Cost and Performance of Woody Biomass Size Reduction for Energy Production

Naimi, Ladan J.; Sokhansanj, Shahab; Mani, Sudhagar; Hoque, Mozammel; Bi, Tony; Womac, A. R.; Narayan, S.

doi:10.13031/2013.22065

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…Также измельчители древесины используются в лесоперерабатывающей промышленности для изготовления щепы с последующей ее переработкой в строительные и отделочные материалы [3,4]. Исследованиям процесса измельчения древесины, а также машинам, для этого предназначенным, посвящено большое количество работ как в нашей стране, так и за границей [5][6][7].…”

Section: объекты и методыunclassified

Waste Wood and Plant Residue Chippers

Pirozhkov,

Lapin

2023

VAGAU

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Section: объекты и методыunclassified

Waste Wood and Plant Residue Chippers

Pirozhkov,

Lapin

2023

VAGAU

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“…However, the maintenance cost estimation is complicated, as the frequency and severity of failures are variable, and the maintenance cost for two seemingly identical chippers could be different [18]. Moreover, the local maintenance costs of parts and labor could affect operators records for the same maintenance intervention [138]. Although there has been different research work on chipper costs in fuel consumption and depreciation [139], repair and maintenance costs are less considered a cost element.…”

Section: Chippers' Maintenancementioning

confidence: 99%

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Maktoubian

Taskhiri

Turner

2021

Forests

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The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

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“…According to the material and required size, this may be performed using a hammer mill equipped with swing hammers and powered by a three-phase induction motor at a speed of 3490 rpm. 49,50 Naimi et al 49 designed a series of experiments for different screen sizes with different initial and final particle sizes. 49 Energy consumption during this operation was measured and reported as summarized in Table 4.…”

Section: ■ Introductionmentioning

confidence: 99%

Wood Chips Components Separation with a New Wet-Milling Process Compared to Chemical Depolymerization: A Technical, Economic, and Environmental Comparison

Arfelis,

Malpartida,

Bala

et al. 2024

ACS Sustainable Chem. Eng.

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This article evaluates two processes, wet milling and chemical depolymerization, for the end-of-life of wood waste in terms of environmental performance (ex ante life cycle assessment), energy balance, and economic analysis of the operating costs. Cellulose, hemicellulose, and lignin are essential components with numerous applications. The study provides valuable insights for industry stakeholders, policymakers, and researchers of the wet milling process (WMP), which is scarcely reported in the literature. The chemical depolymerization process (CDP) is discontinuous and more energy-intensive, while the WMP is a continuous reaction demanding milder conditions and shorter times. However, the milling process requires a pretreatment to reduce the wood chip size. Economic analysis shows that the CDP has lower operational costs when considering the average European electricity price in 2019. This is a result of the price differences between steam and electricity. For lower electricity prices such as in France or using utility-scale solar photovoltaics, the WMP has lower operational costs. The WMP also outperforms the CDP in most environmental indicators, such as global warming potential, particularly when using green electricity technologies.

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Cost and Performance of Woody Biomass Size Reduction for Energy Production

Cited by 12 publications

References 7 publications

Waste Wood and Plant Residue Chippers

Waste Wood and Plant Residue Chippers

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Wood Chips Components Separation with a New Wet-Milling Process Compared to Chemical Depolymerization: A Technical, Economic, and Environmental Comparison

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