Hybrid Soft Computing Approach for Mining of Complex Construction Databases

The paper presents a hybrid soft computing system for mining of complex construction databases. The proposed approach hybridizes soft computing techniques, such as fuzzy logic, artificial neural networks (ANNs), and messy genetic algorithms (mGAs), to form a novel computational method for mining of human understandable knowledge from historical databases. The hybridization combines the merits of explicit knowledge representation of fuzzy logic decision-making systems, learning abilities of ANNs, and global search of mGAs. A hybrid soft computing system (HSCS) is developed for mining complex databases in construction with three characteristics: scarcity, incompleteness, and uncertainty. Real-world construction data repositories are selected to test the capabilities of the proposed HSCS for data-mining under the above-mentioned complex conditions. The testing results show the promising potential of the proposed HSCS for mining of complex databases in construction.     

Multiobjective Optimal Fuzzy Logic Controller Driven Active and Hybrid Control Systems for Seismically Excited Nonlinear Buildings

The third generation benchmark control problem for seismically excited nonlinear buildings is an effort to evaluate the developed control strategies in order to apply them in field applications. As the fuzzy logic control systems have been applied effectively in various fields, including vibration control of structures, a multiobjective optimal fuzzy logic control system has been proposed in this paper. Two types of control devices, namely, active and hybrid, driven by a fuzzy logic controller (FLC) have been considered in the present study. Nondimensionalized peak interstory drift ratio and peak floor acceleration have been used as the two objective functions for the multiobjective optimal design problem. A two-branch tournament genetic algorithm has been used to find a set of Pareto-optimal solutions, as the optimization problem is not necessarily continuous or convex. Performance of the FLC driven active and hybrid control systems have been evaluated for all three third generation benchmark problems for seismically excited nonlinear buildings (3-, 9- and 20-story). Acceleration and velocity information of different floors have been used as feedback to the FLC. This approach provides a set of Pareto optimal designs, from which a controller design can be selected for the required performance. The FLC driven active control system performs better than the sample controller given in the benchmark problem. Though the number of sensors and control devices are far less, the performance of the hybrid is close to the active control system.     

基于模糊神经元PID解耦的精馏塔温度控制

针对精馏塔温度控制这个非线性耦合对象,提出了一种模糊神经元解耦智能控制器。该控制器避免了精馏塔精确数学模型的推导和严格计算解耦算式的麻烦,是模糊逻辑控制与神经元PID控制器的结合。同时,分析了控制器的结构及其学习算法。通过仿真试验,得到了较为理想的控制效果。     

基于神经元模糊控制器的过热汽温控制

工业控制领域的普通模糊控制器都存在着控制精度不理想的问题，为此提出一种把神经元与模糊控制器相结合的方法，使控制系统控制精度得到提高．并应用于发电厂的主气压控制系统中，效果良好。     

水口山铅冶炼污水处理工艺探索及优化

介绍湖南水口山有色金属集团有限公司铅冶炼烟气制酸污水处理系统运行现状,冶炼烟气制酸产生的酸性废水pH值在1以下,且含有大量的Zn、Pb、Cd、As等污染物,采用石灰中和治理工艺,通过不断的研究与试验,对部分工艺条件进行调整和改进,使得污水处理装置运行指标更加合理,设备运行更加稳定,目前污水处理装置运行稳定,治理后污水可达标排放,各项指标达到或超过设计值。处理后的废水满足回用和浇花,处理成本有较大降低,实现了节能减排的目的。该工艺具有良好的经济效益和环境效益。     

软计算及其在钢铁冶金工业中的应用

软计算是解决含有不确定性、不精确性问题的关键技术之一;在众多的实际应用中,软计算已经体现出了卓越的性能.本文分析了软计算中三个主要的方法:模糊逻辑、神经网络和遗传算法,并分别介绍了它们在钢铁冶金工业中的应用.     

Fuzzy Controller for Thermophilic Aerobic Digestion Using Nicotinamide Adenine Dinucleotide Fluorescence

Simple fuzzy logic control of the thermophilic aerobic digestion (TAD) process was conducted to propose the effective control criteria for the TAD process. Nicotinamide adenine dinucleotide (NADH)-dependent fluorescence, which is the effective indicator to give insight into the status of micro-organisms, was used as the control parameter for the TAD process. The NADH-dependent fluorescence was successfully measured in the pure NADH solution, culture solution, batch TAD, and semicontinuous TAD system using a proposed optic sensor system. From a comparison of the fluorescence signal and process status, it was clear that NADH-dependent fluorescence is a good parameter to control the TAD process. Simple fuzzy logic controller was made by the experts’ experience and used to control the semicontinuous TAD process. A proposed fuzzy logic controller using NADH-dependent fluorescence as a control parameter could successfully control the stable operation of the semicontinuous (fill-and-draw) TAD process.     

