Commercial AM60 alloy for semisolid processing:Alloy optimization and thermodynamic analysis

Semisolid processing is now a commercially successful manufacturing route to produce net-shape parts in automotive industry. The conspicuous results of alloy optimization with thermodynamic simulations for semisolid processing of commercial AM60 alloy were present. The results indicate that the available processing temperature range of AM60 alloy is 170 ℃. The temperature sensitivity of solid fraction decreases with increasing solid fraction or with decreasing temperature above eutectic reaction temperature of AM60 alloy. When the solid fraction φs is 0.4, corresponding processing temperature is 603.8 ℃ and the sensitivity -dφs/dT is 0.0184. The effects of various alloying elements on the solidification behavior and SSM processability of AM60 alloy were calculated with Pandat software.

Multi-objective performance optimization&thermodynamic analysis of solar powered supercritical CO_(2)power cycles using machine learning methods&genetic algorithm

The present study is focused on multi-objective performance optimization&thermodynamic analysis from the perspectives of energy and exergy for Recompression,Partial Cooling&Main Compression Intercooling supercritical CO_(2)(sCO_(2))Brayton cycles for concentrated solar power(CSP)applications using machine learning algorithms.The novelty of this work lies in the integration of artificial neural networks(ANN)and genetic algorithms(GA)for optimizing the performance of advanced sCO_(2)power cycles considering climatic variation,which has significant implications for both the scientific community and engineering applications in the renewable energy sector.The methodology employed includes thermodynamic analysis based on energy,exergy&environmental factors including system performance optimization.The system is modelled for net power production of 15 MW thermal output utilizing equations for the energy and exergy balance for each component.Subsequently,thermodynamic model extracted dataset used for prediction&evaluation of Random Forest,XGBoost,KNN,AdaBoost,ANN and LightGBM algorithm.Finally,considering climate conditions,multi-objective optimization is carried out for the CSP integrated sCO_(2)Power cycle for optimal power output,exergy destruction,thermal and exergetic efficiency.Genetic algorithm and TOPSIS(technique for order of preference by similarity to ideal solution),multi-objective decision-making tool,were used to determine the optimum operating conditions.The major findings of this work reveal significant improvements in the performance of the advanced sCO_(2)cycle by 1.68%and 7.87%compared to conventional recompression and partial cooling cycle,respectively.This research could advance renewable energy technologies,particularly concentrated solar power,by improving power cycle designs to increase system efficiency and economic feasibility.Optimized advanced supercritical CO_(2)power cycles in concentrated solar power plants might increase renewable energy use and energy generation infrastructure,potentially opening new research avenues.

Thermodynamic analysis of the Ti-Al-N system

Motivated by the application of （Ti, Al）N alloy compound in the coating layer, the ternary phase diagram of Ti-Al-N was analyzed by the calculation of the phase diagram （CALPHAD） technique. The isothermal sections of the Ti-Al-N ternary system were constructed and compared with the literature experimental results. The thermodynamic parameters of the Ti-Al-N ternary system and the related Ti-N and Al-N binary systems were adopted from literatures, whereas, those of the Ti-Al binary from the literatures were adjusted according to both the ternary and the binary phase equilibria. The consistency between the calculated results and the experimental data shows that considering the ternary thermodynamic relationship, the adjustments to the thermodynamic parameters of the related binaries are necessary.

Multi-objective optimization of high-sulfur natural gas purif ication plant

There exists large space to save energy of high-sulfur natural gas purification process.The multi-objective optimization problem has been investigated to effectively reduce the total comprehensive energy consumption and further improve the production rate of purified gas.A steady-state simulation model of high-sulfur natural gas purification process has been set up by using ProMax.Seven key operating parameters of the purification process have been determined based on the analysis of comprehensive energy consumption distribution.To solve the problem that the process model does not converge in some conditions,back-propagation(BP)neural network has been applied to substitute the simulation model to predict the relative parameters in the optimization model.The uniform design method and the table U21(107)have been applied to design the experiment points for training and testing BP model.High prediction accuracy can be achieved by using the BP model.Nondominated sorting genetic algorithm-II has been developed to optimize the two objectives,and 100 Pareto optimal solutions have been obtained.Three optimal points have been selected and evaluated further.The results demonstrate that the total comprehensive energy consumption is reduced by 13.4%and the production rate of purified gas is improved by 0.2%under the optimized operating conditions.

