立式升温型溴化锂吸收式热泵的设计与变工况研究

本文对立式升温型溴化锂吸收式热泵循环和各部件建立了数学模型,并设计了85℃热水驱动产110℃热水的立式降膜升温型溴化锂吸收式热泵,吸收器输出功率为50 kW。然后对其在热源热水温度和流量变化、冷却水入口温度和流量改变等非设计工况下的工作特性进行了计算与分析研究,得到变工况对于机组性能系数,效率和机组负荷的影响关系图。发现在我们的研究范围内,热源水参数的变化对机组性能的影响要大于冷却水。并且在热源水参数变化过程中,机组性能存在性能急剧变化的拐点,实际运行中应当避免。     

地铁车辆定频空调系统节能方案分析

空调系统是地铁车辆的重要组成部分,承担着为地铁车辆提供良好车内温湿环境的作用,是满足车内乘客舒适性要求的主要设备。由于地铁车辆空调系统能耗占总运行能耗的30%以上,因此有必要对空调系统节能方案进行研究,以减少其能耗。基于北京地铁某线路车辆的空调系统,对空调系统热负荷组成及影响进行分析,并计算了该空调系统的制冷量,通过与常规空调机组的比较、试验的验证以及数值的估算,得出了新型定频空调系统的节能率。分析结果表明:对定频空调机组进行节能设计可以大大提高节能率。     

办公楼地源热泵空调系统夏季运行特性

以泰安市某办公楼地源热泵系统为研究对象,通过对夏季运行工况的实时监测,得到地源侧出水温度、负荷侧回水温度、机组COP和机组制冷量随室外气温的变化特性。研究结果表明,地源侧出水温度波峰较室外气温波峰滞后22h;机组COP和机组制冷量不仅与室外气温有关,还与空调末端供冷需求有关,在供冷需求相同的情况下,室外气温每升高1℃,机组COP、制冷量升高2.5%;在室外温度相同的情况下,地源侧出水温度每升高1℃,机组COP降低5%。该研究结论为地源热泵空调系统的设计、节能运行提供了必要的参考。     

一种冷水式空调机组及其水温检测方法

本文基于冷水式空调机组系统原理及水温控制需求,设计了一种Pt1000铂电阻温度传感器水温检测电路及其检测方法。通过对硬件电路器件精度、外接Pt1000引线电阻等造成的误差分析,采用自动温度补偿手段对Pt1000采集温度值进行校正,消除了各因素引起的误差。通过在冷水式空调机组试验和环温对水温检测的影响试验,结果表明,Pt1000铂电阻传感器采集的温度经温度补偿,精度≤±0.05℃,可满足冷水式空调机组水温高精度采集工况的需求,控制精确,运行范围广,性能较稳定、可靠,提高了用户体验。     

复杂工况下100 kW风热机组运行特性分析

为进一步研究风热机组实际运行情况,基于黄帝城小镇100 k W级风热机组在2020-2021年冬季的实际运行数据,分析了风热机组逐时运行情况、不同风速下的风热机组性能、不同流量下的风热机组性能。得到风热机组逐时特性曲线,机组制热量、COP、一次能源利用率随风速的变化关系,以及流量对机组性能的影响。为风热机组下一步优化运行提供理论支撑。     

纺织厂空调节能探析

分析了纺织厂空调能耗过高的主要原因,并从车间温湿度基数确定、冷负荷计算、气流组织、新风预处理设备及旁通风道使用和冷冻水管路系统设计等方面介绍了减少空调能耗的系统设计;对喷淋室、挡水板等空调设备进行了节能改造,分析了管式间接蒸发冷却器使用的节能效果。在空调运行方面采用智能调节实现自动控制、合理选择冷水机组台数等措施实现节能目的。测试表明,通过挡水板、喷淋室及管式间接蒸发冷却器节能改造,空调室节能1.91%,10.52%和5.07%;制冷机组由三台在80%制冷量减少为一台在90%制冷量下工作,耗电减少653kW/h,减少率为64.33%;采用自动控制系统空调室能耗日减少477kW.h,与人工调节方式比减少45.60%。     

复间冷机组高温时段汽轮机排汽真空优化管理

本文基于复合制冷循环间接空冷(复间冷)系统的结构组成和传热过程,定性分析了环境气温对干、湿冷机组排汽真空影响机理的异同,确定了复间冷机组汽轮机排汽真空的影响因素,建立复间冷机组高温时段变工况特性下排汽真空计算模型,并以最佳真空为目标,给出运行工况复间冷机组高温时段最佳真空的计算方法。以600 MW虚拟复间冷机组参数为基准,通过分析计算,揭示了复间冷机组高温时段最佳真空变化规律以及压缩机的相应运行特性,所得结果可为高温时段复间冷系统设计和运行优化提供可靠理论依据。     

