The design of an efficient controller for a process of batch crystallization is still an important area of ongoing research. In this paper, a -variable adaptive model-free control (-AMFC) has been proposed to control a seeded batch adipic acid crystallization process to get a final unimodal crystal size distribution (CSD) within the required mean weight size. The -AMFC is developed based on online adaptive estimation algorithm to enhance the classic model-free control’s (MFC) performance. It copes with unknown as well as partially known dynamics and the inherent complex behavior of this nonlinear process. The concept consists of using an updated local approximation model. Two structures of -AMFC are considered. The first is a -AMFC based on intelligent Proportional-Integral (-iPI) control and the second is based on intelligent Proportional-Integral control combined with Sliding Mode Control (-iPISMC). To obtain the controller parameters of the two -AMFC structures, the particle swarm optimization (PSO) method was employed. The comparison of alternative approaches is performed by means of simulations including different scenarios. It is found that the -iPISMC has a superior performance: it allows the output convergence 50 minutes earlier than the -iPI and it is more robust against any sudden disturbances.
AbderazekHYildizARMirjaliliS (2020) Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism. Knowledge-Based Systems191: 105237.
2.
AbouaïssaHFliessMJoinC (2017) On ramp metering: Towards a better understanding of ALINEA via model-free control. International Journal of Control90(5): 1018–1026.
3.
AfsiNBakirTOthmanS, et al. (2016) Estimation of the mean crystal size and the moments of the crystal size distribution in batch crystallization processes. In: 2016 4th international conference on control engineering & information technology (CEIT), Hammamet, 16–18 December, pp. 1–6. New York: IEEE.
4.
Al YounesYDrakANouraH, et al. (2014) Model-free control of a quadrotor vehicle. In: 2014 international conference on unmanned aircraft systems (ICUAS), Orlando, FL, 27–30 May, pp. 1126–1131. New York: IEEE.
5.
ArdjalABettayebMMansouriR, et al. (2021) Design and implementation of a model-free fractional order intelligent PI fractional order sliding mode controller for water level tank system. ISA Transactions. Epub ahead of print 3September. DOI: 10.1016/j.isatra.2021.08.042.
6.
AzizNHussainMMujtabaI (2000) Performance of different types of controllers in tracking optimal temperature profiles in batch reactors. Computers Chemical Engineering24(2–7): 1069–1075.
7.
BakirTOthmanSFevotteG, et al. (2006) Nonlinear observer of crystal-size distribution during batch crystallization. AIChE Journal52(6): 2188–2197.
8.
BarrettPSmithBWorlitschekJ, et al. (2005) A review of the use of process analytical technology for the understanding and optimization of production batch crystallization processes. Organic Process Research Development9(3): 348–355.
9.
CarrilloFJRotellaF (2015) Some contributions to estimation for model-free control. IFAC-PapersOnLine48(28): 150–155.
10.
ChristofidesPDEl-FarraNLiM, et al. (2008) Model-based control of particulate processes. Chemical Engineering Science63(5): 1156–1172.
11.
ChungSHMaDLBraatzRD (1999) Optimal seeding in batch crystallization. The Canadian Journal of Chemical Engineering77(3): 590–596.
12.
FliessMJoinC (2013) Model-free control. International Journal of Control86(12): 2228–2252.
13.
FujiwaraMChowPSMaDL, et al. (2002) Paracetamol crystallization using laser backscattering and ATR-FTIR spectroscopy: Metastability, agglomeration, and control. Crystal Growth & Design2(5): 363–370.
14.
GriffinDJGroverMAKawajiriY, et al. (2015) Mass–count plots for crystal size control. Chemical Engineering Science137: 338–351.
15.
GuldnerJUtkinVIAckermannJ (1994) A sliding mode control approach to automatic car steering. In: Proceedings of 19 American control conference-ACC’94, Baltimore, MD, 29 June–1 July, vol. 2, pp. 1969–1973. New York: IEEE.
16.
HanHSuCYStepanenkoY (2001) Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators. IEEE Transactions on Fuzzy Systems9(2): 315–323.
17.
KennedyJEberhartR (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international Conference on Neural Networks, Perth, WA, 27 November–1 December, volume 4, pp. 1942–1948. New York: IEEE.
18.
LiSWangHTianY, et al. (2016) Direct power control of DFIG wind turbine systems based on an intelligent proportional-integral sliding mode control. ISA Transactions64: 431–439.
19.
LiYXYangGH (2017) Observer-based fuzzy adaptive event-triggered control codesign for a class of uncertain nonlinear systems. IEEE Transactions on Fuzzy Systems26(3): 1589–1599.
20.
MesbahAHuesmanAEKramerHJ, et al. (2011) A comparison of nonlinear observers for output feedback model-based control of seeded batch crystallization processes. Journal of Process Control21(4): 652–666.
21.
MesbahAKramerHJHuesmanAE, et al. (2009) A control oriented study on the numerical solution of the population balance equation for crystallization processes. Chemical Engineering Science64(20): 4262–4277.
22.
MichelLJoinCFliessM, et al. (2010) Model-free control of DC/DC converters. In: 2010 IEEE 12th workshop on control and modeling for power electronics (COMPEL), Boulder, CO, 28–30 June, pp. 1–8. New York: IEEE.
23.
MillerSMRawlingsJB (1994) Model identification and control strategies for batch cooling crystallizers. AIChE Journal40(8): 1312–1327.
24.
MoayediHMehrabiMMosallanezhadM, et al. (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Engineering with Computers35(3): 967–984.
25.
PinM (2014) Tecnica MPPT per il controllo ottimizzato di una pompa solare: aspetti hardware = MPPT control of an optimized solar pump electric drive: Hardware features. Available at: https://thesis.unipd.it/handle/20.500.12608/18145
26.
PrecupRERadacMBRomanRC, et al. (2017) Model-free sliding mode control of nonlinear systems: Algorithms and experiments. Information Sciences381: 176–192.
27.
SaengchanAKittisupakornPPaengjuntuekW, et al. (2011) Improvement of batch crystallization control under uncertain kinetic parameters by model predictive control. Journal of Industrial and Engineering Chemistry17(3): 430–438.
28.
SaleemiARiellyCNagyZK (2012) Automated direct nucleation control for in situ dynamic fines removal in batch cooling crystallization. Crystengcomm14(6): 2196–2203.
29.
SarkarDRohaniSJutanA (2006) Multi-objective optimization of seeded batch crystallization processes. Chemical Engineering Science61(16): 5282–5295.
30.
ShiDMhaskarPEl-FarraNH, et al. (2005) Predictive control of crystal size distribution in protein crystallization. Nanotechnology16(7): S562.
31.
SongBWangZZouL (2020) An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree bezier curve. Applied Soft Computing100: 106960.
32.
VuTDurhamRJHouriganJ, et al. (2006) Dynamic modelling optimisation and control of lactose crystallisations: Comparison of process alternatives. Separation and Purification Technology48(2): 159–166.
33.
WangHTianYHanS, et al. (2017) ZMP theory-based gait planning and model-free trajectory tracking control of lower limb carrying exoskeleton system. Studies in Informatics and Control26(2): 161–170.
34.
WangHXuHTianY, et al. (2020) α-variable adaptive model free control of iReHave upper-limb exoskeleton. Advances in Engineering Software148: 102872.
35.
WangMLuZWanW, et al. (2021) A calibration framework for the microparameters of the dem model using the improved PSO algorithm. Advanced Powder Technology32(2): 358–369.
36.
ZhangGRohaniS (2004) Dynamic optimal control of batch crystallization processes. Canadian Chemical Engineering Communication191(3): 356–372.
