• Jyothi Thati
• Anitha Mogilicharla
• Ramya Borgam

This research aims to estimate the kinetics of sulfanilamide crystallization from batch crystallizer data obtained from a linear cooling profile and thereby extended to find the optimum cooling profile for the desired combination of conflicting objectives. Real coded genetic algorithm (RCGA) is applied to estimate the rate parameters from the experimental batch cooling crystallization data of liquid phase concentration. The validated model is used to find the different optimal temperature profiles for the combination of conflicting objectives (i.e. maximization of number mean size (NMS) and minimization of coefficient of variance (CV)). This is done by using real coded non-dominated sorting genetic algorithm (NSGA II). In this effort, together with finding the optimum temperature trajectories, the effect of target choice on the final achievable Pareto front is analyzed using the above two conflicting objective functions.

Crystallization, Sulfanilamide, Kinetic model, Multi objective optimization Coefficient of Variance

"Experimental Validation and Multi-objective optimization of Batch cooling crystallization of sulfanilamide", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.e55-e68, July-2021, Available :http://www.jetir.org/papers/JETIR2107511.pdf

"Experimental Validation and Multi-objective optimization of Batch cooling crystallization of sulfanilamide", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppe55-e68, July-2021, Available at : http://www.jetir.org/papers/JETIR2107511.pdf