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Tian, Hua
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Tian
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Hua
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Hua Tian
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A novel combined cooling and power cycle integrated ejector refrigeration and composition adjustment for stationary engine waste heat recoverySun, Xiaocun; Shi, Lingfeng; Tian, Hua; Shu, GequnThe combined cooling and power cycle has wide applications ascribed to the feasibility of simultaneously providing cooling and electricity. The layout comprised of vapor compression refrigeration cycle and Organic Rankine Cycle by sharing condenser is one of the most common structures. However, for this kind of structure, the high temperatures of working fluid in compressor outlet and expander outlet aggravate the condensation load and lead to the decline of system performance. This study proposes a novel combined cooling and power cycle integrated of ejector refrigeration and composition adjustment running with CO2 mixture. Liquid separation condenser is introduced to realize composition adjustment. Working fluid with higher CO2 mass fraction flows to power sub-cycle and evaporation process of refrigeration sub-cycle. On the other hand, working fluid with smaller CO2 mass fraction is pumped to the inlet of ejector and heated by expansion exhaust. A stationary engine is chosen as the research objective. The co-generation system recovers waste heat of engine exhaust and coolant to provide cooling to refrigerating chamber. The results show the priority of the novel proposed co-generation system. Under the requirement of 0 ℃ chilled fluid, the basic combined cooling and power cycle could export 8 kW cooling capacity and 6.63 kW electricity, while the novel proposed system can export 4.5% more net power output with 8 kW cooling capacity output or export 18.6% higher cooling capacity with 6.63 kW electricity.- Advanced control of compressor inlet temperature in supercritical CO2 Brayton cycleWang, Rui; Wang, Xuan; Tian, Hua; Shu, GequnThe supercritical CO2 (sCO2) Brayton cycle has gained much interest because of its flexibility, compactness and high efficiency. The sCO2 at the inlet of compressor should work near the critical point to obtain high system efficiency, while the physical properties of sCO2 near the critical point change dramatically, which brings great challenges to the control of the compressor inlet temperature. At present, cooling far from the critical point and adjusting the cooling water flow rate with Proportion- Integration-Differentiation (PID) controller is the commonly used method, but it loses system efficiency and may be out of control sometimes. Therefore, in this study Linear Model Predictive Control (LMPC) and Deep reinforcement learning (DRL) are used to control the compressor inlet temperature and compared with PID. A dynamic model of a recompression sCO2 Brayton cycle is established, and the cooler model is carefully validated against experiment data. The results indicate that both the LMPC and DRL can control the sCO2 temperature near the critical temperature much better than the PID. LMPC works the best because the cold end parameters fluctuate slightly and the cooler model can be regarded as approximately linear, thus LMPC can find almost the global optimal solution. Nevertheless, DRL control exhibits the fastest real-time computation ability and proves good extrapolation ability.