R452B应用于空调(热泵)机组的性能研究

对比分析了R452B、R32和R410A应用于空调(热泵)机组理论循环特性,并对R452B在空调(热泵)机组中直接替代R410A的性能进行了试验研究,测试了空调(热泵)机组在不同工况条件下的制冷/热量、功率、性能系数以及压缩机的排气温度等参数。结果表明:相较于使用R410A而言,机组使用R452B的制冷/热能力、功率略有下降,制冷能效提高在5%以内,压缩机的排气温度提高约1℃,压比有所降低。当R452B的充注量达到R410A的85%时,机组性能达到最佳。总体来讲,R452B与R410A的运行性能相近。R452B比R410A具有更低的GWP值,符合行业的制冷剂替代趋势,可以在空调(热泵)机组中作为R410A的直接替代制冷剂。     

太阳能辅助CO2热泵与制冷系统实验研究

本文设计了一种太阳能辅助空气源跨临界二氧化碳热泵空调热水系统,包括太阳能集热系统,二氧化碳热泵系统以及室内室外换热系统;针对春夏秋冬不同天气条件,可采用制热、制冷,热水、制热 热水、制冷 热水五种运行模式,实现热水和空调两大功能.利用搭建的太阳能辅助空气源跨临界CO2热泵热水与空调系统实验台,进行了水-水热泵与制冷循环系统、空气-水热泵与制冷循环系统以及太阳能辅助的热泵循环系统实验研究.结果表明;气体冷却器出口温度越低,系统的性能系数越高;蒸发温度的升高同样也会提高系统的性能系数;在冬季夜间利用太阳能集热系统作为辅助热源可有效提高蒸发温度,同时延长蓄热水箱使用时间,满足整个夜间供热需求.     

联合BIC准则和多重注意力机制的空调能耗预测

建筑空调系统能耗预测对空调系统优化控制至关重要,因此提出一种联合BIC(贝叶斯信息)准则和多重注意力机制的空调能耗预测模型。针对空调能耗影响特征的复杂性,采用BIC分别对气象特征和空调内部特征进行特征选择,并引入双特征注意力机制。为改善双向长短期记忆网络(BiLSTM)梯度消失的问题,改进了网络的激活函数,并在此基础上引入时序注意力机制,以提高模型在长时间序列预测时的稳定性。最后,以某建筑空调系统为例对模型效果进行验证,研究结果表明,所提模型的MAPE、RMSE和MAE相较于传统的BiLSTM模型分别降低了0.013 1,2.929 6和1.841 3,取得了很好的效果,为后续优化控制提供了良好的基础。     

Optimization in gradient networks

Gradient networks can be used to model the dominant structure of complex networks. Previous work has focused on random gradient networks. Here we study gradient networks that minimize jamming on substrate networks with scale-free and Erdos-Renyi structure. We introduce structural correlations and strongly reduce congestion occurring on the network by using a Monte Carlo optimization scheme. This optimization alters the degree distribution and other structural properties of the resulting gradient networks. These results are expected to be relevant for transport and other dynamical processes in real network systems.     

红外光谱与人工神经网络相结合识别栽培、野生黄芩和粘毛黄芩

为了识别栽培黄芩、野生黄芩和粘毛黄芩，采用非线性－线性、线性－线性、非线性－非线性三种模式的人工神经网络（ANN）分别分析各种黄芩的红外谱。我们采用42个样本作训练集，34个样本作检验集，用各种模式的ANN进行了监督性训练。当训练目标误差平方和定为0.01时，各类ANN对训练集中三类黄芩样本识别的正确率均为100%，但对检验集样本识别的结果各不相同，其识别的正确率与隐含层节点数S1有关。我们发现当S1较大时，识别正确率反而下降，可能此时网络的非线性程度过高，使其不适合于该类样本集的训练。线性－线性型ANN识别的结果随S1的变化不很大，但识别的正确率不高，基本在85％左右。非线性－线性型ANN识别的结果最佳。当S1为3时，其识别正确率超过了97％。因此该法可用以简便、快速、准确地识别这三种黄芩药材。     

Detecting community structure from coherent oscillation of excitable systems

Many networks are proved to have community structures. On the basis of the fact that the dynamics on networks are intensively affected by the related topology, in this paper the dynamics of excitable systems on networks and a corresponding approach for detecting communities are discussed. Dynamical networks are formed by interacting neurons; each neuron is described using the FHN model. For noisy disturbance and appropriate coupling strength, neurons may oscillate coherently and their behavior is tightly related to the community structure. Synchronization between nodes is measured in terms of a correlation coefficient based on long time series. The correlation coefficient matrix can be used to project network topology onto a vector space. Then by the K-means cluster method, the communities can be detected. Experiments demonstrate that our algorithm is effective at discovering community structure in artificial networks and real networks, especially for directed networks. The results also provide us with a deep understanding of the relationship of function and structure for dynamical networks.     

