热工网络问题的大范围收敛解法

提出了热工网络问题的大范围收敛迭代解法，该算法避免了普通的直接差分方法所带来的稳定性困难，满足了过程仿真的收敛性与实时性的要求，为工业过程的建模与仿真提供了新的思路． 本文还给出了一个仿真计算的例子．

单相热工流体网络计算软件的开发

本文基于节点压力法,建立了核动力系统流体网络数学模型,开发了适用于可压缩流体(以水或蒸汽为工质)的单相热工流体网络计算软件。仿真结果表明,该软件可应用于复杂管网系统的模拟,具有良好的稳定性。

热工流体网络的实时仿真模型及其算法

本文以电站热力系统中热工流体网络为例,在网络的节点压力方程的基础上,建立了单相可压缩热工流体网络的数学模型。同时采用了隐式欧拉积分算法,使用了稀疏矩阵的双因子分解方法及准优化模拟定序技术,从而使网络系统的计算能够节省存储单元、缩短计算时间,达到了实时仿真的要求。由于电站中存在着许多热工流体网络系统,如风烟系统,给水系统,凝结水系统,主蒸汽系统,再热汽系统,轴封系统,油系统和冷却水系统等。各子系统的网络结构是不一样的。而同一子系统在不同电站中的网络结构可能也不相同。本文采用了网络矩阵算法和模块化建模方法,通用性强,能适用于任意拓扑结构的流体网络计算。

热工流体网络建模在船舶蒸汽动力系统仿真中的应用

本文以船舶蒸汽动力系统仿真建模过程为例,介绍了热工流体网络手工建模的基本方法和特点。配合自动化建模工具JTOPment在两相流建模的优势,结合汽轮机组、凝汽系统、供水系统、供油系统和送风的教学模型,最终确定了该工况下的整体系统模型,从而为下一步系统仿真提供参考。

热工管网的迭代算法及其应用

论文给出了热工网络的迭代算法的改进方案 ,并以一个仿真例子来说明 .该算法可以应用在工业过程的建模与仿真中 .

带遮阳百叶热通道幕墙的热工数学模型

介绍了带遮阳百叶的热通道玻璃幕墙的简单数学模型。该幕墙由外侧单层玻璃幕墙和内侧中空玻璃幕墙组成热通道,通道中内置遮阳百叶以减少太阳能透射得热。在夏热地区,空气在通道中被太阳辐射加热,处于自然通风状态之下。列出了稳态条件下系统的热平衡方程,通过线性矩阵求解了太阳辐射和外界环境温度分别改变时,两个玻璃和通道内两个空气层及百叶的平均温度。计算模拟表明,内置遮阳百叶的热通道幕墙的内层中空玻璃的外表面温度较传统单层幕墙的表面温度有明显降低;与无遮阳百叶的热通道幕墙相比,室内透射得热量有很大减少,传热系数也比中空玻璃和无遮阳百叶的热通道幕墙有显著降低。

单相热工流网计算软件应用研究

基于节点压力法,开发了适用于可压缩流体的单相热工流网计算软件,针对华龙一号安注系统瞬态工况进行了仿真研究。结果表明,该软件可应用于复杂流网系统的运行模拟,具备良好的稳定性。

关于二元网络流算法的一个推广

发展了二元网络流问题的大范围收敛迭代解法，为工业过程建模与仿真提供了新的途径．

热工流体网络的自动建模算法

根据电站模块化图形自动建模的需要，对流体网络模型的求解问题进行了分析研究。利用线性方程组所具有的对称性和稀疏性的特点，提出用新的改进型高斯消去法求解大型稀疏矩阵，并用仿真实例对高斯消去法，高斯塞德尔迭代法，双因子分解法进行分析比较。结果表明新算法优于其它算法。

Prediction of mechanical properties of A357 alloy using artificial neural network

The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property.The mechanical properties of these workpieces depend mainly on solid-solution temperature,solid-solution time,artificial aging temperature and artificial aging time.An artificial neural network（ANN） model with a back-propagation（BP） algorithm was used to predict mechanical properties of A357 alloy,and the effects of heat treatment processes on mechanical behavior of this alloy were studied.The results show that this BP model is able to predict the mechanical properties with a high accuracy.This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy.Isograms of ultimate tensile strength and elongation were drawn in the same picture,which are very helpful to understand the relationship among aging parameters,ultimate tensile strength and elongation.

Application of neural network in heating network leakage fault diagnosis

In order to investigate the leak detection strategy of a heating network,a space-based simulation mathematical model for the heating network under leakage conditions is built by graph theory.The pressure changes of all the nodes in the heating network are obtained from node leak and pipe leak conditions.Then,a leakage diagnosis system based on the back propagation（BP）neural network is established.This diagnosis system can predict the leakage pipe by collecting the pressure change data of the monitoring points,which can preliminary estimate the leak location.The usefulness of this system is proved by an example.The experimental results show that the forecast accuracy by this diagnosis system can reach 100%.

Modeling and Optimization for Heat Exchanger Networks Synthesis Based on Expert System and Genetic Algorithm

A new superstructure form of heat exchanger networks (HEN) isproposed based on expert system system (ES). The new superstructureform is combined with the practical engineering. The differentinvestment cost formula for Different heat exchanger is alsopresented based on ES. The mathematical model for the simultaneousoptimization Of network configuration is established and solved by agenetic algorithm. This method can deal with larger scale HENsynthesis and the optimal HEN configuration is obtainedautomatically. Finally, a case study is presented to Demonstrate theeffectiveness of the method.

Design Energy Efficient Water Utilization Systems Allowing Operation Split

This article deals with the design of energy efficient water utilization systems allowing operation split. Practical features such as operating flexibility and capital cost have made the number of sub operations an important parameter of the problem. By treating the direct and indirect heat transfers separately, target freshwater and energy consumption as well as the operation split conditions are first obtained. Subsequently, a mixed integer non-linear programming （MINLP） model is established for the design of water network and the heat exchanger network （HEN）. The proposed systematic approach is limited to a single contaminant. Example from literature is used to illustrate the applicability of the approach.

Method for Incorporation of Controllability in Heat Exchanger Network Synthesis by Integrating Mathematical Programming and Knowledge Engineering

A method for incorporation of controlling the heat exchanger networks with or without splits is proposed by integrating mathemati-cal programming and knowledge engineering. The simultaneous optimal mathematical model is established. This method can be practically used in the integration of large-scale heat exchanger networks, not only to synthesize automatically but also to satisfy the requirement of struc-tural controllability with more objective human intervention.

A Two-step Design Method for Shaft Work Targeting on Low-temperature Process

In low-temperature processes, there are interactions between heat exchanger network(HEN) and refrigeration system. The modification on HEN of the chilling train for increasing energy recovery does not always coordinate with the minimum shaft work consumption of the corresponding refrigeration system. In this paper, a systematic approach for optimizing low-temperature system is presented through mathematical method and exergy analysis. The possibility of "pockets", which appears as right nose section in the grand composite curve(EGCC) of the process, is first optimized. The EGCC with the pockets cutting down is designed as a separate part. A case study is used to illustrate the application of the approach for a HEN of a chilling train with propylene and ethylene refrigerant system in an ethylene production process.

Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm

In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.

Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.

Prediction of Heat Transfer Rates for Shell-and-Tube Heat Exchangers by Artificial Neural Networks Approach

This work used artificial neural network(ANN)to predict the heat transfer rates of shell-and-tube heatexchangers with segmental baffles or continuous helical baffles,based on limited experimental data.The BackPropagation (BP) algorithm was used in training the networks.Different network configurations were alsostudied.The deviation between the predicted results and experimental data was less than 2%.Comparison withcorrelation for prediction shows ANN superiority.It is recommended that ANN can be easily used to predict theperformances of thermal systems in engineering applications,especially to model heat exchangers for heattransfer analysis.