Machine learning-based multi-objective optimization and thermal assessment of supercritical CO_(2) Rankine cycles for gas turbine waste heat recovery

Technologies for utilizing waste heat for power generation have attracted significant attention in recent years due to their potential to enhance energy efficiency and reduce greenhouse gas emissions.This research focuses on the comparative and optimization analysis of three supercritical carbon dioxide(sCO_(2))Rankine cycles(simple,cascade,and split)for gas turbine waste heat recuperation.The study begins with parametric analysis,investigating the significant effects of key variables,including turbine inlet temperature,condenser inlet temperature,and pinch point temperature,on the thermal performance of advanced sCO_(2) power cycles.To identify the most efficient cycle configuration,a multi-objective optimization approach is employed.This approach combines a Genetic Algorithm with machine learning regression models(Random Forest,XGBoost,Artificial Neural Network,Ridge Regression,and K-Nearest Neighbors)to predict cycle performance using a dataset extracted from cycle simulations.The decision-making process for determining the optimal cycle configuration is facilitated by the TOPSIS(technique for order of preference by similarity to the ideal solution)method.The study's major findings reveal that the split cycle outperforms the simple and cascade configurations in terms of power generation across various operating conditions.The optimized split cycle not only demonstrates superior power output but also exhibits enhanced net power output,heat recovery,system and exergy efficiency of 7.99 MW,76.17%,26.86%and 57.96%,respectively,making it a promising choice for waste heat recovery applications.This research has the potential to contribute to the advancement and widespread adoption of waste heat recovery in energy technologies boosting system efficiency and economic feasibility.It provides a new perspective for future research,contributing to the improvement of energy generation infrastructure.

多种构型超临界CO_(2)循环热力学解构分析与参数优化

新型超临界CO_(2)(S-CO_(2))循环可通过流程改良提高效率,构型复杂多样。为了直观阐明各种流程改良措施对循环效率提升的作用机制,本文将预压缩、后压缩、再压缩、间冷、再热等五种构型的S-CO_(2)循环解构为若干热功转换过程,建立各解构过程与循环效率之间的关联方程,进而开展流程参数优化。研究结果表明,预压缩、后压缩和再压缩方案均是通过增加压缩耗功,减少吸热量实现循环效率提升,其中再压缩方案效果最优,再压缩流量优化后循环效率提高5.1%;采用部分间冷方案,可有效降低压缩功耗,同时避免高品位热量贬值,间冷压力优化后循环效率提高2.2%;再热方案在不改变压缩耗功的前提下,增加透平出功,再热压力优化后循环效率提高1.9%;最后,循环联用再压缩、间冷和再热三种节能措施,可使效率提高9.3%。

液-液二氧化碳储能系统的性能分析

随着间歇性可再生能源的普及,储能系统在电网中发挥着关键作用。与空气储能系统相比,二氧化碳储能具有储能密度高、易液化、环境友好等优势。首先建立了一种液-液二氧化碳储能系统,然后利用Aspen Plus仿真软件对其储能过程进行建模分析,揭示了储能压力、释能压力、压缩机进口压力、节流阀压降、压缩机效率及膨胀机效率等参数变化对系统性能的影响。最后,对这种液-液二氧化碳储能系统进行了参数分析,得到系统最优工艺参数。同时对系统进行[火用]分析,得到[火用]损分布,分析讨论了系统产生[火用]损的内部原因。

CO_(2)跨临界高温热泵干燥系统优化设计

干燥工艺在工、农业生产中被广泛使用但耗能巨大,通过CO_(2)热泵干燥技术有望降低其能耗并减缓环境问题。文中在CO_(2)跨临界基本热泵干燥系统的基础上,提出CO_(2)跨临界复叠式热泵干燥系统和CO_(2)跨临界双级压缩并联加热热泵干燥系统。并且通过建立热力学模型将其最优性能系数C_(OPh)和单位能耗除湿率R_(SME)与R134a双循环热泵干燥系统进行了比较。研究发现,它们相较于CO_(2)基础系统性能均有提升,尤其是双级并联加热系统,其C_(OPh)与R_(SME)均超过R134a系统。进一步的分析显示,增加蒸发温度、降低气冷器出口温度对CO_(2)跨临界热泵干燥系统的性能提升起到了关键作用。此外,文中也探究了补气系数和干燥出风温度对CO_(2)跨临界热泵干燥系统的影响。研究结果表明:经过优化改进的CO_(2)跨临界热泵干燥在高效节能方面表现出色,展现了替代R134a热泵干燥系统的潜力,同时也为解决干燥工艺高能耗的问题提供了创新性和可行性的解决途径。

