Compressive Strength Estimation for the Fiber-Reinforced Polymer (FRP)-Confined Concrete Columns with Different Shapes Using Artificial Neural Networks

An evaluation of existing strength of concrete columns confined with fiber-reinforced polymer( FRP) was presented with extensive collection of experimental data. According to the evaluation results, artificial neural networks( ANNs) model to predict the ultimate strength of FRP confined column with different shapes was proposed. The models had seven inputs including the column length,the tensile strength of the FRP in the hoop direction,the total thickness of FRP,the diameter of the concrete specimen,the elastic modulus of FRP,the corner radius and the concrete compressive strength. The compressive strength of the confined concrete was the output data. The results reveal that the proposed models have good prediction and generalization capacity with acceptable errors.

Prediction of Gas Holdup in Bubble Columns Using Artificial Neural Network

A new correlation for the prediction of gas hold up in bubble columns was proposed based on an extensive experimental database set up from the literature published over last 30 years. The updated estimation method relying on artificial neural network, dimensional analysis and phenomenological approaches was used and the model prediction agreed with the experimental data with average relative error less than 10%.

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.

Dynamic Neural Network Based Nonlinear Control of a Distillation Column

Taking advantage of the knowledge of top and bottom compositions of a distillation column, a dynamic neural network (DNN) is designed to identify the input-output relationship of the column. The weight-training algorithm is derived from a Lyapunov function. Based on this empirical model, a nonlinear H∞ controller is synthesized. The effectiveness of the control strategy is demonstrated using simulation results.

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method.

A Multi-Agent Immune Network Algorithm and Its Application to Murphree Efficiency Determination for the Distillation Column

Artificial Immune Network （aiNet） algorithms have become popular for global optimization in many modem industrial applications. However, high-dimensional systems using such models suffer from a potential premature convergence problem. In the existing aiNet algorithms, the premature convergence problem can be avoided by implementing various clonal selection methods, such as immune suppression and mutation approaches, both for single population and multi-population cases. This paper presents a new Multi-Agent Artificial Immune Network （Ma-aiNet） algorithm, which combines immune mechanics and multiagent technology, to overcome the premature convergence problem in high-dimensional systems and to efficiently use the agent ability of sensing and acting on the environment. Ma-aiNet integrates global and local search algorithms. The perform- ance of the proposed method is evaluated using 10 benchmark problems, and the results are compared with other well-known intelligent algorithms. The study demonstrates that Ma-aiNet outperforms other algorithms tested. Ma-aiNet is also used to determine the Murphree efficiency of a distillation column with satisfactory results.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7?for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4?using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%;and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%;and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.

Simulation and heat exchanger network designs for a novel single-column cryogenic air separation process

To realize the industrialization of the novel single-column air separation process proposed in previous work,steady-state simulation for four different configurations of the single-column process with ternary(nitrogen,oxygen and argon)is developed.Then,exergy analysis of the single-column processes is also carried out and compared with the conventional double-column air separation process at the same capacity.Furthermore,based on the steady-state simulation of single-column processes,the different heat exchanger networks(HENs)for the main heat exchanger and subcooler in each process are designed.To obtain better performance for this novel process,optimization of process configuration and operation is investigated.The optimal condition and configuration for this process is consisted as:feedstock is divided into two streams and the reflux nitrogen is compressed at the approximate temperature of 301 K.In addition,HEN is optimized to minimize the utilities.HENs without utilities are obtained for the four different configurations of single-column process.Furthermore,capital costs of the HEN for different cases are estimated and compared.

Control of liquid column height in electromagnetic casting with fuzzy neural network model

The control of suitable and stable height of liquid column is the crucial point to operate the electromagnetic casting(EMC) process and to obtain ingots with desirable shape and dimensional accuracy. But due to the complicated interact parameters and special circumstances, the measure and control of liquid column are quite difficult. A fuzzy neural network was used to help control the liquid column by predicting its height on line. The results show that the stabilization of the height of liquid column and surface quality of the ingot are remarkably improved by using the neural network based control system.

基于Column-and-constraint的鲁棒配电网重构实用算法

网络重构是配电自动化的重要功能之一,随着智能电网的迅速发展,也越发体现其重要性。但是负荷的波动性和随机性给传统的静态配电网重构带来了严峻的挑战,改进的两阶段鲁棒优化模型可以很好地解决配电网重构中存在的这一问题。第一阶段配置辐射状配电网络,第二阶段寻求满足需求的最优潮流。该策略采用Column-and-constraint实用算法,其主问题和子问题采用整数二阶锥规划算法(mixed-integer second-order cone programming algorithm)来解决。33节点、69节点等典型算例实验结果表明,即使在负荷不确定的情况下,鲁棒配电重构表现出比传统配电网络功率损耗低,可靠性高的特点。实验验证了方法的有效性和良好的计算性能。

Semi-Active TLCD Control of Fixed Offshore Platforms Using Artifical Neural Networks

In this paper, the control method for fixed offshore platforms using semi-active tuned liquid column damper (TLCD) is presented. The equation of motion for the platform-TLCD control system is given and the semi-active control strategy is established. A back propagation artificial neural network (ANN) is used to adjust the orifice opening of TLCD because of the nonlinear motion of liquid in TLCD. The effectiveness of the control method is verified by numerical examples.

Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.

