Computational study for microwave pasteurization of beer and hypothetical continuous flow system design

Beer pasteurization is carried out in conventional systems where low temperature and long time process (56 min process with 13 min pre-heating, 14 min heating, 6 min holding time and 23 min cooling time) is applied for bottled or canned products. Longer process times lead to losses in the sensory properties. Another approach is flash pasteurization where the beer is first pasteurized and then filled into bottles or cans. For flash pasteurization, microwave (MW) process with rapid heating feature might be an innovative alternative. Therefore, the objective of this study was to explore the MW processing for beer pasteurization. For this purpose, a computational mathematical model was developed to determine the temperature change of beer during MW process and experimentally validated in a 2450 MHz lab-scale system. Following the validation, process design studies for a continuous flow system were carried out. These studies demonstrated the MW application as a rapid process (e.g. 60 s of total MW heating time at 5000 W with a short holding time of 27 s) to achieve the required pasteurization unit at a flow rate of 50.4 kg/h. This confirmed the MW process as a possible industrial application. Using these results, continuous flow process optimization studies for industrial process considerations might be planned.     

基于特征的机械设备故障预测系统

在机械设备的使用过程中,如果能准确地提前预测机械设备将要发生的故障并防患于未然,则可为企业节省大量的维修费用,降低企业成本。为此提出了一种依据特征基于神经网络算法的故障预测系统。把机械设备本身的运行特征和环境特征作为输入,把发生故障的概率作为输出,建立映射关系。以历史映射数据训练并建立神经网络,用以预测机械设备在某状态达成后发生故障的概率。通过某厂的机床对系统进行训练和验证,预测故障率与实际故障率的相对误差在15.3%以内。仿真结果表明,该系统可以有效的根据机械设备运行状态特征对其故障率进行准确预测。     

基于机器视觉的直角坐标机器人热误差测量

介绍了一种利用机器视觉标定确定直角坐标机器人线性模组的重复定位精度的方法,通过分析计算得出了模组在热稳定下与从冷却到热稳定过程下的重复定位精度,同时对二者进行比较得出了热误差对重复定位精度的影响结果,并探讨了线性模组在不同速度下热误差对重复定位精度的影响。     

戊二醛交联胶原蛋白/PVA复合纤维的结构与性能

在胶原蛋白与PVA复合后的溶液中加入戊二醛交联剂,得到稳定的复合纺丝原液,再用湿法纺丝得到初生纤维,经热拉伸和热定型、缩醛化处理得到胶原蛋白/PVA复合纤维.复合纤维的强度、模量、伸长率分别达到3.74 cN/dtex、44.60 cN/dtex、31.30%;结晶度为60.79%;水中软化点和回潮率分别为105℃和8.9%.通过扫描电镜观察发现:复合纤维内部有少许孔洞、裂纹,且随纺丝原液中固含量的增加而减少.     

酶制剂的不同组分在脱毛过程中的作用及评价方法研究

本文研究了１６６、１３９８、２０９等酶制剂中酪蛋白水解酶和胶原纤维水解酶解酶组分的脱毛能力和对皮组织的作用。发现在酶脱毛过程中起主要作用的是酪蛋白水解酶，胶原纤维水解酶无明显脱毛作用，但对胶原组织有强烈的溶解能力，并能导致胶原组织热稳定性下降。提出了用活性橙标记胶原法测定制革用酶的胶原纤维水解活力、用热溶率评价胶原组织破坏程度的原理及方法。     

儿童步态时空参数和足底压力的影响因素

儿童的骨骼特征与成年人存在较大差异,儿童期间下肢及足部肌骨系统的发育会持续作用于下肢的运动生物力学特征,使其步态特征与成年人相比更加复杂多变。时空参数和足底压力分布是评价步态特征的两个重要指标,探究和分析儿童步态时空参数及足底压力影响因素,不仅对其足部的正常发育、识别和预防发育偏离现象及针对性干预治疗有重要指导意义,也可以为童鞋的设计提供理论参考和数据支持。     

基于HPLC与主成分分析法区别西南地区不同产地花椒的地域差异:标记化合物的测定和鉴定

为研究西南地区不同产地花椒的地域差别,本文选取了5个产地花椒的15个样品作为实验材料,采用有机溶剂提取并用柱层析方法分离得到不同的组分。通过高效液相色谱法(HPLC)对分离后的组分进行分析。利用主成分分析(PCA)和偏最小二乘判别分析(PLS-DA),找出汉源花椒与其他产地花椒的主要差异物。结果表明,汉源花椒能够与其他产地花椒明显区分,且PLS-DA的分离效果优于PCA。以VIP（变量重要性）>1及p<0.05为指标筛选出汉源花椒和其他产地花椒的主要差异物为新绿原酸、绿原酸、槲皮苷、羟基-β-山椒素和羟基-γ-山椒素,其中汉源花椒的羟基-γ-山椒素含量显著高于其他产地花椒,其他差异物含量均低于其他产地花椒,差异有显著性（p<0.05）。本研究为不同产地花椒的筛选及品质评价提供了一定的理论基础和实验数据。     

基于ATmega16的多路LED光源控制器

为了控制半导体外观缺陷在线检测系统中的照明光源,介绍了一种以ATmega16单片机为核心,使用恒流方式控制LED照明光源的多路LED光源控制器。叙述了光源控制器的总体设计方案、主要硬件电路、软件流程框图。实验结果表明该光源控制器拥有良好的调节光源亮度以及保持光源亮度的性能。     

基于STC11通用数字显示控制器设计

针对市场对显示变化数字的需求,提出了一套基于STC11F02E,采用远程控制更新显示的通用控制器设计方案。详细介绍了系统电源供给电路、通讯接口电路等硬件电路及系统软件设计方法,实现了通过485通讯进行远程更新显示数据。经过实验检测,本设计合理,控制器运行稳定。     

Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO₂ content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions.