Product quality improvement of batch crystallizers by a batch-to-batch optimization and nonlinear control approach

Paengjuntuek, Woranee; Arpornwichanop, Amornchai; Kittisupakorn, Paisan

doi:10.1016/j.cej.2007.08.010

Cited by 25 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Batch crystallization models represented by a reduced order moment model of the nuclei and seed classes of crystals to describe the dynamic behavior of the process are given as follows [4]:…”

Section: Mathematical Models Of a Batch Purified Terephthalic Acid Crmentioning

confidence: 99%

“…Generally, the CSD is directly affected by the non-equilibrium state of the system measured via supersaturation. Since supersaturation is a function of a crystallizer temperature, the slurry temperature is often used as the manipulated variable to control the non-equilibrium driving force of the system [3,4]. However, to achieve the objective of the control, an optimal condition and effective controllers are required.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, optimization of batch crystallization processes has received considerable attention as it is a useful tool to determine an optimal operating temperature which has a direct effect on the final-time CSD [4,5]. For example, Miller and Rawling implemented model identification and an open-loop optimal temperature control strategy on a bench-scale potassium nitrate-water system [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Kittisupakorn¹,

Somsong²,

Hussain³

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases.

show abstract

Section: Mathematical Models Of a Batch Purified Terephthalic Acid Crmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Kittisupakorn¹,

Somsong²,

Hussain³

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, this system didn't consider the effect of crystal content, which would easily lead to pseudo crystal phenomena and result in poor quality of the massecuite. NAHMAT automatic control system, developed by Siemens, integrated PLC control system and PC industrial control software, which was the most reliable control system for sugar cane crystallization and widely used in many countries [4][5]. The key of above systems is using brix sensor (refraction brix sensor or microwave brix sensor) to measure brix during crystallization process, and achieving automatic control by combining traditional PID method.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Intelligent Integrated Measuring and Controlling System for Sugar Cane Crystallization Process

Meng

Zheng

et al. 2015

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Abstract-The deficiency in existing sugar cane crystallization automatic control system is difficult to measure some key parameters on line, such as mother liquor supersaturation, mother liquor purity, crystal content and crystal size distribution. Controlling brix with PID can only reflect the massecuite concentration of sugar cane crystallization process, but it is hard to guarantee the crystal quality. During crystallization process, change of mother liquor purity will affect the crystallization rate and supersaturation. The less mother liquor purity in the final stage is, the better absorption of crystals have. Crystal size distribution, including mean area (MA) and coefficient of variation (CV), influences the quantity and quality of crystals. In order to produce sucrose which has uniform size and small coefficient of variation, it's necessary to study the law of crystal size for sugar cane crystallization. According to the difficulties in measuring some key parameters, an intelligent integrated measuring and controlling system is researched by this paper. The overall structure of this system is designed at first, and also the monitoring system of host computer is developed. Combining with data-driven modeling and hybrid modeling method, the intelligent soft-sensor component for sugar cane crystallization process is implemented. This system realizes automatic monitoring of sugar cane crystallization process, which includes on-line measurement of mother liquor supersaturation, mother liquor purity, crystal content and crystal size distribution (CSD). Experimental results show that this designed intelligent integrated measuring and controlling system for sugar cane crystallization process has not only achieved great on-line prediction for immeasurable parameters, but also has good openness and scalability, which can provide complete parameter detection for the implementation of sugar cane crystallization automatic control system.

show abstract

“…In this aim, several advanced control algorithms (nonlinear model-based predictive control, generic model control, H  -control, etc.) were developed for both continuous and batch crystallization processes in numerous fields of application [1][2][3][4][5][6][7]. Even if modelbased strategies have proved to be efficient, in crystallization, the model design remains a crucial and difficult issue.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Improvement of an Industrial Crystallization Process Using Linearizing Control

Damour¹,

Benne²,

Boillereaux³

et al. 2012

JCPT

View full text Add to dashboard Cite

This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach is declined according to two different strategies: first, a setpoint tracking is proposed for the couple crystal mass/concentration, whereas a second way consists in tracking of crystal content and concentration. The controlled variables, unavailable on-line, are issued from an observer developed in previous works. The performance of these strategies, which application to cane sugar crystallization constitutes a real novelty, are compared with experimental data issued from a PID-controlled industrial plant. The results reveal a significant improvement of energy efficiency, leading to an economy of more than 10% of energy.

show abstract

Product quality improvement of batch crystallizers by a batch-to-batch optimization and nonlinear control approach

Cited by 25 publications

References 13 publications

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Design and Implementation of Intelligent Integrated Measuring and Controlling System for Sugar Cane Crystallization Process

Energy Efficiency Improvement of an Industrial Crystallization Process Using Linearizing Control

Contact Info

Product

Resources

About