绿茶杀青质热传递数学模型

制茶工程中,应用传质传热理论建立数学模型,对茶叶加工过程中所呈现的物理特性进行预测的研究还很欠缺。本文对茶叶杀青工序中茶叶含水率、叶温、杀青时间三者间的关系建立了数学模型,并用孙成(1987)~([1])对 CS-30型小型锅式杀青机的实验数据进行了验证。     

鲜叶水份、环境温度与杀青

茶叶杀青中供需热量 ,会受到鲜叶水份含量和所处环境温度的影响。实践得知 ,水份含量高时 ,杀青时间会相应延长。在环境温度低的春季 ,杀青锅底部必须烧至微红 ;气温高的暑伏季节 ,锅温只需烧至呈灰白色即能满足工艺处理要求 (指加工普通绿茶的大锅 )。因此试验得出在常温 2 0℃下的最佳供需热系数 ,必须结合环境温度和鲜叶含水率的进行修正。但是水份含量与环境温度 ,到底会产生多大的影响 ?其调整幅度如何掌握 ,我们可以按照有关公式求得。1 .鲜叶水份含量的影响 :从茶叶比热计算的经验公式 C=0 .38+ 0 .62W可知 ,鲜叶含水率的多少 ,决定…     

新型杀青技术对采花毛尖茶叶品质的影响

通过研究绿茶杀青的原理,综合滚筒杀青和热风杀青的优点,与热管技术和PLC控制技术集成,研制出一种电热滚筒＋热风＋尾气余热回收的新型杀青机。该杀青机生产的采花名优绿茶汤色、香气、滋味、叶底之感官品质明显优于传统的杀青工艺,最大程度保留了茶叶的品质成分,使茶叶的综合质量大幅提升,并且获得了较高的经济效益。     

茶叶微波加工技术及设备的研究

1茶叶微波杀青、干燥机理微波杀青、干燥是微波发生器将微波辐射到杀青、干燥的物料并穿透到物料内部时,诱使物料的水等极性分子随之同步旋转,例如采用915MHz微波干燥蔬菜类物料制品,其体内极性分子每秒钟旋转9.15亿次,如此高速旋转的结果,使物料瞬时产生摩擦热,导致物料表面与内部同时升温,且内部温度高于物料表面温度,使大量的水分子从物料中逸出而被蒸发带走,这样达到杀青、干燥的目的。这种杀青、干燥方法的特点是加热时间短,内外温度一致,其热传递方向从内向外与湿传递方向也一致。茶叶杀青、干燥的要求是杀青后茶叶的含水率为58%-60%…     

远红外茶叶杀青机在名优绿茶加工中的应用

远红外茶叶杀青机是指利用远红外辐射（线）作为热源,进行杀青作业的茶叶加工设备。其具有杀青均匀、连续化作业、节能环保和有效降低苦涩味等特点。根据使用者的要求不同,红外线划分范围各不相同,在实际应用中通常把2.5μm以上的红外线通称为远红外线。远红外线传递能量速度快,介质损耗小,茶叶干物质在远红外波段约有20%~80%的吸收率,而水在3~6.5μm及15μm以后的波段更有大片的吸收率,即在远红外波段,茶叶干物质和水分具有较强的辐射吸收能力。     

炒青绿茶热解吸特性

对炒青绿茶杀青和炒干过程的热特性进行了跟踪测量。结果表明，杀青过程的热解吸具有等速特性；炒干过程的热解吸则呈现恒率失水和降率失水的双重特性。鲜叶的嫩度不同，其热解吸特性也不尽相同，杀青过程中一级鲜叶的热解吸速率大于三级叶，一级和三级鲜叶杀青前期（２ｍｉｎ），茶叶吸热量分别占总吸热量的３８％和４２％；炒干过程中，一级原料解吸至１５％含水率左右时，开始降率解吸，而三级原料的降率解吸含水率起始点则要提前至２５％—３０％含水率时。温度和解吸速度对炒青品质的形成起着推动作用，而解吸速率又受环境温度和相对湿度的制约     

