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  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 化學工程學系
Please use this identifier to cite or link to this item: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99431
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dc.contributor.advisor吳哲夫zh_TW
dc.contributor.advisorJeffrey D. Warden
dc.contributor.author賴柏圳zh_TW
dc.contributor.authorBo-Zun Laien
dc.date.accessioned2025-09-10T16:16:04Z-
dc.date.available2025-09-11-
dc.date.copyright2025-09-10-
dc.date.issued2025-
dc.date.submitted2025-07-30-
dc.identifier.citation[1] Fang, M.-C.; Tan, P. J.; Ward, J. D. Efficient estimation of crystal filterability using the discrete element method and the Kozeny-Carman equation. Powder Technology 2024, 441, 119820.
[2] Carman, P. C. Fluid ow through granular beds. Trans. Inst. Chem. Eng. London 1937, 15, 150-156
[3] Bourcier, D.; Feraud, J. P.; Colson, D.; Mandrick, K.; Ode, D.; Brackx, E.; Puel, E. Inuence of particle size and shape properties on cake resistance and compressibility during pressure ltration. Chem. Eng. Sci. 2016, 144, 176-187
[4] Zhu, H. P.; Zhou, Z. Y.; Yang, R. Y.; Yu, A. B. Discrete particle simulation of particulate systems: Theoretical developments. Chem. Eng. Sci. 2007, 62 (13), 3378-3396
[5] Tan, P. J.; Fang, M.-C.; Lai, B.-Z; Ward, J. D. Optimal Design of Continuous Integrated Crystallization-Filtration Processes. Powder Technology 2025, in review.
[6] Hill, P., 2005. Batch crystallization. In: Korovessi, E., Linninger, A.A. (Eds.), Batch Processes. Taylor and Francis, CRC Press, New York, USA.
[7] A. Randolph, M. Larson, Theory of Particulate Processes: Analysis and Techniques of Continuous Crystallization, Elsevier, 2012.
[8] Vekilov, P. G. (2010). Nucleation. Crystal Growth & Design, 10(12), 5007-5019.
[9] Boistelle, R., & Astier, J. (1988). Crystallization mechanisms in solution. Journal of crystal growth, 90(1-3), 14-30.
[10] Lacmann, R., Herden, A., & Mayer, C. (1999). Kinetics of nucleation and crystal growth. Chemical Engineering & Technology: Industrial Chemistry‐Plant Equipment‐Process Engineering‐Biotechnology, 22(4), 279-289.
[11] Agrawal, S., & Paterson, A. (2015). Secondary nucleation: mechanisms and models. Chemical Engineering Communications, 202(5), 698-706. Wey, J. S.; Karpinski, P. H. Batch Crystallization. In Handbook of Industrial Crystallization; Elsevier, 2002; pp. 231−248.
[12] Orehek, J., Teslic, D., & Likozar, B. (2020). Continuous crystallization processes in pharmaceutical manufacturing: A review. Organic Process Research & Development, 25(1), 16-42.
[13] Zhang, D. J., Xu, S. J., Du, S. C., Wang, J. K., & Gong, J. B. (2017). Progress of Pharmaceutical Continuous Crystallization. Engineering, 3(3), 354-364. doi: 10.1016/j.Eng.2017.03.023
[14] Wang, T., Lu, H., Wang, J., Xiao, Y., Zhou, Y., Bao, Y., & Hao, H. (2017). Recent progress of continuous crystallization. Journal of industrial and engineering chemistry, 54, 14-29.
[15] Chen, J., Sarma, B., Evans, J. M., & Myerson, A. S. (2011). Pharmaceutical crystallization. Crystal Growth & Design, 11(4), 887-895.
[16] Myerson, A. S., Erdemir, D., & Lee, A. Y. (2019). Handbook of Industrial Crystallization: Cambridge University Press.
[17] Meng, W., Sirota, E., Feng, H., McMullen, J. P., Codan, L., & Cote, A. S. (2020). Effective Control of Crystal Size via an Integrated Crystallization, Wet Milling, and Annealing Recirculation System. Organic Process Research & Development, 24(11), 2639-2650.
