饲料的吸湿解吸平衡规律和颗粒饲料冷却的模型拟合
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摘要
饲料安全作为食品安全的重要环节已得到空前的重视。水分控制对饲料质量、饲料企业效益和养殖户直至终端的肉奶蛋消费者至关重要。国内外饲料行业普遍存在饲料水分(特别是颗粒饲料)难于稳定控制的问题。本研究从饲料原料和成品饲料吸湿解吸平衡以及颗粒饲料冷却两条路线,尝试发现、计算和控制饲料的平衡含水率或水活度、计算安全水分和蒸发潜热,寻找冷却规律,为合理设计和控制饲料水分提供依据。具体结果主要包括：
1.利用饱和盐静态重量法或水活度仪法,研究比较了玉米、普通豆粕、去皮豆粕、发酵豆粕、低温白豆片、不同热处理白豆片、大豆分离蛋白、棉籽粕、脱酚高蛋白棉仁粕、菜籽粕、鱼粉、喷雾干燥血球粉、肉骨粉、酶解羽毛粉、啤酒酵母、DDGS、大豆糖蜜、甜菜糖蜜、魔芋精粉、可溶性淀粉、明胶以及烘干后作为载体的玉米粉、玉米芯粉、麸皮和稻壳粉等主要饲料原料在常温(T=25℃)或者常温范围(T=5℃-45℃)内的吸湿或解吸平衡,并对部分样品的水分吸着等温线进行了模型拟合。平衡水分(EMC)也作为衡量其持水力(WBC)的指标。这些平衡相对湿度(ERH)-平衡水分(EMC)-温度(T)之间的关系对于饲料的干燥、存贮等具有重要价值。研究发现,常见饲料原料中DDGS、糖蜜、发酵豆粕、魔芋精粉具有较高的持水力。持水力的差异在高水活度范围加大。DDGS中水溶性成份的增加线性提高EMC,这为DDGS原料选择和质量控制提供了新的指标。25℃时引入含水溶性成分比例的BY修正模型很好拟合了DDGS的EMC。豆粕经过发酵后提高了EMC。豆粕蛋白溶解度提高有增加EMC的趋势,但除非极端过生或严重加热过度,在正常热处理豆粕的蛋白溶解度的范围内,蛋白溶解度没有对白豆片的EMC产生显著的影响。魔芋精粉除具有较高的EMC外,表现出抗霉变的作用；其与玉米混合后,并未额外提高混合体系的EMC。大豆糖蜜和甜菜糖蜜的ERH-EMC关系显著区别于其他原料,在低水活度区域具有较高EMC,不同水活度时的差异很小(例如15℃时从氢氧化钠到氯化镁饱和盐的Aw,EMC=11.36-11.8%)。甜菜糖蜜的EMC低于大豆糖蜜。从BaCl2饱和盐的ERH以上,温度升高提高了EMC值。动物性原料中肉骨粉、羽毛粉的EMC低于鱼粉和喷雾血球蛋白粉。样品Aw-EMC的关系与优选模型的符合程度,可能有助于判断鱼粉的掺假。菜籽粕在Aw=0.753以下的EMC含量明显低于棉仁粕和棉籽粕,但在Aw≥0.84时基本一致。高蛋白(>50%)的棉仁粕EMC低于普通40%蛋白的棉仁粕。玉米和豆粕的粉碎粒度没有对EMC产生显著的影响,尽管过细的玉米样品表现出降低EMC的趋势,但可能与较低的初始水分有关。从ERH-EMC关系角度,结合水分与流动性的关系判断,稻壳粉是良好的预混料载体(稀释剂)。结合模型的泛化能力和适用性,除糖蜜外,Peleg、Generalized D'Arcy and Watt (GDW)、Blahovec and Yanniotis (BY)、GAB-VR和GAB可作为优选模型。其他经典的Henderson、Oswin、Halsey和Chung-Pfost模型有其具体的适用对象和范围,例如mChung-Pfost模型是拟合玉米解吸平衡的优选模型。当ERH≤84%, Peleg和mPeleg模型能良好地拟合糖蜜在15℃-45℃的ERH-EMC-T的关系。
2.对三种代表性的成品颗粒饲料在15℃～45℃范围的研究表明,肉大鸡料、肉大鸭料和草鱼颗粒饲料的吸湿解吸规律各有特点。在高湿环境下(ERH>90%),肉大鸭和草鱼颗粒料的平衡含水率随温度升高而提高,区别于一般农产品EMC随温度上升而下降的规律。Peleg、GDW、BY、GAB以及GAB-VR是优选的拟合颗粒饲料ERH-EMC关系的模型,而经典的Henderson、Osiwn、Halsey和Chung-Pfost模型及其修正型以及Chen-Clayton模型存在明显的局限性。结合对于原料的模型拟合数据,本研究成功地将温度变量引入到Peleg、GDW和BY模型当中,并通过文献数据得以验证。利用优选模型对三种饲料在15℃(或10℃)～45℃区间吸湿或解吸过程的安全水分进行了估计。草鱼颗粒料的安全水分低于肉大鸭和肉大鸡颗粒饲料。说明安全水分标准的设计还应考虑具体的饲料类别,而不是一刀切。在计算蒸发潜热过程中,选用拟合精度达到要求的经典模型更为便捷。
3.利用自行设计安装的颗粒饲料薄层冷却系统,通过相关与回归分析及神经网络训练证明,存在且可以找到综合各主要变量的数学模型,用于预测进而控制冷却过程。薄层冷却与实际的逆流冷却过程相比,仅缺乏颗粒流量、深床内存在的温度梯度和湿度梯度变量,这为把料层分解为适当厚度的薄层进而实现逐级仿真模拟提供了参考。根据肉大鸭颗粒饲料在不同颗粒初始条件(温度48～87℃,水分12.67%～23.54%db)和环境变量(空气温度7～40℃、相对湿度19%～94%、流速0.28～2.88 m/s)条件下冷却至高于环境温度10℃和5℃时的时间(T10,T5)-水分(M10,M5)变化,建立了含15～22个参数的经验模型。这个过程需要用优选的吸湿解吸模型估计出的EMC。参数模型中,对水分的拟合精度和预测精度优于时间模型,但需要将冷却时间作为自变量。时间模型在包含水分损失速率时的拟合以及预测精度显著优于不含损失速率的模型。不含水分损失速率的时间模型拟合精度满足实用要求,但预测精度有待于改进。对变量进行均值化处理并未显著提高拟合精度。T10与T5、M10与M5间存在显著的线性相关,T5≈1.52T10,M5≈0.99M10。神经网络模型对于水分的估计,即使不采用冷却时间变量,可获得优于本研究所建立的参数模型的结果,然而对于时间的拟合与预测结果欠佳。可能需要结合两者的优点进行建模。