脉冲燃烧技术在太钢步进式加热炉中的应用

本文针对太钢1549mm热连轧厂四座板坯加热炉采用脉冲燃烧技术的特点,设计了基于模糊PID控制理论的数字化燃烧控制系统。通过控制每个烧嘴的开关时间,达到控制燃料和空气给进量从而控制炉温的目的。模糊逻辑控制器以PID控制器为基础,控制动作由常规PID实现。模糊控制程序块根据输入变量的变化,以系统观测、专家经验和操作方法为依据,进行模糊推理后做出决策,在线整定PID参数Kp和Ki。该控制系统投运后,具有动态性能好、控制温度波动小、节约燃料等优点,取得了良好的使用效果。     

Performance of Historic Downstream Canal Control Algorithms on ASCE Test Canal 1

Researchers have developed several algorithms to automatically control water levels in irrigation canals. Proportional-integral (PI) control logic has been used for downstream water-level control, but its performance has not always been satisfactory. Heuristic downstream water-level controllers (e.g., canal automation for rapid demand deliveries, or CARDD) have also been proposed but not rigorously tested. The ASCE Task Committee on Canal Automation Algorithms developed a series of test cases to evaluate the performance of canal control algorithms. In this paper, simulation tests were performed on the ASCE Test Canal 1 using three downstream control algorithms: (1) The standard PI control logic; (2) The PI control logic with hydraulic decouplers; and (3) The heuristic CARDD control logic. These controllers were tuned manually using trial-and-error techniques. Performance of the PI control logic improved with the addition of hydraulic decouplers. CARDD did not perform as well as the PI controllers under the conditions imposed on ASCE Test Canal 1. Robustness of these controllers depends on the aggressiveness of the controller as well as the initial flow rate.     

过热蒸汽温度单神经元模糊自适应控制策略仿真研究

将模糊控制和神经控制相结合,设计出单神经元模糊自适应控制方案.该方案充分利用了模糊逻辑对非线性的逼近性和神经控制自适应的特点.仿真结果表明,本控制策略比PID串级控制、二维PD型模糊控制和单神经元自适应控制具有更好的控制效果.     

Application of Fuzzy Logic Controller to Level Control of Twin-Roll Strip Casting

SymbolList　　D———Stripthickness ;　　E———Levelerror ;　　ΔE———Changeoflevelerror ;　　H———Moltensteellevel;　　L———Widthofcastingroll;　　Q———Volumeofmoltensteel;　　R———Radiusofcastingroll;　　S———Lateralareaofmoltensteelpool;　　u ,u1 ,u2 ———Outputofintelligentfuzzylogiccontrol ler,fuzzylogiccontrollerandPIDcon troller ;　　v———Castingspeed ;　　α———Proportionfactor ;　　τ———Time .　　Thetwin rollstripcastingprocessisanewstripproductionmethod …     

High-Performance Isolated PV-Diesel System

In this paper, a control scheme is proposed for a three-phase isolated photovoltaic (PV)-diesel system without an energy storage element. The scheme aims to: track maximum power from the PV array, regulate the load voltage, compensate the load unbalance viewed by the diesel generator, and to control the diesel engine speed. The first three tasks are achieved by controlling the PWM inverter interfacing the PV array to the system. The fourth is realized by a modified fuzzy logic controller of the diesel engine. The obstacles encountered on operating the system under certain probable loading conditions are addressed. Two strategies are proposed to adapt the control scheme to accommodate all loading scenarios maintaining excellent technical and economic performance. The system operation is investigated under a variety of conditions to prove the aptness of the proposed techniques.     

模糊控制机理在处理宝钢电厂冲灰水上的应用

热电厂冲灰水需经中和处理后才能排放.基于对宝钢电厂冲灰水加酸中和的研究,介绍了宝钢电厂冲灰水处理工艺.通过对现状分析,建立了冲灰水加酸的模式,实现控制器自动投加,从而确保了pH值在6～9之间,使废水在排放标准内稳定排放,实现了良好的社会效益和经济效益.     

