Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks

Aghajani, Mohamad Hosein; Pashazadeh, Ali; Mostafavi, Seyed Hossein; Abbasi, Shayan; Hajibagheri-Fard, Mohammad-Javad; Assadi, Majid; Aghajani, Masoud

doi:10.1208/s12249-015-0293-1

Cited by 10 publications

(9 citation statements)

References 32 publications

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“…To date, some foreign scholars have used neural networks to optimise the reaction performance of microreactors. For example, Aghajani used an artificial neural network to research the size of synthesised nano-iodine in microreactors; it was found that the relationships between flow rate of solvent, flow rate of antisolvent, and size of the synthesised nano-iodine are in inverse relation [21]. Na researched the optimisation of catalyst loading in Fischer-Tropsch microchannel reactors, using the distribution of catalyst loading in microchannel reactors as a variable and considering C5+ productivity and temperature rise in microchannels as optimisation objects by using computational fluid dynamics, it was found that C5+ productivity was increased to 22% and ΔTmax was decreased to 63.2% by using a genetic algorithm (GA) [22].…”

Section: Nomenclaturementioning

confidence: 99%

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Zheng

Zhou

et al. 2019

Chemical Engineering Journal

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To optimise methanol steam reforming performance of cylindrical microreactor for hydrogen production, an error backpropagation algorithm was used to build a mathematical model for reaction performance of different microreactors for hydrogen production. Additionally, a genetic algorithm (GA) was utilised to process the computational model to obtain the optimum reaction parameters. The reliability of the optimum reaction parameters of cylindrical microreactor for hydrogen production was verified by experiments. Firstly, take plate microreactor as an example, the porosity of porous copper fiber sintered sheet (PCFSS), reaction temperature of methanol steam reforming for hydrogen production, injection velocity of the methanol and water mixture, and catalyst loading of PCFSS were considered as input data, whereas methanol conversion was used as output data. The computational model for specific testing system was gained by utilising input and output data from specific testing system to train the mathematical model for different microreactors, combining with matrix laboratory (MATLAB) neural network toolbox and designed MATLAB program. The E max of 5% for plate microreactor and E max of 3.2% for cylindrical microreactor verified the good predictive ability and reliability of the computational model for plate and cylindrical microreactor, indicating the reliability and universal applicability of the mathematical model for different microreactors. Secondly, the effects and mechanisms of PPI, reaction temperature, injection velocity, and catalyst loading on methanol conversion were studied, relying on the computational model. Finally, the optimum reaction parameters were acquired using GA, MATLAB neural network toolbox and designed MATLAB program. The validity of the optimum reaction parameters of cylindrical microreactor for hydrogen production was confirmed by experiments. This study provides a reference method for methanol steam reforming performance optimisation for hydrogen production.Highlights >Error backpropagation algorithm is used to built general mathematical model.>Computational model is established based on general mathematical model.>Computational model shows good reliability and predictive ability. >Relations between reaction parameters and performance are studied. > Optimum reaction parameters are obtained by using genetic algorithm.Graphical abstract

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Section: Nomenclaturementioning

confidence: 99%

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Zheng

Zhou

et al. 2019

Chemical Engineering Journal

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“…Since nanosuspension of stable-iodine ( 127 I) is a deflocculated nanosuspension [7], sedimentation time of formulation can be easily determined by visual observation of a densely packed sediment during a certain period of time as the sedimentation time. In other words, sedimentation time of samples was considered as phase separation time of nanoparticles in nanosuspension, representing an indicator of physical stability [10].…”

Section: Evaluation Of Sedimentation Timementioning

confidence: 99%

“…Since nanosuspension of stable-iodine ( 127 I) is a deflocculated suspension, we used visual observation of sediment to evaluate stability of samples obtained, as mentioned in previous studies [7,10,14]. In a deflocculated suspension, a densely packed sediment with a slow sedimentation rate has a hard structure and cannot be re-dispersed after shaking.…”

Section: Evaluating the Effect Of Input Variables On Sedimentation Timentioning

confidence: 99%

“…Considering a competition between stable-iodine ( 127 I) and radio-iodine to absorb in the thyroid gland, consuming the nanosuspension formulation of stable-iodine ( 127 I) can increase the uptake rate of usual form of stable-iodine ( 127 I) compared to radio-iodine. This phenomena causes a decrease in the uptake of radio-iodine [7].…”

Section: Introductionmentioning

confidence: 99%

“…The possibility of creating formulations with high efficiency is established by achieving favored conditions (less PDI and more sedimentation time). A previous study [7], showed that ANNs is able to create the qualified model to demonstrate the effects of an independent experimental variable on the dependent variable (particle size). Finally, after an analysis of results, a sample was created with an optimized particle size (namely, a nanosuspension formulation with minimum size).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing microfluidic preparation parameters of nanosuspension to evaluate stability in nanoprecipitation of stable-iodine (127I)

et al. 2019

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The aim of the current study was the development of nanosuspension stability in nanoprecipitation using microfluidic devices. Also, it is desirable to understand how the microfluidic preparation parameters influenced the stability of the stable-iodine ( 127 I) nanosuspension. In optimization process through artificial neural networks (ANNs), the relations between input and output variables were investigated for 37 samples obtained by microfluidic nanoprecipitation process. Solvent temperature, antisolvent flow rate, and solvent flow rate were used as input variables, and the sedimentation time and polydispersity index (PDI) were considered as output parameters. Sedimentation time as an indicator of physical stability of nanosuspension was evaluated by observation of a densely packed sediment. Also, size and PDI of different samples were determined by dynamic light scattering. The size of the optimized sample was confirmed by transmission electron microscopy. The result obtained from modeling showed that increasing solvent temperature and antisolvent flow rate led to a decrease in PDI and an increase in the sedimentation time. The antisolvent flow rate was determined as the most important factor that affected the sedimentation time and PDI. Increasing the solvent flow rate was identified as an adverse factor which increased PDI or decreased formulation's sedimentation time. Optimization using ANN showed that microfluidic preparation parameters of nanosuspension as input variables had potential impacts on output parameters.

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Nucleation Rate Prediction of Curcumin Particles in Microfluidic‐Assisted Nanoprecipitation

Valeh-e-Sheyda

Mir

2020

Chem Eng & Technol

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A hybrid computational model was developed to interpret the formation of curcumin particles via liquid antisolvent precipitation (LASP) in three microchannel reactors (MCRs) with different confluence angles. The computational fluid dynamics (CFD) model was numerically combined with a discretized population balance approach. The nucleation kinetics were computed using an expression obtained from the nucleation rate and supersaturation equation from previous experimental data. A quantitative comparison of the particle size distributions (PSDs) demonstrated that the particles with the smallest diameters are observed right after the intersection of the three streams. Besides, the large confluence angle of 135°in the MCR presented higher population density as a result of the smaller size of the precipitated curcumin particles. These findings illustrate the efficient applicability of the hybrid model.

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Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks

Cited by 10 publications

References 32 publications

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Optimizing microfluidic preparation parameters of nanosuspension to evaluate stability in nanoprecipitation of stable-iodine (127I)

Nucleation Rate Prediction of Curcumin Particles in Microfluidic‐Assisted Nanoprecipitation

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