Optimal Design of a Fuzzy Logic Controller for Control of a Cement Mill Process by a Genetic Algorithm

Subbaraj, P.; Anand, P.S. Godwin

doi:10.1080/10739149.2010.545850

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…The proposed approach is compared with a knowledge-based fuzzy control system, offering better results in the handling of uncertainties, and also presenting less variation in the manipulated variables (MVs). Reference [5] proposes a fuzzy approach to control the process, based on operation rules of the grinding circuit. Reference [6] proposes an intelligence-based supervisory control for a grinding circuit, which combines an artificial neural networkbased soft-sensor, a fuzzy logic-based dynamic adjustor, and an expert-based overload diagnosis and adjustment module.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control of a Closed Grinding Circuit System in Cement Industry

Minchala

Zhang

Garza-Castañón

2018

IEEE Trans. Ind. Electron.

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This paper presents the development of a non-linear model predictive controller (NMPC) applied to a closed grinding circuit system in the cement industry. A Markov chain model is used to characterize the cement grinding circuit by modeling the ball mill and the centrifugal dust separator. The probability matrices of the Markovian model are obtained through a combination of comminution principles and experimental data obtained from the particle size distribution (PSD) of cement samples at specific stages of the system. The NMPC is designed as a supervisory controller in order to manage distributed controllers (DCs) installed in the process. Both the model and the controller are validated online through the implementation of the proposed approach in the supervisory control and data acquisition (SCADA) system of an industrial plant. The results show a significant improvement in the performance of the grinding circuit in comparison to the operation of the system without the proposed controller.

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Section: Introductionmentioning

confidence: 99%

Predictive Control of a Closed Grinding Circuit System in Cement Industry

Minchala

Zhang

Garza-Castañón

2018

IEEE Trans. Ind. Electron.

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“…Intelligent controllers based on fuzzy logic infusion with decentralised PID controllers were designed and tested in the real-time plant [14]. Several intelligent controllers designed based on fuzzy logic [13][14][15][16] for the cement ball mill grinding process were able to track the setpoint and reject the disturbance better than the classical controllers addressed in [5][6][7][8][9][10][11][12]. However, the techniques Motivated by this, an attempt is made to design a predictive controller using a state-space model of the cement grinding circuit to provide optimum productivity with a better quality under reduced energy consumption by operating the plant closer to actuator constraints even in the presence of variations in the grindability factor of the clinker as a disturbance.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Several intelligent controllers designed based on fuzzy logic [13][14][15][16] for the cement ball mill grinding process were able to track the setpoint and reject the disturbance better than the classical controllers addressed in [5][6][7][8][9][10][11][12]. However, the techniques discussed in [13][14][15] did not address the constraints and roughly handled the manipulation. A genetic algorithm-based fuzzy logic controller designed for the grinding process addresses the stabilisation of the plant [15].…”

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confidence: 99%

“…However, the techniques discussed in [13][14][15] did not address the constraints and roughly handled the manipulation. A genetic algorithm-based fuzzy logic controller designed for the grinding process addresses the stabilisation of the plant [15]. However, maintaining the product quality is also a prime factor to be considered while developing control algorithms which is not addressed in [16] also.…”

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confidence: 99%

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Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models

Venkatesh

Ramkumar

Amirtharajan

2020

Designs

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Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.

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