Hybrid Intelligent Optimal Control Method for Operation of Complex Industrial Processes
Hybrid Intelligent Optimal Control Method for Operation of Complex Industrial Processes
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DOI：
10.3724/sp.j.1004.2008.00505
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发表时间：
2009-03
期刊：
影响因子：
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通讯作者：
Tianyou Chai;Jinliang Ding;W. Hong;Chun-Yi Su
中科院分区：
文献类型：
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作者：
Tianyou Chai;Jinliang Ding;W. Hong;Chun-Yi Su
工业过程运行过程中，最优控制目标是将代表产品加工质量、效率和消耗的技术指标控制在目标范围内。然而，由于复杂工业过程的工艺指标在线测量困难，工艺与控制回路之间的动态特性具有强非线性、强耦合性，难以用精确模型描述等复杂性质，其动态随过程条件的变化而变化，这样的控制目标目前用现有的控制方法很难实现，因此只能采用手动控制的方式。但手动控制无法根据运行过程的情况及时、准确地调整设定点。因此，很难将各项技术指标控制在理想的范围内，甚至造成故障工况。本文提出了一种过程优化运行的混合智能控制方法，该方法通过根据运行情况在线调整控制环的设定点，使控制系统跟踪调整后的设定点，将工艺指标控制在期望的范围内。该方法由控制环预设模型、前馈和反馈补偿器、技术指标预测模型和故障工况诊断单元加容错控制器组成。通过应用实例说明该方法在某选矿厂22座竖炉的焙烧过程中的应用，应用结果证明了该方法的有效性。
During the operation of the industrial process, the optimal control objective is to control the technique indices that represent the quality, the efficiency, and the consumption of the product processing into its targeted ranges. However, due to the difficulty of measuring the technique indices on-line of the complex industrial process, the dynamics between the techniques and the control loops with complex natures, such as strong nonlinearity, heavy coupling and difficulty of description by the accurate model, and its dynamics varying with the process conditions, such a control objective by far is difficult to achieve by the existing control methods, thus theonly way of manual control is adopted. However, the manual control cannot adjust the setting point according to the conditions of the operation process timely and exactly. Therefore, it is difficult to control the technique indices into its desired ranges and even cause fault work-condition. In this paper, A hybrid intelligent control method for process optimal operation is proposed, which controls the technique indices into the desired ranges by on-line adjusting the set-points of the control loops according to the operation condition, enabling the control system to track the adjusted set-points. The proposed method is comprised of a control loop presetting model, feedforward and feedback compensators, a prediction model of the technique indices and a fault work-condition diagnosis unit plus a fault-tolerance controller. An application case study is given to illustrate the method being applied to a roasting process with 22 shaft furnaces in one ore concentration plant, and the application results have proven the effectiveness of the proposed method.