Strategic evaluation of different direct nucleation control approaches for controlling batch cooling crystallisation via simulation and experimental case studies

摘要

This paper presents a systematic evaluation of direct nucleation control (DNC) approach of controlling batch cooling crystallization systems. DNC, as named is aimed to control nucleation events to achieve the desired crystal size distribution (CSD) in a model free approach that requires no prior knowledge on nucleation or growth kinetics of the system. The performance and robustness of proposed DNC approaches were examined through computer simulations and laboratory experiments. Computer simulations were done in two phases. In the first phase, detailed mathematical models covering reaction kinetics and heat mass balances were developed for a batch cooling crystallization system of Paracetamol in water. Based on these models, rigorous simulation programs were developed in MATLAB (R). This mechanistic model was used to solve an open loop optimal control problem of maximizing the mean crystal length within a fixed batch time. The successive quadratic programming solution provides the optimal temperature trajectory, corresponding number of counts and the total length. In the second phase, treating the MATLAB (R) model as the real processes, the performance of DNC was evaluated by setting the optimal number of counts as the target and applying different DNC strategies: changing DNC structures/algorithm; varying the acceptable range of counts limits; increasing and decreasing the limit of desired number of counts; and changing the heating and cooling rates. The results demonstrate that the fast/slow cooling-fast/slow heating approach yields the best result in terms of reduced number of temperature loops that produced the desired counts in smaller batch duration than the optimal one. Later, the simulated results were justified through laboratory experiments. (C) 2019 Elsevier Ltd. All rights reserved.