利用参数模型计算表明,将初始温度80℃,水分18% db的肉鸭饲料进行冷却,在10℃～40℃,55%RH环境下提高风速可降低冷却时间。湿度影响到降温速率。提高空气湿度增加冷却后的产品含水率(10℃时线性方程的斜率为0.0371,20℃时为0.0247),提高风速降低产品水分(Hoerl函数是描述水分-风速关系的优选模型：M=a*bVair*VairC),但是在30～38℃高温环境里风速对于水分的影响明显不同于一般室温环境(10℃～20℃),大于1.444 m/s的风速将提高冷却后的产品水分。高温下湿度对于冷却产品水分的影响减弱,显示高温环境需要不同于一般室温条件下的冷却策略。由薄层冷却数据采集系统得到的温度水分变化数据还可以用于计算与冷却相关的热质传递参数。这个过程需要用优选的吸湿解吸模型估计出的EMC。
4.采用红外热成像-录像方法在蒸汽调质器出口、冷却器卸料口对料流进行连续温度记录,结合水分分析,发现DDC调制器出料口料流最低和最高温度相差达12℃,且出现低温湿料团；冷却器卸料口平面上的温度和水分不均匀现象因冷却器而异。这些现象与规律为实现自动控制消除不稳定因素提供了参考。实验也表明红外热成像技术在饲料和粮油食品行业可发挥更大作用。
5.合理设计的神经网络模型很好地拟合了一种池塘鱼颗粒饲料的在线冷却结果。利用多层感知器(MLP)网络对自变量重要性分析为控制手段的设计提供了参考。在所研究的产品和设备环境下,粉料水分、颗粒出模水分、平衡含水率、调制温度与水分以及空气温度是影响冷却后产品水分的主要因素,而影响冷却产品温度的主要变量为风量(以风门开度为度量)、平衡含水率、调质水分、空气温度和调制温度。
6.颗粒饲料的吸湿解吸及其适宜模型、蒸发潜热计算、薄层冷却数据,结合颗粒水分与颗粒直径和密度容重等的关系,为采用数值解法求解带有微分方程的热质平衡模型或者ANSYS建模与求解、CFD建模和求解提供了重要的基础数据。本研究利用试验所获得的部分参数,尝试用数值方法对初步设计的热质平衡模型进行求解,结果距离预期还有很大差距,表明模型、算法或者基础数据还有待于完善。
Feed safety as a sector in the food safety system has drawn dramatic public attention; hence moisture control plays an important role in feed quality, feed production margin, animal producers'net profit and the daily consumption of meat, milk and eggs. Stable feed moisture content, esp. in pellet feed, is still a challenge for the global feed industry. Moisture sorption isotherms, which is well known and investigated practically in food industry, is not carrying sufficient awareness it deserves. A relatively systematic research in moisture adsorption and desorption in feed materials and/or compound feeds is absent, thus elementary sorption data and typical predictive equations are deficient. Moisture adsorption and/or desorption equilibration determinations of more than forty feed raw material samples and three typical feed feeds were carried out to find options to improve feed water binding capacity and to reduce water activity, together with choice sorption isotherm equations either original or modified by the authors. A small thin-layer cooling system based upon PAC was established for lab study and online counterflow cooling data acquisition to investigate the possibility to simulate the cooling process by empirical equations and/or artificial neural networks. The research provided preliminary dada and methods to control hot feed pellet cooling practice.
