Performance evaluation of optimal PI controller for ALSTOM gasifier during coal quality variations

Kotteeswaran, R.; Sivakumar, L.

doi:10.1016/j.jprocont.2013.10.006

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…The treatment was carried out as per full factorial design as given in Table 1. Five aqueous solutions of varying concentration (2, 4, 6, 8, and 10%) were prepared, and the fibers were soaked in the prepared solution for various period of time (12,24,36,48, and 60 h). After each interval of time, the fibers were taken out from the solution and rinsed with dilute hydrochloric acid to remove any excess potassium hydroxide sticking over the fiber surface.…”

Section: Fiber Treatmentmentioning

confidence: 99%

Regression modeling and particle swarm optimization of mechanical properties of potassium hydroxide pretreated Dharbai fiber-reinforced polyester composites

Sundaram

Jayabal

2014

Proceedings of the Institution of Mechanical Engineers, Part L:

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This paper aims at improving the mechanical properties of Dharbai fiber-reinforced polyester composites through surface modification of Dharbai fibers using potassium hydroxide alkali treatment. The fibers were subjected to alkali treatment as per full factorial design, and the composites were fabricated by reinforcing the treated fibers in polyester matrix. The response surface methodology was employed to study the effect of treatment parameters namely solution concentration (%) and soaking time (h) on the mechanical properties of Dharbai fiber-reinforced composites. The regression model was developed for correlating the interactions between solution concentrations and soaking time on the tensile, flexural, and impact strength of the composites. The results obtained confirmed that both solution concentration (%) and soaking time (h) play critical role in influencing the mechanical properties of Dharbai fiberreinforced composites. The better treatment conditions for optimum mechanical properties were determined using heuristic optimization method called particle swarm optimization. The optimized value obtained using particle swarm optimization technique was validated using confirmation test, and the results were found to be significant.

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Section: Fiber Treatmentmentioning

confidence: 99%

Regression modeling and particle swarm optimization of mechanical properties of potassium hydroxide pretreated Dharbai fiber-reinforced polyester composites

Sundaram

Jayabal

2014

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…It is seen that MOPSO based baseline PI controller provides better response for wide range of coal quality variations as compared to the existing methods [11,17,[23][24][25]28]. T gas ↓ WStm ↓ Table 8: Comparison of allowed coal quality variation (%).…”

Section: Coal Variation (Model Error)mentioning

confidence: 99%

“…Design and implementation of advanced control schemes are also reported in the literature [14][15][16][17][18][19][20][21][22][23]. Soft computing techniques such as Bat algorithm, Cuckoo search, Nondominated Sorting Genetic algorithm II, Multiobjective Genetic algorithm, and Normalized Normal Constraint algorithm are also found in the literature [6,7], [22][23][24][25][26][27][28], which deals with tuning of baseline PI controller. Cuckoo search algorithm [24] and Bat algorithm [25] are used to retune a portion of baseline PI controller (pressure loop PI controller) since the baseline PI controller did not satisfy PGAS constraints at 0% load for sinusoidal pressure disturbance.…”

Section: Introductionmentioning

confidence: 99%

“…Improved results are obtained for pressure disturbance test and coal quality variation test when compared to baseline PI controller. Further, in order to improve the overall response during pressure disturbance and coal quality variations, all four loops are retuned using multiobjective optimization techniques [22,27,28]. The performance of the gasifier is improved during pressure disturbance test and coal quality variation test.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Tuning of Decentralized PI Controller of Nonlinear Multivariable Process Using Archival Based Multiobjective Particle Swarm Optimization

Kotteeswaran

Sivakumar²

2014

Modelling and Simulation in Engineering

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A Multiobjective Particle Swarm Optimization (MOPSO) algorithm is proposed to fine-tune the baseline PI controller parameters of Alstom gasifier. The existing baseline PI controller is not able to meet the performance requirements of Alstom gasifier for sinusoidal pressure disturbance at 0% load. This is considered the major drawback of controller design. A best optimal solution for Alstom gasifier is obtained from a set of nondominated solutions using MOPSO algorithm. Performance of gasifier is investigated at all load conditions. The controller with optimized controller parameters meets all the performance requirements at 0%, 50%, and 100% load conditions. The investigations are also extended for variations in coal quality, which shows an improved stability of the gasifier over a wide range of coal quality variations.

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“…8, No. 5;2014 Figure 9. Output and Input response for +18% coal quality change with sinusoidal disturbance for 100% load Figure 10.…”

Section: Sinusoidal Disturbance Coupled With Coal Quality Variationsmentioning

confidence: 99%

Design of PID Filter Controller with Genetic Algorithm for Mimo System in Modern Power Generation

Mary¹,

Sivakumar²,

Jayakumar³

2014

MAS

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In this work, a new technique based on Genetic Algorithm for designing multivariable PID filter controller has been developed and applied to gasifier control of ALSTOM benchmark challenge II. The coal gasifier is the main component in Modern power generation. Coal gasifier involves several performance and robustness requirements in addition to actuator constraints under three operating loads (no-load, 50% and 100% load). The proposed GA optimises the tuning parameters of PID constants in terms of robustness and performance. The optimised controller meets all design objectives under all operating conditions. Robustness of the controller is tested for step and sinusoidal pressure disturbances applied at the inlet of throttle valve along with increase and decrease of calorific value of fuel fed-in (coal). Simulation results obtained confirmed the superiority of proposed technique for gasifier problems.

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Performance evaluation of optimal PI controller for ALSTOM gasifier during coal quality variations

Cited by 15 publications

References 21 publications

Regression modeling and particle swarm optimization of mechanical properties of potassium hydroxide pretreated Dharbai fiber-reinforced polyester composites

Regression modeling and particle swarm optimization of mechanical properties of potassium hydroxide pretreated Dharbai fiber-reinforced polyester composites

Optimal Tuning of Decentralized PI Controller of Nonlinear Multivariable Process Using Archival Based Multiobjective Particle Swarm Optimization

Design of PID Filter Controller with Genetic Algorithm for Mimo System in Modern Power Generation

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