Study On Optimization Approaches In Design And Operation For Cooling System Energy-Saving In Electronic Cleanrooms

With the increase in the global temperature and the expansion of the climate crisis,many countries have responded positively and set carbon-neutrality targets.Buildings are one of the high energy consumption and CO₂ emissions industries,which account for 40%of the global society.High-tech manufacturing factories are among the most energy-intensive buildings,which consume 30-50-fold more energy than typical commercial buildings.In these factories,the cooling system is a dominant energy consumer,accounting for 20%-30%of the total energy consumption.Thus,central cooling system energy-saving is crucial for reducing energy consumption and CO₂emission.Optimizing the design and operation of the central cooling system is the key scientific issue that requires to be solved urgently.In this thesis,a comprehensive investigation was conducted to improve the efficiency of the complex cooling system using long-term monitoring,modeling and deep learning methods.Based on the long-term monitoring,a systematic analysis was conducted to reveal the characteristics of electronic cleanrooms.As electronic cleanrooms require cooling all year round,renewable natural cooling sources are available in use in cold seasons.Due to the intensive requirement of energy and water,a novel natural cooling method,tap-water-based free cooling,was proposed to further improve the energy efficiency of the cooling system.This measure has been implemented in the central cooling system and one-year operation data were collected to evaluate the performance of the tap-water-based free cooling system.The performance of the tap-water-based free cooling method was compared with the widely used cooling tower free cooling method.Using a low-grade natural cooling source,the tap-water-based free cooling system,shows better performance with 2.2 times higher EER,4.4 times greater CO₂ emission reduction rate,and a slightly shorter payback period.In electronic cleanrooms,a dual-temperature chiller plant with higher energy efficiency was used as the cooling source in summer.However,in practice,the cooling load distribution of the dual-temperature chiller plant relies on the fixed temperature set point of the air leaving the primary cooling coils,which may lead to energy grade waste under dynamic outdoor weather conditions.Thus,optimal chiller loading strategies for dual-temperature chiller plants have been developed by simultaneously optimizing the partial load ratio of the chillers and the air temperature leaving the primary cooling coils.The performance of the proposed strategies was evaluated in a dual-temperature chilled water plant in the semiconductor factory.The results show that optimizing the cooling load distribution both among chillers in each group and between two groups can improve the operational efficiency of the system by 16.4%throughout the entire cooling season.With the advent of dynamic electricity prices,regulating energy use in different periods helps to improve the cost-efficiency of the system.To obtain this benefit,a thermal storage system was used and an optimization framework for the TES operating strategy was developed.The optimization framework integrated a state-of-the-art deep learning cooling load prediction model with a theoretical heat-transfer model for the ice storage tank.An attention-based dual gated recurrent network（A-d GRU）was built to predict the next 24 h cooling load.The A-d GRU with the CV-RMSE of 0.08 performs better than other data-driven models in previous studies.Then,the optimization framework was evaluated in a central cooling system with ice storage tanks on the dynamic platform built by Dymola.The results show that the operation cost of the system with TES was significantly（13.9%-21.4%）lower than that of the system without TES under the basic control strategy.Under optimal control strategy,the economic benefit was increased by 11.2%compared to the basic strategy throughout the entire ice-cooling season.In summary,the energy-saving measures and optimal control strategies proposed in this thesis can reduce the energy consumption by 6.4%and reduce the operation cost by 16.9%.The energy savings help to reduce the CO₂ emission and the annual CO₂emission reduction was around 2,478 tons,which is of great significance to achieve carbon neutrality of buildings.