1. Adsorption and/or desorption equilibrium moisture content (EMC) of feedstuff samples, including corn, regular soybean meals (SBM), dehulled soybean meal (DHSBM), fermented soybean meal(FSBM), white soybean flakes(WSF) heated at different condition, soybean protein isolates(SPI), cotton seed meal (CSM), degossypololized high-protein CSM(DGCSM), rapeseed meal(RSM), fishmeal(FM), spray-dried blood cells (SDBC), meat and bone meal(MBM), enzymatic hydrolyzed feather meal(HFM), dried beer brewery yeast(DBY), dried distillers'grains and solubles (DDGS), soybean molasses(SBML), sugar beet molasses(BML), choice konjac meal(KJM), soluble starch, gel, and dried ground corn, dried corn cob meal, dried wheat bran and ground rice hull were determined with conventional static gravimetric method with saturated salt solutions and/or the water activity determination instrument, at typical room temperatures (T=25℃, or between 5℃to 45℃). Some of the EMC-Temperature-Aw (water activity, or ERH, equilibrium relative humidity) relationship was modeled. EMC is known as one of the approaches to find the water binding capacity (WBC). This EMC-Aw-Temperature relationship is valuable in feed drying/cooling, handling and storage. In our research, highest WBC were found in quality DDGS, molasses, FSBM and KJM, especially within high water activity range. Water soluble part (WSP) in DDGS linearly increases EMC level, which means new parameters for DDGS selection and quality management. Blahovec and Yanniotis (BY) model with WSP as a variable was the best equation fitting the DDGS EMC at 25℃. Fermentation seemingly increases EMC in soybean meal. Increasing protein solubility of SBM gave a tendency of higher EMC, but did not improve it significantly within the normal solubility range of regular meal, unless in extremely undercooked or overcooked SBM. KJM has very high EMC level, and is inhibitive in mold multiplication. It did not provide higher EMC than the weighted average in mixture with corn. Both the two molasses showed obviously different ERH-EMC relation from other materials, for the high EMC at low ERH range lower than that by saturated MgCl2 solution. Sugar beet molasses took more moisture than soybean molasses. Increasing temperature resulted in higher EMC from ERH provided by saturated BaCl2 solution. Within the animal protein resources, MBM and HFM had lower EMC than fish meal and SDBL. The fitness of Aw-EMC relationship with selected equations could be helpful in fish meal adulteration detection. High protein CSM showed lower EMC than low protein meal. When Aw is under 0.843, RSM EMC is lower than all the four CSMs, but from 0.843 it is very close to that of CSMs. This partially explains the lower EMC in grownout grass carp feed than the level in the broiler feed and the duck feed. Particle size did not affect the EMC of corn and SBM, though the finest corn had seemingly lower EMC, possibly due to the low initial moisture in this fine corn. Ground rice hull is a good premix carrier or dilutor for premix, judged by the Aw-EMC relation and the moisture-flowability interaction. New sorption isotherm equations including Peleg, Generalized D'Arcy and Watt (GDW), Blahovec and Yanniotis (BY) as well as the conventional GAB model fit generally all the materials except for molasses, while other conventional models such as Henderson, Oswin, Halsey and Chung-Pfost performed well for specific feedstuffs and/or specific water activity ranges, e.g. mChung-Pfost for corn desorption process and mHalsey for cottonseed meals. Peleg and mPeleg described well the ERH-EMC-T relationship of molasses when ERH<=84% from 15℃to 45℃.
