钢铁制造烧结过程关键参数检测方法综述

铁矿石烧结是钢铁制造中重要的铁前流程,是高炉炼铁的主要精料来源,也是钢铁行业实施“双碳”战略的前沿阵地。烧结终点、混合料水分以及FeO含量作为烧结过程中的关键参数,直观反映了烧结全流程的工况、能耗水平以及成品质量,是烧结过程在线检测的重点研究对象,本文重点阐述了这三个关键参数的检测方法研究现状,从工艺机理和参数特性出发,分析了现有检测方法的优缺点以及适用性,概括了当前烧结过程关键参数检测面临的问题,并对未来发展趋势进行了展望。

用对数函数描述收敛因子的改进灰狼优化算法及其应用

针对灰狼优化(grey wolf optimization,GWO)算法在求解复杂高维优化问题时存在解精度低、易陷入局部最优等缺点,提出一种基于对数函数描述收敛因子的改进GWO算法。采用佳点集方法初始化种群以保证个体尽可能均匀地分布在搜索空间中;提出一种基于对数函数描述的非线性收敛因子替代线性递减收敛因子,以协调算法的勘探和开采能力;对当前最优的3个个体执行改进的精英反向学习策略产生精英反向个体,以避免算法出现早熟收敛。研究结果表明改进算法具有较好的寻优性能。

顶燃式球式热风炉烧炉过程温度场建模

针对热风炉烧炉过程中蓄热体温度难以实时在线检测的难题,结合计算流体动力学以及传热学,建立顶燃式球式热风炉烧炉过程温度场分布模型。首先,在综合分析炉内的煤气燃烧反应、烟气湍流规律和蓄热体与烟气动态热量交换过程的基础上,建立热风炉的烟气流动模型和融合蓄热球自身热传导的局部非热平衡多孔介质模型;其次,根据实际现场检测的温度、流量和压力合理设定所建模型的定解条件;最后,采用有限体积法对模型进行求解,得到热风炉的温度场分布。研究结果表明:所建立的模型能够正确、有效地模拟热风炉烧炉过程的温度场分布,模型温度计算值与实测拱顶温度的平均相对误差低于1%,与实测废气温度的平均相对误差为2.6%。

基于FGCN的针铁矿沉铁过程建模

针铁矿沉铁过程是由多个连续反应器级联,并且包含氧化反应、还原反应以及中和反应等一系列复杂化学反应的复杂过程,具有强非线性、不确定性的特点,难以建立精确的数学模型。提出一种基于模糊灰色认知网络(fuzzy gray cognitive network,FGCN)的针铁矿沉铁过程的建模方法。根据专家经验和历史数据,建立针铁矿沉铁系统的模糊灰色认知网络模型,利用带终端约束的非线性Hebbian学习算法(nonlinear Hebbian learning,NHL)对权值进行学习。在不同程度的不确定性环境下对系统进行分析,结果表明模糊灰色认知网络能够在不确定性高的环境下对复杂工业系统进行有效模拟,收敛到一个灰度为零或者灰度很小的灰数平衡点,利用白化函数得到一个准确的控制输出。

人工智能助力有色金属工业转型升级

有色金属是国民经济和国防军工的重要原料和战略物资。近年来,我国有色金属工业取得突飞猛进的发展,但仍面临着绿色、高效和智能化发展的挑战。有色金属生产工况复杂、原料多变,资源、能源、环保的要求日益严格,需要通过灵敏感知、精细操作、智能分析和敏捷决策来处理这些复杂变化和严苛要求。人工智能的飞速发展为有色金属生产过程的转型升级提供了核心驱动力。本文从有色金属工业的发展及面临的挑战、人工智能技术助力有色金属生产转型升级案例以及有色金属工业转型升级对人工智能的挑战三个方面阐述人工智能助力有色金属生产过程绿色高效智能化发展的主要内涵。

智慧三维地理信息管理平台在资源管理中的应用

为对辖区资源和资产管理对象实现科学管理,将管理对象整合到"一张图",建立具备方便使用、易于更新、适合存档、具有辅助决策功能的智慧三维地理信息管理平台。对辖区管理对象的现状、资源和时空信息进行采集,建立三维地理信息空间数据库,利用智慧三维地理信息管理平台对数据库进行管理与应用,为资源管理和辅助决策提供服务。采集了基础数据、规划数据、土地利用数据、三维数据和三维实景模型数据、物联网数据和VR数据;利用平台提供的分析决策等工具对上述数据进行操作运用,提高了工作效率、基层管理水平和管理决策能力。

