Research progress of industrial crystallization towards intelligent manufacturing

doi:10.11949/j.issn.0438-1157.20180983

Abstract

Abstract:

Industrial crystallization is a “semi-artistic science” and its process has the characteristics of multi-objective, non-linear and strong coupling. It is well-known by international scholars as one of the most difficult unit operations of chemical engineering to design. On the basis of the domestic and international research progresses on the topic of industrial crystallization and intelligent manufacturing, this review intends to construct a multi-scale research framework of industrial crystallization with intelligent manufacturing technologies to confront with the major strategic demands and historical opportunities of intelligent manufacturing development. The review summarizes the application of core intelligent manufacturing technologies including AI, cloud computing, and internet of things in industrial crystallization with some relevant cases. It will mainly focus on the analysis and discussion of development status and potential integration point of prediction of solubility, polymorphs, crystal habit, and co-crystals with intelligent manufacturing. Some methods for predicting the crystallization conditions of proteins is introduced. Finally, the intelligent control technologies of perception, analysis and decision in crystallization process were reviewed.

Key words: industrial crystallization, solubility, crystal form, crystal habit, co-crystal, prediction, process control

摘要：

工业结晶是一门“半艺术的科学”，具有多目标、非线性和强耦合的特点，在国际上被公认是最难设计的化工单元操作之一。面向智能制造发展的重大战略需求和历史机遇，基于国内外对工业结晶和智能制造的研究现状，拟构建基于智能制造的工业结晶多尺度研究框架。结合相关案例，总结了国内外人工智能、云计算、物联网等核心智能制造技术在工业结晶中的应用，重点分析讨论在溶解度预测、晶型预测、晶习预测、共晶预测与智能制造的发展现状和潜在结合点；总结了结晶过程中的感知、分析、决策的智能控制技术。

关键词: 工业结晶, 溶解性, 晶型, 晶习, 共晶, 预测, 过程控制

CLC Number:

O799

GONG Junbo, SUN Jie, WANG Jingkang. Research progress of industrial crystallization towards intelligent manufacturing[J]. CIESC Journal, 2018, 69(11): 4505-4517.

龚俊波, 孙杰, 王静康. 面向智能制造的工业结晶研究进展[J]. 化工学报, 2018, 69(11): 4505-4517.

References 113

[1]	周济. 智能制造——"中国制造2025"的主攻方向[J]. 中国机械工程, 2015, 26(17):2273-2284. ZHOU J. Intelligent manufacturing-main direction of "made in China 2025"[J].China Mechanical Engineering, 2015, 26(17):2273-2284.
[2]	JONES A G. Crystallization Process Systems[M]. Elsevier, 2002.
[3]	龚俊波, 陈明洋, 黄翠, 等. 面向清洁生产的制药结晶[J]. 化工学报, 2015, 66(9):3271-3278. GONG J B, CHEN M Y, HUANG C, et al. Clean production of pharmaceutical crystallization[J]. CIESC Journal, 2015, 66(9):3271-3278.
[4]	WANG J, XU A, XU R. Solubility of 2-nitro-p -phenylenediamine in nine pure solvents and mixture of(methanol +N-methyl-2-pyrrolidone) from T=(283.15 to 318.15) K:determination and modelling[J]. Journal of Chemical Thermodynamics, 2017, 108:45-58.
[5]	CHEN G, CHEN J, CHENG C, et al. Solubility determination and thermodynamic modelling of 2-amino-5-methylthiazole in eleven organic solvents from T=(278.15 to 313.15) K and mixing properties of solutions[J]. Journal of Molecular Liquids, 2017, 232:226-235.
[6]	KOROTCOV A, TKACHENKO V, RUSSO D P, et al. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets[J]. Molecular Pharmaceutics, 2017, 14(12):4462-4475.
[7]	BOUILLOT B, TEYCHENÉ S, BISCANS B. An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents[J]. Fluid Phase Equilibria, 2011, 309(1):36-52.