2. Moisture sorption behavior of pelleted finishing broiler feed, pelleted finishing duck feed and pelleted grownout grass carp feed differed from each other, within the temperature range from 15℃to 45℃. Duck feed and grass carp feed absorbed much higher moisture at high temperature and humid condition (45℃, ERH>90%), which did not follow the principle of normal agricultural products that increasing temperature reduces the EMC. The model Peleg, GDW, BY, GAB and GAB-VR were choice models for the feed pellets. Henderson, Oswin, Halsey and Chung-Pfost as well as their modified forms and Chen-Clayton model either fits secondary to the choice or have limited generalization and utilization. Also as stated in feed material sorption research, this compound feed sorption study successfully introduced temperature effect as a new variable into either Peleg, GDW or BY model, which was approved by published data. These models were adopted to estimate proper moisture content for safe storage, with Aw=0.65 as the critical Aw point. Traditional sorption isotherm equations were more convenient for latent heat calculation once they fit the data with accepted goodness.
3. A thin layer cooling data acquisition system was established to facilitate the approval of possibility to find empirical parametric equations and/or artificial neural network (ANN) models that integrated all key independent variables for predicting and controlling the cooling process. Thin layer cooling models lack only pellet flow, temperature gradient and relative humidity gradient that exist in deep bed cooling. This system and data provided information for layer by layer cooling simulation after dividing the pellet bed into thin-layers with proper depth increment. Empirical equations with 15 to 22 parameters were obtained for fitting the time to a pellet temperature ten (T10) and five degrees Celsius (T5) above air and for fitting the moisture content at these two time points (M10, M5), of the pelleted duck feed during thin-layer cooling at various pellet initial condition (temperature from 48℃to 87℃,moisture from 12.67% to 23.54% db) and ambient condition (air temperature from 7℃to 40℃, relative humidity from 19% to 94%, and air velocity from 0.28 m/s to 2.88 m/s). EMC calculated by choice sorption equation(s) was involved in the regression. The empirical moisture equation with cooling time as an independent variable performed better than the time equation. The cooling time equation including average moisture loss rate gave much better fitting goodness than the equation without the rate. If the moisture loss rate was excluded the cooling time model gave a fitting accuracy close to acceptance, but the predictive accuracy for the validation data group required improvement. Equalization of variables did not improve the fit significantly. T5 could be linearly estimated by T10 (T5≈1.52T10) and M5 linearly by M10 (M5≈0.99M10). For moisture content, ANN gave much better prediction and validation even without the cooling time variable, than the established empirical moisture equation, but neither of them could fit or simulate the cooling time excellently. A combination of the merits of ANN and the empirical parametric model might be another approach. Simulation of the duck feed (assigned initial temperature 80℃and moisture:18% db) at 55% RH from 10℃to 40℃with the empirical equation found that faster air movement reduced exponentially the cooling time. Relative humidity changed the reductive effect. Higher RH increased the moisture content in cooled feed linearly (slope was 0.0371 at 10℃and 0.0247 at 20℃), raised air velocity reduced moisture level and the Hoerl model (M=a*bVair*VairC) described well this function. However, the moisture increasing effect was different at hot environment (30℃～38℃) from that at normal range (10℃～20℃) because on the contrary the velocity higher than 1.444 m/s increased the moisture percentage in the cooled pellets. High air temperature weakened the effect of relative humidity on moisture in the product, thus the cooling configuration requires adjustment from that in lower temperature condition. The temperature and water data obtained by the thin-layer cooling system are useful for calculation of coupled heat and mass transfer coefficients.
4. Infrared thermo-videoing system were managed to record the temperature variation in the steam conditioned mash flow at the steam conditioner exit and in the cooled pellets at the cooler exit plain. The average lowest temperature was about 12℃lower than the average highest in the mash flow. Occasional cool and wet clots were found as well. Deviation of temperature and moisture of feed at the cooler downloading plain varied from cooler to cooler. These findings will help avoid fluctuation in the planned cooling automation system. The trial suggested extensive potential utilization of thermo videoing in both research and production in feed, food, cereal and oilseed industries.
5. A proper ANN network simulated well the online counterflow cooling of a pelleted aqua feed for fresh water farming. Evaluation of variable importance by a multi-layer preceptor network (MLP) provided references for automation design. For the specific product manufactured by the specific processing line within the air circumstances, mash moisture, pellet moisture at the die exit, EMC, conditioned mash temperature and moisture as well as the air temperature were profound factors on product moisture, while the air flow (determined by air valve openings), EMC, conditioned mash moisture, air temperature and the conditioned mash temperature were critical factors on cooled pellet temperature.
