The LCA model framework for mixed biomass briquetting

The LCA model framework for mixed biomass briquetting

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Purpose Previous Life Cycle Assessment (LCA) studies of biomass briquetting have shown wide variations in the LCA outcomes as a result of variations in LCA methodological parameters and briquetting technological parameters. An LCA model of biomass briquetting was therefore developed to enable transparent comparison of life cycle environmental impac...

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... Often missing in most of the research on the LCA of biomass densification is an understanding of the relevance of process variables to the environmental effects of the life cycle. For example, biomass properties, such as density and moisture content, and densification technology, can affect the energy requirements for densification [17]. It is therefore vital to explore the suitability of various biomass resources for potential utilisation as bioenergy sources via sustainability assessments, to ensure the sustainable utilisation of these resources. ...
... The current study employs a comparative LCA model of biomass densification system [17] to simulate the process and feed parameters associated with various Mexican biomass types. The specific biomass range used and composition are from published studies [2,4,18]. ...
... A functional unit of 1 MJ densified biomass energy content at the plant gate was defined for the LCA modelling. A system boundary of gate-to-gate was utilised, as established in the parent model ( Figure 1) [17]. The case study focuses on identifying variations in the environmental impact of densifying different biomass resources, and the feed biomass used was assumed to have suitable moisture and particle sizes for densification. ...
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Due to the global energy crisis and high energy consumption, it is commonly understood that a considerable amount of energy from alternative fuels is required. Briquetting is a revolutionary thermal process that is mostly used to transform agricultural waste into cylindrical shape products that may be utilized as a fuel in burning chambers and power stations. In the present paper, Stochastic analysis is performed on a two-unit cold standby briquetting system. The system considers two kinds of faults, either major or minor. The inspection of a unit’s failure shows system feasibility under the observation either of expert or ordinary repairmen. It is assumed that if a minor/major fault occurs in the operational unit, the system would fail partially, but if a fault occurs in both units, the system will fail. We include a detailed study of system parameters such as availability, mean time to system failure, busy period, and profit by using the regenerative point graphical technique. Furthermore, we applied the grey wolf optimization technique to optimize the system profit.KeywordsRegenerative point graphical technique (RPGT)Briquetting systemOptimizationProfit
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All participating countries of the Paris agreement 2015 agreed on a reduction of the overall rise in temperature by less than 2 °C. To achieve this target, renewable energy plays a vital role due to its non-depleting and sustainable nature. The process of condensation of biomass residues into solid fuels is famously known as briquetting. It increases the efficacy of thermal capacity, rate of combustion, and calorific value of the fuel. Sometimes neglected faults in a briquette machine usually do not affect the overall performance of the system but their ignorance leads us towards major faults. Moreover, when a neglected fault occurs, the aspect of preventive maintenance of the system will also come into the picture to avoid the extension of minor faults into major faults. In this paper, we consider the neglected faults like overheating, extensive vibration, abnormal sounds, etc. along with other minor/ major faults by means of preventive maintenance. For such kinds of minor faults, an ordinary repairman is enough for the handling of the entire unit. A detailed analysis of various parameters like availability, profit, busy period, mean time to system failure, etc. are presented. Furthermore, we used the Artificial Bee Colony algorithm to optimize the value of system profit. We studied the graphical representation of evaluated parameters for a clear picture of the proposed work.