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The dynamic control of the heat exchanger network is important for developing energy‐efficient and safe industrial processes. In such a system, the control is achieved through the bypass stream around the heat exchanger. This work aims to track the setpoint temperature of the mixed stream by manipulating the bypass fraction of the cold stream around the heat exchanger. The implemented control is in a non‐linear model predictive control (NMPC) framework. The first‐principles model of a shell and a tube heat exchanger is used. The orthogonal collocation technique is used to discretize the first‐principles model into the system of algebraic equations. In this work, uncertainty is also considered in the inlet temperature of the hot stream. The uncertain optimal control problem is dealt with by using a scenario tree‐based approximation along with the affine policy‐based method. The results show that, under different scenarios of uncertainty, the controlled variable efficiently tracks the setpoint. In comparison, considering the same scenarios of uncertainty used, the deterministic optimization approach shows significant deviation in the controlled variable from the setpoint as time passes.

In this paper, the problem of input design for identification of continuous time output error models is considered. The input design problem is formulated as maximization of a measure of the Fisher information matrix, which defines the accuracy with which the system parameters can be estimated. The optimization problem involving the Fisher information matrix depends on the true system parameters, which are unknown. Existing methods use an iterative approach to tackle this circular dependency. In this paper, the system transfer function is represented using generalized orthonormal basis functions which makes the Fisher Information matrix independent of the true system parameters. Therefore, the input design problem reduces to solving a single optimization problem instead of a series of optimizations that are performed in existing methods. Further, in existing approaches, the computational complexity of the optimization grows with the length of the input vector to be identified. In the current paper, the input is also represented using smooth basis functions, thereby making the complexity independent of the length of the input vector. A least-squares approach for parameter estimation is given and it is shown that the estimates are unbiased for the optimal inputs. The accuracy of the proposed method is demonstrated with the help of two examples. Further, input design for a continuous stirred tank reactor model and a quadruple tank experimental set up is considered to demonstrate the practical applicability of the new method.

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