斯特林热电联产装置的性能测试研究

测试了一台带有烟气余热回收装置的斯特林热电联产装置的热力性能。结果表明,斯特林热电联产装置在噪声、污染物排放等环节具有非常突出的优点,采用烟气冷凝技术可以较大幅度地提高系统的总效率。     

我国天然气热电联产的发展及障碍

对我国天然气热电联产的发展状况进行了分析，指出了制约我国天然气热电联产发展的主要障碍，对此提出了建议。     

我国发展燃气热电冷联产系统的探讨

通过分析我国燃气热电联产系统和燃气热电冷联产系统的的节能性和环保性,指出大型燃气蒸汽联合循环系统比中小型燃气热电冷联产系统更具优势,燃气热电联产系统比燃气冷电联产系统更节能。探讨了中小型燃气热电冷联产系统的适用范围。     

积极准备小型燃气热电联产系统应用

小型燃气热电联产具有发电效率高、能源能充分利用、良好的环保、投资成本低、建设时间短及运行简单等优点,可作为今后发展热电联产的首选。根据用户对热力产品的需求不同,联产系统可以有不同的组合形式。但在推广燃气热电联产时必须要有必要的技术准备以及相应的政策。     

微型热电联产——适用于家庭的热电联产

热电联产是一项成熟而高效的技术，它既可以为用户节省能源支出，又能降低对环境的排放量。然而由于经济和技术上的原因，目前热电联产并未使大多数家庭用户受益。但热电联产技术的最新进展预示着适用于居家的热电联产（微型热电联产）在不久的将来将成为现实。英国燃气研究与技术中心对这一概念作了分析并开展了广泛的家庭能源需求及可能的发电技术方面的研究。现已确认，微型热电联产可为英国节省可观的能源并可作为现有集中供热锅     

大力发展热电联产是保护环境的重要途径

本文首先介绍了热电联产在全世界的发展概况，然后论证了在降低灰渣排放量，粉尘排放量方面以及在降低引发酸雨的气体，有毒气体，温室效应气体和破坏大气臭氧层的气体（ＳＯｘ，ＣＯ，ＮＯｘ，Ｎ２Ｏ）的排放量方面，热电联产相对于分散供热的强大优势，同时，本文介绍了我国热电联产的现状，在环保方面的初步成果及其近期规划和长远目标，最后，本文分别就发展热电联产所应注意的几个问题进行了进一步的探讨。     

基于热力学第一定律的热电联产节能分析

以热力学第一定律为基础,通过建立热电联产和热电分产的能耗分析模型,研究了两者的供热和发电标准煤耗量差,进而得到热电联产节能的最小热电比。根据最小热电比,分析研究得到了热电联产供热最小抽气量和最佳热效率,为热电厂的节能改造和合理运行提供了理论基础。     

杭州市区热电联产的发展前景

文中叙述了杭州市区热电联产的现状,指出热电联产有利于环保和节约能源,以及天然气在热电(冷)联产中的应用。最后,对发展热电联产提出了建议。     

我国热电联产概况与政策

我国政府历来鼓励发展热电联产。在五十年代就支持建设了第一批大型区域性热电厂。在《大气污染防治法》、《节约能源管理暂行条例》、《节能技术政策大纲》、《节能法》等文件中都明确提出要鼓励发展热电联产。在1998年开始执行的国家重点鼓励发展的产业、产品和技术目录中也包括热电联产。国家计委、国家经贸委、电力部、建设部于1998年2月17日又颁布了《关于发展热电联产的若干规定》。现在国家计委、国家经贸委、建设部、环保总局又在拟定新的《关于发展热电联产的规定》,以促进热电联产在新形势下的健康发展。　　热电联产要比热电…     

煤气热电联产系统的热载体循环率

本文介绍了煤气热电联产系统中的关键技术，建立了热载体流化床干馏炉的物料和热量平衡模型，讨论了热载体温度，干馏温度，流化介质温度等对热载体循环率的影响，对煤气热电联产系统的设计和运行具有指导作用。     