基于机器视觉的茶叶微波杀青中品质变化与预测研究

茶多酚、氨基酸、含水率是茶叶品质的重要指标,传统检测方法周期长且过程复杂。本研究利用机器视觉对微波杀青过程中茶叶的色泽和纹理特征实时监测,在线检测含水率,同时检测茶多酚和氨基酸含量。结果表明,色泽、纹理特征与含水率、茶多酚、氨基酸含量均呈规律性变化且显著相关。对色泽和纹理特征进行主成分分析,以前3个主成分为输入建立极限学习机（ELM）、遗传神经网络（GA-BP）、卷积神经网络（CNN）模型对品质成分含量进行预测。结果表明,ELM、GA-BP、CNN模型分别适用于含水率、茶多酚含量和氨基酸含量的预测,精度均在0.99以上。研究表明,通过实时监测茶叶的色泽和纹理特征来预测其在杀青过程中含水率、茶多酚和氨基酸含量是可行的。     

不同杀青工艺原料茶加工饮料品质稳定性的研究

将不同杀青工艺绿茶原料加工成绿茶饮料,通过对茶饮料高温瞬时灭菌以及37℃、4℃下贮藏7d后茶多酚、氨基酸、蛋白质、总糖等常规化学成分、儿茶素组分、香气组分以及色差、浊度、感官品质变化的系统研究,明确了不同杀青处理对浸出特性、理化成分含量、风味品质等的影响,提出了饮料用原料茶加工的适宜杀青工艺。     

浅析30型滚筒杀青机的杀青技术

杀青是名优绿茶品质形成的关键工序.传统的手工杀青方式由于工作效率低,劳动强度大,因而逐渐被机械杀青所取代.近年来,我国已成功地开发出专用于名优绿茶杀青的系列滚筒连续杀青机.机械杀青不仅台时产量高,而且所制茶叶色泽绿,香气高,因而在生产上广泛应用.然而,由于对这些机械的杀青工艺缺乏系统研究,导致生产上仅凭经验掌握,影响杀青效果.据此,笔者根据近年来华中农业大学茶学专业的研究成果,结合生产实际,谈谈30型柴式滚筒杀青机的杀青技术.     

茶叶热风杀青机简介

茶叶热风杀青机采用热风杀青,杀青匀、透,杀青叶色泽翠绿.热风杀青的原理很简单,即通过高温热风和鲜叶接触,把热量传递给鲜叶,由于叶片与热风温度的温差很大,故热量迅速穿透鲜叶,使叶温快速升高,达到钝化酶活性的目的,完成杀青工序.     

基于DMC-PID串级控制的茶叶远红外烘干机设计与试验

根据茶叶物料烘焙时具有迟滞、大惯性和非线性的特点,采用传统PID控制时温度控制精度不高,超调量较大,鲁棒性差。设计了一种基于动态矩阵控制的茶叶烘干机,烘干机采用多层隧道式,上加热方式,加热元件为电加热远红外辐射板,运用DMC-PID串级温度控制系统,前级的DMC算法提高温度控制系统的动态响应能力与鲁棒性;后级PID算法提高系统的抗干扰性能。对样机进行绿茶烘焙试验,试验表明,采用PID温度控制系统时烘干机超调量为10.5%,而采用DMC–PID串级温度控制其超调量为5.9%。DMC-PID可显著提高茶叶烘干机的温度控制精度及成茶品质。     

基于KFCM-RBF优化算法的肥力评价与玉米产量预测

采用带有基于高斯核函数的模糊聚类算法(KFCM),优化传统模糊聚类算法,提出一个基于模糊聚类的土壤肥力评价模型,以提高肥力聚类的准确性、高效性;融合RBF神经网络,创建一个基于RBF神经网络模型,提出KFCM-RBF优化算法的玉米产量预测模型。将模型运用于吉林省农安县的土壤肥力评价与玉米产量预测仿真试验。结果表明,该模型预测精度高,可用于玉米产量的预测,并为精准施肥提供决策依据。该模型具有结构稳定、训练速度快、适应性强、鲁棒性好、预测精度高的特点。     

