浓密机生产过程综合自动化系统

针对浓密机生产过程工艺机理复杂,具有大滞后、非线性的特点以及给矿性质波动频繁、难以实现浓密机自动控制的难题,提出了由过程管理和过程控制两层结构组成的浓密机生产过程综合自动化系统。讨论了系统的结构、功能和浓密机底流浓度智能优化控制策略。实际应用结果表明,该方法实现了浓密机生产过程精细化,降低了浓密机压耙等生产事故,提高了浓密机生产效率,减轻了现场岗位劳动强度,实现了浓密机生产过程优化控制、优化运行和优化管理,取得了显著的应用效果。     

基于PCS7的铜钴回收冗余控制系统设计

针对新型铜钴回收工艺仍然处于人工手动操作、自动化程度低的问题,该文在西门子PCS7过程控制系统下设计铜钴回收冗余控制系统对整个生产流程进行自动化监控,针对铜钴回收浓密机工艺机理复杂,操作环境恶劣,具有大惯性、非线性以及来料性质波动频繁等缺点,分析浓密机的结构,设计浓密机的参数自整定模糊PID控制.通过在青海某矿业集团50万吨铜钴回收项目的实际应用证明,该系统能很好地满足控制要求.浓密机运行安全可靠,提高了综合回收效率,减轻了劳动强度,实现了安全稳定的自动化生产.     

基于数据驱动的浓密-压滤过程协调优化控制

针对某选矿厂由于浓密-压滤过程关键变量没有实现在线检测,导致该工序生产操作无序、生产指标难以达标、能耗经济指标高等问题,利用浓密-压滤过程的生产运行数据,提出一种基于数据驱动的浓密-压滤过程协调优化控制方法.首先,通过偏最小二乘(PLS)方法建立浓密-压滤过程的数据模型;然后,在阶梯电价、浓密机运行安全、生产指标的约束...     

常见浓密机升耙结构介绍故障分析及其解决措施

浓密机是一种连续作业液固分离的大型专用设备,广泛应用于选矿治金、水处理等行业。浓密机在工作过程中随着池体底部矿浆的沉积、高浓度矿浆对旋转轴和耙架形成扭矩。当扭矩达到峰值时、浓密机需要提耙、以确保耙齿及旋转轴不受破坏。因此浓密机的升耙机构是确保浓密机正常运行的重要部件。以往浓密机多采用手动升耙的模式,不具备扭矩追踪功能、无扭矩反馈功能,事故率高。随着自动化技术的发展、先进的压力传感器及扭矩传感器被逐步应用,升耙也由手动人工调整升级为液压或机械自动提耙模式。使得浓密机向着更高层次的自动化方向发展。由于中心传动浓密机和周边传动浓密机的升降耙机构适用于不同的工况条件,试图通过阐述升降机构的结构特点和应用现状。讨论不同工况条件下采用相应的升降机构的优缺点。     

多智能体协调技术在热连轧中的应用

本文以典型的流程工业过程为对象,结合分布式工业过程控制的特点,分析了多智能体应用于复杂工业过程控制的可行性、必要性;提出了一种基于工业过程控制的多智能体系统的体系结构及其协调控制过程.板带热连轧机过程控制是典型的分布式过程控制对象,文章从综合控制的角度出发提出热连轧机工业生产过程多智能体系统的体系结构,给出了体系结构中各功能模块的组成及其处理过程,并重点分析了加热炉控制的协调过程.     

基于混合整数线性规划的浓密脱水过程协调优化

浓密脱水过程是有色金属选冶领域重要的固液分离工序.但由于该过程关键变量难以在线检测、生产设备间相互耦合以及人工经验操作等问题,导致其能耗较高、过程安全性难以保证.对此,以浓密脱水过程为背景,构建一种基于混合整数线性规划的协调优化模型.利用工业现场的历史数据,建立底流浓度预测模型以及底流泵与压滤泵运行时间预测模型;在考虑阶梯电价的条件下,以最小化生产过程能耗为目标,以生产工艺条件、设备安全等为约束条件,建立浓密脱水过程的协调优化模型;通过引入辅助决策变量,对优化模型进行线性化处理,将复杂的非线性过程问题转化为更易于求解的混合整数线性规划问题.最后,将所提出的方法应用于某选矿厂的浓密脱水过程,结果显示,平均放矿底流浓度可以提高13.5%,能耗经济指标降低46.8%.     

基于OPC技术的上位机与西门子PLC的通信

OPC是一种被广为接受的开放式的工业通信标准,在工业控制领域越来越得到广泛应用.本文介绍了基于OPC技术上位机VB程序与西门子PLC通信的实现过程,PC机能实时监控变频调速风机系统.VB不仅是上位机监控组态程序,也是OPC的客户端,需要开发OPC客户端的应用程序,实现上位机与PLC的通信.     

