Literature Review Of Comparison between modeling of batch crystallization and continuous crystallization of L-Glutamic Abstract The batch and continuous crystallization of L-glutamic are modeled to compare. The different operate conditions in batch and continuous crystallization are studied though modeling. Currently, the convert of batch to continuous is popular, but there are many factors need to consider and analysis. It is of great significance to compare the model between batch and continuous. Key words: model, crystallization, batch, continuous 1. Introduction Model building is an important part of research in crystallization, which is a separation and purification technique employed to produce a wide variety of materials, and was used commonly in chemical and pharmaceutical industry[1]. The force of the crystallization is supersaturation, which is defined as the difference between the solution and saturated concentration at a given temperature[2]. There are some methods to create supersaturation, including cooling temperature, addition of antisolvent and evaporation[3]. There are two type of operation of crystallization, batch and continuous[4]. In different operation condition, a suitable operation should be chosen. Compared with continuous crystallization, batch crystallization can be simple, easy to clean and get larger mean particle size, but continuous crystallization can be cheaper, less labor and more suitable for mass production[5]. The qualities of crystals, such as purity, polymorphic form, crystal size distribution and crystal shape are influenced by the operation condition, like seeding condition and temperature profile[6]. Through modeling, the process of crystallization can be simulated and the quality of crystal can be evaluated before the experiment be done. To build a model, three balance equation, including population, mass and energy balance equation, and parameters of solubility, nucleation and growth are required to be prepared[7]. The methods be used to solve the equation are the method of moments and the method of classes, which can#8217;t give analytical solution but numerical solution[6]. 2. Review Jochen Scholl et all test the parameters of L-glutamic by fitting the model to the experiment data, give the birth, growth and solubility equation, which can be used to build a model[8]. The theory of model of crystallization and particle systems was proposed by Hulburt and Katz[9]. Ramkrishna applied the population balance into the particulate system in engineering[10]. The numerical solution of population balance equation (PEB) can be obtained by the method of moments and the method of classes[11]. The modeling of batch crystallization of L-glutamic is studied by Ehsan Sheikholeslamzadeh[6]. In three different temperature profile and seeding condition, the result of average size of particles, mass of stable polymorph and the mass of metastable form were compared with the optimal cooling, listed in stable 1. Objective function Natural cooling Linear cooling Nonlinear cooling Optimal cooling J1(g/kg of solvent) 8.71 13.02 13.41 13.85 J2(μm) 65.54 98.60 148.50 153.83 J3(g/kg of solvent) 5.87 1.62 0.67 7.80 Table 1: Optimal of the mass of stable form (J1), mass of metastable form (J3) and the size of stable form (J2) with their value from conventional cooling policies[6] It is concluded that the optimal profiles have better performance comprising with conventional policies. In addition, the breakage, stir and agglomeration are neglected in this model[6]. Jorg Worlitschek and Marco Mazzotti found that the particles size distribution of paracetamol is sensitive to small change in the applied temperature profile for batch cooling crystallization[12]. Noriaki Kubota et al studied the effect of seeding on crystal size distribution (CSD) for cooling crystallization of potassium alum[13]. The product can be unimodal regardless of the temperature profile if the seed concentration is higher than a critical point[13]. According to what Qinglin Su et al have studied, despite the smaller mean crystal size in batch crystallization, the proposed MSMPR operation show higher production capacity with shorter residence time and comparable product yield as in continuous crystallization[14]. In the result of the experiment by Simon Lawton et al, the scale up of the continuous crystallization with COBC is linear and need very small changes in diameter of the tubular crystallization, which ensures that the governing science of solution crystallization remains the same in the scale up, a very important factor that cannot be achieved in the scaling up of batch[15]. 3. Conclusion Based on review above, batch and continuous operation have superiority in their scope of application. It is of great significance to compare the model between batch and continuous. Currently, the convert of batch to continuous is popular, but there are many factors need to consider and analysis. A good model can simulate the process approximatively and guide your next step. 4. Reference [1] Anwar J, Boateng P K. Computer simulation of crystallization from solution[J]. Journal of the American Chemical Society, 1998, 120(37): 9600-9604. [2] Anwar J, Boateng P K. Computer simulation of crystallization from solution[J]. Journal of the American Chemical Society, 1998, 120(37): 9600-9604. [3] Kordylla A, Krawczyk T, Tumakaka F, et al. Modeling ultrasound-induced nucleation during cooling crystallization[J]. Chemical Engineering Science, 2009, 64(8): 1635-1642. [4] Gerstlauer A, Motz S, Mitrovi#263; A, et al. Development, analysis and validation of population models for continuous and batch crystallizers[J]. chemical engineering science, 2002, 57(20): 4311-4327. [5] Li H, Kawajiri Y, Grover M A, et al. Modeling of nucleation and growth kinetics for unseeded batch cooling crystallization[J]. Industrial Engineering Chemistry Research, 2017, 56(14): 4060-4073. [6] Sheikholeslamzadeh E, Rohani S. Modeling and optimal control of solution mediated polymorphic transformation of L-glutamic acid[J]. Industrial Engineering Chemistry Research, 2013, 52(7): 2633-2641. [7] Kwon J S I, Nayhouse M, Christofides P D, et al. Modeling and control of protein crystal shape and size in batch crystallization[J]. AIChE Journal, 2013, 59(7): 2317-2327. [8] Schouml;ll J, Bonalumi D, Vicum L, et al. In situ monitoring and modeling of the solvent-mediated polymorphic transformation of L-glutamic acid[J]. Crystal Growth Design, 2006, 6(4): 881-891. [9] Hulburt H M, Katz S. Some problems in particle technology: A statistical mechanical formulation[J]. Chemical engineering science, 1964, 19(8): 555-574. [10] Ramkrishna D. Population balances: Theory and applications to particulate systems in engineering[M]. Elsevier, 2000. [11] Marchisio D L, Vigil R D, Fox R O. Quadrature method of moments for aggregation#8211;breakage processes[J]. Journal of colloid and interface science, 2003, 258(2): 322-334. [12] Worlitschek J, Mazzotti M. Model-based optimization of particle size distribution in batch-cooling crystallization of paracetamol[J]. Crystal Growth Design, 2004, 4(5): 891-903. [13] Kubota N, Doki N, Yokota M, et al. Seeding policy in batch cooling crystallization[J]. Powder Technology, 2001, 121(1): 31-38. [14] Su Q, Nagy Z K, Rielly C D. Pharmaceutical crystallisation processes from batch to continuous operation using MSMPR stages: Modelling, design, and control[J]. Chemical Engineering and Processing: Process Intensification, 2015, 89: 41-53. [15] Lawton S, Steele G, Shering P, et al. Continuous crystallization of pharmaceuticals using a continuous oscillatory baffled crystallizer[J]. Organic Process Research Development, 2009, 13(6): 1357-1363.