NEURAL NETWORK INTELLIGENT SYSTEM FOR THE ON-LINE OPTIMIZATION IN CHEMICAL PLANTS

A strategy of developing on-line optimization intelligent systems based on combiningflowsheeting simulation and optimization package with artificial neural networks(ANN)is presented inthis paper.A number of optimization cases for a certain chemical plant are obtained off-line byusing PROCESS-Ⅱ or other flowsheeting programming with optimization.Then,taking these cases astraining examples,we establish a neural network systems which can be used on-line as an optimizer toobtain setpoints from input data sampled from distributed control system through gross error detectionand data reconciliation procedures.Such an on-line optimizer possesses two advantages over nonlinearprogramming package:first of all,there is no convergence problem for the trained ANN to be usedonline;secondly,the frequency for setpoints updating is not limited because only algebraic calculationrather than optimization is required to be carried out on-line.Here two key problems ofimplementing ANN approaches to the on-line optimization

Model calibration concerning risk coefficients of driving safety field model

Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.

On-Line Measurement of the Chemical Oxygen Demand in Wastewater in a Pulp and Paper Mill Using Near Infrared Spectroscopy

Although near infrared (NIR) spectroscopy has been evaluated for numerous applications, the number of actual on-line or even on-site industrial applications seems to be very limited. In the present paper, the attempts to produce online predictions of the chemical oxygen demand (COD) in wastewater from a pulp and paper mill using NIR spectroscopy are described. The task was perceived as very challenging, but with a root mean square error of prediction of 149 mg/l, roughly corresponding to 1/10 of the studied concentration interval, this attempt was deemed as successful. This result was obtained by using partial least squares model regression, interpolated reference values for calibration purposes, and by evenly distributing the calibration data in the concentration space. This work may also represent the first industrial application of online COD measurements in wastewater using NIR spectroscopy.

Study on intelligent digital welding machine with a self-learning function

A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.

Intelligent Automation Module-Based Gear Edge Grinding System

As industry progresses toward intelligent production and development,on-site workers that perform repetitive tasks will be replaced by intelligent machines.Currently,automation applications still have the following problems:(1)on-site personnel are required to line up the workpieces before a robot arm can pick it up;(2)the trajectory generated by offline programming software must be adjusted by on-site personnel in accordance with the processing results;and(3)because of workpiece positioning errors and tool wear,achieving acceptable processing results is difficult.This study developed intelligent application modules that solve the aforementioned automation application problems.These modules predict processing quality,generate trajectory,enable robot arms to load and unload randomly positioned workpieces,and automatically calibrate the system.An automatic gear edge grinding system was developed by integrating each module;the system increases the processing efficiency and solves the current problem of manual grinding being required after gear processing.

基于人工智能技术的雨量校准故障诊断与预警辅助系统研究

基于人工智能技术的雨量校准故障诊断与预警辅助系统,通过气象观测数据“云”获取设备计量数据进行预处理,采用多种数据驱动和人工智能算法,利用深度学习及神经网络,对采集的数据进行分析,对数据样本进行训练学习,诊断设备是否存在故障并对设备存在的风险进行预警判断。采用神经网络分析设备故障,根据分析出的设备故障情况,系统以大数据为核心、智能算法为底层逻辑模式分析并推送解决方案,有效地提升了户外计量工作效能,对气象自动站其他高精度传感器检定、校准的多源数据分析和诊断具有较好的开拓意义。

基于物联网的流量仪表智能标定系统设计

针对流量仪表因采用手工或半自动方式标定所造成的费时、低效等问题,设计了基于物联网的流量仪表智能标定系统。在物联网边缘通过数字化感知、边缘计算、数据网关,实现数据采集和传输,满足了各种标定设备的数据互联互通。根据流量仪表标定过程的特殊性,在云端建立了基于规则的流量仪表标定专家系统。通过数据共享、数据可视化等数字化、智能化的系统设计,模拟人类标定专家的思维过程。运用知识进行推理,根据不同的流量仪表产品,及时给出满意的标定解决方案。该方案赋能流量仪表的智能标定过程,实现了标定生产过程的全自动化。该设计使标定周期由原来的120min缩短到40min,提升了流量计生产装备智能化程度,提高了生产效益。

面向手眼标定的改进灰狼优化方法

为解决眼在手上的机器视觉智能机器人手眼标定精度较低的问题,提出了一种改进灰狼算法的手眼标定方法。首先建立了眼在手的机器视觉智能机器人的手眼标定数学模型,通过分析影响手眼标定误差的因素,提出一种用于降低手眼标定误差的拍照位姿生成方案。然后,融合维度学习和差分进化策略,利用改进的灰狼算法对经由传统手眼标定算法求得的解析解进行非线性优化,避免了传统优化算法在迭代过程中,容易提前收敛,陷入局部最优解等缺陷。最后利用实物设备进行手眼标定实验,实验结果证明了该方法对降低手眼标定误差的可行性和有效性。

智能激光3D投影空间目标辐射照度校准技术

由于激光3D投影系统的光路复杂,且受空间目标的形状和大小的影响,导致在特定空间内难以取得理想的投影效果,使得投影目标与实际投影点之间存在较大的偏差,因此,提出一种辐射照度校准方法。分别利用矩形、圆形两种不同形状的辐射面和敏感面搭建辐射照度测量仪,得到4种不同辐射照度测量结果;利用斯忒藩-玻尔兹曼全辐射定律,对测量结果校准,进而完成对智能激光3D投影空间目标辐射照度的校准。实验结果表明,所提方法辐射照度校准结果与实际结果基本一致,且辐射照度校准误差保持在6 W·m^(2)以内。

电梯制动器中的控制检验技术分析

阐述电梯制动器电气控制的原理、功能、重要性,分析常见问题,并介绍检验方法,包括检验目标、标准、流程和常见问题的识别与解决方案。提出提升电梯制动器电气控制可靠性和安全性的方法。

智能水表全寿命期应用管理关键问题研究

水表是供水计量的基本器具,加快智能水表应用是供水行业当前的重要任务。围绕智能水表全寿命期性能可靠及结算公平等需求,研究了智能水表应用管理中的防冻、在线校准、计量争议处置等关键问题。寒潮冰冻会导致水表损坏及计量失准,需结合地区特点提升水表抗冻性能;采用外夹式超声波流量计为标准表可有效实现大口径电子式水表的在线校准,给出了校准操作的相关建议;用户对水量计量争议关心的重点是处置程序和费用退补的公平性,介绍了水表计量争议处置和水量计量差错退补核算方法。

基于机器视觉的智能制造实践平台应用研究

随着信息技术和数字技术的快速发展,新一轮的工业科技革命蓬勃兴起。多年来,我国一直被誉为“世界工厂”,制造业稳居世界第一。然而,随着经济结构转型和产业结构不断深化调整,我国的经济逐渐朝着多元结构、多重挑战、高中速增长的方向发展。智能制造成为制造业发展的新方向,特别是《中国制造2025》制造强国战略的推行实施后,坚持创新驱动、智能化转型已经成为我国制造业发展的主要目标,该文结合机器视觉技术的概念及系统组成,阐述基于机器视觉的智能制造实践平台的应用设计,分析基于机器视觉的智能制造系统的优势,并探讨未来的发展趋势,旨在为相关人士提供借鉴和参考。

KA-NC-1智能钻孔校检仪在糯东煤矿精准化治理瓦斯的应用

针对突出矿井复杂地质条件下的煤层群瓦斯涌出量预测、瓦斯治理措施以及瓦斯治理效果评价等关键问题,详细介绍了KA-NC-1智能钻孔校检仪的结构、原理以及施工方法,并在糯东煤矿进行了现场应用。应用结果表明,通过该智能钻孔校检仪得到20号煤层的绝对瓦斯涌出量预测值为41.52 m^(3)/min与实际测量值基本符合,成功实现了对矿井绝对瓦斯涌出量的精准预测;智能钻孔校检仪随钻精确测量钻孔倾角、方位角、轨迹、轨迹绝对偏移量等校检参数,使得钻孔的布置更符合设计要求,从而实现工作面瓦斯治理精准化措施;利用钻孔空白带自动识别技术,确保了抽采钻孔“压茬”布置,从而实现了工作面瓦斯治理效果的智能化校检评价。

基于机器视觉的衣领智能缝纫系统

传统的衣领的缝纫都为手工缝纫,缝纫质量差异化严重、效率低、人工成本高,针对这些不足,搭建一个基于机器视觉的智能缝纫系统,该系统将视觉定位技术、运动控制技术应用在电动缝纫机上,完成衣领的智能缝纫。系统通过预先标定方法获得图像中的点移动到缝纫机缝针正下方时图像坐标与机械运动坐标的关系,工作时运用机器视觉技术获得待缝纫衣领的图像并计算缝纫路径和缝纫点坐标,通过得到的图像坐标与机械运动坐标的转换关系,再结合运动控制技术和电动缝纫机技术完成衣领的智能缝纫。该系统可减少一线缝纫工的劳动强度和技术难度,提高衣领缝纫质量和缝纫效率。

制衡还是共生:生成式人工智能治理的技术校准与行动框架

生成式人工智能的发展突出了技术的加速度与社会治理框架的不协调问题。本文以生成式人工智能对社会结构的突变性影响为切入点,分析以数据安全为代表的技术风险、技术利维坦和隐私安全等社会风险以及以认知思维构陷为代表的决策风险等突出问题,在技术权力置换与社会格局重组中寻找生成式人工智能技术校准的基准点,并以中观与微观数智素养培育为行动起点,以期在“有形边界”之外,为生成式人工智能发展提供“无形边界”与伦理素养规范的治理逻辑与行动框架。具体来说,首先,在宏观层面,需要构建一个全球性的治理框架,以应对生成式人工智能的全球性挑战,在跨国际的治理合作中构建一个安全、公平、可持续的全球性生成式人工智能治理体系,实现广域的数字化联合。其次,在中观层面,需要建立健全制度体系,以规范生成式人工智能的研发、应用和推广的政策法律等“有形边界”,同时进行社会层面的道德规制,找准技术校准点,为保障该技术的健康发展提供更有益的社会环境。最后,在微观层面,需要关注个人的惯习和认知,以无形之力搭建起人类与生成式人工智能技术之间的认知边界、信任边界和文化边界,以期在个体层面上提升人们对生成式人工智能的认识和理解。

基于机器视觉和卷积神经网络的无人化智能装卸研究

利用机器人可以大批量、不间断作业的优势,设计基于机器视觉和卷积神经网络的无人化智能装卸方法。使用机器视觉系统采集无人化智能装卸机器人装卸时的图像数据,通过自适应卷积神经网络的无人化智能装卸图像识别方法获得待装卸物体,将其在图像坐标系下的坐标值运用张氏标定法转化为机器人坐标系下的坐标值,根据转化结果采用快速扩展随机树算法驱动机器人运动到坐标位置,实现待装卸物体的无人化智能装卸。实验结果表明:该方法能识别出全部待装卸物体;标定所得重投影误差值小,无人化智能装卸机器人运动路径短,并能有效避开所有障碍物,能高效、精准地装卸全部待装卸物体。

工业仪表的自动化校验与智能化管理分析

阐述工业仪表中的常见故障原因,分析工业仪表自动化校验要点,包括明确计算参数、优化校验模式、落实数据信息。探讨工业仪表智能化管理策略,提升工业仪表的自动化校验与智能化管理水平。

集中供热热量校准和供热调节系统分析

以某工业园区热力站为研究对象,提出了采用热量在线校准及集中供热智能化调节的方案,解决了该工业园区热力站目前存在的供热管网规模大、设备分散、管理困难,供热不合理及供热收费难等问题。采用热量在校准及供热智能化调节模式后,节省了很大一部分需日常维护、检修的人力资源,并对改造前后的能耗进行对比分析,对该工业园区热力站供热结构进行改造后综合节电率可达到50%,因供热所需的耗水量可节省18%,热力站一年整体的能耗费用可以节省32.5%,整体的经济成本可节约749.02万元左右,改造效果显著,为工业集中供热系统的智能化建设提供了支持。