混合选别浓密过程双速率智能切换控制

赤铁矿混合选别浓密过程是以底流矿浆泵频率为输入,以底流矿浆流量为内环输出,以底流矿浆浓度为外环输出的强非线性串级工业过程.由于受到频繁的浮选过程产生的中矿矿浆和污水的随机干扰,底流矿浆浓度外环和流量内环始终处于动态变化之中,控制器积分作用失效,内外环相互影响,使被控系统的动态性能变坏,底流矿浆浓度与流量超出工艺规定的控制目标的范围,甚至产生谐振.本文针对上述问题利用提升技术建立基于内环流量闭环动态模型的浓度外环动态模型,将基于未建模动态补偿驱动的一步最优PI控制和基于模糊推理与规则推理的切换控制相结合,提出了由浓度外环控制和流量内环控制组成的混合选别浓密过程的双速率智能切换控制算法,建立了由机理主模型和神经网络补偿模型组成的混合选别浓密过程动态模型.所提算法通过混合选别浓密过程的半实物仿真实验结果表明本文所提控制方法的有效性.     

基于Modbus协议的电能监控系统网络通信的实现

为适应远程工业控制的需要,研究了电能监控系统中基于Modbus协议的网络通信的实现.该系统以PLC为核心,在Modbus和TCP/IP协议的基础上,实现了PC监控终端通过Internet对现场设备进行远程监控,分析了具体的通信过程,并利用VB6.0开发了上位机监控平台.所构建的电能监控系统实现了现场数据采集、处理及通信等功能,为工业控制网络通信提供了一种有效的技术方法.     

基于支持向量机的非线性系统预测控制

针对离散非线性系统, 提出一种可用于非线性过程的支持向量机预测控制方法, 并给出了控制律的收敛性分析. 该方法将复杂的非线性预测方程转化成直观而有效的线性形式, 同时利用线性预测控制方法求得解析的控制律, 避免了复杂的非线性优化求解, 对非线性工业焦化装置温度控制的仿真结果表明了算法的有效性.     

混合选别浓密机底流矿浆浓度和流量区间智能切换mm控制方法

赤铁矿磁选-浓密-浮选过程中浓密机的底流矿浆浓度受到大而频繁的浮选过程产生的中矿矿浆随机干扰的影响,造成底流矿浆流量频繁波动在工艺规定的范围之外,使得矿浆在浮选机中选别时间缩短,液位波动造成有用金属流失,从而减少精矿品位和金属回收率. 本文分析了难以采用现有的底流矿浆浓度闭环控制策略的原因,提出了由流量设定和跟踪流量设定值控制组成的矿浆浓度与流量区间双闭环控制;提出了基于静态模型的流量预设定、模糊推理的流量设定补偿、流量设定保持器和规则推理的切换机制组成的流量设定智能切换控制方法. 与矿浆浓度闭环控制方法的仿真对比实验和在国内某大型赤铁矿混合选别浓密机的成功应用,表明所提出的方法在浮选中矿干扰下,不仅将底流矿浆浓度和流量控制在目标值范围内,而且明显减少底流矿浆流量波动,从而在保证金属回收率不变的条件下,显著提高了精矿品位.     

基于ESN的多指标DHP控制策略在污水处理过程中的应用

针对污水处理过程(Wastewater treatment process, WWTP)溶解氧(Dissolved oxygen, DO)及硝态氮浓度控制问题, 提出了一种多评价指标的DHP (Dual heuristic dynamic programming)控制策略. 该策略能够降低评价指标的复杂性, 提高评价网络的逼近精度. 采用回声状态网络(Echo state networks, ESNs)实现评价函数及控制策略的逼近, 研究了控制器的在线学习算法. 实验表明, 该策略在控制性能上优于单评价指标的DHP策略及常规PID控制策略.     

基于Volterra模型的预测控制及应用

由于工业实践的需要,非线性预测控制近年来受到广泛地关注.Volterra模型是一类特殊的非线性模型,非常适合描述工业过程中的无记忆非线性对象.传统的基于Volterra模型的控制器合成法及迭代计算预测控制器法计算量大,且不便于处理控制约束.非线性模型预测控制求解是典型的非线性规划问题,序列二次规划(sequential quadratic program,SQP)算法是求解非线性规划问题常用方法之一.针对Volterra非线性模型预测控制求解问题,本文将滤子法与一种信赖域SQP算法相结合,提出一种改进SQP算法用于基于非线性Volterra模型的带控制约束的多步预测控制求解,并分析了所提方法的收敛性.工业实例仿真结果证实了所提方法的可行性与有效性.     

