基于人工鱼群优化算法中央空调制冷系统优化研究

发布时间：2018-11-04 19:24

【摘要】：近年来，随着中国对节能减排的重视，提高能源利用率，成为人们的共识，建筑能耗成为我国主要能源消耗领域之一，而中央空调是建筑能耗的最主要环节，减少中央空调系统的能耗，提高空调系统的能效比，成为一个关键性的课题。首先，综述了中央空调系统节能技术的发展和研究现状。分析了中央空调系统制冷系统的结构与工艺原理，它是在蒸发器内被气化的制冷剂流经制冷机组的压缩机时被压缩成高压高温的气体，当高温高压的制冷剂流经冷凝器时候被来自冷却塔的冷却水冷却成低温高压的气体，低温高压的制冷剂通过膨胀阀后重新变成了低温低压的液体，而后再在蒸发器内气化，完成一次能量循环。将能量守恒定律和热传导作为依据，，针对中央空调制冷系统各工作环节的能耗特点的主要工艺流程，分析制冷设备的特点，运用最小二乘法建立了关于制冷系统中能耗设备（制冷机、冷却水泵和冷却塔风扇）的静态模型。对中央空调制冷系统的非线性、对变量系统特性，确定了制冷系统的优化目标和约束条件。其次，分析了多目标多约束条件的智能优化算法--遗传算法、粒子群算法、蚁群算法和基本群群算法特点，针对常用的智能优化算法缺点和中央空调制冷系统的运行特点，提出了改进鱼群算法，利用改进的人工鱼群优化算法求解中央空调制冷系统的最小功率。通过改变算法中的人工鱼群的视野与步长的大小，提出了视步系数，克服了基本人工鱼群优化算法的收敛速度慢，收敛精度低，出现局部最优，提高了优化算法的搜索速度和精度，为中央空调制冷系统的节能优化提供了一种新的有效方法。最后，通过MATLAB仿真程序，对中央空调制冷系在不同外界环境条件下，分别用基本和改进后的人工鱼群优化算法进行了仿真实验，从仿真图可知改进后的人工鱼群优化算法明显提高了收敛速度，同时也提高了收敛精度。
[Abstract]:In recent years, with the importance of energy saving and emission reduction in China, it has become a common understanding for people to improve energy utilization efficiency. Building energy consumption has become one of the main energy consumption areas in China, and central air conditioning is the most important link in building energy consumption. Reducing the energy consumption of central air-conditioning system and improving the energy-efficiency ratio of air-conditioning system has become a key issue. Firstly, the development and research status of energy-saving technology in central air-conditioning system are summarized. The structure and technological principle of the refrigeration system of central air conditioning system are analyzed. The refrigerant vaporized in the evaporator is compressed into a high pressure and high temperature gas as it passes through the compressor of the refrigeration unit. When the high-temperature and high-pressure refrigerant flows through the condenser, it is cooled by the cooling water from the cooling tower into a low-temperature and high-pressure gas. The low-temperature and high-pressure refrigerant passes through the expansion valve to become a low-temperature and low-pressure liquid again, and then vaporized in the evaporator. Complete an energy cycle. Based on the law of conservation of energy and heat conduction, the characteristics of refrigeration equipment are analyzed according to the main technological process of energy consumption characteristics of each working link of central air-conditioning refrigeration system. The static model of energy consumption equipment (refrigerator, cooling water pump and cooling tower fan) in refrigeration system is established by using the least square method. For the nonlinearity of central air-conditioning refrigeration system and the characteristic of variable system, the optimization objective and constraint conditions of refrigeration system are determined. Secondly, the characteristics of multi-objective and multi-constraint intelligent optimization algorithms, such as genetic algorithm, particle swarm optimization algorithm, ant colony algorithm and basic swarm optimization algorithm, are analyzed, aiming at the shortcomings of common intelligent optimization algorithms and the operation characteristics of central air-conditioning refrigeration system. An improved fish swarm algorithm is proposed. The improved artificial fish swarm optimization algorithm is used to solve the minimum power of central air-conditioning refrigeration system. By changing the visual field and step size of artificial fish swarm in the algorithm, the apparent step coefficient is proposed, which overcomes the slow convergence speed, low convergence precision and local optimum of the basic artificial fish swarm optimization algorithm. The search speed and precision of the optimization algorithm are improved, which provides a new and effective method for energy saving optimization of central air-conditioning refrigeration system. Finally, through the MATLAB simulation program, the simulation experiments are carried out on the central air-conditioning refrigeration system under different environment conditions, respectively, using the basic and improved artificial fish swarm optimization algorithm. From the simulation diagram, we can see that the improved artificial fish swarm optimization algorithm obviously improves the convergence speed and the convergence accuracy.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP18;TB657

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