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In this work a computational framework is proposed for the synthesis of flexible and controllable Heat Exchanger Networks. The synthesis is projected to operate over a specified range of expected variations in the inlet temperatures and flowrates of the process streams using a decentralized control system, such that the Total Annual Cost involving the utility consumption and the investment are optimized simultaneously. The framework is based on a two-stage strategy. A design stage is performed prior to the operability analysis where the design variables are chosen. In the second stage, the control variables are adjusted during operation on the realizations of the uncertain parameters. The framework yields a HEN design, which is guaranteed to operate with the designed control system under varying conditions ensuring stream temperature targets and optimal energy integration. The application of the proposed framework and its computational efficiency are illustrated with some numerical examples.

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The model quality for a model predictive control (MPC) is critical for the control loop performance. Thus, assessing the effect of model–plant mismatch (MPM) is fundamental for performance assessment and monitoring the MPC. This paper proposes a method for evaluating model quality based on the investigation of closed-loop data and the nominal output sensitivity function, which facilitates the assessment procedure for the actual closed-loop performances. The effectiveness of the proposed method is illustrated by a multivariable case study, considering linear and nonlinear plants.

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Stiction is a well-known villain in industry because of the limit-cycle imposed on the controller. Several methodologies are reported in the literature to automatically detect this problem using only normal operating data. However, this becomes more difficult when the loop with stiction is affected by disturbances or the sticky valve is inside a cascade loop. This study proposes two methods to automatically diagnose valve stiction when the reference signal is variable and centers primarily on recognizing triangular or sinusoidal patterns. The first method is based on the slope of the signal peaks and the second on data segmentation. These techniques were compared to a curve-fitting method, providing similar results when the reference is fixed. However, for processes significantly affected by disturbances or when the sticky valve was inside a cascade loop, stiction detection was better for both methods proposed. These results are corroborated by simulation and industrial data.

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