Fast property prediction in an industrial rubber mixing process with local ELM model

Abstract

Online property prediction in industrial rubber mixing processes is not an easy task. An efficient data-driven prediction model is developed in this work. The regularized extreme learning machine (RELM) is utilized as the fundamental soft sensor model. To better capture distinguished characteristics in multiple recipes and operating modes, a just-in-time RELM modeling method is developed. The number of hidden neurons and the value of regularization parameter of the just-in-time RELM model can be efficiently selected using a fast leave-one-out strategy. Consequently, without the time-consuming laboratory analysis process, the Mooney viscosity can be online predicted once a mixing batch has been discharged. The industrial Mooney viscosity prediction results show its better prediction performance in comparison with traditional approaches.

Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method

“…Machine learning is not only proven to be a powerful tool for learning the material properties of the experimental data as well as to predict the unlearned data (Mueller et al, 2016;Zhang and Friedrich, 2003), but is also widely used in the study of the MR materials (Imaduddin et al, 2017;Wang and Liao, 2005). The methods can be selected from the existing studies, such as backpropagation artificial neural network (BP-ANN) (Shahriar and Nehdi, 2011;Vani et al, 2015), support vector regression (SVR) (Liu and Chen, 2013;Liu et al, 2014), extreme learning machine (ELM) (Jin et al, 2017;Zheng et al, 2017), and deep learning (DL) (Liu et al, 2017(Liu et al, , 2018. ELM (Jin et al, 2017;Zheng et al, 2017) is known for its shorter training time as well as its better accuracy (Li et al, 2016;Zheng et al, 2018) and generalization levels than the conventional SVR and BP-ANN methods (Huang et al, 2011(Huang et al, , 2015.…”

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

“…Current data-driven soft sensors for the Mooney viscosity information are generally divided into two categories, supervised and semi-supervised, according to the training datasets being labeled or semi-labeled. Most of the existing Mooney viscosity soft sensors belong to the first category, such as shallow neural networks (NNs) [10,11], partial least squares (PLS) [12,13], Gaussian process regression (GPR) [12][13][14][15], and extreme learning machine (ELM) [16]. Generally, they learn a labeled dataset S l = X l , Y with N pairs of input and output samples, denoted as…”

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

“…In most rubber and tire factories, however, the Mooney viscosity can only be determined through manual analysis, which often takes 4∼6 h after a batch has been discharged, while the duration of a batch run of mixing process is only about 2∼5 min. erefore, in recent years, soft sensor methods have been widely applied to provide real-time estimations of the Mooney viscosity to obtain the optimal and uniform rubber product quality [3][4][5][6][7][8][9][10][11][12].…”

“…Generally, there are two categories of local learning methods: ensemble methods [24][25][26][27] and just-in-time learning (JIT) methods [6,7,9,12]. ese methods employ the divide-and-conquer philosophy to model the relationships between the inputs and output by building a set of locally valid models.…”

“…In particular, JIT learning, known as a representative local learning paradigm, has gained growing attention in soft sensor applications for Mooney viscosity estimation due to its strong capability of handling nonlinearity, time-varying behavior, multiphase and multimode process characteristics, etc. [9,11,12]. However, traditional JIT soft sensors attempt to build a globally optimal encapsulation of local modeling techniques, similarity measures, input variables, model hyper-parameters, etc., while the diversity of JIT learning is ignored.…”