6. The data obtained on pellet feed EMC and corresponding choice equation(s), the latent heat, and the thin-layer cooling information as well as the moisture effect on pellet diameter, pellet density and bulk density etc. facilitate substantially the numerical solution of mass and heat problems with differentiation equations, problems by ANSYS and/or that in CFD modeling. This study tried to establish and solve numerically the heat and mass balance equation groups with assistance by the collected data. However the report was not good as expected, which means further effort is necessary to improve the modeling, the solving and/or the data base.
1.利用饱和盐静态重量法或水活度仪法,研究比较了玉米、普通豆粕、去皮豆粕、发酵豆粕、低温白豆片、不同热处理白豆片、大豆分离蛋白、棉籽粕、脱酚高蛋白棉仁粕、菜籽粕、鱼粉、喷雾干燥血球粉、肉骨粉、酶解羽毛粉、啤酒酵母、DDGS、大豆糖蜜、甜菜糖蜜、魔芋精粉、可溶性淀粉、明胶以及烘干后作为载体的玉米粉、玉米芯粉、麸皮和稻壳粉等主要饲料原料在常温(T=25℃)或者常温范围(T=5℃-45℃)内的吸湿或解吸平衡,并对部分样品的水分吸着等温线进行了模型拟合。平衡水分(EMC)也作为衡量其持水力(WBC)的指标。这些平衡相对湿度(ERH)-平衡水分(EMC)-温度(T)之间的关系对于饲料的干燥、存贮等具有重要价值。研究发现,常见饲料原料中DDGS、糖蜜、发酵豆粕、魔芋精粉具有较高的持水力。持水力的差异在高水活度范围加大。DDGS中水溶性成份的增加线性提高EMC,这为DDGS原料选择和质量控制提供了新的指标。25℃时引入含水溶性成分比例的BY修正模型很好拟合了DDGS的EMC。豆粕经过发酵后提高了EMC。豆粕蛋白溶解度提高有增加EMC的趋势,但除非极端过生或严重加热过度,在正常热处理豆粕的蛋白溶解度的范围内,蛋白溶解度没有对白豆片的EMC产生显著的影响。魔芋精粉除具有较高的EMC外,表现出抗霉变的作用；其与玉米混合后,并未额外提高混合体系的EMC。大豆糖蜜和甜菜糖蜜的ERH-EMC关系显著区别于其他原料,在低水活度区域具有较高EMC,不同水活度时的差异很小(例如15℃时从氢氧化钠到氯化镁饱和盐的Aw,EMC=11.36-11.8%)。甜菜糖蜜的EMC低于大豆糖蜜。从BaCl2饱和盐的ERH以上,温度升高提高了EMC值。动物性原料中肉骨粉、羽毛粉的EMC低于鱼粉和喷雾血球蛋白粉。样品Aw-EMC的关系与优选模型的符合程度,可能有助于判断鱼粉的掺假。菜籽粕在Aw=0.753以下的EMC含量明显低于棉仁粕和棉籽粕,但在Aw≥0.84时基本一致。高蛋白(>50%)的棉仁粕EMC低于普通40%蛋白的棉仁粕。玉米和豆粕的粉碎粒度没有对EMC产生显著的影响,尽管过细的玉米样品表现出降低EMC的趋势,但可能与较低的初始水分有关。从ERH-EMC关系角度,结合水分与流动性的关系判断,稻壳粉是良好的预混料载体(稀释剂)。结合模型的泛化能力和适用性,除糖蜜外,Peleg、Generalized D'Arcy and Watt (GDW)、Blahovec and Yanniotis (BY)、GAB-VR和GAB可作为优选模型。其他经典的Henderson、Oswin、Halsey和Chung-Pfost模型有其具体的适用对象和范围,例如mChung-Pfost模型是拟合玉米解吸平衡的优选模型。当ERH≤84%, Peleg和mPeleg模型能良好地拟合糖蜜在15℃-45℃的ERH-EMC-T的关系。
2.对三种代表性的成品颗粒饲料在15℃～45℃范围的研究表明,肉大鸡料、肉大鸭料和草鱼颗粒饲料的吸湿解吸规律各有特点。在高湿环境下(ERH>90%),肉大鸭和草鱼颗粒料的平衡含水率随温度升高而提高,区别于一般农产品EMC随温度上升而下降的规律。Peleg、GDW、BY、GAB以及GAB-VR是优选的拟合颗粒饲料ERH-EMC关系的模型,而经典的Henderson、Osiwn、Halsey和Chung-Pfost模型及其修正型以及Chen-Clayton模型存在明显的局限性。结合对于原料的模型拟合数据,本研究成功地将温度变量引入到Peleg、GDW和BY模型当中,并通过文献数据得以验证。利用优选模型对三种饲料在15℃(或10℃)～45℃区间吸湿或解吸过程的安全水分进行了估计。草鱼颗粒料的安全水分低于肉大鸭和肉大鸡颗粒饲料。说明安全水分标准的设计还应考虑具体的饲料类别,而不是一刀切。在计算蒸发潜热过程中,选用拟合精度达到要求的经典模型更为便捷。