[8]	SKYNER R E, MCDONAGH J L, GROOM C R, et al. A review of methods for the calculation of solution free energies and the modelling of systems in solution.[J]. Physical Chemistry Chemical Physics, 2015, 17(9):6174-6191.
[9]	LUSCI A, POLLASTRI G, BALDI P. Deep architectures and deep learning in chemoinformatics:the prediction of aqueous solubility for drug-like molecules[J]. Journal of Chemical Information & Modeling, 2013, 53(7):1563-1575.
[10]	SHAYANFAR A, FAKHREE M A A, JOUYBAN A. A simple QSPR model to predict aqueous solubility of drugs[J]. Journal of Drug Delivery Science & Technology, 2010, 20(6):467-476.
[11]	LEACH A R, GILLET V J. An Introduction to Chemoinformatics[M]. Kluwer Academic Publishers, 2007.
[12]	PORTUGAL I, ALENCAR P, COWAN D. The use of machine learning algorithms in recommender systems:a systematic review[J]. Expert Systems with Applications, 2018, 97:205-227.
[13]	PALMER D S, NOEL M O, AND R C G, et al. Random forest models to predict aqueous solubility[J]. Journal of Chemical Information & Modeling, 2007, 47(1):150.
[14]	WU K, ZHAO Z, WANG R, et al. TopP-S:persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility[J]. Journal of Computational Chemistry, 2018, 39(20):1444-1454.
[15]	WU Z, RAMSUNDAR B, FEINBERG E N, et al. MoleculeNet:a benchmark for molecular machine learning[J]. Chemical Science, 2017, 9(2):513-530.
[16]	ERI?A S, POPOVI? A, ZLOH M, et al. Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks[J]. International Journal of Pharmaceutics, 2012, 437(1/2):232-241.
[17]	LUSCI A, POLLASTRI G, BALDI P. Deep architectures and deep learning in chemoinformatics:the prediction of aqueous solubility for drug-like molecules[J]. Journal of Chemical Information & Modeling, 2013, 53(7):1563-1575.
[18]	MCDONAGH J L, NATH N, FERRARI L D, et al. Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules[J]. Journal of Chemical Information & Modeling, 2014, 54(3):844.
[19]	LOUIS B, AGRAWAL V K, KHADIKAR P V. Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses[J]. European Journal of Medicinal Chemistry, 2010, 45(9):4018-4025.
[20]	LUSCI A, POLLASTRI G, BALDI P. Deep architectures and deep learning in chemoinformatics:the prediction of aqueous solubility for drug-like molecules[J]. Journal of Chemical Information & Modeling, 2013, 53(7):1563-1575.
[21]	BOOBIER S, OSBOURN A, MITCHELL J. Can human experts predict solubility better than computers?[J]. J. Cheminform, 2017, 9(1):63.
[22]	HOU J, WU S, LI R, et al. The induction time, interfacial energy and growth mechanism of maltitol in batch cooling crystallization[J]. Crystal Research and Technology, 2012, 8(47):888-895.
[23]	HULME A T, PRICE S L, TOCHER D A. A new polymorph of 5-fluorouracil found following computational crystal structure predictions[J]. Journal of the American Chemical Society, 2005, 127(4):1116-1117.
[24]	NEUMANN M A, PERRIN M. Can crystal structure prediction guide experimentalists to a new polymorph of paracetamol?[J]. CrystEngComm, 2009, 11(11):2475.
[25]	GROOM C R, REILLY A M. Sixth blind test of organic crystal-structure prediction methods[J]. Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials, 2014, 70(4):776-777.
[26]	OLIYNYK A O, ADUTWUM L A, RUDYK B W, et al. Disentangling structural confusion through machine learning:structure prediction and polymorphism of equiatomic ternary phases ABC[J]. Journal of the American Chemical Society, 2017, 139(49):17870-17881.
