Nonlinear model predictive control for improved water recovery and throughput stability for tailings reprocessing

Burchell, J.J.; Roux, J.D. le; Craig, I.K.

doi:10.1016/j.conengprac.2022.105385

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…Combing eqs. (5)(6)(7)(8), the general state space model of asymmetric hydraulic cylinder is obtained.…”

Section: Du Of Bcr Nonlinear Model Establishmentmentioning

confidence: 99%

“…Physics-based methods rely on the fundamental principles and equations of the hydraulic system, offering a profound understanding of the underlying physics and establishing clear relationships among various components. These approaches are flexible and easily adaptable, allowing for modifications to accommodate changes in system parameters, component characteristics, or operating conditions [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Lu,

Cheng

et al. 2024

J Bionic Eng

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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.

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“…Combing eqs. (5)(6)(7)(8), the general state space model of asymmetric hydraulic cylinder is obtained.…”

Section: Du Of Bcr Nonlinear Model Establishmentmentioning

confidence: 99%

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

show abstract

“…This paper extends on the work in Wiid et al to include a model of the natural gas wells feeding the pipeline. The dynamic model is validated against online industrial plant data. ,− The novelty in this study is the implementation of SEM to a large number of interconnected pipelines and the subsequent development of suitable boundary conditions for gas wells, choke valves, and consumers with the aim of obtaining an overall numerically stable gas network model.…”

Section: Introductionmentioning

confidence: 99%

Natural Gas Well Production Network State-Space Model Development and Validation for Process Control

Dzedzemane,

Wiid,

le Roux

et al. 2024

Ind. Eng. Chem. Res.

Self Cite

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A transient state-space nonlinear model is developed for a natural gas system fed from multiple gas wellheads. The dynamic models are developed by making use of the spectral element method for pipeline spatial discretization. Wellhead models are integrated into the pipeline models by making use of suitable boundary conditions based on the characteristic compatibility method. The models are validated against a large scale natural gas well production network. The validation shows that the model has a good prediction performance based on a low normalized root-mean-square error of at most 5.08% and a high Pearson correlation coefficient with measured plant data of at least 0.94. The good prediction response of the developed transient models makes them suitable for use in model-based optimal control of natural gas well production networks. The resulting dynamic model can be easily adapted to a gas network of any configuration due to its modular form.

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“…System identification of real-world processes is imperative for developing effective process control strategies and precise fault detection and diagnosis methods. A benefit of such identification is enhanced plant stability and performance, which can be achieved by integrating the model with advanced model-based process control, outperforming traditional control methods [7], [35], [40], [43].…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

Cedeño,

Coronel,

Orellana

et al. 2024

IEEE Access

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In this paper, we have proposed a new paradigm for modeling of SAG mills. Typically, important parameters found in the modeling of such processes are described as state-space system model rather than unknown parameters. Here, we propose to estimate the system model using the maximum likelihood approach. Additionally, we propose using a new measurement that has not been considered in other modeling approaches. The benefits of our proposal are illustrated via numerical simulations. The results demonstrate that incorporating this new measurement within the framework of maximum likelihood estimation improves the accuracy of estimating the unknown parameters.

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Nonlinear model predictive control for improved water recovery and throughput stability for tailings reprocessing

Cited by 7 publications

References 33 publications

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

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

Natural Gas Well Production Network State-Space Model Development and Validation for Process Control

Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

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