[18] Hintz, R. J., & Johnson, K. C. (1989). The effect of particle size distribution on dissolution rate and oral absorption. International Journal of Pharmaceutics, 51(1), 9-17.
[19] Wey, J. S.; Karpinski, P. H. Batch Crystallization. In Handbook of Industrial Crystallization; Elsevier, 2002; pp. 231−248.
[20] Chianese, A., Cave, S. D. and B. Massarotta, “Investigation on some Operating Factors Influencing Batch Cooling Crystallization”, In S. J. Jancic and E. J. de Jong (Eds.). “Industrial Crystallization 84”, Elsevier, Amsterdam (1984), pp. 443-446.
[21] Bohlin, M. and A. C. Rasmuson, “Application of Controlled Cooling and Seeding in Batch Crystallization”, Can. J. of Chem. Eng. 70, 120-126 (1992).
[22] Rawlings, J. B., C. W. Sink and S. M. Miller, “Control of crystallization processes”. in “Handbook of Industrial Crystallization”, A. S. Myerson (Ed.), Butterworth-Heinemann, Boston, MA (1993), pp. 179-207.
[23] Wey, J.S. (1981). In Preparation and Properties of Solid State Materials, vol. 6 (Wilcox, W.R., ed.), p. 67, Marcel Dekker, New York.
[24] Moyers, Jr., C.G., and Rousseau, R.W. (1987). In Handbook of Separation Process Technology (Rousseau, R.W., ed.), p. 587, Wiley-Interscience, New York.
[25] R.C. Bennett, Crystallizer selection and design, in: Handbook of Industrial Crystallization, Elsevier, 2002, pp. 115–140.
[26] J.D. Ward, D.A. Mellichamp, M.F. Doherty, Choosing an operating policy for seeded batch crystallization, AICHE J. 52 (6) (2006) 2046–2054.
[27] Y.-T. Tseng, J.D. Ward, Comparison of objective functions for batch crystallization using a simple process model and Pontryagin’s minimum principle, Comput. Chem. Eng. 99 (2017) 271–279.
[28] H.-J. Pan, J.D. Ward, Computationally efficient algorithm for solving population balances with size-dependent growth, nucleation, and growth-dissolution cycles, Ind. Eng. Chem. Res. 60 (34) (2021) 12614–12628.
[29] H.-J. Pan, J.D. Ward, Dimensionless framework for seed recipe design and optimal control of batch crystallization, Ind. Eng. Chem. Res. 60 (7) (2021) 3013–3026.
[30] R. Wakeman, E.S. Tarleton, Solid/Liquid Separation: Principles of Industrial Filtration, Elsevier, 2005.
[31] C. Wibowo, W.-C. Chang, K.M. Ng, Design of integrated crystallization systems, AIChE J 47 (11) (2001) 2474–2492.
[32] R. Wakeman, The influence of particle properties on filtration, Sep. Purif. Technol. 58 (2) (2007) 234–241.
[33] R. Beck, A. H¨akkinen, D. Malthe-Sørenssen, J.-P. Andreassen, The effect of crystallization conditions, crystal morphology and size on pressure filtration of l- glutamic acid and an aromatic amine, Sep. Purif. Technol. 66 (3) (2009) 549–558.
[34] D. Bourcier, J.P. F´ eraud, D. Colson, K. Mandrick, D. Ode, E. Brackx, F. Puel, Influence of particle size and shape properties on cake resistance and compressibility during pressure filtration, Chem. Eng. Sci. 144 (2016) 176–187.
[35] D. Ramkrishna, Population Balances: Theory and Applications to Particulate Systems in Engineering, Elsevier, 2000.
[36] N. Ouchiyama, T. Tanaka, Porosity estimation from particle size distribution, Ind. Eng. Chem. Fundam. 25 (1) (1986) 125–129.