[27]	ARTRITH N, URBAN A. An implementation of artificial neural-network potentials for atomistic materials simulations:performance for TiO2[J]. Computational Materials Science, 2016, 114:135-150.
[28]	FELIX MUSIL S D J Y. Machine learning for the structure-energy-property landscapes of molecular crystals electronic supplementary information[J]. Chemical Science, 2018, 5(9):1289-1300.
[29]	CAI Z, LIU Y, SONG Y, et al. The effect of tailor-made additives on crystal growth of methyl paraben:experiments and modelling[J]. Journal of Crystal Growth, 2017, 461:1-9.
[30]	AL-JIBBOURI S, STREGE C, ULRICH J. Crystallization kinetics of epsomite influenced by pH-value and impurities[J]. Journal of Crystal Growth, 2002, 236(1):400-406.
[31]	SRIRAMBHATLA V K, GUO R, PRICE S L, et al. Isomorphous template induced crystallisation:a robust method for the targeted crystallisation of computationally predicted metastable polymorphs[J]. Chemical Communications, 2016, 52(46):7384-7386.
[32]	PADRELA L, ZEGLINSKI J, RYAN K M. Insight into the role of additives in controlling polymorphic outcome:a CO2-antisolvent crystallization process of carbamazepine[J]. Crystal Growth & Design, 2017, 17(9):4544-4553.
[33]	WEISSBUCH I, TORBEEV V Y, LEISEROWITZ L, et al. Solvent effect on crystal polymorphism:why addition of methanol or ethanol to aqueous solutions induces the precipitation of the least stable beta form of glycine[J]. Angewandte Chemie International Edition, 2005, 44(21):3226-3229.
[34]	BRAVAIS A. Études Crystallographic[M]. Paris:Gauthier-Villars, 1866.
[35]	DONNAY J D H. A new law of crystal morphology extending the law of Bravais[J]. Amer. Mineral, 1937, 22:446-467.
[36]	HARTMAN P, BENNEMA P. The attachment energy as a habit controlling factor(Ⅰ):Theoretical considerations[J]. Journal of Crystal Growth, 1980, 49(1):145-156.
[37]	ZHANG C, JI C, LI H, et al. Occupancy model for predicting the crystal morphologies influenced by solvents and temperature, and its application to nitroamine explosives[J]. Crystal Growth & Design, 2012, 13(1):282-290.
[38]	MARKOV I V. Crystal Growth for Beginners[M]. World Scientific Europe, 1995:355.
[39]	DEIJ M A, CUPPEN H M, MEEKES H, et al. Steps on surfaces in modeling crystal growth[J]. Crystal Growth & Design, 2007, 7(10):1936-1942.
[40]	DEIJ M A, MEEKES H, VLIEG E. The step energy as a habit controlling factor: application to the morphology prediction of aspartame, venlafaxine, and a yellow isoxazolone dye[J]. Crystal Growth & Design, 2007, 7(10):1949-1957.
[41]	SNYDER R C, DOHERTY M F. Predicting crystal growth by spiral motion[J]. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, 2009, 465(2104):1145-1171.
[42]	TILBURY C J, GREEN D A, MARSHALL W J, et al. Predicting the effect of solvent on the crystal habit of small organic molecules[J]. Crystal Growth & Design, 2016, 16(5):2590-2604.
[43]	TILBURY C J, GREEN D A, MARSHALL W J, et al. Predicting the effect of solvent on the crystal habit of small organic molecules[J]. Crystal Growth & Design, 2016, 16(5):2590-2604.
[44]	LOVETTE M A, DOHERTY M F. Predictive modeling of supersaturation-dependent crystal shapes[J]. Crystal Growth & Design, 2012, 12(2):656-669.
[45]	SHIM H, KOO K. Prediction of growth habit of b-cyclotetramethylene-tetranitramine crystals by the first-principles models[J]. Crystal Growth & Design, 2015, 15(8):3983-3991.