3.利用自行设计安装的颗粒饲料薄层冷却系统,通过相关与回归分析及神经网络训练证明,存在且可以找到综合各主要变量的数学模型,用于预测进而控制冷却过程。薄层冷却与实际的逆流冷却过程相比,仅缺乏颗粒流量、深床内存在的温度梯度和湿度梯度变量,这为把料层分解为适当厚度的薄层进而实现逐级仿真模拟提供了参考。根据肉大鸭颗粒饲料在不同颗粒初始条件(温度48～87℃,水分12.67%～23.54%db)和环境变量(空气温度7～40℃、相对湿度19%～94%、流速0.28～2.88 m/s)条件下冷却至高于环境温度10℃和5℃时的时间(T10,T5)-水分(M10,M5)变化,建立了含15～22个参数的经验模型。这个过程需要用优选的吸湿解吸模型估计出的EMC。参数模型中,对水分的拟合精度和预测精度优于时间模型,但需要将冷却时间作为自变量。时间模型在包含水分损失速率时的拟合以及预测精度显著优于不含损失速率的模型。不含水分损失速率的时间模型拟合精度满足实用要求,但预测精度有待于改进。对变量进行均值化处理并未显著提高拟合精度。T10与T5、M10与M5间存在显著的线性相关,T5≈1.52T10,M5≈0.99M10。神经网络模型对于水分的估计,即使不采用冷却时间变量,可获得优于本研究所建立的参数模型的结果,然而对于时间的拟合与预测结果欠佳。可能需要结合两者的优点进行建模。
利用参数模型计算表明,将初始温度80℃,水分18% db的肉鸭饲料进行冷却,在10℃～40℃,55%RH环境下提高风速可降低冷却时间。湿度影响到降温速率。提高空气湿度增加冷却后的产品含水率(10℃时线性方程的斜率为0.0371,20℃时为0.0247),提高风速降低产品水分(Hoerl函数是描述水分-风速关系的优选模型：M=a*bVair*VairC),但是在30～38℃高温环境里风速对于水分的影响明显不同于一般室温环境(10℃～20℃),大于1.444 m/s的风速将提高冷却后的产品水分。高温下湿度对于冷却产品水分的影响减弱,显示高温环境需要不同于一般室温条件下的冷却策略。由薄层冷却数据采集系统得到的温度水分变化数据还可以用于计算与冷却相关的热质传递参数。这个过程需要用优选的吸湿解吸模型估计出的EMC。
4.采用红外热成像-录像方法在蒸汽调质器出口、冷却器卸料口对料流进行连续温度记录,结合水分分析,发现DDC调制器出料口料流最低和最高温度相差达12℃,且出现低温湿料团；冷却器卸料口平面上的温度和水分不均匀现象因冷却器而异。这些现象与规律为实现自动控制消除不稳定因素提供了参考。实验也表明红外热成像技术在饲料和粮油食品行业可发挥更大作用。
5.合理设计的神经网络模型很好地拟合了一种池塘鱼颗粒饲料的在线冷却结果。利用多层感知器(MLP)网络对自变量重要性分析为控制手段的设计提供了参考。在所研究的产品和设备环境下,粉料水分、颗粒出模水分、平衡含水率、调制温度与水分以及空气温度是影响冷却后产品水分的主要因素,而影响冷却产品温度的主要变量为风量(以风门开度为度量)、平衡含水率、调质水分、空气温度和调制温度。
6.颗粒饲料的吸湿解吸及其适宜模型、蒸发潜热计算、薄层冷却数据,结合颗粒水分与颗粒直径和密度容重等的关系,为采用数值解法求解带有微分方程的热质平衡模型或者ANSYS建模与求解、CFD建模和求解提供了重要的基础数据。本研究利用试验所获得的部分参数,尝试用数值方法对初步设计的热质平衡模型进行求解,结果距离预期还有很大差距,表明模型、算法或者基础数据还有待于完善。
Feed safety as a sector in the food safety system has drawn dramatic public attention; hence moisture control plays an important role in feed quality, feed production margin, animal producers'net profit and the daily consumption of meat, milk and eggs. Stable feed moisture content, esp. in pellet feed, is still a challenge for the global feed industry. Moisture sorption isotherms, which is well known and investigated practically in food industry, is not carrying sufficient awareness it deserves. A relatively systematic research in moisture adsorption and desorption in feed materials and/or compound feeds is absent, thus elementary sorption data and typical predictive equations are deficient. Moisture adsorption and/or desorption equilibration determinations of more than forty feed raw material samples and three typical feed feeds were carried out to find options to improve feed water binding capacity and to reduce water activity, together with choice sorption isotherm equations either original or modified by the authors. A small thin-layer cooling system based upon PAC was established for lab study and online counterflow cooling data acquisition to investigate the possibility to simulate the cooling process by empirical equations and/or artificial neural networks. The research provided preliminary dada and methods to control hot feed pellet cooling practice.
