| 基于GA-BPNN的采后蜜桃预冷效果预测模型 |
| |
| 引用本文： | 谌英敏,王贺,苏勤,赵锐,宋海燕.基于GA-BPNN的采后蜜桃预冷效果预测模型[J].农业工程学报,2021,37(23):264-272. |
| |
| 作者姓名： | 谌英敏 王贺 苏勤 赵锐 宋海燕 |
| |
| 作者单位： | 1. 山西农业大学农业工程学院，太谷 030801;;2. 南京农业大学工学院，南京 210031; |
| |
| 基金项目： | 国家重点研发计划项目（2018YFD0700300）；山西农业大学博士科研启动项目（2021BQ86） |
| |
| 摘 要： | 为精准监控采后蜜桃预冷效果以提高商业经济价值，该研究以大久保蜜桃为研究对象，建立了包含衬垫和果实内部热源项的单箱多层CFD-WIHS（Computational Fluid Dynamics-With Internal Heat Source）传热传质数值模型。通过对比试验结果发现该模型预测果温时的最大均方根误差为1.668 °C，平均绝对百分比误差为13.65%，验证了该建模方法的可行性和通用性。在此基础上，利用高精度三维时空温度及流场分布数据，构建了GA-BPNN采后蜜桃预冷效果网络预测模型。该模型在冷藏转移时间和整体预冷均匀度方面的网络预测与测试值之间决定系数达0.962 5和0.863 8，且均方根误差和平均绝对百分比误差较BP模型分别低了30.22%、32.84%以及21.91%、39.45%，GA-BPNN比BP神经网络模型能更好表达采后蜜桃预冷效果与主控因素之间的非线性关系。研究结果为中小型果园实时监控蜜桃采后预冷均匀性和冷却时间以保障果实最佳预冷品质提供可靠的依据。
|
| 关 键 词： | 贮藏 品质控制 蜜桃 预冷效果 GA-BPNN 计算流体力学 强制风冷 |
| 收稿时间： | 2021/7/19 0:00:00 |
| 修稿时间： | 2021/11/12 0:00:00 |
Prediction model of post-harvest peach pre-cooling effectiveness based on GA-BPNN |
| |
| Chen Yingmin,Wang He,Su Qin,Zhao Rui,Song Haiyan.Prediction model of post-harvest peach pre-cooling effectiveness based on GA-BPNN[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(23):264-272. |
| |
| Authors: | Chen Yingmin Wang He Su Qin Zhao Rui Song Haiyan |
| |
| Institution: | 1. College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, China;;2. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China; |
| |
| Abstract: | This study aims to rapidly and accurately monitor the pre-cooling performance of post-harvest peach in Forced-Air Cooling (FAC) for a better commercial and economic potential of fruits. Taking the Okubo peach as a research object, a single-box multi-layer Computational Fluid Dynamics-With Internal Heat Source (CFD-WIHS) heat and mass transfer numerical model was established to consider the internal heat source term from the respiration and evaporative latent heat. The transient mathematical model consisted of an unsteady Shear Stress Transport (SST) k-w turbulence model, the pressure-velocity coupling, and the semi-implicit for pressure-linked equations (SIMPLE). A pressure-based split solver was also utilized to select the second-order upwind scheme for the discrete format of momentum, energy, turbulent kinetic energy, and diffusivity. The outlet of the computational domain was set as a pressure-outflow boundary condition, whereas, the inlet was set as a velocity-inlet boundary condition before simulating. The result showed that the maximum Root Mean Square Error (RMSE) of the fruit temperature was 1.668 °C, the Mean Absolute Percentage Error (MAPE) was 13.65%, and the maximum relative error of predicted Seven-Eight Cooling Time (SECT) was 19.23 %, indicating the excellent feasibility and versatility of the model. Correspondingly, a Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) prediction model was constructed to adopt a large amount of high-precision three-dimensional space-time temperature and flow field distribution data. Specifically, the velocity of air inflow, the temperature of pre-cooling air, the initial temperature of peach, the vent number, and opening diameter were taken as the input variables, while the peach Seven-Eight Cooling Time (SECT) and the Overall Heterogeneity Index (OHI) were used as the output variables. After that, 100 simulated datasets were divided into the BP neural network training set and test set at a ratio of 4:1, in order to determine the optimal number of hidden nodes and GA setting parameters. It was found that the correlation coefficients between the predicted and test value of the GA-BPNN model were 0.962 5 and 0.863 8, respectively, for the Seven-Eight Cooling Time (SECT) and the Overall Heterogeneity Index (OHI). Compared with the BP, the RMSE and MAPE of GA-BPNN model decreased by 30.22 % and 32.84 %, respectively, for the Seven-Eight Cooling Time. In pre-cooling uniformity, the RMSE and MAPE of GA-BPNN model decreased by 21.91 % and 39.45 %, respectively. Consequently, the GA-BPNN model demonstrated a better non-linear relationship between the pre-cooling performance of post-harvest peach and the main control factors, compared with the BP neural network model without optimization. The findings can also provide practical support to the real-time prediction on the pre-cooling performance of post-harvest peach for better pre-cooling quality in small- and medium-sized orchards. |
| |
| Keywords: | storage quality control peach pre-cooling effectiveness GA-BPNN computational fluid dynamics Forced-Air Cooling (FAC) |
|
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
