Cotton Gin Electrical Energy Use Trends and 2009 Audit Results

Funk, Paul; Hardin, Robert G.

doi:10.13031/2013.42078

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…There can also be a categorization which does not distinguish between production and support processes. One example is energy audit of cotton gin factories in USA (Funk & Hardin, 2012). This categorization is based on sector-specific energy-using processes.…”

Section: Categorization Of Eeu Datamentioning

confidence: 99%

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International study on energy end-use data among industrial SMEs (small and medium-sized enterprises) and energy end-use efficiency improvement opportunities

Thollander

Paramonova

Cornelis

et al. 2015

Journal of Cleaner Production

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The industrial sector is important to study in terms of improved energy efficiency, being one of the major energy-using sectors and responsible for a major share of CO2 emissions.The energy end-use (EEU) in the industrial sector is complex in general as processes are intertwined and interrelated. Moreover, bottom-up data of EEU on an aggregated level is scarce.Data for total energy supply like electricity, oil, coal, and natural gas exists but bottom-up data of what processes these energy carriers are used in, and moreover, where the major potential for implementation of energy efficiency measures (EEMs) exists, is less prevalent.This holds in particular for industrial small and medium-sized enterprises (SMEs). This makes policy formulation and design for industry a great challenge. Knowledge on where and how energy is used, as well as where opportunities for improvement exist, may provide good support for developing the most effective policies. Therefore, the aim of this study has been to present and compare available bottom-up energy data for industrial SMEs in four countries, namely Belgium, Italy, Japan and Sweden. Results show that the existence and quality of bottom-up EEU data differs largely between the countries and the development of a general taxonomy of structuring EEU data as well as EEMs is needed. Without the development of such a general taxonomy, the deployment level of EEMs and carbon dioxide emission reductions is unlikely to ever reach its full potential as knowledge is missing on how large the potential is, in which processes the major potential is found, how far industry has reached in terms of deployment levels, and in which areas future energy policies are needed. In conclusion, this paper of EEU and EEM in industrial SMEs addresses the high importance of future research in creating a harmonized data categorization, as this will greatly support the transition towards sustainable industrial energy systems.

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Section: Categorization Of Eeu Datamentioning

confidence: 99%

“…This categorization is based on sector-specific energy-using processes. However, these processes were not named as production processes (Funk & Hardin, 2012). Differences in categorization can appear due to differences in objectives for different studies, e.g.…”

Section: Categorization Of Eeu Datamentioning

confidence: 99%

International study on energy end-use data among industrial SMEs (small and medium-sized enterprises) and energy end-use efficiency improvement opportunities

Thollander

Paramonova

Cornelis

et al. 2015

Journal of Cleaner Production

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“…Energy audits conducted in US cotton gins representing a range of capacities between 2009 and 2013 revealed the average participating saw gin used between 34 and 40 kWh to process one cotton bale, i.e. 227 kg ( Funk and Hardin 2012 ; Funk et al 2013 ). While gins have become larger and more efficient again in the nearly 10 years since those audits, energy costs as a proportion of gin’s operating costs have become larger and now exceed >20% of the total cost of ginning cf.…”

Section: Introductionmentioning

confidence: 99%

“…While gins have become larger and more efficient again in the nearly 10 years since those audits, energy costs as a proportion of gin’s operating costs have become larger and now exceed >20% of the total cost of ginning cf. 6–10% in the 1970s ( Funk and Hardin 2012 ). Ginning of seed cotton without fuzz fibers, or fuzzless, generally consumes less power and causes less damage to the lint fibers as much less force is required to remove the lint from the seed coat than fuzzy-seeded cotton ( Bechere et al 2009 , 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Genetic mapping and transcriptomic characterization of a new fuzzless-tufted cottonseed mutant

