A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramat- ically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms （partial least squares （PIE）, recursive PIE （RPLS）, and kernel PIE （KPIE）） to form novel regression ones, QPLS, QRPIE and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

OpenMP-based parallel transient stability simulation for large-scale power systems

This paper provides an OpenMP-based parallel Very Dishonest Newton(VDHN) algorithm with variable step size,running transient stability simulations(TSA) on multi-core computers.Under the framework of simultaneous solution method of TSA,the step-size control strategy is used according to the local truncation error theory firstly.Then,computation of the generation units,which is the most time-consuming part of the simulation,is dynamically dispatched to several cores using an α dynamic scheduling scheme to obtain workload balancing based on OpenMP.Due to the convergence of Newton-type iterations,an adaptive Jacobian update control strategy is applied to reduce the sequential part of the simulation and the overhead generated by OpenMP.Several large scale test cases verify the validity and practicability of the proposed parallel algorithm,showing that the proposed approach achieves high speed-up and a considerable reduction in parallel overheads.

Experimental investigation of control updating period monitoring in industrial PLC-based fast MPC： Application to the constrained control of a cryogenic refrigerator

In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the validation is a cryogenic refrigerator that is used to cool the supra-conductors involved in particle accelerators or experimental nuclear reactors. Two recently predicted features are validated： the first states that it is sometimes better to use less efficient （per iteration） optimizer if the lack of efficiency is overmcompensated by an increase in the updating control frequency. The second is that for a given solver, it is worth monitoring the control updating period based on the on-line measured behavior of the cost function.