Enhancing and multi-objective optimising of the performance of Stirling engine using third-order thermodynamic analysis

Stirling engine is an external combustion engine which uses eternal heat sources like solar radiation for heating a compressible fluid inside cylinders. In the recent years, significant attention is drawn to Stirling engines due to the clear advantages, high efficiency potential, flexible fuel, lower nitrogen oxides, quiet and minimal vibration, high reliability and highest specific output work for any closed regenerative cycle. The third order thermal analysis is one of the analyses which has been applied in several studies which have been carried out on Stirling engines. NSGA-II algorithm is applied to optimise the differential regenerator pressure (bar) and the power output (kW) for a Stirling engine system. In this study, three decision-making techniques are utilised to optimise the solutions, obtained of the results. At last, the employed techniques are compared with the data of an experimental research work.     

Artificial neural networks modelling of the performance parameters of the Stirling engine

The Stirling engine can theoretically be very efficient to convert heat into mechanical work at Carnot efficiency. Various parameters could affect the performance of the addressed Stirling engine which is considered in optimisation of the Stirling engine for designing purpose. Through addressed factors, torque has the highest effect on the robustness of the Stirling engines. Due to this fact, determination of the referred parameters with low uncertainty and high precision is needed. To solve the mentioned obstacle, throughout this paper, a generation of intelligent model called ‘artificial neural network’ (ANN) was implemented to estimate the torque of the Stirling heat engine. In addition, highly accurate actual values of the required parameters which were gained from open literature surveys from previous studies were implemented to develop a robust intelligent model. Based on the outcomes of the ANN approach, the output results of an ANN model were close to relevant actual values with a high degree of performance.     

Artificial neural network,ANN-PSO and ANN-ICA for modelling the Stirling engine

Nowadays, shortage of fossil fuels resources, especially oil, and also global attention to environmental hazards produced by the internal combustion process have caused extensive researches on the development of renewable energy engine technology. Among all kinds of renewable resources, solar energy Stirling engines have their own special situation for energy generation with lower pollutants and sustainable sources. The Stirling engine is an external combustion engine that uses any external heat source to generate mechanical power. Various parameters affect the performance of the Stirling engine. In this study, artificial neural network (ANN) was applied to estimate the power and torque values obtained from a Stirling heat engine (Philips M102C engine). It employs the Levenberg–Marquardt algorithm for training ANN with back propagation network for estimating the power and torque of the Stirling heat engine. The performances of the imperialist competitive algorithm (ICA)-ANN and ANN-particle swarm optimisation (PSO) are compared with the performance of the ANN based on mean square error (MSE) and correlation coefficient. PSO and ICAs are applied to determine the initial weights of the neural network. The obtained results indicate that ANN-PSO has a better performance than ICA-ANN and ANN alone; also the MSE for the ANN-PSO is lower as well. Considering the results obtained from this study, there is very good agreement between the output of the testing phase of the ANN-PSO model with experimental data and they are very close to each other.     

A dish-Stirling solar-thermal power system

This paper presents results of a preliminary design/economic study of a first-generation Point Focusing Distributed Receiver (PFDR) solar-thermal electric system optimized for application to industrial and small community power plants at power levels up to 10 MW_e. Power conversion is provided by small Stirling cycle engines mounted at the focus of paraboloidal solar concentrators. The output of multiple power modules (concentrator, receiver, engine, and electric generator) is collected by means of a conventional electrical system and interfaced with a utility grid. Based on the United Stirling (Sweden) P-75 engine, a 1 MW_e system employing mass-produced components (100 000 modules/year) could produce electricity at costs competitive with those projected for electricity generated by more conventional means, eg with fossil fuels.     

