An experimental investigation on plug formation using fuzzy cottonseeds

Rajabnia, H.; Orozovic, O.; Lavrinec, A.; Ilić, Dušan; Williams, Kenneth C.; Jones, Mark; Klinzing, George E.

doi:10.1016/j.powtec.2022.117131

Cited by 3 publications

(12 citation statements)

References 35 publications

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Section: Analysis Of Biomass Pneumatic Conveying Model: Single-batch ...mentioning

confidence: 99%

“…In this study, it is essential to mention our previous investigation on cottonseed plug formation [43], which concluded that cottonseed possesses the ability to maintain plug flow during conveying. Rajabnia et al [43] further emphasized the importance of the rear section of the plug in sustaining its stability throughout the conveying process. Although the author has submitted another paper discussing the performance of three additional materials, the results from all four materials collectively demonstrate their potential to maintain plug flow during both single batch and continuous conveying.…”

Section: Analysis Of Biomass Pneumatic Conveying Model: Single-batch ...mentioning

confidence: 99%

“…Conversely, higher density materials demonstrated enhanced efficiencies at greater heights. In a recent study, Rajabnia et al [43] explored the flow modes of cottonseed conveying and aimed to identify the conditions that lead to Plug-1 formation [27]. The authors concluded that the feeding method was crucial in determining plug formation, indicating a reliance on local particle arrangements during fixed bed formation.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Pressure Prediction Models for Pneumatic Conveying of Biomass: A Comprehensive Approach to Minimize Trial Tests and Enhance Accuracy

Rajabnia¹,

Orozovic²,

Williams³

et al. 2023

Processes

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This study investigates pneumatic conveying of four different biomass materials, namely cottonseeds, wood pellets, wood chips, and wheat straw. The performance of a previously proposed model for predicting pressure drop is evaluated using biomass materials. Results indicate that the model can predict pressure with an error range of 30 percent. To minimize the number of trial tests required, an optimization algorithm is proposed. The findings show that with a combination of three trial tests, there is a 60 percent probability of selecting the right subset for accurately predicting pressure drop for the entire range of tests. Further investigation of different training subsets suggests that increasing the number of tests from 3 to 7 can improve the probability from 60% to 90%. Moreover, thorough analysis of all three-element subsets in the entire series of tests reveals that when considering air mass flow rate as the input, having air mass flow rates that are not only closer in value but also lower increases the likelihood of selecting the correct subset for predicting pressure drop across the entire range. This advancement can help industries to design and optimize pneumatic conveying systems more effectively, leading to significant energy savings and improved operational performance.

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Section: Analysis Of Biomass Pneumatic Conveying Model: Single-batch ...mentioning

confidence: 99%

Section: Analysis Of Biomass Pneumatic Conveying Model: Single-batch ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Pressure Prediction Models for Pneumatic Conveying of Biomass: A Comprehensive Approach to Minimize Trial Tests and Enhance Accuracy

Rajabnia¹,

Orozovic²,

Williams³

et al. 2023

Processes

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“…In a more recent investigation, Rajabnia et al [39] analysed the flow modes for cottonseed conveying and sought to discern the conditions leading to Plug-1 formation [40]. Their findings indicated the significant influence of the feeding method in determining plug formation that they proposed was contingent on local particle arrangements during fixed bed formation.…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, the study also demonstrated that the base or rear of a plug served a critical function in maintaining the plug structure by acting as a piston to push the material bed ahead. An essential contribution of the study of Rajabnia et al [39] was the development of a reliable and repeatable method to generate Plug-1 flow for cottonseeds while taking into account such factors as feeding, precompaction, orientation, and batch sizes.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between the Time Constant Ratio and Plug-Flow Behaviour in the Pneumatic Conveyance of Biomass Material

Rajabnia¹,

Orozovic²,

Williams³

et al. 2023

Processes

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This study introduces a novel methodology to evaluate the behaviour of biomass material by examining the ratio of aeration and deaeration time constants. To this end, a series of tests were conducted on four different materials, namely, cottonseed, wood chips, wood pellets, and wheat straw, in order to investigate their aeration and deaeration behaviours. The study derives the aeration and deaeration pressure drop equations, and discusses the corresponding time constant expression. Subsequently, the four materials were conveyed in 12 m long batch-fed and continuous pneumatic conveying pipelines to examine their behaviour in longer pipelines. The results indicate that the aeration and deaeration time constants increased with an increase in air superficial velocity. However, the ratio of the aeration and deaeration time constants was identified as a unique number, where a value close to 1 indicates a higher likelihood of plug flow. On the basis of the results, cottonseed, with the lowest ratio of time constant, was more likely to form a stable plug flow in both batch-fed and continuous pneumatic conveying. Given the unique properties of biomass and the limited research on the pneumatic conveyance of biomass, this methodology represents a novel approach for predicting modes of flow in materials with complex properties.

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Predictive modelling approach for cottonseed plug velocity applying a circuit theory analogy

Rajabnia¹,

Orozovic²,

Williams³

et al. 2023

Powder Technology

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An experimental investigation on plug formation using fuzzy cottonseeds

Cited by 3 publications

References 35 publications

Optimizing Pressure Prediction Models for Pneumatic Conveying of Biomass: A Comprehensive Approach to Minimize Trial Tests and Enhance Accuracy

Optimizing Pressure Prediction Models for Pneumatic Conveying of Biomass: A Comprehensive Approach to Minimize Trial Tests and Enhance Accuracy

Investigating the Relationship between the Time Constant Ratio and Plug-Flow Behaviour in the Pneumatic Conveyance of Biomass Material

Predictive modelling approach for cottonseed plug velocity applying a circuit theory analogy

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