Time -Sharing Power Supply Optimal Decision forElectrolytic Deposition Process of Zinc Based onSimulated Annealing Algorithm

According to time-sharing valuation principle (TSVP) of power supply, the relationships of current density and current efficiency at different acidities are obtained based on the processed data of electrolytic deposition process of zinc (EDPZ) with the least square method (LSM). Thus an optimal model of time-sharing power supply system for EDPZ is established, which has been optimized by use of an improved efficient simulated annealing algorithm (SAA). Practical results show that industrial and mining enterprises can obtain enormous economic benefits every year.

Robust Feedback Set Stabilization of Logic Networks with State-Dependent Uncertain Switching and Control Constraints

This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs)with state-dependent uncertain switching and control constraints.First,based on the properties of the semi-tensor product of matrices and the vector representation of logic,an SLCN with state-dependent uncertain switching and control constraints is expressed in algebraic form.Second,an input transformation and a switching model are constructed to transfer the original SLCN into one with a free control input and arbitrary switching.The equivalence between the set stabilizability of the original SLCN and that of the resulting SLCN is established.Based on such equivalence,the authors propose a necessary and sufficient condition for robust feedback set stabilizability.Finally,an example is presented to demonstrate the application of the results obtained.

Fixed-time distributed robust optimization for economic dispatch with event-triggered intermittent control

This paper proposes a fixed-time distributed robust optimization approach for solving economic dispatch problems.Based on an integral sliding mode control scheme,the proposed multi-agent system converges to an optimal solution to an economic dispatch problem before a fixed time.In addition,the proposed multi-agent system can suppress the disturbance in a fixed time.To reduce the cost of sliding mode controls,we propose a distributed event-triggered intermittent control which reduces the sliding mode control time by setting a control triggering rule on the basis of two boundary functions of a Lyapunov function.The simulation results of three power systems illustrate the characteristics and effectiveness of the theoretical results.

Finite-Time Observability of Probabilistic Logical Control Systems

This study investigates finite-time observability of probabilistic logical control systems(PLCSs)under three definitions(i.e.,finite-time observability with probability one,finite-time singleinput sequence observability with probability one,and finite-time arbitrary-input observability with probability one).The authors adopt a parallel extension technique to recast the finite-time observability problem of a PLCS as a finite-time set reachability problem.Then,the finite-time set reachability problem can be transferred to stabilization problem of a logic dynamical system by using the state transfer graph reconstruction method.Necessary and sufficient conditions for finite-time observability under the three definitions are derived respectively.Finally,the proposed methods are illustrated by numerical examples.

人工智能的回顾与展望

基于第194期"双清论坛",本文分析了我国人工智能发展和人工智能助力制造业优化升级面临的挑战问题,从脑启发计算、人工智能基础前沿和流程制造业智能化三个方面总结了近5年主要研究进展,探讨了未来5年前沿研究领域和科学基金重点资助方向。

Asynchronous Fuzzy Cognitive Networks Modeling and Control for Goethite Iron Precipitation Process

Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.

Process modeling issues arising in zinc hydrometallurgy

As typical nonferrous metallurgical processes,hydrometallurgical processes are used extensively to recover elemental zinc from raw ores.Accurate system models must be established to increase automation of hydrometallurgical zinc processes with strict energy and environmental requirements.However,the modeling of such processes is hindered by their complex production characteristics and process mechanisms.In this review,we summarize some process modeling issues arising in zinc hydrometallurgy and provide several possible solutions from the perspective of our research work.

LMI-Based Delay-Dependent Robust Control for Value Bounded Uncertain Systems with Time Delay

The present paper investigated the delay-dependent robust control for linear value bounded uncertain systems with state delay. By introducing the idea of matrix decomposition into the synthesis problem, incorporating with Lyapunov-Krasovskii functional method and adding ＂zeros＂ matrix through the correlation of each item in Newton-Leibniz formula, we present a sufficient condition via the feedback stabilization based on linear matrix inequality （LMI）. LMI is a new delay dependent condition that is much less conservative, and it guarantees that the system is robust asymptotically stable via state feedback controller. Neither the model transformation nor the bounding cross terms is employed. Finally, a numerical example is presented and it demonstrates the effectiveness of the offered method.