School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
J Adv Res. 2023 Apr;46:189-197. doi: 10.1016/j.jare.2022.07.003. Epub 2022 Jul 22.
Image recognition technology has immense potential to be applied in industrial energy systems for energy conservation. However, the low recognition accuracy and generalization ability under actual operation conditions limit its commercial application.
To improve the recognition accuracy and generalization ability, a novel image recognition method integrating deep learning and domain knowledge was applied to assist energy saving and emission reduction for industrial energy systems.
As a typical industrial scenario, the defrosting control in the refrigeration system was selected as the specific optimization object. By combining deep learning algorithm with domain knowledge, a residual-based convolutional neural network model (RCNN) was proposed specifically for frosty state recognition, which features the residual input and average pooling output. Based on the real-time recognition of frosty levels, a defrosting control optimization method was proposed to initiate and terminate the defrosting operation on demand.
By combining the advanced image recognition technique with specific energy domain knowledge, the proposed RCNN enables both high recognition accuracy and strong generalization ability. The recognition accuracy of RCNN reached 95.06% for the trained objects and 93.67% for non-trained objects while that of only 75.86% for the conventional CNN. By adopting the presented system optimization method assisted by RCNN, the defrosting frequency, accumulated time and energy consumption were 53.8%, 57.02% and 34.5% less than the original control method. Furthermore, the environmental and cost analysis illustrated that the annual reduction in CO emissions is 2145.21 to 3412.84 kg and the payback time was less than 2.5 years which was far below the service life.
The technical feasibility and significant energy-saving benefits of deep learning-based image recognition method were demonstrated through the field experiment. Our study shows the great application potential of image recognition technology and promotes carbon neutrality in industrial energy systems.
图像识别技术在工业能源系统节能领域具有巨大的应用潜力。然而，实际运行条件下的低识别精度和泛化能力限制了其商业应用。
为了提高识别精度和泛化能力，将一种新的深度学习与领域知识相结合的图像识别方法应用于工业能源系统的节能减碳。
选择制冷系统除霜控制作为典型工业场景的具体优化对象。通过将深度学习算法与领域知识相结合，提出了一种基于残差的卷积神经网络模型（RCNN），专门用于霜层状态识别，具有残差输入和平均池化输出的特点。基于霜层实时识别，提出了一种除霜控制优化方法，按需启动和终止除霜操作。
将先进的图像识别技术与特定的能源领域知识相结合，所提出的 RCNN 具有较高的识别精度和较强的泛化能力。RCNN 对训练对象的识别精度达到 95.06%，对非训练对象的识别精度达到 93.67%，而传统的 CNN 仅为 75.86%。采用 RCNN 辅助的提出的系统优化方法，除霜频率、累计时间和能耗分别比原始控制方法减少了 53.8%、57.02%和 34.5%。此外，环境和成本分析表明，CO2 排放量的年减排量为 2145.21 至 3412.84kg，投资回收期小于 2.5 年，远低于服务寿命。
通过现场试验验证了基于深度学习的图像识别方法的技术可行性和显著的节能效益。我们的研究表明了图像识别技术的巨大应用潜力，推动了工业能源系统的碳中和。
