Twodimensional recursive least squares identification based on local polynomial modeling for batch processes Abstract   In this paper, a twodimensional recursive least squares identification method based on local polynomial modeling for batch processes is proposed. Using local polynomial modeling method to parameterize the timevarying characteristics of batch processes, a twodimensional cost function along both time and batch directions is minimized to design the recursive least squares identification algorithm. Because this proposed method employs local polynomial modeling and utilizes twodimensional data information to estimate model parameters, it can effectively improve the estimation accuracy and accelerate the convergence rate. Furthermore, the convergence property of the proposed method is analyzed. Finally, the simulation results show the superiority of the proposed method.

Datadriven modeling of semibatch manufacturing: a rubber compounding test case The continuously growing amount of available data from manufacturing processes supports the development of datadriven models. The typical target application of these models is optimal control and continuous quality management within an objective of zerodefect manufacturing. However, data obtained from batch processes are characterized by its high dimensionality that exceeds the computational capabilities of online applications and datadriven modelu0027s reliability must be guaranteed for proper industrial implementation. We explore two approaches to reduce problemu0027s size: feature extraction and feature selection; several multivariate regression methods are also compared regarding it precision and robustness. We base our analysis on an industrial rubber compounding process where natural rubber is blended in a semibatch mixer with several additives, then it is further mixed up using cylinders and it is conditioned in bands for storing. For this process, real production data is collected and stored in the manufacturing execution system of the company. The objective of the analysis is to predict mechanical properties of the rubber at the end of the processes. Based on the provided data, several datadriven models are built and tested. From the comparison among them it is concluded: models based on feature extraction and artificial neural networks yield the highest accuracy, while featureselected models provide better physical interpretability and increased robustness regarding industrial deployment.