[46]	SHIM H, KOO K. Molecular approach to the effect of interfacial energy on growth habit of e-HNIW[J]. Crystal Growth & Design, 2016, 16(11):6506-6513.
[47]	TILBURY C J, GREEN D A, MARSHALL W J, et al. Predicting the effect of solvent on the crystal habit of small organic molecules[J]. Crystal Growth & Design, 2016, 16(5):2590-2604.
[48]	TILBURY C J, JOSWIAK M N, PETERS B, et al. Modeling step velocities and edge surface structures during growth of non-centrosymmetric crystals[J]. Crystal Growth & Design, 2017, 17(4):2066-2080.
[49]	NIEH R S, ULRICH J. Designing crystal morphology by a simple approach[J]. Crystal Research and Technology, 1995, 30(3):389-395.
[50]	SCHMIDT C, ULRICH J. Crystal habit prediction-including the liquid as well as the solid side[J]. Crystal Research and Technology, 2012, 47(6):597-602.
[51]	SCHMIDT C, ULRICH J. Morphology prediction of crystals grown in the presence of impurities and solvents-an evaluation of the state of the art[J]. Journal of Crystal Growth, 2012, 353(1):168-173.
[52]	WINN D, DOHERTY M F. Modeling crystal shapes of organic materials grown from solution[J]. AIChE Journal, 2010, 46(7):1348-1367.
[53]	SEGLER M H S, PREUSS M, WALLER M P. Planning chemical syntheses with deep neural networks and symbolic AI[J]. Nature, 2018, 555(7698):604-610.
[54]	SANPHUI P, GOUD N R, KHANDAVILLI U B R, et al. Fast dissolving curcumin cocrystals[J]. Crystal Growth & Design, 2011, 11(9):4135-4145.
[55]	CHOW S F, SHI L, NG W W, et al. Kinetic entrapment of a hidden curcumin cocrystal with phloroglucinol[J]. Crystal Growth & Design, 2014, 14(10):5079-5089.
[56]	KULESHOVA L N, HOFMANN D W M, BOESE R. Lattice energy calculation-a quick tool for screening of cocrystals and estimation of relative solubility. Case of flavonoids[J]. Chemical Physics Letters, 2013, 564(12):26-32.
[57]	HOFMANN D W M, APOSTOLAKIS J. Crystal structure prediction by data mining[J]. Journal of Molecular Structure, 2003, 647(1):17-39.
[58]	HONG C, XIE Y, YAO Y, et al. A novel strategy for pharmaceutical cocrystal generation without knowledge of stoichiometric ratio:myricetin cocrystals and a ternary phase diagram[J]. Pharmaceutical Research, 2015, 32(1):47-60.
[59]	JUNG I H, KIM J. Thermodynamic modeling of the Mg-Ge-Si, Mg-Ge-Sn, Mg-Pb-Si and Mg-Pb-Sn systems[J]. Journal of Alloys & Compounds, 2010, 494(1):137-147.
[60]	ABRAMOV Y A, LOSCHEN C, KLAMT A. Rational coformer or solvent selection for pharmaceutical cocrystallization or desolvation[J]. Journal of Pharmaceutical Sciences, 2012, 101(10):3687-3697.
[61]	LOSCHEN C, KLAMT A. Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering[J]. J. Pharm. Pharmacol., 2015, 67(6):803-811.
[62]	LANGE L, SADOWSKI G. Thermodynamic modeling for efficient cocrystal formation[J]. Crystal Growth & Design, 2015, 15(9):4406-4416.
[63]	PERLOVICH G L. Thermodynamic characteristics of cocrystal formation and melting points for rational design of pharmaceutical two-component systems[J]. Crystengcomm, 2015, 17(37):7019-7028.
[64]	KARAMERTZANIS P G, KAZANTSEV A V, ISSA N, et al. Can the formation of pharmaceutical cocrystals be computationally predicted?(Ⅱ):Crystal structure prediction[J]. Journal of Chemical Theory & Computation, 2009, 5(5):1432.