烘干机温度PID控制系统及大红袍烘焙效果试验

阐述了新研制的烘干机温度PID控制系统结构原理。用该系统进行大红袍精制烘焙应用试验,研究表明,设定温度为180℃,PID控制系统平均热风温度为181.9℃,人工控制平均热风温度为188.9℃,PID控制烘焙温度比人工控制更加稳定,节煤率为22.97%~26.83%;PID控制与人工控制烘焙的两种等级大红袍茶多酚含量均达到显著水平,一级大红袍的氨基酸含量达到极显著水平,二级大红袍的氨基酸和可溶性糖含量达到显著水平;PID控制烘干的茶叶感官品质优于人工控制。     

A comparative study on the estimation of evapotranspiration using backpropagation neural network: Penman�CMonteith method versus pan evaporation method

The study compares the prediction performances of evapotranspiration by the FAO56 Penman–Monteith method and the pan evaporation method using the artificial neural network. A backpropagation neural network was adopted to determine the relationship between meteorological factors and evapotranspiration or evaporation. The evapotranspiration in the ChiaNan irrigated area of Tainan was considered. Weather data compiled by Irrigation Experiment Station of ChiaNan Irrigation Association were the input layer variables, including (1) the highest temperature, (2) the lowest temperature, (3) the average temperature, (4) the relative humidity, (5) the wind speed, (6) hours of sunlight, (7) amount of solar radiation, (8) the dew point, (9) morning ground temperature and (10) afternoon ground temperature. The importance of the ten weather factors was ranked by the general influence (GI) factor. Results show that the correlation coefficient between the evapotranspiration in 2004 calculated by FAO56 Penman–Monteith method and the one predicted by the neural network model with a hidden layer of ten nodes is 0.993. The actual evapotranspiration is 911.6 cm, and value prediction by the neural network is 896.4 cm, between which two values the error is 1.67%. The results reveal that the backpropagation neural network based on the FAO56 Penman–Monteith method can accurately predict evapotranspiration. However, the correlation coefficient between the actual evaporation in 2004 and the value prediction by the neural network with a hidden layer of ten nodes and an output layer with the pan evaporation as its target output is 0.708. The pan evaporation is 1,673.1 cm, while the value predicted by the backpropagation neural network is 1,451.7 cm, between which values the error is 13.23%. The backpropagation neural networks with pan evaporation as target outputs predict the evaporation with large errors. Moreover, the use of four agricultural weather factors (determined by the GI) including wind speed, average temperature, dew point and maximum temperature as input variables, and a hidden layer of three nodes in the backpropagation neural network model can successfully predict evapotranspiration based on the FAO56 Penman–Monteith method (R = 0.98, error = 1.35%).     

Real-time estimation and irregularity control of the sliver draft system

High regularity is important in manufacturing spun yarn. Among controllers used in regularity control, existing PID controller is suitable for linear time-invariant systems but not for the control of sliver draft systems with non-linear characteristic. Thus, the present study designed a knowledge-based fuzzy controller in consideration of disturbance and non-linear characteristic of sliver draft systems. RLS (Recursive Least Squares) was used as an estimation algorithm to formulate a model used in computer simulation for implementing the controller, and real time estimation was made using real data from draft systems to get a model close to actual systems. This research used a estimation model in designing a knowledge-based controller that minimizes the variation of control response to the sliver linear density of the system, and the result of linear density control was used to prove the superiority of the controller in control performance through CV%.     

