Search

In batch processes, existing soft sensing methodologies encounter substantial challenges when confronted with nonlinearity and multi‐phase issues. In response to these challenges, an innovative soft sensing framework known as the multi‐phase stacking ensemble model with self‐selected primary learner is proposed. The main innovation of this framework lies in the solution to the primary learner selection issue within the stacking model. To commence, the batch process is divided into multiple phases employing a Gaussian mixture model, thereby establishing local ensemble models for each phase. Subsequently, the iterative self‐selection of primary learners strategy is proposed, which iteratively selects suitable primary learners for these models, optimizing their combination of primary learners for each local model. This primary learner selection strategy effectively enhances the accuracy of predictions in the stacking ensemble model. To further enhance the performance, Bayesian optimization is utilized to tune the hyperparameters of each local ensemble model. This step guarantees optimal performance of the model across diverse phases. Extensive simulation experiments are conducted on an industrial penicillin fermentation process to validate the effectiveness of the proposed framework. According to the findings, the model demonstrated superior performance compared to existing single‐learner soft sensing methods and commonly utilized ensemble‐based soft sensing methods in terms of both R2 score (0.97584) and RMSE (0.0513). Overall, this framework offers a novel approach for selecting primary learners in stacking ensemble models and enhancing the predictive performance in batch processes for soft sensing. [ABSTRACT FROM AUTHOR]

Abstract: Screen printed Ba0.6Sr0.4TiO3 (BST6/4) thick films were fabricated by reactive sintering at a low temperature below 900°C. The dielectric properties in radio frequency range were measured on samples of sandwich structure MIM capacitors by impedance analyzer, while that in microwave frequency range were measured on samples of thick films without top and bottom electrodes by split-post dielectric resonator method. The thick films exhibited a low permittivity, while at the same time, maintained a high tunability. The permittivity and dielectric loss at 1MHz were 228.8 and 0.007, respectively. The corresponding values measured at 9.9GHz were 82.24 and 0.109, respectively. The tunability was as high as 72.4% (150kV/cm, 10kHz). This method provides a simple and effective route to obtain thick films with great potential in applications in Low Temperature Co-fired Ceramic (LTCC) and microwave tunable devices. [Copyright &y& Elsevier]