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Modelling and control of crystallization process

机译:结晶过程的建模和控制

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Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinear nature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why an optimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation has constraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller like Generic Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error, thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Model predictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints are fed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method. Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settling time and percentage overshoot.
机译:批式结晶器主要用于化学工业,如制药,食品工业和特种化学品。当目标是获得均匀的晶体尺寸分布(CSD)时,批处理过程的非线性性质会带来困难。在这项研究中,使用经典的控制器调整方法设计了线性PI控制器,以通过控制入口夹套温度来控制结晶器出口温度。但是,反应并不令人满意。简单的PID控制器不能保证令人满意的响应,这就是为什么设计最佳控制器以将浓度和温度保持在适合我们需求的范围内的原因。任何典型的过程操作都对状态,输入和输出有约束。因此,需要运行一个满足约束条件的非线性过程。因此,实现了类似于PI控制器的非线性控制器,如通用模型控制器(GMC)。它最小化平方误差的导数,从而改善了过程的输出响应。晶体尺寸变化的最小化被认为是这项研究的目标函数。还设计了模型预测控制,该模型预测控制使用高级优化算法来最小化误差,同时使过程线性化。将约束输入到MATLAB中的MPC工具箱中,并使用Sridhar和Cooper方法调整预测,控制范围和性能权重。比较了所有三个控制器(PID,GMC和MPC)的性能,发现在建立时间和超调百分比方面,MPC是最出色的一种。

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