1. Adsorption and/or desorption equilibrium moisture content (EMC) of feedstuff samples, including corn, regular soybean meals (SBM), dehulled soybean meal (DHSBM), fermented soybean meal(FSBM), white soybean flakes(WSF) heated at different condition, soybean protein isolates(SPI), cotton seed meal (CSM), degossypololized high-protein CSM(DGCSM), rapeseed meal(RSM), fishmeal(FM), spray-dried blood cells (SDBC), meat and bone meal(MBM), enzymatic hydrolyzed feather meal(HFM), dried beer brewery yeast(DBY), dried distillers'grains and solubles (DDGS), soybean molasses(SBML), sugar beet molasses(BML), choice konjac meal(KJM), soluble starch, gel, and dried ground corn, dried corn cob meal, dried wheat bran and ground rice hull were determined with conventional static gravimetric method with saturated salt solutions and/or the water activity determination instrument, at typical room temperatures (T=25℃, or between 5℃to 45℃). Some of the EMC-Temperature-Aw (water activity, or ERH, equilibrium relative humidity) relationship was modeled. EMC is known as one of the approaches to find the water binding capacity (WBC). This EMC-Aw-Temperature relationship is valuable in feed drying/cooling, handling and storage. In our research, highest WBC were found in quality DDGS, molasses, FSBM and KJM, especially within high water activity range. Water soluble part (WSP) in DDGS linearly increases EMC level, which means new parameters for DDGS selection and quality management. Blahovec and Yanniotis (BY) model with WSP as a variable was the best equation fitting the DDGS EMC at 25℃. Fermentation seemingly increases EMC in soybean meal. Increasing protein solubility of SBM gave a tendency of higher EMC, but did not improve it significantly within the normal solubility range of regular meal, unless in extremely undercooked or overcooked SBM. KJM has very high EMC level, and is inhibitive in mold multiplication. It did not provide higher EMC than the weighted average in mixture with corn. Both the two molasses showed obviously different ERH-EMC relation from other materials, for the high EMC at low ERH range lower than that by saturated MgCl2 solution. Sugar beet molasses took more moisture than soybean molasses. Increasing temperature resulted in higher EMC from ERH provided by saturated BaCl2 solution. Within the animal protein resources, MBM and HFM had lower EMC than fish meal and SDBL. The fitness of Aw-EMC relationship with selected equations could be helpful in fish meal adulteration detection. High protein CSM showed lower EMC than low protein meal. When Aw is under 0.843, RSM EMC is lower than all the four CSMs, but from 0.843 it is very close to that of CSMs. This partially explains the lower EMC in grownout grass carp feed than the level in the broiler feed and the duck feed. Particle size did not affect the EMC of corn and SBM, though the finest corn had seemingly lower EMC, possibly due to the low initial moisture in this fine corn. Ground rice hull is a good premix carrier or dilutor for premix, judged by the Aw-EMC relation and the moisture-flowability interaction. New sorption isotherm equations including Peleg, Generalized D'Arcy and Watt (GDW), Blahovec and Yanniotis (BY) as well as the conventional GAB model fit generally all the materials except for molasses, while other conventional models such as Henderson, Oswin, Halsey and Chung-Pfost performed well for specific feedstuffs and/or specific water activity ranges, e.g. mChung-Pfost for corn desorption process and mHalsey for cottonseed meals. Peleg and mPeleg described well the ERH-EMC-T relationship of molasses when ERH<=84% from 15℃to 45℃.
2. Moisture sorption behavior of pelleted finishing broiler feed, pelleted finishing duck feed and pelleted grownout grass carp feed differed from each other, within the temperature range from 15℃to 45℃. Duck feed and grass carp feed absorbed much higher moisture at high temperature and humid condition (45℃, ERH>90%), which did not follow the principle of normal agricultural products that increasing temperature reduces the EMC. The model Peleg, GDW, BY, GAB and GAB-VR were choice models for the feed pellets. Henderson, Oswin, Halsey and Chung-Pfost as well as their modified forms and Chen-Clayton model either fits secondary to the choice or have limited generalization and utilization. Also as stated in feed material sorption research, this compound feed sorption study successfully introduced temperature effect as a new variable into either Peleg, GDW or BY model, which was approved by published data. These models were adopted to estimate proper moisture content for safe storage, with Aw=0.65 as the critical Aw point. Traditional sorption isotherm equations were more convenient for latent heat calculation once they fit the data with accepted goodness.
