科学研究的早期历程,评述了近年来中国微重力科学研究进展,特别是利用实践十号科学实验卫星、天宫二号空间实验室等空间平台开展的微重力科学与技术应用研究取得的最新成果,并对中国载人空间站时代微重力科学发展的前景予以瞻望,推动微重力科学与应用研究在中国的快速、可持续发展

~(12)C+~(209)Bi中能对称三分裂实验研究

在云母上蒸镀靶物质,做成叠层靶进行辐照,用云母的2π几何空间探测对称三分裂,给出了4π几何空间的三分裂相对二分裂的几率比,测量~(12)C(25—47.5MeV/A)+~(209)Bi反应形成的热核的对称三分裂相对于对称二分裂的激发函数,并与级联裂变的理论分析结果相比较

车厘子核油的制备及品质分析Preparation and quality analysis of cherry kernel oil

以车厘子核为原料，采用索氏抽提法提取车厘子核油，并对其理化性质、脂肪酸组成、脂溶性营养成分、抗氧化活性及挥发性成分进行分析。结果表明：车厘子核油得率为（10.13±0.19）%，其酸值、过氧化值均符合GB 2716—2018要求；车厘子核油中有8种脂肪酸，主要是油酸（58.69%）和亚油酸（26.03%）；车厘子核油富含甾醇，含量高达530.04 mg/100 g，以β-谷甾醇（428.00 mg/100 g）为主；车厘子核油抗氧化能力强，对DPPH自由基和羟自由基均有较好的清除效果，其IC50值分别为12.51 mg/mL和1.92 mg/mL；车厘子核油中检出58种挥发性成分，以醛类物质（44.27%）和烃类物质（28.44%）为主，有特殊的果香味。 The cherry kernel oil was extracted by Soxhlet method with cherry kernel as raw material, and its physicochemical properties, fatty acid composition, fat-soluble nutritional component, antioxidant activity and volatile components were analyzed. The results showed that the yield of cherry kernel oil was (10.13±019)%, and the acid value and peroxide value of cherry kernel oil met the requirement of GB 2716-2018. There were eight kinds of fatty acids in the cherry kernel oil, mainly oleic acid (58.69%) and linoleic acid (26.03%).Cherry seed oil was rich in sterols, which reached 530.04 mg/100 g, and β-sitosterol was dominant (428.00 mg/100 g). Cherry kernel oil had strong antioxidant ability and had good scavenging effects on DPPH free radical and hydroxyl free radical, and their IC50 values were 12.51 mg/mL and 1.92 mg/mL, respectively. There were 58 volatile components detected in the cherry kernel oil, and aldehydes(44.27%) and hydrocarbons(28.44%) were the main compounds.The cherry kernel oil had special fruit flavor

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel