Studies on crystallization process for pharmaceutical compounds using ANN modeling and model based control

Solvent selection and Controlling of operating parameters play a crucial role in batch cooling crystallization process. Choosing a best solvent for crystallization process involves more experimentation and time. To overcome this problem, an Artificial Neural Network (ANN) model technique is used to predict the carbamazepine form Ⅲ solubility by considering the thermodynamic properties of different solvents i.e. critical temperature, critical pressure, temperature, molecular weight, and acentric factor. The ANN model was trained and evaluated for solubility at various input data sets using experimental solubility data available in the literature. The ANN model with 20 hidden neurons has given the R2 value of 0.9943 which shows that the developed ANN model can be used for the selection of best solvent for batch crystallization process. Further, to determine the optimal cooling profile of batch cooling crystallization process, a multi-objective optimization problem is formulated by considering objectives as minimizing the coefficient of variation (CV) and maximizing the Number mean size (NMS) of crystals subjected to population balance equations using “method of moments” technique. Two types of temperature strategies i.e., piece-wise constant and piece-wise linear are developed and solved using NSGA-Ⅱ dynamic optimization procedure. The optimal NMS value attained through piece-wise linear strategy was 197.1 µm. This value has been increased by 28.3 µm from the nominal case (without optimization) and the coefficient of variation has decreased from 0.951 to 0.76. Further, optimal NMS value attained through piece-wise constant strategy was 205 µm. The value has been increased by 36.2 µm and the coefficient of variation has decreased from 0.951 to 0.73. This proves that the crystal attributes can be improved by optimal cooling temperature profile obtained by multi-objective optimization framework. For implementing the optimal cooling profile an advanced model-based control, i.e., Generic Model Control (GMC) was developed. It was observed that the GMC controller has the good tracking profile with no offset with/without disturbances and small value of root mean square error (RMSE) of 0.0016 using piece-wise constant as set point temperature. Using piece-wise linear as set point temperature, the RMSE value was 0.0018. In particular, it is advantageous to operate the batch cooling crystallization process with piece-wise linear strategy for set point trajectory tracking problems

Ultrafiltration study of the polysulfone membrane modified with branched polyethyleneimine

This work discussed the fabrication of polysulfone (PSF) ultrafiltration membranes with hydrophilic behaviour by adding branched polyethyleneimine (PEI) as an additive. By directly blending the base polymer and the additive in the organic solvent, the casting solution is prepared. An asymmetric ultrafiltration membrane was fabricated by the phase inversion method. The presence of PEI was confirmed by comparing the IR spectra of the plain PSF membrane and the modified PSF membrane. A scanning electron microscope was used for the comparison of morphological changes in plain and modified membranes. The membrane was characterised with respect to bovine serum albumin (BSA) adsorption, pure water flux, permeability, compaction factor, humic acid (HA) rejection, and water uptake. The fouling resistance behaviour is prompted due to the presence of hydrophilic PEI chains in the membrane. As a result, pure water flux and flux recovery ratio increased from 28.84 to 326.54 L/m2h and from 0.526 to 0.954 L/m2hkPa for the modified membrane with respect to the plain membrane, respectively. HIGHLIGHTS Polysulfone (PSF) ultrafiltration membranes with hydrophilic behaviour by adding the polyethyleneimine branched (PEI) as an additive were fabricated.; High percentage of HA rejection was achieved with good antifouling properties.; BSA adsorption also decreased with respect to the weight percentage of PEI.

Study on water and gas permeation characteristics with ZIF-8 mixed matrix membranes

The membrane separation process lacks intrinsic permeation characteristics to compete with other separation technologies like adsorption, sedimentation, coagulation, skimming, and distillation. A mixed matrix membrane (MMM) is one of the strategies to improve the separation characteristics with embedded nanofillers particles. Zeolite imidazolate framework (ZIF) has a new subclass of inorganic–organic hybrid materials that are being introduced as new fillers for incorporation into the polymer matrix for various applications such as oily wastewater separation, wastewater treatment, natural gas dehydration, landfill gas upgrading, and mixed gas separation. In this experimental work, a metal-organic framework called ZIF-8 was synthesized and used as a filler for modification of MMMs and characterized with FTIR and SEM. ZIF-8 nanoparticles up to 5 wt% loading were added to PSF casting solution then the permeation characteristics of MMMs showed an improved result like the pure water flux of the modified membrane at 2.5 bar was increased up to 456.38 L/m2h. In the case of pure gas separation, at 5 wt% ZIF-8 loading in PSF, the pure gas CO2 permeability at 9 bar pressure had increased to 10.54 barrer. HIGHLIGHTS We have studied water and gas permeation characteristics incorporated with ZIF-8 containing mixed matrix membranes.; ZIF-8 was made as a gateway for quick transport of CO2 gas molecules and water molecules through the polymer matrix.; As per the observed results, higher permeability of the MMMs can be possible with higher loading of ZIF-8.