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Modeling, identification and predictive control of pH processes.

机译：pH过程的建模，识别和预测控制。

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This thesis presents in a complete and detailed manner the modeling, simulation, identification and control of pH processes. The model is strictly based on the physical balance equations of mass and charge, and it is proven that such a model can be reduced to a minimal realization based on monoprotic equivalent substances, which makes the influent states observable. Identification is done in two different ways: identification of the concentrations using the Nonnegative Least Squares (NNLS) method when the influent components are known, and identification of the concentrations and dissociation constants using the Constrained Nonlinear Optimization algorithm in cascade with the NNLS when there is insufficient information about the influent stream. If the influent does not change or changes occur slowly, an Inline arrangement is proposed as a way of reducing cost. When influent changes are important a small CSTR is proposed as the best way to achieve reliable control. These identification approaches are implemented on-line, so that the model can be quickly updated to a new model resulting from changes in the number or concentration of influent components. Identification provides the inverse of the static nonlinear pH mapping, such a model is used in the feedback path to linearize the system, and a predictive control is used to regulate pH in the resulting nonlinear dynamic system. Parameters in the controller allow the adjustment of the receding horizon, the maximum actuator variation per sample, etc. Control and Identification were implemented using BridgeVIEW 2.0 and MATLAB in two Pentium II computers, experiments were done using a modified version of a LabVolt Analytic Station.
机译：本文以完整，详细的方式介绍了 pH 过程的建模，仿真，识别和控制。该模型严格基于质量和电荷的物理平衡方程，并且已证明可以将这种模型简化为基于单质子等效物质的最小实现，从而可以观察到进水状态。识别有两种不同的方法：当已知进水成分时，使用非负最小二乘（NNLS）方法对浓度进行识别；当存在进水成分时，使用约束非线性优化算法与NNLS进行级联来识别浓度和解离常数。有关入流的信息不足。如果进水口不变或变化缓慢，建议采用内联装置以降低成本。当进水变化很重要时，建议使用小型CSTR作为实现可靠控制的最佳方法。这些识别方法是在线实施的，因此可以根据进水组分的数量或浓度的变化将模型快速更新为新模型。识别提供了静态非线性 pH 映射的逆函数，这种模型用于反馈路径以线性化系统，而预测控制则用于调节系统中的 pH 。由此产生的非线性动力系统。控制器中的参数允许调整后退范围，每个样本的最大执行器变化等。控制和识别是在两台Pentium II计算机上使用BridgeVIEW 2.0和MATLAB进行的，实验是使用LabVolt分析站的改进版进行的。

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