Prediction of Axial Capacity of Concrete-Filled Square Steel Tubes Using Neural Networks

The application of artificial neural network to predict the ultimate bearing capacity of CFST （ concrete-filled square steel tubes） short columns under axial loading is explored. Input parameters consiste of concrete compressive strength, yield strength of steel tube, confinement index, sectional dimension and width-to-thickness ratio. The ultimate bearing capacity is the only output parameter. A multilayer feedforward neural network is used to describe the nonlinear relationships between the input and output variables. Fifty-five experimental data of CFST short columns under axial loading are used to train and test the neural network. A comparison between the neural network model and three parameter models shows that the neural network model possesses good accuracy and could be a practical method for predicting the ultimate strength of axially loaded CFST short columns.

基于SO-LSTM的立柱液压系统故障诊断方法研究

针对目前无法快速、准确地诊断矿用立柱液压系统故障等问题,在建立仿真模型分析单一故障机制的基础上,基于优化算法提出多种故障诊断方法。将立柱物理模块与立柱液压系统模块相结合,建立立柱液压系统仿真模型;基于Simulink分析单一故障的影响,基于蛇优化LSTM神经网络建立诊断模型;最后,根据实际数据进行模型的实例验证。结果表明:蛇优化LSTM模型对液压立柱故障仿真数据识别率达到99.5%,对液压立柱故障真实数据识别率达到97%,与模型仿真数据的预测精度仅相差2.5%,预测精度较高,达到了预期目标。

基于ANN的RECFST短柱轴压承载力预测

目的针对相关设计规范和文献在计算圆端形截面钢管混凝土短柱轴压承载力上的局限性,开发高精高效的轴压承载力预测模型。方法首先,基于国内外已有的RECFST短柱轴压试验研究结果建立有限元模型,并通过验证;其次,基于Python脚本批量生成有限元模型,建立涵盖广泛输入参数的数据集;然后,利用数据集开发高精度的ANN模型并与相关规范和文献结果进行比较;最后,基于ANN模型开发GUI图形用户界面工具。结果ANN模型预测值与试验结果之比的平均值N ANN/N u=0.98,模型预测误差远低于相关规范和文献公式预测误差;ANN模型的均方误差K MSE=7.3734×10-7,总数据样本回归值R=0.99963,表明了ANN模型的有效性以及预测结果的精确性。结论ANN模型可以准确预测RECFST短柱的轴压承载力,基于模型开发的GUI工具简便实用。

基于粒子群优化BP神经网络的中空夹层钢管混凝土柱轴压承载力研究

圆中空夹层钢管混凝土(concrete filled double-skin steel tube,CFDST)柱因其独特的结构形式与优异的力学性能,已成为现代工程结构中的主要受力构件。然而外钢管、内钢管与核心混凝土之间的相互约束作用导致其受力比较复杂。为此,采用PSO-BP混合神经网络算法对圆CFDST柱的轴压承载力进行了研究。收集了167组数据建立数据库,并选取8种影响因素作为输入层参数,轴压承载力作为输出层参数,分析了传统BP神经网络模型所存在的缺陷,建立了PSO-BP神经网络模型。此外,将机器学习模型与3种规范的结果进行比较,结果表明机器学习模型的精度比3种规范的精度更高。相较于BP神经网络模型,PSO-BP神经网络模型具有更好的预测能力,更有助于预测CFDST柱的轴压承载力,对工程上研究CFDST柱的力学性能有着重要意义。

工业过程关键指标预测的知识协同进化增强图卷积网络方法

在流程工业关键变量预测领域,已有研究致力于将过程知识与大数据相结合,以实现更高的准确性,降低过拟合风险和提高可解释性.然而,现有工作存在准确的先验知识构建成本高、无法从丰富的数据中挖掘知识等问题,限制了这些方法在实际工业过程中的广泛应用.为了解决这些挑战,本文提出了一种基于知识协同进化的增强图卷积网络方法.首先,利用易获取的过程流图构建低成本的粗粒度流程知识.然后,在图卷积神经网络模型训练中引入图探索,实现知识更新.最后,为了降低知识复杂度并保持一致性,设计了一种知识过滤机制.所提出的方法在基准的脱丁烷塔工艺过程上进行了验证.实验结果表明,该方法具有出色的预测准确性,并获得高质量的新知识.

经验知识监督的RC墩柱力学性能神经网络分析方法

基于试验或数值模拟的单一墩柱力学性能分析方法难以兼顾计算精度和效率,纯数据驱动的分析方法存在可解释性差和对数据依赖性强等问题。为此,本文通过研究钢筋混凝土(RC)墩柱力学性能试验数据、经验知识和机器学习的融合机制,提出了经验知识监督的RC墩柱力学性能神经网络(knowledge-guided neural network,KGNN)分析方法。首先,建立了包含761组RC墩柱拟静力试验样本的数据库;随后,基于经验知识分析了RC墩柱主要特征对其力学性能的影响规律,构建了相应的数学表征方法;最后,将RC墩柱试验数据及经验知识融入人工神经网络架构和训练过程,建立了高精度、可解释、可通用且不依赖大量训练数据的RC墩柱力学性能KGNN分析模型。本文提出的KGNN分析方法与纯数据驱动神经网络(BPNN)的结果对比表明:BPNN在测试集上表现更好,在分析墩柱承载力时均方根误差(E)和拟合系数(R^(2))分别为0.070和0.978,KGNN模型的E和R^(2)分别为0.108和0.942;但由于BPNN所预测的墩柱特征对承载力的影响规律与经验知识并不吻合,即未能准确反映墩柱特征与其力学性能间的关系,BPNN模型发生了过拟合;而KGNN方法不仅可以快速准确获得RC墩柱力学性能,且预测规律与经验知识吻合较好,具有更高的可靠性和实用性。因此,融合试验数据与经验知识的神经网络有望成为一种新的RC结构力学性能分析方法。