A comprehensive review on Advanced Process Control of cement kiln process with the focus on MPC tuning strategies

Ramasamy, Valarmathi; Ramkumar, K.; Muralidharan, G.; Sidharthan, Rakesh Kumar; Veerasamy, Gomathi; Venkatesh, S.; Amirtharajan, Rengarajan

doi:10.1016/j.jprocont.2022.12.002

Cited by 20 publications

(7 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rahman et al in [29] sketched out a generic framework for using learning-based technologies to solve problems in complex industrial processes. Ramasamy et al focused on the strategies of tuning predictive controls model and conducted a comprehensive survey covering both analytical methods such as ordinary differential equations and learning-based methods such as neural networks [30].…”

Section: Related Workmentioning

confidence: 99%

“…Here, we particularly focus on the applications of optimization techniques in the concrete and cement industries and present an up-to-date survey about the adoptions of machine learning (ML) and other advanced control methods in various stages and processes of the concrete and cement productions, spanning from energy efficiency [13,31], concrete porosity [12], free lime content [32][33][34], ball milling [6,7], to advanced process control and optimization [30,35]. Lin et al back in 2006 [36] already adopted data-driven approaches to develop soft sensors for systematically monitoring the cement quality.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

Lyu,

Chu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Recent advances in artificial intelligence (AI) technologies such as deep learning open up new opportunities for various industries, such as cement manufacturing, to transition from traditional human-aided manually controlled production processes to the modern era of “intelligentization”. More and more practitioners have started to apply machine learning methods and deploy practical applications throughout the production process to automate manufacturing activities and optimize product quality. In this work, we employ machine learning methods to perform effective quality control for cement production through monitoring and predicting the density of free calcium oxide (f-CaO) in cement clinker. Based upon the control data measured and collected within the distributed control system (DCS) of cement production plants and the laboratory measurements of the density of free lime in cement clinker, we are able to train effective models to stabilize the cement production process and optimize the quality of cement clinker. We report the details of the methods used and illustrate the superiority and benefits of the adopted machine learning-based approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

Lyu,

Chu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Then, a line search is used to calculate the best distance to go in the direction of the current search, as given in equation (12).…”

Section: = -( )mentioning

confidence: 99%

“…Adaptive controllers, on the other hand, can struggle to maintain stability and performance when the process dynamics change significantly. To address these limitations, there has been a growing interest in developing more advanced control strategies, such as deep learning controllers [7,11,12]. Deep learning controllers are based on artificial neural networks that can learn to model the process dynamics directly from data, without requiring a priori knowledge of the system [13,14].…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of various training algorithms of deep learning based controller

2023

View full text Add to dashboard Cite

Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10−14 at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.

show abstract

“…However, the control performance of MPC-type control algorithms can be susceptible to inaccuracies in the mathematical model, external disturbances, and controller parameters such as the prediction domain, control domain, control weights, etc. [29]. To enhance the control performance of MPC/NMPC, various methods have been employed recently, encompassing aspects such as model accuracy, parameter optimization, supplementation with other control methods, and more, across a range of system applications.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Nonlinear Model Predictive Motion Control of a Heavy-duty Bionic Caterpillar-like Robot

Li,

Lu,

Cheng

et al. 2024

J Bionic Eng

View full text Add to dashboard Cite

This paper investigates the motion control of the heavy-duty Bionic Caterpillar-like Robot (BCR) for the maintenance of the China Fusion Engineering Test Reactor (CFETR). Initially, a comprehensive nonlinear mathematical model for the BCR system is formulated using a physics-based approach. The nonlinear components of the model are compensated through nonlinear feedback linearization. Subsequently, a fuzzy-based regulator is employed to enhance the receding horizon optimization process for achieving optimal results. A Deep Neural Network (DNN) is trained to address disturbances. Consequently, a novel hybrid controller incorporating Nonlinear Model Predictive Control (NMPC), the Fuzzy Regulator (FR), and Deep Neural Network Feedforward (DNNF), named NMPC-FRDNNF is developed. Finally, the efficacy of the control system is validated through simulations and experiments. The results indicate that the Root Mean Square Error (RMSE) of the controller with FR and DNNF decreases by 33.2 and 48.9%, respectively, compared to the controller without these enhancements. This research provides a theoretical foundation and practical insights for ensuring the future highly stable, safe, and efficient maintenance of blankets.

show abstract

A comprehensive review on Advanced Process Control of cement kiln process with the focus on MPC tuning strategies

Cited by 20 publications

References 75 publications

Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

Performance analysis of various training algorithms of deep learning based controller

Hybrid Nonlinear Model Predictive Motion Control of a Heavy-duty Bionic Caterpillar-like Robot

Contact Info

Product

Resources

About