[37] B. Nagy, B. Szil´ agyi, A. Domokos, K. Tacsi, H. Pataki, G. Marosi, Z.K. Nagy, Z. K. Nagy, Modeling of pharmaceutical filtration and continuous integrated crystallization-filtration processes, Chem. Eng. J. 413 (2021) 127566.
[38] S. S¨oren, T. Jürgen, Simulation of a filtration process by DEM and CFD, Int. J. Mech. Eng. Mechatron. 1 (2) (2012) 28–35.
[39] F. Qian, N. Huang, J. Lu, Y. Han, CFD–DEM simulation of the filtration performance for fibrous media based on the mimic structure, Comput. Chem. Eng. 71 (2014) 478–488.
[40] C. Yue, Q. Zhang, Z. Zhai, Numerical simulation of the filtration process in fibrous f ilters using CFD-DEM method, J. Aerosol Sci. 101 (2016) 174–187.
[41] B. Li, K.M. Dobosz, H. Zhang, J.D. Schiffman, K. Saranteas, M.A. Henson, Predicting the performance of pressure filtration processes by coupling computational fluid dynamics and discrete element methods, Chem. Eng. Sci. 208 (2019) 115162.
[42] R. Deshpande, S. Antonyuk, O. Iliev, DEM-CFD study of the filter cake formation process due to non-spherical particles, Particuology 53 (2020) 48–57.
[43] D. Hund, P. L¨ osch, M. Kerner, S. Ripperger, S. Antonyuk, CFD-DEM study of bridging mechanisms at the static solid-liquid surface filtration, Powder Technol. 361 (2020) 600–609.
[44] Hulburt, H. M. and S. Katz, “Some Problems in Particlc Technology”, Chem. Eng. Sci. 19, 555-574 (1964).
[45] Randolph, A. and M. A. Larson, “Theory of Particulate Processes”, Academic Press, San Diego, 2nd edition.
[46] J. Garside, “Advances in Characterisation of Crystal Growth”, i n “Advances in Crystallization from Solutions”. volume 80 of AlChE Symposium Series No. 240, AIChE, New York (1984).
[47] O’ Hara, M. and R. C. Reid, “Modelling Crystal Growth Rates from Solution”, Prentice-Hall, Englewood Cliffs, NJ (1973).
[48] Burton, W., N. Cabrera and F. Frank, “The Growth of Crystals and the Equilibrium Structure of their Surfaces”, Philosophical Trans. of the Royal Society of London Series B - Biological Sciences, 243,29%358 (1951).
[49] Nyvlt, J., O. Sohnel, M. Matuchova and M. Broul, “The Kinetics of Industrial Crystallization”, volume 19 of Chemical Engineering Monographs, Elsevier, Amsterdam (1985).
[50] Chung, S.H., Ma, D.L., Braatz, R.D., 1999. Optimal seeding in batch crystallization. Can. J. Chem. Eng. 77, 590–596.
[51] S. M. Miller, “Modelling and Quality Control Strategies for Batch Cooling Crystallizers”, PhD thesis, Univ. of Texas at Austin (1993).
[52] W.-C. Chang, K.M. Ng, Synthesis of processing system around a crystallizer, AICHE J. 44 (10) (1998) 2240–2251.
[53] Tarleton, S.; Wakeman, R. Solid/liquid separation: equipment selection and process design; Elsevier, 2006.
[54] D. Bourcier, J.P. F´ eraud, D. Colson, K. Mandrick, D. Ode, E. Brackx, F. Puel, Influence of particle size and shape properties on cake resistance and compressibility during pressure filtration, Chem. Eng. Sci. 144 (2016) 176–187.
[55] P.C. Carman, Fluid flow through a granular bed, Trans. Inst. Chem. Eng. Lond. 15 (1937) 150–156.
[56] G. Perini, F. Salvatori, D.R. Ochsenbein, M. Mazzotti, T. Vetter, Filterability prediction of needle-like crystals based on particle size and shape distribution data, Sep. Purif. Technol. 211 (2019) 768–781.
