摘要
伴随能源紧缺问题的突显,低温余热的开发利用成为绿色能源发展的趋势。将铸造企业生产过程中产生的大量废水余热进行回收,为吸附式制冷装置提供热源,可以持续不断地为铸件冷却塑形提供冷量,达到节能减排的目的。在吸附式制冷系统的冷凝器冷却回水与金属模具生产线的废热水源之间,采用BP神经网络控制的方法,通过定量混合的手段获得65℃的最佳热源,使吸附式制冷装置的COP达到0.43,优化了制冷装置、充分利用了废热为铸件降温塑性。
With the highlight of the energy shortage,the development and utilization of the low temperature waste heat become the trend of green energy.The large amount of waste water and waste heat produced by foundry plants can be recovered to provide as heat source for the adsorption refrigeration unit and as constant cooling capacity for cooling molding so as to realize the purposes of the energy saving and emission reduction.Between the cooling backwater of condenser and the waste heat source of metal mould production line in the adsorption refrigeration system,the optimal heat source temperature of 65℃could be obtained by the method of BP neural network control,and the adsorption refrigeration system COP could be 0.43,which not only reduced the environmental pollution,but also made use of waste heat from foundry plants.
作者
杜芳莉
申慧渊
Du Fangli;Shen Huiyuan(Department of Energy and Architecture,Xi’an Aeronautical University,Xi'an 710077,China)
出处
《低温与超导》
CAS
北大核心
2020年第11期101-104,共4页
Cryogenics and Superconductivity
关键词
余热回收
制冷装置
神经网络控制
高效
Recovery of waste heat
Refrigerating plant
Neural network control
Highly active
