Use of machine learning tool to elucidate and characterize the growth mechanism of an in-situ fluid bed melt granulation

Korteby, Yasmine; Kristó, Katalin; Sovány, Tamás; Regdon, Géza

doi:10.1016/j.powtec.2018.03.052

Cited by 10 publications

(6 citation statements)

References 46 publications

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“…This led to excellent flow properties in the resulting granules. Similarly, Korteby et al [6] observed that when a low binder content was used, fragile and low-density granules with a low degree of sphericity were formed.…”

Section: (%)mentioning

confidence: 88%

“…The air distributor is a stainless-steel perforated plate (2). The fluidization air was supplied by a centrifugal blower (3) with a bypass system that can be actioned by means of a manual butterfly valve (6). This bypass allows better control of the air flow within the chamber.…”

Section: Methodsmentioning

confidence: 99%

“…In this sense, fluidized bed melt granulation (FBMG) has received wide interest in the pharmaceutical research field, being the most common application the preparation of pharmaceutical granules for tableting [6]. Although granulation processes involving the incorporation of the binder as droplets have received significantly more attention from a scientific point of view, both for wet and melt granulation, it is notable that co-melt processes are much more practical from an industrial point of view, since they avoid the spraying stage with hot-melt flows, leading to a simpler production process [7].…”

Section: Introductionmentioning

confidence: 99%

“…Kukec et al [7] granulated Carvediol and lactose with a poloxamer to study the binder/seed mass ratio, fluidization air temperature, and endpoint temperature on granules' PSD, morphology, flow properties, and drug release through a Circumscribed Central Composite Design. More recently, Korteby et al [6] related granules' PSD, aspect ratio, porosity, and granulation yield with the binder/seed mass ratio, viscosity and size of the binder, and outlet temperature through another empirical technique as Artificial Neural Networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

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Fluidized Bed Co-Melt Granulation: New Insights in the Influence of Process Variables and Validation of Regime Map Theory

Lobos de Ponga,

Piña,

Cotabarren

2023

Powders

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The understanding of granule growth mechanisms and the effects of formulation and operating conditions over product quality and process performance in fluidized bed co-melt granulation is nowadays of great interest. In this sense, this work systematically studies the combined effects of binder content (WPEG) and fluidization air flowrate (FA) and temperature (TA) on granules’ quality and process-related variables (product mass (MP), elutriated fines (Mf), mass stuck on walls (MW)) by using a Box–Behnken-type design of experiments (DoE), as it is a statistical tool suggested by the Quality by Design (QbD) initiative. It was found that the granules’ size and powder flowability are significantly affected by WPEG (higher WPEG, higher granule size and better flowability). Interestingly, TA is the process variable that significantly affects MP, enhancing process performance at high temperature values. Regarding FA, it significantly affects d10, promoting the formation of small particles due to breakage at high flowrates and the presence of non-elutriated powder at low flowrates. As a consequence, intermediate FA is the optimum for obtaining higher MP. Regarding regime map studies, most runs experienced a rapid growth regime, which is in accordance with the granules’ high pore saturation. This result agrees with the observed high increment in particle size and the morphology of the final granules, allowing researchers to validate and extend existing previous maps.

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Section: (%)mentioning

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fluidized Bed Co-Melt Granulation: New Insights in the Influence of Process Variables and Validation of Regime Map Theory

Lobos de Ponga,

Piña,

Cotabarren

2023

Powders

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“…As Ibrahim [28] stated, the RW of an input variable is its contribution that predicts the dependent variable, and ANNs give only minimal information about the influence of input variables on the dependent variable. Thus, Garson [29] developed the GA to interpret the black box of ANN and identify the relative contributions of input variables with a trained ANN [28,[30][31][32][33][34]. This study integrated ANN with GA to train the network based on the collected database in the first phase and calculate the RWs of risk factors respectively.…”

Section: Factor Ranking Using Aimentioning

confidence: 99%

Construction Schedule Risk Assessment and Management Strategy for Foreign General Contractors Working in the Ethiopian Construction Industry

Cheng

Darsa

2021

Sustainability

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Construction project schedule delay is a worldwide concern and especially severe in the Ethiopian construction industry. This study developed a Construction Schedule Risk Assessment Model (CSRAM) and a management strategy for foreign general contractors (FGCs). 94 construction projects with schedule delay were collected and a questionnaire survey of 75 domain experts was conducted to systematically select 22 risk factors. In CSRAM, the artificial neural network (ANN) inference model was developed to predict the project schedule delay. Integrating it with the Garson algorithm (GA), the relative weights of risk factors with rankings were calculated and identified. For comparison, the Relative Importance Index (RII) method was also applied to rank the risk factors. Management strategies were developed to improve the three highest-ranked factors identified using the GA (change order, corruption/bribery, and delay in payment), and the RII (poor resource management, corruption/bribery, and delay in material delivery). Moreover, the improvement results were used as inputs for the trained ANN to conduct a sensitivity analysis. The findings of this study indicate that improvements in the factors that considerably affect the construction schedule can significantly reduce construction schedule delays. This study acts as an important reference for FGCs who plan to enter or work in the Ethiopian construction industry.

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Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry

Maharjan,

Lee,

Lee

et al. 2023

J. Pharm. Investig.

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Use of machine learning tool to elucidate and characterize the growth mechanism of an in-situ fluid bed melt granulation

Cited by 10 publications

References 46 publications

Fluidized Bed Co-Melt Granulation: New Insights in the Influence of Process Variables and Validation of Regime Map Theory

Fluidized Bed Co-Melt Granulation: New Insights in the Influence of Process Variables and Validation of Regime Map Theory

Construction Schedule Risk Assessment and Management Strategy for Foreign General Contractors Working in the Ethiopian Construction Industry

Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry

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