This paper reports on the application of dynamic time warping (DTW) to evaluate controller performance of batch processes. Batch processes often are characterized by unequal durations due to changing batch-to-batch op...
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This paper reports on the application of dynamic time warping (DTW) to evaluate controller performance of batch processes. Batch processes often are characterized by unequal durations due to changing batch-to-batch operational conditions. To compare conveniently the performance different batch runs and different controllers, one requires equal batch duration. Therefore, batch data lengths are synchronized by compressing and expanding batch trajectories of various lengths in order to provide a unified controller performance index. Synchronization can be achieved by using DTW algorithm. The DTW algorithm is nonlinear and flexible pattern matching technique. The applicability of the proposed method for controller performance assessment of batch processes is illustrated through an industrial example.
The aim of this paper is to study the determination of the stability regions for continuous-time systems subject to actuator saturation. Using an affine saturation-dependent Lyapunov function, a new method is proposed...
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The aim of this paper is to study the determination of the stability regions for continuous-time systems subject to actuator saturation. Using an affine saturation-dependent Lyapunov function, a new method is proposed to obtain the estimation of the domain of attraction of the closed-loop system. A family of linear matrix inequalities （LMIs） that provides sufficient conditions for the existence of this type of Lyapunov function are presented. The results obtained in this paper can reduce the conservativeness compared with the existing ones. Numerical examples are given to illustrate the effectiveness of the proposed results.
This paper presents a novel approach for refinery crude oil operations under uncertainty. Due to the flexibility of the crude oil scheduling, decisions made by deterministic optimizations are often conservative or lac...
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An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong ...
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An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.
In this article,an approach for economic performance assessment of model predictive control(MPC) system is *** method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG)...
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In this article,an approach for economic performance assessment of model predictive control(MPC) system is *** method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG) benchmark other than conventional minimum variance control(MVC) to estimate the potential of reduction in *** LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance,and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability *** the LQG benchmark directly with benefit potential of MPC control system,both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization *** proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.
To find global frequent itemsets in a multiple, continuous, rapid and time-varying data stream, a fast, incremental, real-time, and little-memory-cost algorithm should be used. Based on the max-frequency window model,...
Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance con...
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Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.
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