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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Neurofuzzy-Based Plant-Wide Hierarchical Coordinating Optimization and Control: An Application to Zinc Hydrometallurgy Plant
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Neurofuzzy-Based Plant-Wide Hierarchical Coordinating Optimization and Control: An Application to Zinc Hydrometallurgy Plant

机译：基于神经模糊的全工厂层级协调优化与控制：在湿法炼锌厂中的应用

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摘要

In this paper, a novel three-layer coordinating optimization and control strategy involving fuzzy neural networks (FNNs) is proposed for industrial plant-wide control. This strategy includes a coordinator (top layer), local optimizers (middle layer), and local controllers (bottom layer). The coordinator determines and coordinates set-points of production indices for each process in the plant, aiming to achieve plant-wide optimal control. The local optimizers optimize individual unit and deliver the information to the coordinator. The controllers calculate optimal control inputs for the processes according to the set-points. Model predictive control (MPC) is used in this layer. Also used are FNNs with a gradient-based learning algorithm, which are trained to represent the models of the processes that are controlled by MPC. By constructing a Lyapunov function, convergence of the FNNs is established. Experiments are carried out in the largest zinc hydrometallurgy plant in China. The results demonstrate performance of our strategy in tracking the set-points and saving processing costs. Moreover, the strategy achieves a more optimal overall plant operation when compared to the same FNN-based MPC scheme that does not use the coordinator.
机译：在本文中，提出了一种新颖的涉及模糊神经网络的三层协调优化控制策略，用于工业工厂范围的控制。该策略包括协调器（顶层），本地优化器（中间层）和本地控制器（底层）。协调员为工厂中的每个过程确定和协调生产指标的设定点，以实现工厂范围内的最佳控制。本地优化器优化单个单元，并将信息传递给协调器。控制器根据设定值为过程计算最佳控制输入。模型预测控制（MPC）用于此层。还使用了具有基于梯度的学习算法的FNN，这些FNN经过训练可以表示由MPC控制的过程模型。通过构造李雅普诺夫函数，建立了神经网络的收敛性。实验是在中国最大的锌湿法冶金厂进行的。结果证明了我们的策略在跟踪设定点和节省处理成本方面的性能。此外，与不使用协调器的基于FNN的相同MPC方案相比，该策略可实现最佳的整体工厂运营。

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