[65]	GAMIDI R K, RASMUSON Å C. Estimation of melting temperature of molecular co-crystals using artificial neural network model[J]. Crystal Growth & Design, 2017, 17(1):175-182.
[66]	WICKER J, CROWLEY L, ROBSHAW O, et al. Will they co-crystallize?[J]. Crystengcomm, 2017, 19(36):5336-5340.
[67]	GIEGÉ R. A historical perspective on protein crystallization from 1840 to the present day[J]. FEBS Journal, 2013, 280(24):6456-6497.
[68]	王岸娜, 苏子豪, 吴立根, 等. 蛋白质结晶研究进展[J]. 河南工业大学学报(自然科学版), 2014, 35(5):107-115. WANG A N, SU Z H, WU L G, et al. Research progress in protein crystallization[J]. Journal of Henan University of Technology(Natural Science Edition), 2014, 35(5):107-115.
[69]	SLABINSKI L, JAROSZEWSKI L, RYCHLEWSKI L, et al. XtalPred:a web server for prediction of protein crystallizability[J]. Bioinformatics, 2007, 23(24):3403-3405.
[70]	SMIALOWSKI P, SCHMIDT T, COX J, et al. Will my protein crystallize? A sequence-based predictor[J]. Proteins Structure Function & Bioinformatics, 2010, 62(2):343-355.
[71]	PRICE Ⅱ W N, CHEN Y, HANDELMAN S K, et al. Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data[J]. Nature Biotechnology, 2009, 27(1):51-57.
[72]	CHEN K, KURGAN L, RAHBARI M. Prediction of protein crystallization using collocation of amino acid pairs[J]. Biochemical and Biophysical Research Communications, 2007, 355(3):764-769.
[73]	CHAROENKWAN P, SHOOMBUATONG W, LEE H C, et al. SCMCRYS:predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs[J]. Plos One, 2013, 8(9):e72368.
[74]	OVERTON I M, PADOVANI G, GIROLAMI M A, et al. ParCrys:a Parzen window density estimation approach to protein crystallization propensity prediction[J]. Bioinformatics, 2008, 24(7):901-907.
[75]	OVERTON I M, BARTON G J. A normalised scale for structural genomics target ranking:the OB-Score[J]. FEBS Letters, 2006, 580(16):4005-4009.
[76]	CHAYEN N E. Turning protein crystallisation from an art into a science[J]. Current Opinion in Structural Biology, 2004, 14(5):577-583.
[77]	DELGADO-FRIEDRICHS O, O'KEEFFE M, YAGHI O M. Three-periodic nets and tilings:edge-transitive binodal structures[J]. Acta Crystallographica Section A Foundations of Crystallography, 2006, 62(5):350-355.
[78]	ALEXANDROV E V, SHEVCHENKO A P, ASIRI A A, et al. New knowledge and tools for crystal design:local coordination versus overall network topology and much more[J]. CrystEngComm, 2015, 17(15):2913-2924.
[79]	FABER F, LINDMAA A, VON LILIENFELD O A, et al. Crystal structure representations for machine learning models of formation energies[J]. International Journal of Quantum Chemistry, 2015, 115(16):1094-1101.
[80]	BHARDWAJ R M A J. A random forest model for predicting the crystallisability of organic molecules[J]. CrystEngComm, 2015, 23(17):4272-4275.
[81]	BHARDWAJ R M, JOHNSTON A, JOHNSTON B F, et al. A random forest model for predicting the crystallisability of organic molecules[J]. Crystengcomm, 2015, 17(23):4272-4275.
[82]	OVERTON I M, VAN NIEKERK C A J, BARTON G J. XANNpred:neural nets that predict the propensity of a protein to yield diffraction-quality crystals[J]. Proteins Structure Function & Bioinformatics, 2015, 79(4):1027-1033.