Overlapping community detection using a generative model for networks

Detecting overlapping communities is a challenging task in analyzing networks, where nodes may belong to more than one community. Many present methods optimize quality functions to extract the communities from a network. In this paper, we present a probabilistic method for detecting overlapping communities using a generative model. The model describes the probability of generating a network with the model parameters, which reflect the communities in the network. The community memberships of each node are determined based on a probabilistic approach using those model parameters, whose values can be obtained by fitting the model to the network. This method has the advantage that the node participation degrees in each community are also computed. The proposed method is compared with some other community detection methods on both synthetic networks and real-world networks. The experiments show that this method is efficient at detecting overlapping communities and can provide better performance on the networks where a majority of nodes belong to more than one community.     

粉末涂料电脑配色的人工神经网络模型

提出一种基于多层BP人工神经网络的粉末涂料配方预测模型;用BP算法人工神经网络建立粉末涂料反射样品的标准色度参数与配方浓度参数之间的映射关系。把人工神经网络的配方预测模型应用到典型的粉末涂料样品的测配色实验过程中。实验结果表明,基于多隐层BP网的模型可以实现粉末涂料样品的配方浓度空间与标准三刺激值颜色空间的相互映射,对64个节点的平均训练精度达到了1个CIELAB色差单位。     

Blocking in wavelength-routed all-optical WDM network with or without wavelength conversion

The blocking probability in wavelength-routed all-optical networks is a very important measure of performance of the network. This blocking probability can be affected by many factors such as network topology, traffic load, number of links, algorithms employed and whether wavelength conversion is available or not. In this paper we have proposed a mathematical model to reduce the blocking probability of the WDM optical network for both wavelength convertible networks as well as for wavelength non-convertible networks. The model is can be used to evaluate the blocking performance of any network topology also it can be useful to improve its performance of the given network topology.     

Kohonen网络与BP网络用于钨和钼的同时测定

Kohonen与BP人工神经网络结合用于解析钨和钼的吸收光谱,讨论了Kohonen网络输出层的拓扑结构,并利用确定的结构对钨和钼的重叠光谱进行波长选择,在全光谱中选择最能代表光谱特征的不同类波长。所选波长处的吸光度作为三层BP-ANN网络的输入集,分光光度法同时测定了钨和钼。利用Kohonen网络选择全谱特征波长,优化了BP-ANN的输入层。与常规的波长选择方法进行比较,分析结果表明, 经K-ANN方法进行波长选择后,提高了BP-ANN的预测能力。确立了Kohonen网络作为选择最优波长集的一种工具。     

Finding community structures in complex networks using mixed integer optimisation

The detection of community structure has been used to reveal the relationships between individual objects and their groupings in networks. This paper presents a mathematical programming approach to identify the optimal community structures in complex networks based on the maximisation of a network modularity metric for partitioning a network into modules. The overall problem is formulated as a mixed integer quadratic programming (MIQP) model, which can then be solved to global optimality using standard optimisation software. The solution procedure is further enhanced by developing special symmetry-breaking constraints to eliminate equivalent solutions. It is shown that additional features such as minimum/maximum module size and balancing among modules can easily be incorporated in the model. The applicability of the proposed optimisation-based approach is demonstrated by four examples. Comparative results with other approaches from the literature show that the proposed methodology has superior performance while global optimum is guaranteed.     

基于白术FTIR的径向基函数神经网络鉴别研究

为了鉴定白术及其伪品,采用径向基函数神经网络(RBF)分别测试了白术及其伪品的傅里叶变换红外光谱。采用36个样本作训练集,27个样本作检验集,用各种模式的BPF进行了监督性训练。当训练目标误差平方和定为0.01时,各类RBF对训练集中白术样本识别的正确率均为100%,但对检验集样本识别的结果各不相同,其识别的正确率与隐含层节点数S₁有关。发现当S₁较大时,识别正确率反而下降,可能此时网络的非线性程度过高,使其不适合于该类样本集的训练。线性—线性型RBF识别的结果随S₁的变化不是很大,但识别的正确率不高,基本在85%左右。非线性—线性型RBF识别的结果最佳。当S₁为3时,其识别正确率超过了97%。因此该法可用于简便、快速、准确地识别白术及其伪品。