INTELLIGENT CONTROL SYSTEM FOR STREAM SAMPLING

This article introduces an intelligent sampling controller to assist the actions taken by a plant operator to correct sampling conditions. This hybrid system includes a sampling error filter (SEF), a sampling performance indexer (SPI), a sampling correctness inspector (SCI), and a sampling error evaluator (SEE). First, the SEF upgrades the measured variables in a mineral-processing system by material balance. Then, the SPI employs fuzzy logic to assess the sampling performance. In addition, the expert system SCI checks the correctness of the sampling process. Finally, the sampling error is computed by the expert system SEE.     

智能集成优化控制技术及其在工业中的应用

研究和讨论了复杂工业过程的优化控制技术 ,提出了智能集成优化控制的理论框架。以冶金工业生产中三个典型的复杂生产过程为应用背景 ,分别将模糊神经网络集成建模、基于改进模拟退火算法的混合罚函数目标优化技术、基于混沌神经网络的优化技术及基于模型的专家优化控制技术应用于工业生产过程控制 ,为企业带来了巨大的经济效益和社会效益。     

基于模糊推理的自适应PID控制器研究

针对常规PID控制器当控制对象变化时,控制器的参数难以在线自动调整,难以很好地控制非线性、时变的复杂系统和模型不清楚的系统,介绍了一种基于模糊控制原理的自适应PID控制器,同时利用MATLAB中的Simulink和Fuzzylogic工具箱进行了仿真研究,仿真结果具有令人满意的动、静态特性。     

Finishing mill thread speed set-up and control by interval type 1 non-singleton type 2 fuzzy logic systems

Abstract

The set-up of the finishing mill (FM) thread speed to achieve the desired strip temperature as measured by the finish mill delivery temperature sensor is made by an intelligent controller based on interval type 2 fuzzy logic system. The controller calculates the FM thread speed required to achieve the strip FM exit target temperature, and the interstand strip surface temperature profiles. The interval type 2 fuzzy set-up controller uses as inputs the predicted transfer bar FM entry temperature, the transfer bar thickness, the target strip FM exit temperature, the target strip FM exit gauge, last stand draft reduction, last stand work roll diameter, last stand rolling force, %C of strip, and target width. Taking into account that the measurements are modelled as type 1 non-singleton fuzzy numbers, the authors present the so called interval type 1 non-singleton type 2 fuzzy logic system controllers. As reported in the literature, interval type 2 fuzzy logic systems have greater approximation capacity than that of its type 1 counterpart and it has the advantage to develop more robust and reliable solutions than the latter. The experiments of these applications were carried out for three different types of coils, from a real hot strip mill. The results proved the feasibility of the developed system for FM thread speed set-up and control, and for interstand strip temperature profile prediction. Comparisons with the online mathematical based models show that the proposed interval type 2 fuzzy logic system equalises the performance in FM strip thread speed, and temperature set-up and control under the tested conditions characterised by high uncertainty levels.     

自适应模糊模型在非线性系统中的仿真研究

针对一类仿射单输入单输出非线性系统采用模糊控制、模糊逻辑系统逼近和滑模控制相结合的模糊控制器,利用滑模控制及Lyapunov函数方法提出了直接模糊自适应控制方案。直接自适应控制器充分利用模糊控制规则,通过仿真表明该方法具有较好的跟踪性,能保证闭环系统稳定,跟踪误差收敛,具有一定的鲁棒性,系统中所涉及的所有变量有界,系统的跟踪误差渐近收敛于零或零的一个邻域内。     

Control of the Molten Steel Level in the Top Side-Pouring Twin-Roll Casting Process Based on Fuzzy Rules Optimized by Particle Swarm Optimization Algorithm

Twin-roll strip casting is a near-net-shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time-varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model-based controller. Top side-pouring twin-roll casting is a new kind of twin-roll strip casting technology. This study introduces the control system of the top side-pouring twin-roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side-pouring twin-roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed.     

Dynamic Prediction of Project Success Using Artificial Intelligence

The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an evolutionary project success prediction model (EPSPM). The model is developed based on a hybrid approach that fuses genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs). In EPSPM, GAs are primarily used for optimization, FL for approximate reasoning, and NNs for input-output mapping. Furthermore, the model integrates the process of continuous assessment of project performance to dynamically select factors that influence project success. The validation results show that the proposed EPSPM, driven by a hybrid artificial intelligence technique, could be used as an intelligent decision support system, for project managers, to control projects in a real time base.