Fuzzy-aided neural network for real-time state estimation and process prediction in the alcohol formation step of production-scale beer brewing

1993 ◽  
Vol 27 (2) ◽  
pp. 203-215 ◽  
Author(s):  
R. Simutis ◽  
I. Havlik ◽  
A. Lübbert
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Real Time ◽  
Production Scale ◽  
Beer Brewing ◽  
Process Prediction ◽  
Alcohol Formation ◽  
Formation Step

Multi-Parameter Prediction for Steam Turbine Based on Real-Time Data Using Deep Learning Approaches

2021 ◽  
Author(s):  
Lei Sun ◽  
Tianyuan Liu ◽  
Yonghui Xie ◽  
Xinlei Xia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Plant ◽  
Steam Turbine ◽  
Real Time ◽  
Learning Approaches ◽  
Time Data ◽  
Time Parameters ◽  
Process Prediction ◽  
Parameter Prediction

Abstract Accurate and real-time parameters forecasting is of great importance to the turbine control and predictive maintenance which can help the improvement of power system. In this study, deep-learning models including recurrent neural network (RNN) and convolutional neural network (CNN) for multi-parameter prediction are proposed, and are applied to predict real-time parameters of steam turbine based on data from a power plant. Firstly, the prediction results of RNN and CNN models are compared by the overall performance. The two models show good performance on forecasting of six state parameters while RNN performs better. Moreover, the detailed performance on a certain day show that the relative error of two models are both less than 2%. Finally, the influence of model designs including loss function, training size and input time-steps on the performance of RNN model are also explored. The effects of the above parameters on the prediction performance, training and prediction time of the models are studied. The results can provide a reference for model deployment in the power plant. It is convinced that the proposed method has a high potential for dynamic process prediction in actual industrial scenarios through the above research.

scholarly journals State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1124
Author(s):  
Wasiq Ali ◽  
Yaan Li ◽  
Muhammad Asif Zahoor Raja ◽  
Wasim Ullah Khan ◽  
Yigang He
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Interacting Multiple Model ◽  
Bayesian Filtering ◽  
Multiple Model ◽  
Neural Computing ◽  
Narx Neural Network

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

scholarly journals Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 550
Author(s):  
Wasiq Ali ◽  
Wasim Ullah Khan ◽  
Muhammad Asif Zahoor Raja ◽  
Yigang He ◽  
Yaan Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Nonlinear Filtering ◽  
Actual State ◽  
Filtering Algorithms ◽  
Feedback Neural Network ◽  
Nonlinear Autoregressive

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.

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