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首页> 外文会议>Control systems conference on the development and application of process control and management technology in the pulp and paper industry, information tools to match the evolving operator role >CONSTRAINED MODEL PREDICTIVE CONTROL OF A PAPER MACHINE SAVEALL
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CONSTRAINED MODEL PREDICTIVE CONTROL OF A PAPER MACHINE SAVEALL

机译：造纸机Saveall的受限模型预测控制

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摘要

Efficient operation of a paper machine disk saveall requires that the saveall vat level be kept constant by manipulating the rotational speed of the disk, the flow rate of "cloudy" water to the vat, or both. On some savealls, the disk speed must be limited, otherwise mat formation deteriorates. To stabilize the stock composition in the headbox, it is desirable to constrain cloudy flow changes since sweetener addition is made in proportion to the cloudy flow. Thus, it is difficult to satisfy all of the control objectives (close control of the vat level, protection against disk speed saturation, low cloudy flow manipulation, etc.) simultaneously. To address this problem, a multivariable, constrained, model predictive controller (MPC) was applied. A notable feature of the implementation is that the standard MPC performance index was simplified to include only the quadratic errors at a single future point, and the current control increments, rather than summing over horizons. This simplified the calculations significantly so that the algorithm could be implemented on a multifunction controller with limited capabilities. Mill application results indicated that MPC showed some improvement over the existing control scheme, and was simple to implement and tune. Furthermore, MPC proved to be robust to process-model mismatch and coped well during upset conditions.
机译：造纸机磁盘的高效操作要求通过操纵盘的旋转速度，为增值税的“阴天”水的流速，或两者之间的流速保持恒定。在一些SaveAll上，磁盘速度必须受到限制，否则垫片形成劣化。为了稳定头部框中的储备组成，希望限制多云的流动变化，因为甜味剂添加与多云流量成比例。因此，难以满足所有控制目标（密切控制VAT水平，同时保护磁盘速度饱和，低云流量操作等）。为了解决这个问题，应用了多变量，约束的模型预测控制器（MPC）。实施方式的值得注意的特征是简化了标准MPC性能索引，仅包括单个未来点的二次误差，以及当前的控制增量，而不是通过地平线求和。这简化了计算，使得算法可以在具有有限功能的多功能控制器上实现。 MILL申请结果表明，MPC对现有的控制方案进行了一些改进，易于实施和调整。此外，MPC被证明是在不安条件下处理模型不匹配和应对良好的稳健性。

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