A dividing wall column (DWC), characterized by multivariable control, strong nonlinearity, and highly coupled systems, shows effective distillation capacity with a significant reduction in energy consumption and capital cost. Although multivariable control strategies for DWCs have attracted certain attention from both academia and industry, relatively little work has focused on data-driven multivariable controllers for such a complex system that is not easy to model. In this work, a novel different-factor full-form model-free adaptive controller (DF-FFMFAC) is first proposed for DWCs aiming to solve the problem of simultaneous control of the liquid level, column pressure, and temperature channels with quite different characteristics between them, which may be a challenging task for the prototype FFMFAC. Taking such complex dynamics into account, a parameter selection technique for the DF-FFMFAC based on neural networks is also developed, where gradient descent for the neural network is improved by the full-form dynamic linearization technique utilized in the DF-FFMFAC. Furthermore, the stability of the parameter tuning process is guaranteed by Lyapunov theory. The present work makes a noteworthy contribution to the multivariable control of DWCs in a purely online data-driven way without any offline training procedure and mathematical information. In terms of the separation of an ethanol–n-propanol–n-butanol DWC, the controller is cosimulated in MATLAB/SIMULINK and Aspen Plus Dynamics and tested against a series of feed flow rate and feed composition disturbances. As a result, the proposed method achieves encouraging control performance with smaller oscillations and faster responses compared with model predictive control and proportional–integral–derivative controllers, proving to be a promising data-driven method for the multivariable control of DWCs. Finally, the efficacy of the proposed scheme for the practical control of DWCs in the presence of measurement noise has also been demonstrated by adding white noise to the simulation.

中文翻译：

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使用不同因子全形式无模型自适应控制器的分隔墙柱的非线性多变量控制

具有多变量控制、强非线性和高度耦合系统的分隔壁塔 (DWC) 显示出有效的蒸馏能力，同时显着降低了能源消耗和资本成本。尽管 DWC 的多变量控制策略已经引起了学术界和工业界的一定关注，但对于这样一个不容易建模的复杂系统，数据驱动的多变量控制器的工作相对较少。在这项工作中，为了解决同时控制具有完全不同特性的液位、柱压和温度通道的问题，首次提出了一种用于 DWC 的新型不同因子全型无模型自适应控制器（DF-FFMFAC）。在它们之间，这对于原型 FFMFAC 来说可能是一项具有挑战性的任务。考虑到如此复杂的动态，还开发了一种基于神经网络的 DF-FFMFAC 参数选择技术，其中通过 DF-FFMFAC 中使用的全形式动态线性化技术改进了神经网络的梯度下降。此外，Lyapunov 理论保证了参数调整过程的稳定性。目前的工作以纯在线数据驱动的方式对 DWC 的多变量控制做出了值得注意的贡献，而无需任何离线训练程序和数学信息。在乙醇的分离方面—— Lyapunov 理论保证了参数整定过程的稳定性。目前的工作以纯在线数据驱动的方式对 DWC 的多变量控制做出了值得注意的贡献，而无需任何离线训练程序和数学信息。在乙醇的分离方面—— Lyapunov 理论保证了参数整定过程的稳定性。目前的工作以纯在线数据驱动的方式对 DWC 的多变量控制做出了值得注意的贡献，而无需任何离线训练程序和数学信息。在乙醇的分离方面——正丙醇-正丁醇 DWC，控制器在 MATLAB/SIMULINK 和 Aspen Plus Dynamics 中进行联合仿真，并针对一系列进料流速和进料组成干扰进行了测试。因此，与模型预测控制和比例-积分-微分控制器相比，所提出的方法以更小的振荡和更快的响应实现了令人鼓舞的控制性能，被证明是一种用于 DWC 多变量控制的有前途的数据驱动方法。最后，通过在模拟中添加白噪声也证明了所提出的方案在存在测量噪声的情况下实际控制 DWC 的有效性。