[83]	DUROS V, GRIZOU J, XUAN W, et al. Human versus robots in the discovery and crystallization of gigantic polyoxometalates[J]. Angewandte Chemie International Edition, 2017, 56(36):10815-10820.
[84]	卞正岗. 高端智能装备与自动化技术[J]. 自动化博览, 2011, 28(s1):2-5. BIAN Z G. Automation technique and advanced intelligent equipment[J]. Automation Panorama, 2011, 28(s1):2-5.
[85]	SIMON L L, SIMONE E, OUCHERIF K A. Chapter 9-Crystallization process monitoring and control using process analytical technology[J]. Computer Aided Chemical Engineering, 2018, 41:215-242.
[86]	王娜, 陶晓龙, 史欢欢, 等. 过程分析技术在晶体多晶型研究中的应用[J]. 化学工业与工程, 2017, 34(2):1-9. WANG N, TAO X L, SHI H H, et al. Application of process analytical tools in polymorphism of crystalline materials[J]. Chemical Industry and Engineering, 2017, 34(2):1-9.
[87]	SIMON L L, NAGY Z K, HUNGERBUHLER K. Comparison of external bulk video imaging with focused beam reflectance measurement and ultra-violet visible spectroscopy for metastable zone identification in food and pharmaceutical crystallization processes[J]. Chemical Engineering Science, 2009, 64(14):3344-3351.
[88]	POWELL K A, CROKER D M, RIELLY C D, et al. PAT-based design of agrochemical co-crystallization processes:a case-study for the selective crystallization of 1:1 and 3:2 co-crystals of p-toluenesulfonamide/triphenylphosphine oxide[J]. Chemical Engineering Science, 2016, 152:95-108.
[89]	PEÑA R, NAGY Z K. Process intensification through continuous spherical crystallization using a two-stage mixed suspension mixed product removal(MSMPR) system[J]. Crystal Growth & Design, 2015, 15(9):4225-4236.
[90]	LIU W, WEI H, ZHAO J, et al. Investigation into the cooling crystallization and transformations of carbamazepine using in situ FBRM and PVM[J]. Organic Process Research & Development, 2013, 17(11):1406-1412.
[91]	LUO Y, TU Y, GE J, et al. Monitoring the crystallization process of methylprednisolone hemisuccinate(MPHS) from ethanol solution by combined ATR-FTIR-FBRM-PVM[J]. Separation Science & Technology, 2013, 48(12):1881-1890.
[92]	KLAPWIJK A R, SIMONE E, NAGY Z K, et al. Tuning crystal morphology of succinic acid using a polymer additive[J]. Crystal Growth & Design, 2016, 16(8):4349-4359.
[93]	KADAM S S, WINDT E V D, DAUDEY P J, et al. A comparative study of ATR-FTIR and FT-NIR spectroscopy for in-situ concentration monitoring during batch cooling crystallization processes[J]. Crystal Growth & Design, 2010, 10(6):2629-2640.
[94]	SALEEMI A, RIELLY C, NAGY Z K. Automated direct nucleation control for in situ dynamic fines removal in batch cooling crystallization[J].Crystengcomm, 2012, 14(6):2196-2203.
[95]	SIMONE E, SALEEMI A N, NAGY Z K. In situ monitoring of polymorphic transformations using a composite sensor array of Raman, NIR, and ATR-UV/vis spectroscopy, FBRM, and PVM for an intelligent decision support system[J]. Organic Process Research & Development, 2015, 19(1):167-177.
[96]	SIMONE E, SALEEMI A N, NAGY Z K. Application of quantitative Raman spectroscopy for the monitoring of polymorphic transformation in crystallization processes using a good calibration practice procedure[J]. Chemical Engineering Research & Design, 2014, 92(4):594-611.
