Abstract
Objective: To improve the energy efficiency of mango drying in the air source heat pump system so as to save energy. Methods: The process of drying mangoes was subdivided, and a variable structure control was used to adjust the temperature and humidity of drying room intelligently and dynamically to improve energy efficiency. Each drying process stage was divided into three parts, namely far away from the conversion point, near the conversion point, and closing to the conversion point. For the first two parts, a constrained nonlinear autoregressive neural network (NARX) with external inputs was used to intelligently adjust the temperature and humidity settings so as to save electricity, while for the third part, a PI controller was used to accurately control the dehumidification amount at the conversion point of the drying process so as to ensure the quality of mango drying. Results: Compared with conventional segmented constant temperature and humidity drying methods, the proposed control method could save 8.63% of electricity with a guaranteed quality of mango drying. Conclusion: The proposed subdivided variable structure control method can significantly improve the energy efficiency of heat pump drying systems, and achieve drying quality similar to conventional segmented constant temperature and humidity methods.
Publication Date
12-26-2023
First Page
100
Last Page
104,145
DOI
10.13652/j.spjx.1003.5788.2023.80197
Recommended Citation
Xiaoxuan, GUO; Min, GUO; Shuai, HAN; and Leping, SUN
(2023)
"Variable structure intelligent control for mango drying with air source heat pump,"
Food and Machinery: Vol. 39:
Iss.
10, Article 15.
DOI: 10.13652/j.spjx.1003.5788.2023.80197
Available at:
https://www.ifoodmm.cn/journal/vol39/iss10/15
References
[1] 戚玉欣, 陶志国. 空气源热泵干燥技术的研究现状与发展展望[J]. 资源节约与环保, 2016(5): 69.
QI Y X, TAO Z G. Research status and development prospects of air source heat pump drying technology[J]. Resources Economization and Environmental Protection, 2016(5): 69.
[2] 蔡志敏, 李凡, 李春来. 空气源热泵烘干机组研究现状及发展趋势[J]. 科技创新与应用, 2022, 12(21): 77-80.
CAI Z M, LI F, LI C L. Research status and development trend of air source heat pump drying unit[J]. Technology Innovation and Application, 2022, 12(21): 77-80.
[3] KIVEVELE T, HUAN Z J. Experimental comparative study of an open and completely closed air source heat pump for drying sub-tropical fruits[C]// 12th International Conference on the Industrial and Commercial Use of Energy (ICUE 2015). [S.l.]: IEEE Computer Society, 2015: 232-238.
[4] 黄燕婷, 罗朝丹, 黎新荣, 等. 浅析芒果干生产中干燥技术及装备[J]. 农业研究与应用, 2022(2): 48-53.
HUANG Y T, LUO Z D, LI X R, et al. Analysis on drying technology and equipment in dried mango production[J]. Agricultural Research and Application, 2022(2): 48-53.
[5] 罗彩连, 林羡, 吴继军, 等. 芒果高温热泵间歇干燥特性的研究[J]. 热带作物学报, 2015(12): 2 295-2 299.
LUO C L, LIN X, WU J J, et al. Study on intermittent drying characteristics of mango with high temperature heat pump[J]. Journal of Tropical Crops, 2015(12): 2 295-2 299.
[6] 段宙位, 窦志浩, 谢辉, 等. 芒果果肉太阳能—热泵干燥工艺优化[C]// 中国食品科学技术学会第十一届年会论文摘要集. 杭州: 中国食品科学技术学会, 2014: 217.
DUAN Z W, DOU Z H, XIE H, et al. Optimization of solar-heat pump drying process for mango pulp[C]// Abstract Collection of Papers from the 11th Annual Meeting of Chinese Institute of Food Science and Technology. Hangzhou: Chinese Institute of Food Science and Technology, 2014: 217.
[7] 李珊珊. 中等水分芒果果脯的研制与保藏研究[D]. 无锡: 江南大学, 2020: 65.
LI S S. Study on preparation and preservation of preserved mango with medium moisture content[D]. Wuxi: Southern Yangtze University, 2020: 65.
[8] 段宙位, 窦志浩, 谢辉, 等. 太阳能—热泵干燥过程中芒果果肉的品质变化[J]. 食品工业科技, 2014, 35(20): 125-127, 132.