A self-learning scheme for residential energy system control and management

In this paper, we apply intelligent optimization method to the challenge of intelligent price-responsive management of residential energy use, with an emphasis on home battery use connected to the power grid. For this purpose, a self-learning scheme that can learn from the user demand and the environment is developed for the residential energy system control and management. The idea is built upon a self-learning architecture with only a single critic neural network instead of the action-critic dual network architecture of typical adaptive dynamic programming. The single critic design eliminates the iterative training loops between the action and the critic networks and greatly simplifies the training process. The advantage of the proposed control scheme is its ability to effectively improve the performance as it learns and gains more experience in real-time operations under uncertain changes of the environment. Therefore, the scheme has the adaptability to obtain the optimal control strategy for different users based on the demand and system configuration. Simulation results demonstrate that the proposed scheme can financially benefit the residential customers with the minimum electricity cost.     

Probabilistic dual heuristic programming-based adaptive critic

Adaptive critic (AC) methods have common roots as generalisations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, non-linear and non-stationary environments. In this study, a novel probabilistic dual heuristic programming (DHP)-based AC controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) AC method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterised by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the probabilistic critic network is then calculated and shown to be equal to the analytically derived correct value. Full derivation of the Riccati solution for this non-standard stochastic linear quadratic control problem is also provided. Moreover, the performance of the proposed probabilistic controller is demonstrated on linear and non-linear control examples.     

Fuzzy adaptive dynamic programming-based optimal leader-following consensus for heterogeneous nonlinear multi-agent systems

In this paper, a novel online iterative scheme, based on fuzzy adaptive dynamic programming, is proposed for distributed optimal leader-following consensus of heterogeneous nonlinear multi-agent systems under directed communication graph. This scheme combines game theory, adaptive dynamic programming together with generalized fuzzy hyperbolic model (GFHM). Firstly, based on precompensation technique, an appropriate model transformation is proposed to convert the error system into augmented error system, and an exquisite performance index function is defined for this system. Secondly, on the basis of Hamilton–Jacobi–Bellman (HJB) equation, the optimal consensus control is designed and a novel policy iteration (PI) algorithm is put forward to learn the solutions of the HJB equation online. Here, the proposed PI algorithm is implemented on account of GFHMs. Compared with dual-network model including critic network and action network, the proposed scheme only requires critic network. Thirdly, the augmented consensus error of each agent and the weight estimation error of each GFHM are proved to be uniformly ultimately bounded, and the stability of our method has been verified. Finally, some numerical examples and application examples are conducted to demonstrate the effectiveness of the theoretical results.

     

基于学习的鲁棒自适应评判控制研究进展

在作为人工智能核心技术的机器学习领域,强化学习是一类强调机器在与环境的交互过程中进行学习的方法,其重要分支之一的自适应评判技术与动态规划及最优化设计密切相关.为了有效地求解复杂动态系统的优化控制问题,结合自适应评判,动态规划和人工神经网络产生的自适应动态规划方法已经得到广泛关注,特别在考虑不确定因素和外部扰动时的鲁棒自适应评判控制方面取得了很大进展,并被认为是构建智能学习系统和实现真正类脑智能的必要途径.本文对基于智能学习的鲁棒自适应评判控制理论与主要方法进行梳理,包括自学习鲁棒镇定,自适应轨迹跟踪,事件驱动鲁棒控制,以及自适应H_∞控制设计等,并涵盖关于自适应评判系统稳定性、收敛性、最优性以及鲁棒性的分析.同时,结合人工智能、大数据、深度学习和知识自动化等新技术,也对鲁棒自适应评判控制的发展前景进行探讨.     

Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

It is crucial to predict the outputs of a thickening system, including the underflow concentration(UC) and mud pressure, for optimal control of the process. The proliferation of industrial sensors and the availability of thickening-system data make this possible. However, the unique properties of thickening systems, such as the non-linearities, long-time delays, partially observed data, and continuous time evolution pose challenges on building data-driven predictive models. To address the above ...     

Adaptive and optimal control of a non-linear process using intelligent controllers

A time-optimal control for set point changes and an adaptive control for process parameter variations using neural network for a non-linear conical tank level process are proposed in this work. Time-optimal level control was formulated using dynamic programming algorithm and basic properties of the solutions were analysed. It was found that the control is of bang–bang type and there is only one switching. In this method, a mathematical step-by-step procedure is used to obtain the optimal valve position path with one switching and is trained by neural network, based on the back-propagation algorithm. The dynamic programming procedure allows the set point to be reached as fast as possible without overshoot. An adaptive system is also designed and proved to be useful in adjusting the trained parameter of the dynamic programming based neural network for the process parameter variations. A prototype of conical tank level system has been built and implementation of dynamic programming based neural network control algorithm for set point changes and implementation of adaptive control for process parameter variations are performed. Finally, the performance is compared with conventional control. The results prove the effectiveness of the proposed optimal and adaptive control schemes.