[97]	SIMON L L, OUCHERIF K A, NAGY Z K, et al. Histogram matching, hypothesis testing, and statistical control-chart-assisted nucleation detection using bulk video imaging for optimal switching between nucleation and seed conditioning steps[J]. Industrial & Engineering Chemistry Research, 2010, 49(20):9932-9944.
[98]	SIMON L L, OUCHERIF K A, NAGY Z K, et al. Bulk video imaging based multivariate image analysis, process control chart and acoustic signal assisted nucleation detection[J]. Chemical Engineering Science, 2010, 65(17):4983-4995.
[99]	KEE N C S, TAN R B H, BRAATZ R D. Selective crystallization of the metastable a-form of L-glutamic acid using concentration feedback control[J]. Crystal Growth & Design, 2009, 9(7):3044-3051.
[100]	FUJIWARA M, NAGY Z K, JIE W C, et al. First-principles and direct design approaches for the control of pharmaceutical crystallization[J]. Journal of Process Control, 2005, 15(5):493-504.
[101]	XING Y W, NAGY Z K, TAN R B H, et al. Adaptive concentration control of cooling and antisolvent crystallization with laser backscattering measurement[J]. Crystal Growth & Design, 2009, 9(1):182-191.
[102]	ALATALO H M, HATAKKA H, LOUHI KULTANEN M, et al. Closed-loop control of reactive crystallization(Ⅰ):Supersaturation-controlled crystallization of L-glutamic acid[J]. Chemical Engineering & Technology, 2010, 33(5):743-750.
[103]	KLEINERT T, WEICKGENANNT M, JUDAT B, et al. Cascaded two-degree-of-freedom control of seeded batch crystallisations based on explicit system inversion[J]. Journal of Process Control, 2010, 20(1):29-44.
[104]	KHAN S, MA C Y, MAHMUD T, et al. In-process monitoring and control of supersaturation in seeded batch cooling crystallization of L-glutamic acid:from laboratory to industrial pilot plant[J]. Organic Process Research & Development, 2011, 15(3):540-555.
[105]	ZHOU G, MOMENT A, YAUNG S, et al. Evolution and application of an automated platform for the development of crystallization processes[J]. Organic Process Research & Development, 2013, 17(10):1320-1329.
[106]	KACKER R, SALVADOR P M, STURM G S J, et al. Microwave assisted direct nucleation control for batch crystallization:crystal size control with reduced batch time[J]. Crystal Growth & Design, 2016, 16(1):440-446.
[107]	SALEEMI A N, RIELLY C D, NAGY Z K. Comparative investigation of supersaturation and automated direct nucleation control of crystal size distributions using ATR-UV/vis spectroscopy and FBRM[J]. Crystal Growth & Design, 2012, 12(12):1792-1807.
[108]	PATAKI H, CSONTOS I, NAGY Z K, et al. Implementation of Raman signal feedback to perform controlled crystallization of carvedilol[J]. Organic Process Research & Development, 2013, 17(3):493-499.
[109]	SIMONE E, SALEEMI A N, TONNON N, et al. Active polymorphic feedback control of crystallization processes using a combined Raman and ATR-UV/vis spectroscopy approach[J]. Crystal Growth & Design, 2014, 14(4):1839-1850.
[110]	YANG Y, SONG L, NAGY Z K. Automated direct nucleation control in continuous mixed suspension mixed product removal cooling crystallization[J]. Crystal Growth & Design, 2016, 15(12):5839-5848.
[111]	YANG Y, SONG L, ZHANG Y, et al. Application of wet milling-based automated direct nucleation control in continuous cooling crystallization processes[J]. Industrial & Engineering Chemistry Research, 2016, 55(17):4987-4996.
[112]	LAI T T C, CORNEVIN J, FERGUSON S, et al. Control of polymorphism in continuous crystallization via mixed suspension mixed product removal systems cascade design[J]. Crystal Growth & Design, 2016, 15(7):1433097091.
[113]	TACHTATZIS C, SHERIDAN R, MICHIE C, et al. Image-based monitoring for early detection of fouling in crystallisation processes[J]. Chemical Engineering Science, 2015, 133(22):82-90.