[57] H.P. Zhu, Z.Y. Zhou, R.Y. Yang, A.B. Yu, Discrete particle simulation of particulate systems: theoretical developments, Chem. Eng. Sci. 62 (13) (2007) 3378–3396, https://doi.org/10.1016/j.ces.2006.12.089.
[58] P.A. Cundall, R.D. Hart, Numerical modelling of discontinua, Eng. Comput. 9 (2) (1992) 101–113.
[59] J. Seville, U. Tüzün, R. Clift, Processing of Particulate Solids vol. 9, Springer Science & Business Media, 2012.
[60] C. Coetzee, Simplified Johnson-Kendall-Roberts (SJKR) Contact Model, University of Stellenbosch report, 2020.
[61] S.J. Burns, P.T. Piiroinen, K.J. Hanley, Critical time step for DEM simulations of dynamic systems using a Hertzian contact model, Int. J. Numer. Methods Eng. 119 (5) (2019) 432–451, https://doi.org/10.1002/nme.6056 (accessed 2023/04/06).
[62] R.R. Craig Jr., E.M. Taleff, Mechanics of Materials, John Wiley & Sons, 2020.
[63] R. Turton, R.C. Bailie, W.B. Whiting, J.A. Shaeiwitz, Analysis, Synthesis and Design of Chemical Processes, Pearson Education, 2008.
[64] Hojjati H, Rohani S. Cooling and seeding effect on supersaturation and final crystal size distribution (CSD) of ammonium sulphate in a batch crystallizer. Chem Eng Process. 2005;44:949–957.
[65] Ward JD, Yu CC, Doherty MF. A new framework and a simpler method for the development of batch crystallization recipes. AIChE J. 2011;57:606–617.
[66] Doki N, Kubota N, Yokota M, Chianese A. Determination of critical seed loading ratio for the production of crystals of uni-modal size distribution in batch cooling crystallization of potassium alum. J Chem Eng Jpn. 2002;35:670–676.
[67] Doki N, Kubota N, Sato A, Yokota M, Hamada O, Masumi F. Scaleup experiments on seeded batch cooling crystallization of potassium alum. AIChE J. 1999;45:2527–2533.
[68] Jagadesh D, Kubota N, Yokota M, Sato A, Tavare NS. Large and mono-sized product crystals from natural cooling mode batch crystallizer. J Chem Eng Jpn. 1996;29:865–873.
[69] Kubota, N., Doki, N., Yokota, M. and Sato, A., 2001, Seeding policy in batch cooling crystallization, Powder Technol, 121(1): 31–38.
[70] S.M. Miller, J.B Rawlings, Model identification and control strategies for batch cooling crystallizers, AIChE J. 40 (1994) 1312-1327.
[71] Mullin, J. W. and J. Nyvlt; “Programmed Cooling of Batch Crystallizers,” Chem. Eng. Sci., 26, 369–377 (1971)
[72] Jones, A. G. and A. Chianese; “Fines Destruction during Batch Crys tallization,” Chem. Eng. Commun., 62, 5–16 (1987)
[73] Tavare, N. S.; Industrial Crystallization, pp. 93–139, Plenum Press, New York, USA (1995)
[74] Mullin, J. W.; Crystallization, 4th ed., pp. 423–429, Butterworth Heinemann, London, UK (2001)
[75] Doki, N., N. Kubota, A. Sato and M. Yokota; “Effect of Cooling Mode on Product Crystal Size in Seeded Batch Crystallization of Potassium Alum,” Chem. Eng. J., 81, 319–322 (2001)
[76] Jagadesh, D., N. Kubota, M. Yokota, N. Doki and A. Sato; “Seed ing Effect on Batch Crystallization of Potassium Sulfate under Natural Cooling Mode and a Simple Design Method of Crystallizer,” J. Chem. Eng. Japan, 34, 514–520 (1999)
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99431-
dc.description.abstract批次結晶廣泛應用於化學、製藥及相關產業中,用以將晶體產品從溶液中分離出來。相較於連續結晶,批次結晶具有更高的操作彈性,更適合用來獲得狹窄分布的產品晶體粒徑分布(CSD)。而產品的晶體粒徑分布對過濾性能及後續製程有顯著影響。為了有效優化批次結晶過程,必須理解晶體粒徑分布與濾餅可過濾性之間的關係。然而,這種估算具有挑戰性,目前針對優化批次結晶濾餅可過濾性的研究仍相當稀少。
