Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy ( IF 1.7 ) Pub Date : 2021-01-12 , DOI: 10.1177/0957650920983102 Chengcheng Tian 1, 2 , Ziwen Xing 1 , Xi Pan 1 , Haojie Wang 1
Performance of varying speed screw chiller is affected by many uncertainties. High precision prediction of its characteristics can guide the chiller to reach a better performance. This study presents an artificial intelligence model named least square support vector machine (LSSVM) with genetic algorithm (GA). Five parameters are predicted with the model, including COP, discharge pressure, suction temperature, suction pressure and cooling capacity. By comparing the simulation results with the test results, this model shows a high precision ability to predict the performance of the on-site chiller. Additionally, a newly control strategy is introduced to help the chiller with optimizing performance. Cooling capacity and superheat degree are separately used as input to train the model to control openness of EXV. The prediction of this control strategy process shows enough ability to predict openness of EXV. The results can be used to guide the chiller to reach better performances by adjusting the corresponding parameters.
中文翻译:
GA-LSSVM预测和控制螺杆冷却器的方法
变速螺杆冷却器的性能受许多不确定因素的影响。对其特性的高精度预测可以指导冷却器达到更好的性能。这项研究提出了一种具有遗传算法(GA)的名为最小二乘支持向量机(LSSVM)的人工智能模型。该模型预测了五个参数,包括COP,排气压力,吸入温度,吸入压力和冷却能力。通过将仿真结果与测试结果进行比较,该模型显示了预测现场冷水机组性能的高精度能力。此外,还引入了新的控制策略来帮助冷却器优化性能。制冷量和过热度分别用作输入,以训练模型以控制EXV的开度。该控制策略过程的预测显示了足够的能力来预测EXV的开放性。通过调整相应的参数,结果可以指导冷却器达到更好的性能。