Constructing regions of attainable sizes and achieving target size distribution in a batch cooling sonocrystallization process

• Published in: Ultrasonics sonochemistry Vol. 42; pp. 162 - 170
• Main Authors: Bhoi, Stutee, Sarkar, Debasis
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2018
• Subjects: Dynamic optimization; Generic Model Control algorithm; Regions of attainable particle sizes; Target crystal size distribution; Ultrasound; Target crystal size distribution; Ultrasound; Generic Model Control algorithm; Regions of attainable particle sizes; Dynamic optimization
• ISSN: 1350-4177; 1873-2828; 1873-2828
• DOI: 10.1016/j.ultsonch.2017.11.017

•An improved model is developed for sonocrystallization to track temperature rise.•Population balance equation and Generic Model Control algorithm are integrated.•The regions of attainable crystal sizes were computed.•Dynamic optimization was used to achieve target crystal size distribution.•The experimental evidence demonstrates the efficiency of the proposed approach. The application of ultrasound to a crystallization process has several interesting benefits. The temperature of the crystallizer increases during ultrasonication and this makes it difficult for the temperature controller of the crystallizer to track a set temperature trajectory precisely. It is thus necessary to model this temperature rise and the temperature-trajectory tracking ability of the crystallizer controller to perform model-based dynamic optimization for a given cooling sonocrystallization set-up. In our previous study, we reported a mathematical model based on population balance framework for a batch cooling sonocrystallization of l-asparagine monohydrate (LAM). Here we extend the previous model by including energy balance equations and a Generic Model Control algorithm to simulate the temperature controller of the crystallizer that tracks a cooling profile during crystallization. The improved model yields very good closed-loop prediction and is conveniently used for studies related to particle engineering by optimization. First, the model is used to determine the regions of attainable particle sizes for LAM batch cooling sonocrystallization process by solving appropriate dynamic optimization problems. Then the model is used to determine optimal operating conditions for achieving a target crystal size distribution. The experimental evidence clearly demonstrates the efficiency of the particle engineering approach by optimization.