This article studies the performance monitoring problem for potassium chloride flotation process, which is a critical component of fertilizer processing. To address its froth image segmentation problem, this proposes multiscale feature extraction and fusion network (MsFEFNet) to overcome weak edge characteristics images. MsFEFNet performs simultaneous at multiple scales automatically learns spatial information interest each scale achieve efficient fusion. In addition, process multistage...

In process industries, accurate prediction of critical quality variables is particularly important for control and optimization. Usually, soft sensors have been developed to estimate the via variables. However, there are often that far apart in topology but high correlations, presenting challenges such feature learning sensor. To solve these difficulties, a variable correlation analysis based convolutional neural network (VCA-CNN) proposed this paper topological extraction, which generates...

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties extracting multiscale local features multicoupled process data harnessing them to their full potential improve performance. Therefore, a attention-based CNN (MSACNN) is proposed this article alleviate such problems. In MSACNN, convolutional...

Localization is one of the most difficult and costly problems in mobile robotics. To avoid this problem, paper presents a new controller for trajectory tracking nonholonomic robots using visual feedback without direct position measurement. This works on basis novel adaptive algorithm estimating global robot online natural features measured by vision system its orientation velocity odometry Attitude Heading Reference System (IMU&Compass) sensors. The motion constraint fully taken into...

Due to the existence of complex disturbances and frequent switching operational conditions characteristics in real industrial processes, process data under different subject distributions, which means there exist manifold structures broad operations. Globally, entire are distributed a multimanifold structure. Nevertheless, existing data-driven quality prediction methods do not consider relationships among manifolds just treats as single manifold. How extract effective structure feature...

Soft sensors have been extensively developed to estimate the difficult-to-measure quality variables for real-time process monitoring and control. Process nonlinearities dynamics are two main challenges accurate soft sensor modeling. To cope with these problems, temporal convolutional network (TCN)-based models in this paper. With a hierarchy of convolution kernels large receptive fields, TCN is able describe long dynamic dependence variables. Thus, model first designed nonlinear sensing....

Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor faults, and transmission failures. The incomplete obstruct the effective use degrade performance data-driven models. Numerous imputation algorithms have been proposed to deal with missing values, primarily based on supervised learning, that is, imputing by constructing a prediction model remaining complete data. They limited when amount is overwhelming. Moreover, many methods not considered...

In complex process industries, multivariate time sequences are omnipresent, whose nonlinearities and dynamics present two major challenges for soft sensing of important quality variables. Consequently, due to the potent representational capabilities, nonlinear dynamic models like gated recurrent unit (GRU) long short-term memory (LSTM) networks have been used data sequence modeling. Though it is a common occurrence in many industrial plants, series with heterogeneous sample intervals missing...

In industrial processes, frequent communication failures and information corruption may result in the loss of entire blocks process data, which is also known as blackout missing data. The imperfect data time series impede performance subsequent modeling control tasks. However, traditional matrix factorization or supervised learning imputation methods are hardly applicable to challenging task recovering difficulty imputing stems from two major factors: lacks reference co- evolutionary...

Batch process quality prediction is an important application in manufacturing and chemical industries. The complexity of batch processes characterized by multiphase, nonlinearity, dynamics, uneven durations so that modeling these rather difficult. Moreover, there are other challenges the face prediction. Specifically, trajectories over whole running duration potentially make specific contributions to final targets issue embraces tremendously high-dimensional inputs but very low-dimensional...

In modern industrial processes, dynamics and nonlinearities are two main difficulties for soft sensing of key quality variables. Thus, nonlinear dynamic models like long short-term memory (LSTM) network have been applied data sequence modeling due to its powerful representation ability. Nevertheless, most methods cannot deal with series irregular sampling intervals, which is a common phenomenon in many plants. To handle this problem, novel sampling-interval-aware LSTM (SIA-LSTM) proposed...

In industrial processes, long short-term memory (LSTM) is usually used for temporal dynamic modeling of soft sensor. The process data have various correlations under different time scales due to the continuous physical and chemical reactions. However, LSTM model can only extract features at a specific scale, which affects feature learning capability accuracy. this article, new hierarchical sequential generative network (HSGN) proposed mining multiscale using large amount unlabeled To quality...

Real-time prediction of key quality variables based on data-driven soft sensor modeling is an important way to monitor flotation status and product in the froth process. However, existing methods have limitations terms nonlinear feature extraction interpretability. In addition, prevalent correlations between process can help improve model interpretability but there are still challenges exploring potential due complexity industrial mechanism presence high noise data. These relationships be...

Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires high-quality model describing the corresponding nonlinear dynamic behavior. The proposed is constructed using deep neural networks (DNNs) to represent state transition observation generation, both of which constitute stochastic state-space model. A new bidirectional recurrent network (RNN), creating connection hidden layer between forward RNN backward RNN,...