Zhu

Stiller

Moncuquet

et al. 2020

G3 Genes|Genomes|Genetics

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Fiber mutants are unique and valuable resources for understanding the genetic and molecular mechanisms controlling initiation and development of cotton fibers that are extremely elongated single epidermal cells protruding from the seed coat of cottonseeds. In this study, we reported a new fuzzless-tufted cotton mutant (Gossypium hirsutum) and showed that fuzzless-tufted near-isogenic lines (NILs) had similar agronomic traits and a higher ginning efficiency compared to their recurrent parents with normal fuzzy seeds. Genetic analysis revealed that the mutant phenotype is determined by a single incomplete dominant locus, designated N5. The mutation was fine mapped to an approximately 250-kb interval containing 33 annotated genes using a combination of bulked segregant sequencing, SNP chip genotyping, and fine mapping. Comparative transcriptomic analysis using 0–6 days post-anthesis (dpa) ovules from NILs segregating for the phenotypes of fuzzless-tufted (mutant) and normal fuzzy cottonseeds (wild-type) uncovered candidate genes responsible for the mutant phenotype. It also revealed that the flanking region of the N5 locus is enriched with differentially expressed genes (DEGs) between the mutant and wild-type. Several of those DEGs are members of the gene families with demonstrated roles in cell initiation and elongation, such as calcium-dependent protein kinase and expansin. The transcriptome landscape of the mutant was significantly reprogrammed in the 6 dpa ovules and, to a less extent, in the 0 dpa ovules, but not in the 2 and 4 dpa ovules. At both 0 and 6 dpa, the reprogrammed mutant transcriptome was mainly associated with cell wall modifications and transmembrane transportation, while transcription factor activity was significantly altered in the 6 dpa mutant ovules. These results imply a similar molecular basis for initiation of lint and fuzz fibers despite certain differences.

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“…lectricity is a significant cost for cotton gins, accounting for approximately 20% of variable costs (Valco et al, 2012). Over half of the electricity consumed at gins is used by centrifugal fans for pneumatic conveying (Funk and Hardin, 2012;Hardin and Funk, 2012). For a centrifugal fan, the volumetric flow rate and air velocity vary linearly with the fan speed.…”

mentioning

confidence: 99%

Pneumatic Conveying of Seed Cotton: Minimum Velocity and Pressure Drop

2014

Trans. ASABE

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Electricity is a major cost for cotton gins, representing approximately 20% of variable costs. Fans used for pneumatic conveying consume the majority of electricity at cotton gins. Development of control systems to reduce the air velocity used for conveying seed cotton could significantly decrease electricity use and cost. A greater understanding of the pneumatic conveying characteristics of seed cotton is necessary for development of these systems. A negative pressure conveying system was constructed with a feed control, conveying pipe, separator, and fan. Air velocity was measured at the system inlet and in the conveying section when testing with air only. A differential pressure measurement was taken in the conveying pipe, and temperature and relative humidity were recorded. Two pipe diameters, two cultivars, two moisture content levels, and three seed cotton feed rates were included in the experimental design. Seed cotton was fed into the conveying system, and the fan speed was decreased until choking occurred. The minimum differential pressure measurement indicated the saltation velocity. A segmented linear model was fit to the log transformed data to identify the mass flow ratio and Froude number (Fr) corresponding to the minimum pressure. This model accurately fit the data (R2 = 0.88) and resulted in the following equation for finding the saltation velocity: ? = 8.90 × 10-5 × Frmin5.04, where Frmin is the Fr at the saltation velocity. The solids resistance factor at velocities greater than saltation was found to be 0.179 × Fr-1. This model had an R2 of 0.91 and predicted the pressure drop with 14.2% error. Pipe diameter, cultivar, and moisture content level did not have a practically significant effect on the models developed to predict saltation velocity or the solids resistance factor. These models may be useful in designing control systems for cotton gin conveying systems, resulting in significant electricity and cost savings.

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Cotton Gin Electrical Energy Use Trends and 2009 Audit Results

Cited by 13 publications

References 10 publications

International study on energy end-use data among industrial SMEs (small and medium-sized enterprises) and energy end-use efficiency improvement opportunities

International study on energy end-use data among industrial SMEs (small and medium-sized enterprises) and energy end-use efficiency improvement opportunities

Genetic mapping and transcriptomic characterization of a new fuzzless-tufted cottonseed mutant

Pneumatic Conveying of Seed Cotton: Minimum Velocity and Pressure Drop

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