Modeling and differential evolution optimization of PAN carbon fiber production process

In this research, results of an experimental and artificial neural network fuzzy interface system (ANFIS) modeling of operating parameters on tensile strength of the carbon fibers are investigated. To do these experiments, the commercial polyacrylonitrile (PAN) fiber of Polyacryl Iran Corporation (PIC) was used as the precursors. The results show that increasing all of parameters improves tensile strength performance. ANFIS was applied to predict tensile strength of carbon fibers as a function of stabilization temperature at first stage (STFIS), stabilization temperature at second stage (STSS), stabilization temperature at third stage (STTS), stabilization temperature at fourth stage (STFOS), and carbonization temperature (CT). The optimum levels of influential factors, determined for tensile strength are STFIS 200 °C, STSS 225 °C, STTS 240 °C, STFOS 260 °C, CT, and 1400 °C. The modeling results showed that there is an excellent agreement between the experimental data and the predicted values. Furthermore, the fiber process is optimized applying differential evolution (DE) algorithm as an effective and robust optimization method.     

基于模糊算法的茶叶理条机温度控制设计

为了防止茶叶在理条过程中出现色泽变黄、变暗或产生焦味的现象,需要有效地控制理条过程中的温度,提高茶叶的加工质量。将模糊算法与理条过程的温度控制结合,运用Matlab对设计进行仿真及进行理条实验,表明主副加热部件模糊温度控制精度高,超调量小,碎茶率为6%（传统方式碎茶率为11.8%,单一加热部件模糊控制碎茶率为8.3%）,理条温度为90℃,评审分数为932.5分,优于传统理条方式与单一加热部件模糊控制理条方式,该研究为提高茶叶理条质量提供了参考。     

常规粳稻在安徽沿江稻区的特征特性研究

以常规粳稻为试验材料,于2010-2015年在安徽沿江稻区的桐城市范岗镇杨安村试验基地,以杂交籼稻、杂交粳稻、常规籼稻为对照,对常规粳稻的特征特性进行了研究。结果表明,常规粳稻具有茎秆矮壮、叶片挺直且短、穗形上翘或直立、穗短且着粒密的植株形态特征和足蘖中穗的经济性状特征;与籼稻相比,常规粳稻表现为感光性强、分蘖率弱,但成穗率高、花时迟,幼穗分化Ⅵ期前后和抽穗扬花期对高温敏感,中后期耐肥等特性。不同播期对常规粳稻经济性状的影响主要表现在单位面积有效穗数和每穗总粒数上,对结实率和千粒重影响较小,常规粳稻随着播期的推迟表现为生物学产量逐渐下降而收获指数逐渐提高。试验结果表明,5月下旬播种、8月底至9月初抽穗的常规粳稻品种为安徽沿江稻区的优势品种,并提出了优势品种的高产栽培技术。     

Development of prediction system using artificial neural networks for the optimization of spinning process

This article correlates draw frame settings with quality characteristics of sliver and ring spun yarn using artificial neural networks. Considering the importance of draw frame as the last quality improvement machine in the spinning process, the quality influencing parameters of the draw frame were used as input for artificial neural networks. The neural networks were trained using a combination of Levenberg-Marquardt algorithm and Bayesian regularization for better generalization of the networks. Cross validation was performed for each trained network to test the performance of networks. The promising results achieved by this research work emphasize the ability of neural networks to predict the quality characteristics of sliver and yarn using the artificial neural networks. Therefore, draw frame parameters can be adjusted on the basis of required sliver and yarn quality. Furthermore, machines can be involved in the decision making process in spinning mills.     

基于学习矢量量化神经网络的水稻白穗和正常穗的高光谱识别

水稻病虫害的发生会导致大量白穗的出现,对白穗和正常穗的区分是采取植保措施和灾害评估的基础。通过研究获取了由水稻二化螟和穗瘟造成的白穗和正常穗的室内光谱,选取红边斜率、红边面积、绿峰幅值和绿峰面积等4个高光谱变量作为输入向量,利用学习矢量量化（LVQ）神经网络对水稻白穗和正常穗进行分类。利用测试样本对网络进行测试,结果显示对白穗和正常稻穗的分类精度高达100%。研究表明,基于LVQ神经网络对水稻白穗和正常穗进行辨别的方法是切实可行的,可以补充和替代肉眼观测。