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2024年4月20日,Elsevier 旗下国际著名期刊《Renewable Energy》在线发表了云南师范大学能源与环境科学学院王云峰教授课题组的最新研究成果《Machine learning, mathematical modeling and 4E (energy, exergy, environmental, and economic) analysis of an indirect solar dryer for drying sweet potato》。云南师范大学能源与环境科学学院王云峰教授通讯作者,合作单位有埃及法尤姆大学(Fayoum University)、爱资哈尔大学( Al-Azhar University)、开罗大学(Cairo University)、本哈大学(Benha University)、艾因·夏姆斯大学(Ain Shams University)、阿联酋大学(United Arab EmiratesUniversity),以及中国华中农业大学(Huazhong Agricultural University)。
文章研究和分析了太阳能红薯模型烘干机的机器学习、数学建模和4E(能量、热力学平衡
、环境和经济)关系。
https://www.sciencedirect.com/science/article/pii/S0960148124006001
Abstract
A developed indirect solar dryer is built and operated to dry sweet potato cubes. Since, numerous instruments have gathered experimental data to comprehensively evaluate the system's energy, exergy, environmental, and economical aspects. Additionally, four machine learning algorithms, namely Decision Trees (DT), Gradient Boosting Regression (GBR), Multiple Linear Regression (MLR), and Random Forest (RF), are evolved to forecast the solar collector's energy () and exergy efficiency () as well as the drying chamber's mean drying temperature and exergy efficiency (). In addition, ten drying kinetics mathematical models were employed to fit with sweet potato moisture ratio variation over the experiment. Also, Color and bioactive compounds were analyzed. Results show that, and was 72.9 %, and 5.6 %, respectively. Storage unit thermal ()and exergy efficiency () were 43.4 %, and 18.4 %, respectively, the discharging lasted around 5.5 h. Theoretical drying chamber thermal efficiency () was from 21.9 to 97.2 %. And av. was 46.1 %. RF algorithm achieved the best results for , , , and forecasting, because of its superior adaptability and generalization. The overall dryer efficiency was 15 % with a specific energy consumption of 4.509 kWh/kg moisture. The Page model showed the best fitting with sweet potato moisture ratio data. In addition, CO2 mitigation reached 20.2 with earned carbon credit is 56771 RMB. The economic payback period is 29.24 months, the annual total revenue is 8464 RMB and 0.7 RMB as a Return on investment.
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