Fang 等人 [1] 提出了一種根據特定晶體粒徑分布有效預測濾餅可過濾性的方法。該方法結合了由 Bourcier 等人 [3] 修改的 Kozeny-Carman 方程式 [2],以及離散元法(DEM)[4]。在他們的方法中,首先利用結晶器模型根據結晶器的操作條件(如停留時間與晶體成長速率)預測產品的晶體粒徑分布;接著使用離散元法預測晶體濾餅的結構與孔隙率;然後,再使用經 Bourcier 等人修正、可應用於具有粒徑分布晶體濾餅的 Kozeny-Carman 方程式,來預測濾餅的過濾阻力;最後,根據濾餅過濾阻力進行過濾器設計,並估算整個結晶與過濾系統的總成本。此方法亦已被 Tan 等人 [5] 應用於包含細晶溶解與產品分級的連續結晶系統中。
在本研究中,該方法被用來評估不同操作條件下批次結晶器所產生晶體產品的濾餅可過濾性,目的是找出最佳的批次結晶操作條件。研究探討了三項對結晶操作影響重大的變數:晶種添加量、批次時間以及冷卻軌跡。結果顯示,濾餅過濾阻力對晶種添加量與冷卻軌跡的變化並不敏感。線性與二次冷卻曲線所產生的晶體產品,其粒徑分布相似。進一步結果指出,雖然晶種添加量主要影響大晶體的生成,但濾餅過濾阻力主要由小晶體的粒徑分布所決定,而該分布在不同晶種添加量下變化不大。在所有操作變數中,批次時間的影響最大。延長批次時間可降低過飽和度與成核速率,進而產生較大晶體並改善濾餅的可過濾性。此外,本研究也進行了參數分析,探討不同成核與成長速率對結果的影響。
zh_TW
dc.description.abstractBatch crystallization is widely used in the chemical, pharmaceutical and related industries to separate crystalline products from solution. Compared to continuous crystallization, batch crystallization offers greater flexibility and is better suited for achieving narrow product crystal size distributions (CSD). The product CSD significantly impacts filterability and downstream processes. In order to properly optimize batch crystallization processes it is necessary to understand the relationship between CSD and cake filterability. However, such estimation is challenging, and studies on optimizing cake filterability in batch crystallization are rare.
Fang et al. [1] proposed an efficient method for estimating cake filterability based on a specified product CSD. Their method combines the Kozeny-Carman equation [2], as modified by Bourcier et al. [3] with the discrete element method (DEM) [4]. In their method a crystallizer model is used to predict a product crystal size distribution based on crystallizer properties such as residence time and crystal growth rate. Then the crystal cake structure and porosity are predicted using the discrete element method. Next, the Kozeny-Carman equation, modified by Bourcier et al. so that it can be applied to crystal cakes with size dispersity, is used to predict the filter cake resistance. Finally, the filter cake resistance is used to design the filter and the total cost of the combined system can be estimated. The method was further applied to continuous crystallizers with fines dissolution and product classification by Tan et al. [5].
In the present study, this method is applied to evaluate the filterability of crystalline products from a batch crystallizer under various operating conditions with the goal of determining the optimal recipe for batch crystallizer operation. The effect of three important crystallizer operating variables are studied: seed loading, batch time, and cooling trajectory. The results suggest that that cake resistance is relatively insensitive to seed loading and cooling trajectory. The linear cooling trajectory and quadratic cooling trajectory are found to produce crystalline product with a similar CSD. Results further suggest that while seed loading primarily affects the size of large crystals, cake resistance is predominantly determined by the CSD of small crystals, which remains nearly the same for different seed loadings. Among the three operating variables, the most significant factor is batch time. Extending the batch time reduces supersaturation and nucleation rates, leading to larger crystals and improved cake filterability. A parametric analysis was also conducted to study the effect of different nucleation and growth rates on the results.
