Research Status and Prospect on Green and Low-carbon Technology of Grain Drying
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摘要:
粮食干燥是保障国家粮食安全的重要生产环节,但干燥低碳减排技术处于落后状态。基于粮食机械化干燥技术现状,分别从干燥系统供热方式、干燥工艺设计和干燥控制技术3个方面分析了粮食干燥高能耗的主要原因,重点比较了机械化干燥工艺与节能改进方法;梳理干燥理论与智能调控研发历程,讨论粮食干燥智能化技术特征和进展,分析了粮食干燥智能化存在信息感知困难、算法精度差、控制策略单一等问题;提出了粮食干燥理论研究范式创新、干燥装备设计与工艺优化、干燥智能控制方法体系构建等产业发展建议,为粮食干燥节能减排新技术创新及产业高质量发展提供借鉴。
Abstract:Grain drying is a critical element in production chain and plays a key role in ensuring national food security.However, progress of drying industry toward high-quality development is hampered by outdated dying techniques that lack effective measures for carbon reduction and emissions.Starting from current status of mechanized grain drying technology, main reasons for high energy consumption in grain drying were analyzed from perspectives of heating methods in drying systems, drying process design, and drying control technology.The focus was on comparing mechanized drying technology with energy-saving improvement methods.Research and development process of drying theory and intelligent regulation was reviewed, characteristics and progress of intelligent technology for grain drying were discussed, and problems of information perception difficulties, poor algorithm accuracy, and single control strategy in intelligent grain drying were analyzed.Suggestions for future development of industry, including innovative research paradigms in grain drying theory, design and process optimization of drying equipment, and construction of an intelligent control method system for drying, have been proposed, providing reference for innovation of new technologies for energy conservation and emission reduction in grain drying and high-quality development of industry.
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Keywords:
- grain drying /
- green and low-carbon /
- intelligent /
- food security
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表 1 不同粮食干燥工艺与节能减排性能分析
Table 1. Analysis of different grain drying processes and performance of energy saving and emission reduction
干燥工艺 工艺特点 研究结论 绿色发展方向 多段变温缓苏[18] 综合考虑粮食品质、干燥能耗、环保、粮食用途及成本等因素;根据水分去除机理和粮食受热等分为若干段不同温度干燥粮食;缓苏为了减少粮食干燥爆腰 以稻谷为例,其干燥含水率均匀度较高99.6%,能有效缩短干燥时间,干燥速率0.036%/min,缓苏工艺对爆腰率有显著降低的效果 多段变温和缓苏工艺有效减少干燥能耗,对干燥产物品质保持较好,其对温度稳定调节和水分、品质的在线检测技术要求较高 顺逆流组合干燥[19-20] 将顺流与逆流干燥技术组合,并采用其独特的通风节布风结构,适合于干燥低温高水分粮食 顺逆流干燥技术比顺流干燥技术加逆流冷却工艺的能耗率低5%,比逆流干燥技术的能耗率低8%;建立回归分析模型预测出机水分,减少能源消耗 最大限度地进行水分与热量的转移,烘干废气温度40 °C,冷却段由于粮温热量也可使得空气温度达到35 °C,可以采取余热回收方式将两种空气重新收集送入换热器中达到节能效果 顺混流组合干燥[21] 将顺流与混流干燥技术组合,并采用其独特的通风节布风结构及密布角状管的一种组合干燥技术和设备,适合于干燥低温高水分粮食,尤其适合于低温高水分粮食的节能保质干燥 以HSHT15型顺混流干燥机为例,具有热效率高、节能明显、烘后粮食品质好、烘后粮食干燥不均匀度1.6%、破碎率增值1.%和裂纹率增值3.0% 选择余热回收技术,收集顺流段烘干废气,混流段的干燥效率较高,整体顺混流系统热量利用率较高 逆顺流组合干燥[22] 将逆流与顺流干燥技术组合,并采用均匀布风技术,实行多段、变温和缓苏干燥,适合于稻谷、油料等非耐热物料的保质节能干燥 以HNST500环保型连续式逆顺流高水分稻谷保质干燥机为例,逆流和顺流交替进行,每一级逆流和顺流干燥后设置一级缓苏,干燥缓苏级数随降水幅 度的增大而增加 采用废气回收装置,循环利用干燥段和冷却段废气,控制废气出口风速来减少废气排放带出的粉尘 高低温组合干燥[23] 粮食先经高温连续干燥,当水分降到17%~18%时,将粮食转移到通风干燥仓内,采用就仓干燥技术,低温大风量通风干燥去除剩余的水分 高低温组合干燥技术和设备具有能耗低、干燥后粮食品质好的优点,但是要配置大容量的通风干燥仓,占地面积大 热量利用率较高,但缺少余热回收装置和机构,对于就仓干燥来说干燥均匀性和品质保持都有一定的技术难度 表 2 基于人工智能的粮食干燥控制算法分析
Table 2. Analysis of grain drying control algorithm based on artificial intelligence
对象类型 运用算法和控制器 性能分析 稻谷干燥[38] 长短期记忆神经网络(LSTM)和模型预测控制(MPC)耦合控制器 与常规PID控制器相比,LSTM-MPC控制器响应速度提升15%~30%,干燥后出机水分控制精度提升0.2%以上,具有更强的鲁棒性 稻谷干燥[39] 优化长短期记忆神经网络(LSTM) 与BP、ELMAN、NAEX等算法及普通LSTM网络进行比较,结果发现优化的LSTM模型可以更好地预测稻谷出机水分,平均绝对误差0.12% 玉米干燥[40] 极限学习机(ELM) 构建了出机水分含量预测模型,水分含量预测误差0.2811%~0.3821%,未出现过拟合情况 玉米干燥[41] BP神经网络 建立出机含水率预测模型,模型预测排粮电机转速误差−5~5 r/min,相关系数0.98419,可以有效预测排粮电机转速和出机玉米含水率 玉米干燥[42] BP神经网络 快速准确地建立模型描述排粮频率变化规律,模型相关系数0.9491,用于自动控制排粮频率来控制干燥机内粮食流量,进而控制粮食含水率及过度干燥问题 5HNH-15型连续式
粮食干燥机[43]8-11-1的BP神经网络 模型决定系数0.998,绝对误差±0.1,解决了建立被控对象模型难度大问题,可为智能控制应用提供基础 多筒式烘干机[44] BP神经网络模型的Sigmoid函数 模拟生物神经元,实现非线性数据处理,增强神经网络的非线性映射能力,开发了智能预测控制系统软件,系统提高了整体粮食烘干塔干燥处理效率 智能模拟系统[45] 包括在线水分、温度、视频监控系统和计算机模拟控制与管理系统 软件主要用于预测干燥机干燥粮食的效果,分析粮食干燥过程,辅助优化最佳干燥参数,建立对应的干燥控制程序 物联网监控系统[46] 粮食烘干储藏一体化物联网 实现了粮食干燥、粮仓环节的关键参数实时远程监测、报警及关键设备自动控制功能,为粮食高效节能烘干提供了新思路,有广阔的应用前景 -
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