3. A thin layer cooling data acquisition system was established to facilitate the approval of possibility to find empirical parametric equations and/or artificial neural network (ANN) models that integrated all key independent variables for predicting and controlling the cooling process. Thin layer cooling models lack only pellet flow, temperature gradient and relative humidity gradient that exist in deep bed cooling. This system and data provided information for layer by layer cooling simulation after dividing the pellet bed into thin-layers with proper depth increment. Empirical equations with 15 to 22 parameters were obtained for fitting the time to a pellet temperature ten (T10) and five degrees Celsius (T5) above air and for fitting the moisture content at these two time points (M10, M5), of the pelleted duck feed during thin-layer cooling at various pellet initial condition (temperature from 48℃to 87℃,moisture from 12.67% to 23.54% db) and ambient condition (air temperature from 7℃to 40℃, relative humidity from 19% to 94%, and air velocity from 0.28 m/s to 2.88 m/s). EMC calculated by choice sorption equation(s) was involved in the regression. The empirical moisture equation with cooling time as an independent variable performed better than the time equation. The cooling time equation including average moisture loss rate gave much better fitting goodness than the equation without the rate. If the moisture loss rate was excluded the cooling time model gave a fitting accuracy close to acceptance, but the predictive accuracy for the validation data group required improvement. Equalization of variables did not improve the fit significantly. T5 could be linearly estimated by T10 (T5≈1.52T10) and M5 linearly by M10 (M5≈0.99M10). For moisture content, ANN gave much better prediction and validation even without the cooling time variable, than the established empirical moisture equation, but neither of them could fit or simulate the cooling time excellently. A combination of the merits of ANN and the empirical parametric model might be another approach. Simulation of the duck feed (assigned initial temperature 80℃and moisture:18% db) at 55% RH from 10℃to 40℃with the empirical equation found that faster air movement reduced exponentially the cooling time. Relative humidity changed the reductive effect. Higher RH increased the moisture content in cooled feed linearly (slope was 0.0371 at 10℃and 0.0247 at 20℃), raised air velocity reduced moisture level and the Hoerl model (M=a*bVair*VairC) described well this function. However, the moisture increasing effect was different at hot environment (30℃～38℃) from that at normal range (10℃～20℃) because on the contrary the velocity higher than 1.444 m/s increased the moisture percentage in the cooled pellets. High air temperature weakened the effect of relative humidity on moisture in the product, thus the cooling configuration requires adjustment from that in lower temperature condition. The temperature and water data obtained by the thin-layer cooling system are useful for calculation of coupled heat and mass transfer coefficients.
4. Infrared thermo-videoing system were managed to record the temperature variation in the steam conditioned mash flow at the steam conditioner exit and in the cooled pellets at the cooler exit plain. The average lowest temperature was about 12℃lower than the average highest in the mash flow. Occasional cool and wet clots were found as well. Deviation of temperature and moisture of feed at the cooler downloading plain varied from cooler to cooler. These findings will help avoid fluctuation in the planned cooling automation system. The trial suggested extensive potential utilization of thermo videoing in both research and production in feed, food, cereal and oilseed industries.
5. A proper ANN network simulated well the online counterflow cooling of a pelleted aqua feed for fresh water farming. Evaluation of variable importance by a multi-layer preceptor network (MLP) provided references for automation design. For the specific product manufactured by the specific processing line within the air circumstances, mash moisture, pellet moisture at the die exit, EMC, conditioned mash temperature and moisture as well as the air temperature were profound factors on product moisture, while the air flow (determined by air valve openings), EMC, conditioned mash moisture, air temperature and the conditioned mash temperature were critical factors on cooled pellet temperature.
6. The data obtained on pellet feed EMC and corresponding choice equation(s), the latent heat, and the thin-layer cooling information as well as the moisture effect on pellet diameter, pellet density and bulk density etc. facilitate substantially the numerical solution of mass and heat problems with differentiation equations, problems by ANSYS and/or that in CFD modeling. This study tried to establish and solve numerically the heat and mass balance equation groups with assistance by the collected data. However the report was not good as expected, which means further effort is necessary to improve the modeling, the solving and/or the data base.
引文
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