Study On Transcritical Parallel Rankine Cycle For Waste Heat Recovery Of Internal Combustion Engine

China is a large energy consumer and carbon emitter.Clean and low-carbon utilization of energy and its sustainable development is the top priority of China and even the world's development.Recently,China has proposed stronger policies,in which China will realize peak carbon emission by 2030 and Carbon Neutrality by 2060.Improving the utilization of primary energy is one of the important measures for realizing the policies.Internal combustion engine(ICE)is the main consumer of primary energy,and it is commonly-used in various fields.Therefore,the research on ICE waste heat recovery and utilization is very important for the development of China and even the world,which has great significance for alleviating the energy shortage crisis,reducing carbon dioxide emissions,air pollution and so on.In view of the high temperature of exhaust gas and the large temperature difference to the coolant,the study on transcritical parallel Rankine cycle is carried out to improve ICE fuel utilization rate,and the intelligent algorithm is used to optimize the system,which provides important references for its engineering applications.The main work of this paper includes design of system structure,working fluid selection of system,analysis of system performance at different working conditions,intelligent optimization and dynamic analysis of system.A novel transcritical-subcritical parallel organic Rankine cycle(TSPORC)for ICE waste heat recovery is proposed,the thermodynamic and techno-economic analyses are carried out.Eight parameters including net power output,thermal efficiency,exergy efficiency,net power output per unit area,specific investment cost,electricity production cost,improvement of ICE power output,and decrease of brake specific fuel consumption are.considered as evaluation indicators of system performance.The TSPORC system is compared with other typical ORC systems and shows the best performance.Three design parameters including turbine inlet temperature and pressure of high-temperature branch,the evaporation temperature of low-temperature branch are evaluated.According to the results,for a given engine load,the design parameters can be optimized for maximizing system performance,an optimal evaporation temperature of low-temperature branch can be obtained to make ICE coolant waste heat utilized 100%,and there are corresponding turbine inlet temperature and pressure of high-temperature branch optimized for maximizing system performance.Moreover,for a given engine load,the net power output of the system,the improvement of ICE power output,and decrease of brake specific fuel consumption have the same trend,and the specific investment cost and electricity production cost have the same evaluation results.Ten fluids with zero ODP and low GWP are considered as candidate working fluids,and the study on working fluid selection is carried out.The result shows that R1233zd is the best working fluid.Effects of composition of zeotropic mixtures on system performance are investigated.The results show that adopting zeotropic mixtures can significantly reduce the total exergy loss and improve the system performance,and the system based on R600/R601 performs better than based on R600a/R601a.Aiming at the advantages and disadvantages of CO2,a transcritical CO2 parallel Rankine cycle(T-CO2-PRC)for ICE waste heat recovery is proposed,and liquefied natural gas(LNG)is introduced as the cold source of the system to solve the problem of low efficiency.The thermodynamic,economic and parametric sensitivity analyses are conducted.Both in high and low-temperature branches,there are corresponding turbine inlet temperature and pressure optimized for maximizing system performance.When the condensation temperature changes from 30? to-10?,each performance indicator of the T-CO2-PRC system is improved significantly,which proves that the introduction of LNG cold source can effectively solve the inefficiency caused by high critical pressure and low critical temperature of CO2.In addition,performance indicators such as net power output,energy efficiency and the decrease of brake specific fuel consumption and so on of T-CO2-PRC system are better than that of TSPORC system,and the net power output generated by LNG regasification process can improve 2%energy efficiency of T-CO2-PRC system.Combined with the many advantages of CO2,the results fully demonstrate that T-CO2-PRC system has great potential in the application of ICE waste heat recovery.Considering that the ICE may run under different loads in practical application,the model of waste heat distribution under different engine load is established,and the system operating condition under different ICE load is discussed.The results show that with the increase of engine load,the net power output of both TSPORC and T-CO2-PRC systems increases,while the specific investment cost and electricity production cost decrease.At low or high engine load,two performance indexes of improvement of ICE power output and decrease of brake specific fuel consumption is higher,and the two performance indicators of TSPORC and T-CO2-PRC systems reach the maximum value at 20%and 10%engine load respectively.However,at medium engine load,the two performance indicators are relatively low.At 50%engine load,the two performance indicators of both TSPORC and T-CO2-PRC systems reach the minimum value.Considering the high complexity of the system caused by many factors,such as double heat sources of the ICE,variable load of the engine,uncontrollable ambient temperature,many design parameters of the system and the uncertainty of the transcritical heat transfer process,an intelligent optimization model of the waste heat recovery system is established by intelligent algorithm including artificial neural network(ANN)and genetic algorithm(GA).Firstly,the fast prediction model of performance of TSPORC system is established by ANN.The results show that the back propagation neural network(BPNN)has higher accuracy than radial biased function neural network(RBFNN)and adaptive neuro fuzzy interface system(ANFIS).BPNN can accurately predict the performance of TSPORC system,and the prediction model presents very low root mean square error,average absolute deviation and very high coefficient of correlation.At the same time,the correlation analysis demonstrates that there is a strong correlation between the input and output variables of proposed ANN model.Based on the BPNN prediction model,the intelligent optimization models of single objective opt imization and weight optimization under different boundary conditions are established using genetic algorithm,and the multi-objective optimization model under specific boundary condition is established.The single objective optimization and weight optimization models cover the whole range of boundary conditions,and the models provide the intelligent optimization results of different performance indicators and the corresponding optimal working condition designs of TSPORC system under different boundary conditions.That is,for any engine load and ambient temperature,the optimal performance indicators of the system and its corresponding optimal working condition design can be quickly and accurately obtained through intelligent optimization results.Multi-objective optimization model provides the Pareto frontiers of different performance indicators and corresponding vector distributions of design parameters.Dynamic analysis of TSPORC system driven by ICE waste heat is carried out,and the dynamic response processes of heat exchanger and system at given engine load are investigated,and the dynamic response processes of heat exchanger and system under variable engine load are studied.The results show that the reasonable parameter setting can ensure the system operates safely and stably,and it is supposed to adjust parameters such as mass flow rate for system operating reasonably and efficiently under variable engine load.