DUAN Z W, DOU Z H, XIE H, et al. Quality changes of mango pulp during solar-heat pump drying[J]. Science and Technology Food Industry, 2014, 35(20): 125-127, 132.
[9] MUDALIAR R K, SHAH S B. Mathematical modelling of dried mango (Khatai) [C]// 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). [S.l.]: Institute of Electrical and Electronics Engineers Inc, 2017: 1-7.
[10] GRECIA K J, ALBERT LUCE A, BUENAVENTURA M A, et al. Design and evaluation of a mango solar dryer with thermal energy storage and recirculated air[C]// 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). [S.l.]: Institute of Electrical and Electronics Engineers Inc, 2019: 1-5.
[11] TOMA T, BASU K, RODRIGUES W, et al. A deep learning based method for heat pump dryer user classification[C]// IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. [S.l.]: Institute of Electrical and Electronics Engineers Inc, 2018: 3 455-3 460.
[12] 李昌宝, 孙健, 辛明, 等. 番木瓜热泵干燥特性及数学模型的研究[J]. 轻工科技, 2018, 34(2): 1-4.
LI C B, SUN J, XIN M, et al. Study on drying characteristics and mathematical model of papaya by heat pump[J]. Light Industry Science and Technology, 2018, 34(2): 1-4.
[13] 韩明, 贾英新. 空气源热泵烘干大枣系统的研究[J]. 河北省科学院学报, 2017, 34(2): 42-46.
HAN M, JIA Y X. Study on air source heat pump drying jujube system[J]. Journal of the Hebei Academy of Sciences, 2017, 34(2): 42-46.
[14] 何冠成, 黄兴钊, 郭庆盛. 干燥用空气源热泵机组开式运行除湿能效的分析[J]. 日用电器, 2019(11): 69-73.
HE G C, HUANG X Z, GUO Q S. Analysis of dehumidification energy efficiency of air source heat pump unit for drying in open operation[J]. Electrical Appliances, 2019(11): 69-73.
[15] 沈伟, 张培兰, 牛俊乐, 等. 不同干燥方式对芒果果皮理化特性的影响[J]. 保鲜与加工, 2021, 21(9): 87-92.
SHEN W, ZHANG P L, NIU J L, et al. Effects of different drying methods on physicochemical properties of mango peel[J]. Storage and Process, 2021, 21(9): 87-92.
[16] 朱想, 丁云龙, 郭力, 等. 基于改进NARX神经网络算法的光伏发电功率短期预测[J]. 武汉大学学报(理学版), 2020, 66(5): 505-511.
ZHU X, DING Y L, GUO L, et al. Short-term prediction of photovoltaic power based on improved NARX neural network algorithm[J]. Journal of Wuhan University (Natural Science Edition), 2020, 66(5): 505-511.
[17] 付青, 单英浩, 朱昌亚. 基于NARX神经网络的光伏发电功率预测研究[J]. 电气传动, 2016, 46(4): 42-45.
FU Q, SHAN Y H, ZHU C Y. Research on photovoltaic power prediction based on NARX neural network[J]. Electric Drive, 2016, 46(4): 42-45.
[18] 冯胜, 刘明远, 冯旭. 基于NARX神经网络的工业水质智能处理[J]. 工业水处理, 2018, 38(3): 69-72.
FENG S, LIU M Y, FENG X. Intelligent treatment of industrial water quality based on NARX neural network[J]. Industrial Water Treatment, 2018, 38(3): 69-72.
[19] LIU J W, LI T Y, ZHANG Z Y, et al. NARX prediction-based parameters online tuning method of intelligent PID system[J]. Journals and Magazines (IEEE Access), 2020, 8: 130922-130936.
[20] WU W H, LI L B, YIN J C, et al. A modular tide level prediction method based on a NARX neural network[J]. Journals and Magazines (IEEE Access), 2021, 9: 147416-147429.
[21] LI C, ZHANG W J, ZHOU T X, et al. Prediction of ship rolling motion based on NARX neural network[C]// 2021 33rd Chinese Control and Decision Conference (CCDC). [S.l.]: Institute of Electrical and Electronics Engineers Inc, 2021: 4 664-4 668.