氦低温系统关键参数及流程优化研究进展

氦低温系统应用于极低温技术领域,高效节能的氦低温系统将极大推动低温超导技术的发展。因此,研究系统中主要部件对其性能的影响、针对系统中主要热力学参数进行遗传算法优化,对提高系统制冷效率和经济效益以及保持其长期运行的稳定性十分重要。基于此,对国内外氦低温系统主要影响部件膨胀机和换热器的关键参数以及[火用]分析和遗传算法优化进行了梳理分析,总结了现存技术难点和发展趋势。

超临界-超临界二氧化碳储能系统的性能分析

随着间歇性可再生能源的普及,储能系统在电网中发挥着关键作用。与空气储能系统相比,二氧化碳储能具有储能密度高、易液化、环境友好等优势。首先建立了一种超临界-超临界二氧化碳储能系统,然后利用Aspen Plus软件对其储能过程进行建模分析,揭示了储能压力、释能压力、加热器加热温度、节流阀压降、压缩机效率、膨胀机效率等参数变化对系统性能的影响。最后,对二氧化碳储能系统进行参数分析,得到系统最优工艺参数。同时对系统进行[火用]分析,得到[火用]损分布,并分析讨论了系统产生[火用]损的内部原因。

Highview Power液化空气储能中试装置热力学分析

[目的]构建以新能源为主体的新型电力系统,储能成为必不可少的支撑技术。液化空气储能是一种新兴的技术经济可行的大规模储能解决方案,具有广泛的应用前景。Highview Power液化空气储能中试装置是目前唯一公开了现场测试数据的液化空气储能系统。为探究液化空气储能的热力学原理,寻求提升循环效率的方法。[方法]根据Highview Power液化空气储能中试装置的工艺流程建立了热力学建模,利用测试数据验证了热力学模型的准确性。通过?分析研究制约循环效率的关键设备,通过控制变量法研究关键操作参数对储能过程和释能过程的影响。[结果]结果表明:制约循环效率的关键设备是循环压缩机和汽化器;增加高压压力和节流后压力、提高增压膨胀机的分流质量和入口温度、回收释能过程回热器的冷量有利于提升储能过程液化率、降低液化能耗;提高释能高压压力和膨胀机组入口温度有助于提升系统的输出功率和循环效率。[结论]提出了回收压缩热、提高压缩机等熵效率、减小汽化器换热温差等改进措施以提升循环效率。

基于NSGA-Ⅱ算法的变温度热源有机朗肯循环系统性能分析及工质选择

针对120~150℃烟气余热,建立亚临界有机朗肯循环系统的数学模型。首先以R245fa为例,在不同热源温度下对系统进行了热力性能和经济性能分析;其次选取6种纯工质,基于NSGA-Ⅱ算法对系统进行了多目标优化研究;最后通过TOPSIS法和灰色关联分析对不同热源温度下的系统进行了工质选择和性能分析。结果表明:在研究温度范围内,过热度和蒸发器夹点温差的升高均不利于提升系统性能;不同热源温度下的最佳工质不同,热源温度为120℃时,R601的热力性能最优,R245fa的经济性最优,R1233zd的综合性能最优;热源温度的升高有利于提升系统的经济性能,各工质的最佳蒸发温度随着热源温度的升高而升高。灰色关联分析表明,6种工质的综合性能均随着热源温度升高有所提升。

碳中和背景下热电联产机组抽汽分配节能优化

为了研究热电联产机组在全工况不同供热负荷分配下电厂煤耗量的变化规律,实现电厂深度节能与减少碳排放量的目标,以某2台630 MW热电联产机组作为研究对象,利用EBSILON软件建模,并基于弗留格尔公式和热量法原理,进行单、双台机组不同热负荷分配方案的研究分析。研究发现:单台机组的热电负荷变化对煤耗的影响存在一个临界供热抽汽流量点,当供热流量小于该临界点时,电负荷越大,机组发电热效率越高;当供热流量大于该临界点时,电负荷越小,机组发电热效率越高。对于2台运行机组,当供热负荷集中分配给某一台机组时,在不同工况下的全厂煤耗量最小,节能效果更好。寻找最佳供热分配方式,有利于降低电厂的煤耗,对电厂节能减排以及响应国家的“碳中和”政策具有重要意义。

