Adaptive MIMO neuro-fuzzy logic control of a seeded and an unseeded anti-solvent semi-batch crystallizer
ABSTRACT This study explores the implementation of a two input/two output adaptive neuro-fuzzy logic controller on an anti-solvent semi-batch crystallization process. The solution concentration and the solubility curve of paracetamol (PA) in a mixture of water and isopropanol in the range of temperatures between 10 and were determined using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. The in situ chord length distribution of crystals was obtained from laser backscattering by focus beam reflectance measurement (FBRM) probe. The controlled variables were the supersaturation and the difference in the chord length counts between two sampling times, and the manipulated variables were the cooling rate and anti-solvent flow rate. The ‘direct’ objectives of this study were to keep the controlled variables inside their predetermined ranges. The ‘indirect’ objectives were to improve the end-of-batch properties that included batch time, yield, and particle size distribution. Performance of the adaptive neuro-fuzzy logic controller for the closed-loop system was evaluated based on meeting the ‘direct’ and ‘indirect’ objectives. The best results in terms of batch time and product yield for unseeded experiments were 280 min and 95%, respectively. However, the most significant improvement was noted in the seeded set of experiments that resulted in 225 min batch time, an increase of the volume weighted mean size by , and 99% product yield.
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ABSTRACT: This paper presents an output feedback nonlinear model-based control approach for optimal operation of industrial batch crystallizers. A full population balance model is utilized as the cornerstone of the control approach. The modeling framework allows us to describe the dynamics of a wide range of industrial batch crystallizers. In addition, it facilitates the use of performance objectives expressed in terms of crystal size distribution. The core component of the control approach is an optimal control problem, which is solved by the direct multiple shooting strategy. To ensure the effectiveness of the optimal operating policies in the presence of model imperfections and process uncertainties, the model predictions are adapted on the basis of online measurements using a moving horizon state estimator. The nonlinear model-based control approach is applied to a semi-industrial crystallizer. The simulation results suggest that the feasibility of real-time control of the crystallizer is largely dependent on the discretization coarseness of the population balance model. The control performance can be greatly deteriorated due to inadequate discretization of the population balance equation. This results from structural model imperfection, which is effectively compensated for by using the online measurements to confer an integrating action to the dynamic optimizer. The real-time feasibility of the output feedback control approach is experimentally corroborated for fed-batch evaporative crystallization of ammonium sulphate. It is observed that the use of the control approach leads to a substantial increase, i.e., up to 15%, in the batch crystal content as the product quality is sustained.IEEE Transactions on Control Systems Technology 01/2012; DOI:10.1109/TCST.2011.2160945 · 2.52 Impact Factor
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ABSTRACT: The nucleation and growth kinetic parameters of paracetamol in an isopropanol−water antisolvent batch crystallizer were estimated by nonlinear regression in terms of the moments of the crystal population density. The moments were calculated using the measured chord length distribution (CLD) generated by the FBRM. The measured supersaturation by ATR-FTIR spectroscopy was also used to calculate the nucleation and growth rates using power law correlations. Using the estimated kinetic parameters, the crystallization model based on the population and mass balance, was validated using the open-loop experimental particle size distribution and supersaturation results. Subsequently, the solution to the optimal antisolvent flow-rate profiles was obtained by applying nonlinear constrained single- and multiobjective optimization on the validated model. These profiles were implemented on the crystallizer and crystal-size distributions were compared with the open-loop experiments. The bimodality in the particle size distribution (PSD), which was present in the open-loop experiments, was either minimized or completely eliminated with the optimal profile policies. The results of the multiobjective optimization showed an improvement of 27.5 μm and 3% in the volume weighted mean size and yield, respectively, in comparison to the best results obtained from the open-loop experiments.Industrial & Engineering Chemistry Research 02/2008; 47(5). DOI:10.1021/ie071125g · 2.24 Impact Factor
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ABSTRACT: This article is Restricted Access. It was published in the journal, Crystal Growth and Design [© American Chemical Society] and is available from: http://pubs.acs.org/doi/abs/10.1021/cg800131r The paper presents a thorough simulation and experimental evaluation of the concentration control approach for batch and semibatch crystallization. The sensitivity of concentration feedback control is assessed in the case of various disturbances that result in excessive nucleation events. The enhanced robustness of the concentration control is demonstrated against the widely used direct operation approach, which directly implements the temperature or anti-solvent addition rate versus time. Two adaptive supersaturation control approaches are proposed that employ measurement of the number of particle counts per unit time provided by in-situ laser backscattering, to detect the onset of nucleation and adapt the operating curve accordingly, further enhancing the robustness of the approach. Simulation and experimental results indicate that adaptive concentration control is robust to variations in the nucleation, growth, or dissolution rates due to scale-up or other changes in the process conditions. Restricted accessCrystal Growth & Design 01/2009; 9(1). DOI:10.1021/cg800131r · 4.56 Impact Factor