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dc.description.tableofcontents誌謝 i
中文摘要 iii
ABSTRACT iv
CONTENTS vi
LIST OF FIGURES viii
LIST OF TABLES xv
Chapter 1 Introduction 1
1.1 Overview of the Crystallization Process 1
1.2 Batch Crystallization 2
1.3 The Design of Integrated Batch Crystallization–Filtration Process 3
1.4 Structure of the Thesis 6
Chapter 2 Model and theory 7
2.1 Batch Crystallizer Model 7
2.2 Centrifugal Filter Model 10
2.3 Evaluation of the Filterability of Crystal Cakes 13
2.4 DEM Settings 15
2.5 Estimation of Capital Cost 19
Chapter 3 Results and discussion 20
3.1 Base Case 20
3.2 Case 2&3 - Increasing Nucleation Rate 34
3.3 Case 4&5 - Increasing Growth Rate 59
3.4 Economic Analysis 84
Chapter 4 Conclusion 91
Nomenclature 93
References 96

Figure 1 1 The flowsheet for case 2 through 5 5
Figure 2 1 The schematic diagram of batch crystallizer 9
Figure 2 2 Schematic diagram of a peeler centrifuge cycle (a)Filtration (b)Displacement washing (c)Deliquoring(d)Cake discharge by plough 12
Figure 3 1 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under linear cooling conditions for base case 23
Figure 3 2 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under quadratic cooling conditions for base case 24
Figure 3 3 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.001 g/L for base case 25
Figure 3 4 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.01 g/L for base case 26
Figure 3 5 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.1 g/L for base case 27
Figure 3 6 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and linear cooling conditions for base case 28
Figure 3 7 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and linear cooling conditions for base case 29
Figure 3 8 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and linear cooling conditions for base case 30
Figure 3 9 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and quadratic cooling conditions for base case 31
Figure 3 10 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and quadratic cooling conditions for base case 32
Figure 3 11 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and quadratic cooling conditions for base case 33
Figure 3 12 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under linear cooling conditions for case 2 36
Figure 3 13 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under quadratic cooling conditions for case 2 37
Figure 3 14 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.001 g/L for case 2 38
Figure 3 15 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.01 g/L for case 2 39
Figure 3 16 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.1 g/L for case 2 40
Figure 3 17 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and linear cooling conditions for case 2 41
Figure 3 18 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and linear cooling conditions for case 2 42
Figure 3 19 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and linear cooling conditions for case 2 43
Figure 3 20 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and quadratic cooling conditions for case 2 44
Figure 3 21 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and quadratic cooling conditions for case 2 45
Figure 3 22 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and quadratic cooling conditions for case 2 46
Figure 3 23 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under linear cooling conditions for case 3 48
Figure 3 24 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under quadratic cooling conditions for case 3 49
Figure 3 25 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.001 g/L for case 3 50
Figure 3 26 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.01 g/L for case 3 51
Figure 3 27 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.1 g/L for case 3 52
Figure 3 28 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and linear cooling conditions for case 3 53
Figure 3 29 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and linear cooling conditions for case 3 54
Figure 3 30 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and linear cooling conditions for case 3 55
Figure 3 31 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and quadratic cooling conditions for case 3 56
Figure 3 32 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and quadratic cooling conditions for case 3 57
Figure 3 33 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and quadratic cooling conditions for case 3 58
