以往悬浮结晶冷冻浓缩工艺多采用分立单元设备组合，系统庞大、操作复杂。本研究对一台多级智能控制的冷冻浓缩仪的原型机进行了测试和验证。该设备采用将刮面换热器、结晶器、洗涤柱三位一体的结构设计，并配置了一个智能控制单元。基于该设备，本研究以浓缩苹果汁为例对冷冻浓缩仪的性能参数进行了实验测试。第一级、第二级和第三级冷冻浓缩的浓缩比分别为1.84、1.40和1.32；结晶率分别为0.44、0.36和0.22；固溶物在冰晶和母液中的分配系数分别为0.03、0.04和0.11；固溶物的回收得率分别为0.99、0.99和0.98；刮面换热器冷却阶段的换热系数分别为0.23±0.00 kJ·m-2·℃-1、0.11±0.02 kJ·m-2·℃-1和0.22±0.02 kJ·m-2·℃-1；冷冻阶段的换热系数分别为0.54±0.03 kJ·m-2·℃-1、0.44±0.02 kJ·m-2·℃-1和0.29±0.01 kJ·m-2·℃-1；产冰速率分别为0.90±0.05 kg/h、0.90±0.05 kg/h和0.30±0.06 kg/h。结果表明该系统简约高效，并可将冷冻浓缩复杂的操作控制交由机器完成。

Combinations of discrete equipment units were often used in suspension crystallization freeze concentration process in the past, and as a result, the system was large and complicated to run. In this study, a multi-stage intelligently controlled freeze concentrator prototype was tested and validated. This prototype adopted a trinity structure, which combined a scraped surface heat exchanger (SSHE), a crystallizer and a washing column and equipped with an intelligent control unit. Based on the equipment, this study used the concentrated apple juice as an example to test the performance parameters of the freeze concentrator. The concentration ratios of the first, second and third stages of freeze concentration were 1.84, 1.40 and 1.32, respectively. The crystallization rates were 0.44, 0.36 and 0.22, respectively. The partition coefficients of solid solutes in ice crystals and mother liquor were 0.03, 0.04 and 0.11, respectively. The recovery of solid solutes were 0.99, 0.99 and 0.98, respectively. The heat transfer coefficients of SSHE at the cooling stage were 0.23±0.00 kJ·m-2·℃-1, 0.11±0.02 kJ·m-2·℃-1 and 0.22±0.02 kJ·m-2·℃-1, respectively. The heat transfer coefficients of SSHE at the freezing stage were 0.54±0.03 kJ·m-2·℃-1, 0.44±0.02 kJ·m-2·℃-1 and 0.29±0.01 kJ·m-2·℃-1, respectively. The rates of ice production were 0.90±0.05 kg/h, 0.90±0.05 kg/h and 0.30±0.06 kg/h, respectively. The results showed that the system is simple and efficient, and the operational control of complex freeze concentration can be completed by the machine.

“十三五”国家重点研发计划项目（2016YFD04003032）；国家自然科学基金项目（21376052）