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 tra...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.展开更多
The key of production planning of refineries is to determine the production planning of units and blending schemes of blends in each period of the plan horizon,since they affect the effective utilization of components...The key of production planning of refineries is to determine the production planning of units and blending schemes of blends in each period of the plan horizon,since they affect the effective utilization of components of refineries and hence profits.The optimization is difficult,because of many complicated product production–consumption relationships in production processes,which are closely related to the running modes of the units.Additionally,the blending products,such as gasoline and diesel,may use multiple blending schemes for their production that increase the complexity of the problem.This paper models the production planning problem as a mixed integer nonlinear programming.Computational experiments for a refinery show the effectiveness of the model.The optimal results give the effective utilization of the self-produced components and increase of the profit.展开更多
文摘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.
基金Supported by the State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds(2013ZCX02)
文摘The key of production planning of refineries is to determine the production planning of units and blending schemes of blends in each period of the plan horizon,since they affect the effective utilization of components of refineries and hence profits.The optimization is difficult,because of many complicated product production–consumption relationships in production processes,which are closely related to the running modes of the units.Additionally,the blending products,such as gasoline and diesel,may use multiple blending schemes for their production that increase the complexity of the problem.This paper models the production planning problem as a mixed integer nonlinear programming.Computational experiments for a refinery show the effectiveness of the model.The optimal results give the effective utilization of the self-produced components and increase of the profit.