Figure 3 34 Effect of varying seed loading on (a) cake resistance over time and (b)、(c) the corresponding CSD under linear cooling conditions for case 4 61
Figure 3 35 Effect of varying seed loading on (a) cake resistance over time and (b)、(c) the corresponding CSD under quadratic cooling conditions for case 4 62
Figure 3 36 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.001 g/L for case 4 63
Figure 3 37 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.01 g/L for case 4 64
Figure 3 38 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.1 g/L for case 4 65
Figure 3 39 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and linear cooling conditions for case 4 66
Figure 3 40 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and linear cooling conditions for case 4 67
Figure 3 41 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and linear cooling conditions for case 4 68
Figure 3 42 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and quadratic cooling conditions for case 4 69
Figure 3 43 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and quadratic cooling conditions for case 4 70
Figure 3 44 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and quadratic cooling conditions for case 4 71
Figure 3 45 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under linear cooling conditions for case 5 73
Figure 3 46 Effect of varying seed loading on (a) cake resistance over time and (b) the corresponding CSD under quadratic cooling conditions for case 5 74
Figure 3 47 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.001 g/L for case 5 75
Figure 3 48 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.01 g/L for case 5 76
Figure 3 49 Effect of varying cooling trajectory on (a) cake resistance over time and (b) the corresponding CSD under a seed loading of 0.1 g/L for case 5 77
Figure 3 50 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and linear cooling conditions for case 5 78
Figure 3 51 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and linear cooling conditions for case 5 79
Figure 3 52 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and linear cooling conditions for case 5 80
Figure 3 53 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.001 g/L and quadratic cooling conditions for case 5 81
Figure 3 54 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.01 g/L and quadratic cooling conditions for case 5 82
Figure 3 55 (a) Variation of cake resistance with residence time and (b) the corresponding CSD under a seed loading of 0.1 g/L and quadratic cooling conditions for case 5 83
Figure 3 56 (a)The cost for crystallizer and filter (b)The total capital cost (base case) 86
Figure 3 57 (a)The cost for crystallizer and filter (b)The total capital cost (case 2) 87
Figure 3 58 (a)The cost for crystallizer and filter (b)The total capital cost (case 3) 88
Figure 3 59 (a)The cost for crystallizer and filter (b)The total capital cost (case 4) 89
Figure 3 60 (a)The cost for crystallizer and filter (b)The total capital cost (case 5) 90

Table 2 1 Crystallization Kinetic Parameters for Batch Crystallizer Model[50-51] 9
Table 2 2 Parameters used for DEM Simulation. 18
Table 2 3 The Cost Parameters of Crystallizer and Filter. 19
Table 3 1 The conditions used for the base case. 22
Table 3 2 The values of nucleation and growth parameters for Cases 2 and 3. In case 2, kb is increased by a factor of 1000 from 2.78×10−5 (1/(μm3∙min)) to 2.78×10−2 (1/(μm3∙min)). In Case 3. The nucleation exponent is increased from 1.78 to 2.5. 34
Table 3 3 The values of nucleation and growth kinetic parameters for cases 4 and 5. In case 4, kg is increased by a factor of ten from 6967 (μm/min) to 69,670 (μm/min). In case 5, g is increased from 1.32 to 1.9. 59
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dc.language.isoen-
dc.subject濾餅可過濾性zh_TW
dc.subject最佳化zh_TW
dc.subject批次結晶器zh_TW
dc.subject結晶zh_TW
dc.subjectOptimizationen
dc.subjectBatch Crystallizeren
dc.subjectCake Filterabilityen
dc.subjectCrystallizationen
dc.title在批次結晶槽中濾餅的過濾阻力之最適化zh_TW
dc.titleCake Filterability Optimization in a Batch Crystallizeren
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee陳誠亮;李豪業zh_TW
dc.contributor.oralexamcommitteeCheng-Liang Chen;Hao-Yeh Leeen
dc.subject.keyword濾餅可過濾性,最佳化,批次結晶器,結晶,zh_TW
dc.subject.keywordCake Filterability,Optimization,Batch Crystallizer,Crystallization,en
dc.relation.page103-
dc.identifier.doi10.6342/NTU202502597-
dc.rights.note未授權-
dc.date.accepted2025-08-01-
dc.contributor.author-college工學院-
dc.contributor.author-dept化學工程學系-
dc.date.embargo-liftN/A-
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