危废焚烧处理耦合有机朗肯循环系统工质筛选与热力学优化

危险废弃物焚烧处理产生的热量除了用于满足自身系统热量需求和损失外,其余大部分热量可以作为低温发电系统热源。利用该余热锅炉产生的饱和蒸汽作为潜热型热源驱动有机朗肯循环系统做功,并基于热力学和热经济学分析进行工质筛选。研究进一步对危废焚烧系统的基础流程进行优化,并分析了回热对ORC系统的热力学性能的影响。结果表明,工质Benzene表现最优,且危废系统优化后做功能力的提升,相较于基础结构增加了24.5%,回热后ORC系统净输出功提升了19.98%,热效率提升了35.57%,?效率提升了19.99%。

Multi-objective evolutionary optimization and thermodynamics performance assessment of a novel time-dependent solar Li-Br absorption refrigeration cycle

This research paper aims to perform dynamics analysis,3E assessment including energy,exergy,exergoeconomic,and the multiobjective evolutionary optimization on a novel solar Li-Br absorption refrigeration cycle.The research is time-dependent,owing to solar radiation variability during different timelines.Theoretically,all the necessary thermodynamic,energy,and exergy equations are applied initially.This is followed by the thermoeconomic analysis,which takes place after defining the designing variables during the thermoeconomic optimization process and is presented together with the economic relations of the system and its thermoeconomic characteristics.Furthermore,the sensitivity analysis is undertaken,the source of system inefficiency is determined,the multi-objective evolutionary optimization of the whole system is carried out,and the optimal values are compared with the primary stage.Engineering Equation Solver(EES)software has been used to accomplish comprehensive analyses.As part of the validation process,the results of the research are compared with those published previously and are found to be relatively consistent.

热电联产机组抽汽性能分析及优化

为了优化双机热电联产机组运行,采用Ebsilon软件搭建某2×350 MW热电联产机组的仿真模型,并对系统进行详细的热力学分析计算,得到不同抽汽分配策略下机组的运行煤耗,进而得出机组经济性最优的抽汽分配策略。结果表明:两台机组相同电负荷时,由2号机组承担全部供热、供汽负荷,总体煤耗量最低;双机平均分配供热、供汽负荷时总体煤耗量最高;两种抽汽分配方案引起的机组总煤耗量差值会随负荷的升高而逐渐减小。

水电联产机组低负荷工况海水淡化系统供汽方案研究

为保障海水淡化系统在发电机组处于低负荷工况下的制水能力,提高全厂的经济性,以某4台水电联产机组为例进行研究,利用EBSILON软件对4台机组进行热力学建模,并验证了模型的准确性;分析符合海水淡化制水条件的汽源,并提出满足低负荷工况下海水淡化制水的3种抽汽方案;基于热力学经典理论,利用热力学模型对3种抽汽方案进行论证分析,并通过对3种抽汽方案的煤耗量和改造成本进行比较分析,得到经济性最优的低负荷供汽方案。研究结果对电厂节能减排、提高电厂运行经济效益具有一定指导意义。

具有非理想气体工质的往复式Brayton循环多目标优化

基于前人建立的不可逆往复式Brayton循环模型,在考虑多种不可逆损失项和非理想气体比热模型的情况下,以压缩比为优化变量,对无因次功率、效率、无因次生态学函数和无因次功率密度进行性能分析与多目标优化,并通过比较3种决策方式得到的的偏差指数,获得优化方案。结果表明:在进行单目标分析时,四个目标函数性能指数会随着不可逆损失项系数的增大而减小;多目标优化结果相比单目标优化更优,其设计方案更理想。

电站锅炉烟气余热利用与空气预热器综合优化

针对常规电站锅炉余热利用系统中低温省煤器入口烟气温度的限制,对锅炉尾部受热面进行综合优化,将空气预热器分两级布置,在两级空气预热器之间布置低温省煤器;并通过改变Ⅱ级空气预热器出口空气温度,优化低温省煤器所处的烟气温度范围,从而得到热力学最优工况;最后结合技术经济性分析,得出符合工程实际的综合优化结果.结果表明:通过空气预热器分级设计和合理设定关键运行参数,在热力学最佳方案下机组供电煤耗降低约6.7g/(kW·h),结合技术经济性分析,在年平均运行5 500h工况下,每年净收益可达2 100余万元,经济效益显著.

电站锅炉烟气余热利用系统的热力学分析和优化

通过烟气余热利用系统的热力学分析发现,提高烟气余热利用的节能效果,需要对烟气热量的释放过程和蒸汽回热系统进行耦合优化。结合典型百万kW燃煤电厂机组,采用能量梯级利用的方法,对锅炉烟气余热利用系统进行了优化设计,相对于将低温省煤器布置在空气预热器出口之后的传统方案,采用优化方案可使机组供电煤耗降低值由2.18 g/kW·h提高至5.19 g/kW·h,节能优势明显。