[114]	BELLO O, ZEADALLY S. Intelligent device-to-device communication in the internet of things[J]. IEEE Systems Journal, 2016, 10(3):1172-1182.43-750.
[100]	KLEINERT T, WEICKGENANNT M, JUDAT B, et al. Cascaded two-degree-of-freedom control of seeded batch crystallisations based on explicit system inversion[J]. Journal of Process Control, 2010,20(1):29-44.
[101]	KHAN S, MA C Y, MAHMUD T, et al. In-Process Monitoring and Control of Supersaturation in Seeded Batch Cooling Crystallisation of l-Glutamic Acid:From Laboratory to Industrial Pilot Plant[J]. Organic Process Research & Development, 2011,15(3):540-555.
[102]	ZHOU G, MOMENT A, YAUNG S, et al. Evolution and Application of an Automated Platform for the Development of Crystallization Processes[J]. Organic Process Research & Development, 2013,17(10):1320-1329.
[103]	KACKER R, SALVADOR P M, STURM G S J, et al. Microwave Assisted Direct Nucleation Control for Batch Crystallization:Crystal Size Control with Reduced Batch Time[J]. Crystal Growth & Design, 2016,16(1).
[104]	SALEEMI A N, RIELLY C D, NAGY Z K. Comparative Investigation of Supersaturation and Automated Direct Nucleation Control of Crystal Size Distributions using ATR-UV/vis Spectroscopy and FBRM[J]. Crystal Growth & Design, 2012,12(12):1792-1807.
[105]	PATAKI H, CSONTOS I, NAGY Z K, et al. Implementation of Raman Signal Feedback to Perform Controlled Crystallization of Carvedilol[J]. Organic Process Research & Development, 2013,17(3):493-499.
[106]	SIMONE E, SALEEMI A N, TONNON N, et al. Active Polymorphic Feedback Control of Crystallization Processes Using a Combined Raman and ATR-UV/Vis Spectroscopy Approach[J]. Crystal Growth & Design, 2014,14(4):1839-1850.
[107]	YANG Y, SONG L, NAGY Z K. Automated Direct Nucleation Control in Continuous Mixed Suspension Mixed Product Removal Cooling Crystallization[J]. Crystal Growth & Design, 2016,15(12):5839-5848.
[108]	YANG Y, SONG L, ZHANG Y, et al. Application of Wet Milling-Based Automated Direct Nucleation Control in Continuous Cooling Crystallization Processes[J]. Industrial & Engineering Chemistry Research, 2016,55(17).
[109]	LAI T T C, CORNEVIN J, FERGUSON S, et al. Control of Polymorphism in Continuous Crystallization via Mixed Suspension Mixed Product Removal Systems Cascade Design[J]. Crystal Growth & Design, 2016,15(7):1433097091.
[110]	TACHTATZIS C, SHERIDAN R, MICHIE C, et al. Image-based monitoring for early detection of fouling in crystallisation processes[J]. Chemical Engineering Science, 2015,133(22):82-90.
[111]	BELLO O, ZEADALLY S. Intelligent Device-to-Device Communication in the Internet of Things[J]. IEEE Systems Journal, 2016,10(3):1172-1182.
[112]	侯志霞, 邹方, 王湘念, 等. 关于建设航空智能生产线的思考[J]. 航空制造技术, 2015(8):50-52. Hou Zh X, Zhou F, Wang X N, et al. Suggestions on Aeronautic Intelligent Production Line Construction[J]. Aeronautical Manufacturing Technology, 2015(8):50-52.
[113]	孙柏林. 未来智能装备制造业发展趋势述评[J]. 自动化仪表, 2013(01):1-5. Sun B L. Commentary of the Development Trend for Intelligent Equipment Manufacturing Industry in the Future[J]. Process Automation Instrumentation, 2013(01):1-5.