A parametric study on the effects of displacer-cylinder-circumferential-wall thermal conditions on the performance of a γ-type LTD Stirling engine

ABSTRACT

Regarding a Stirling engine’s heat source and heat sink, most of the studies in the literature focus only on the magnitude of temperature difference between them. However, different Stirling engines adopt very different heat-source and heat-sink configurations. This study is aimed at understanding the effects of different displacer-cylinder-wall thermal conditions on engine performance using computational fluid dynamics (CFD). Results include p–V diagrams, heat flux distributions, temperature variations, and effects of three displacer-cylinder-wall parameters on indicated power and efficiency. It is found that the thermal conditions on the displacer-cylinder-circumferential wall (DCCW) impose significant effects on engine performance. Within the ranges of parameters investigated in this study, extending the coverage of heat source and heat sink on this wall improves up to 28% in indicated power at the cost of losing about 10% in efficiency, proving the significance of DCCW conditions on engine performance.     

The effect of regenerative losses on the efficiency of a Stirling heat engine at maximum power output

A more realistic cycle model is established for discussing the performance of the Stirling heat engine with regenerative losses. The power output is adopted as an objective function for optimisation. The maximum power output and the corresponding efficiency of the Stirling heat engine are derived. The effect of regenerative losses on the efficiency at maximum power output is expounded. The times of four processes in the Stirling cycle are allotted optimally. Some new conclusions are obtained.     

Optimisation of a combined Stirling cycle–organic Rankine cycle using a genetic algorithm

In a Stirling cycle a huge amount of energy is wasted due to the losses. This wasted energy may be utilised as a heat source for the boiler of an organic Rankine cycle (ORC). Combination of these two cycles leads to an increased cycle efficiency compared to a single Stirling cycle and the analysis and optimisation of the integrated system is carried out. Optimisation is performed using the genetic algorithm and considering three decision variables: the temperature of the cold tank of the Stirling cycle, the pressure ratio and the temperature of the ORC condenser. In optimisation, the efficiency is considered as the objective function and the highest value is achieved by adjusting the decision variables. Using this method, the efficiency of the overall combined cycle was improved in which the highest efficiency was obtained to be 41.5%.     

Numerical investigations on the Dish–Stirling engine system

The Stirling engine is an environmentally friendly external combustion heat engine and reduces the complexities of the combustion process, and indirectly helps in reduction of CO₂ emission. Modelling based on cyclic analysis is performed for a Beta configuration Stirling engine of 1.5?kW_e capacity using a rhombic drive for the solar-dish-supported Stirling engine. The analysis helps in estimating the overall efficiency of the system using the experimental correlation of the solar concentrator ARUN160 at the engine operating temperature. The analysis shows that the system will have overall efficiency around 25% in the range of 750–1050?K at the expansion space. The degradation of performance compared to that at an operating temperature of 1025?K is only marginal and makes 750?K a more preferred temperature. The present study evaluates a range of possible design goals and provides suitable alternatives and thus provides a clear understanding of the system design considerations.     

Optimisation of the thermodynamic performance of the Stirling engine

In this communication, the thermodynamic performance of an ideal Stirling cycle engine has been investigated. In this regard, the first law of thermodynamics has been employed to determine state of total heat addition, network output, and thermal efficiency with changes in dead volume percentage and regenerator effectiveness. Second law analysis is applied to obtain the trends for the total entropy generation of the cycle. Moreover, the entropy generation of each element involving the Stirling cycle processes is measured. Three objective functions including the output power per rate of mass of the ideal gas working fluid (w_net) and the thermal efficiency (η_t) have been considered simultaneously for maximisation, and the ratio of total entropy generation to rate of mass of the ideal gas working fluid of the Stirling engine is minimised at the same time. Multi-objective evolutionary algorithms based on the NSGA-II algorithm have been employed, while effectiveness of the regenerator, effectiveness of low- and high-temperature heat exchangers, effectiveness of high-temperature heat exchanger, temperatures of the hot side and cold side, and dead volume ratio are considered as decision variables. After the definition of the Pareto optimal frontier, the final optimal solution has been selected using different decision-making methods such as the fuzzy Bellman–Zadeh, LINMAP and TOPSIS.     

Environmental science at Stirling

The growth and development of environmental science at the University of Stirling is presented. After discussing various definitions of environmental science the organization of the four years honours degree programme is described. Some of the anticipated developments in this programme are discussed.