Bioenergy, material, and nutrients recovery from household waste: Advanced material, substance, energy, and cost flow analysis of a waste refinery process

Tonini, Davide; Dorini, Gianluca; Astrup, Thomas Fruergaard

doi:10.1016/j.apenergy.2014.01.058

Cited by 28 publications

(10 citation statements)

References 42 publications

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“…For these reasons, an upgrading of the existing MBT plants is still called for and a further reduction and stabilization of the organic content of the OFMS along with a lower energy demand need to be pursued [23]. Upgrading the MBT technology to enhance the production of a solid refined fuel can be an effective solution [24,25].…”

Section: Introductionmentioning

confidence: 98%

Refuse recovered biomass fuel from municipal solid waste. A life cycle assessment

et al. 2017

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Section: Introductionmentioning

confidence: 98%

Refuse recovered biomass fuel from municipal solid waste. A life cycle assessment

et al. 2017

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“…This is why the present study accounted for both obvious actors involved in carbon absorption (Forest, Grassland) and less obvious actors (Agriculture, Bodies of Water). The research on carbon flows among sectors mainly focused on the production and distribution of crops (Rimhanen and Kahiluoto, 2014), the consumption of family foods (Luo et al, 2008), consumption of energy, paper products, plastics, and other materials related to the family life (Fissore et al, 2012), and finally on the disposal of household garbage (Tonini et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Some researchers also focused on the subsequent waste disposal. Tonini et al (2014) analyzed the carbon flow processes involved in refining and processing wastes, including waste inputs, pretreatment, screening, post-processing, recycling, and final disposal, and used elemental analysis (i.e., tracking the flows of an individual element) to quantify the carbon flows. Similarly, Zhou et al (2015) studied the garbage disposal system using empirically derived coefficients to quantify the waste carbon stock and carbon inputs of the system.…”

Section: Introductionmentioning

confidence: 99%

Flow analysis of the carbon metabolic processes in Beijing using carbon imbalance and external dependence indices

Zhang

Liu

et al. 2018

Journal of Cleaner Production

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Maintaining urban systems consumes a large amount and variety of materials and leads to waste flows. Carbon is a basic element that intuitively characterizes the metabolic characteristics of urban resource consumption and pollution emission. In this study, we integrated the carbon metabolic flows among 18 metabolic actors and between these actors and the atmosphere, and calculated flows of material in many categories using empirically derived coefficients to estimate the associated carbon flows (emission and absorption). Taking Beijing as an example, we analyzed the dynamic changes in the carbon metabolism and the structural characteristics of material utilization. We defined two indices to characterize the metabolism (the carbon imbalance and external dependence indices), and identified key actors responsible for changes in the indices. The total carbon metabolism (inputs and outputs) increased by 64% and 200%, respectively, from 1995 to 2015, mainly driven by energy consumption, which accounted for more than 78% of the total. In addition, input growth was driven by food, accounting for up to 6% of the total. The carbon imbalance and external dependence indices increased to nearly two and four times their 1995 values, respectively, mainly due to the Manufacturing, Electricity and Heat Production, and Energy Conversion actors' demand for food or energy during the early part of the study period, and by the rapid growth of food or energy required by the Urban Life and Transportation actors. Identifying and comparing the key metabolic actors provided a novel way to analyze data to determine targets for carbon regulation and emission reduction measures for Beijing.

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“…In recent years, many sector studies have used material flow analysis to understand better the volume and composition of wastes (Moriguchi & Hashimoto, 2016;Velis et al, 2013) thus assisting decision making (Turner, Williams, & Kemp, 2016) and improving material recovery and treatment (Al Sabbagh, Velis, Wilson, & Cheeseman, 2012;Allesch & Brunner, 2015, 2017Arena & Di Gregorio, 2014;Habib, Schibye, Vestbø, Dall, & Wenzel, 2014;Pivnenko, Laner, & Astrup, 2016;Stanisavljevic & Brunner, 2014;Tonini, Dorini, & Astrup, 2014). Knowing the composition of solid waste within process streams in real-time reduces uncertainty (Laner, Rechberger, Feketitsch, & Fellner, 2016;Rechberger, Cencic, & Frühwirth, 2014), eliminating the input-output lag during flow analysis (Pivnenko et al, 2016;Zoboli, Laner, Zessner, & Rechberger, 2016).…”

Section: Introductionmentioning

confidence: 99%

Deep learning in material recovery: Development of method to create training database

Vrancken

Longhurst

Wagland

2019

Expert Systems with Applications

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Increasing the rate of material identification, separation and recovery is a priority in resource management and recovery, and rapid, low cost imaging and interpretation is key. This study uses different combinations of cameras, illuminations and data augmentation techniques to create databases of images to train deep neural networks for the recognition of fibre materials. Using a limited set of 24 material samples sized 1,200 cm 2 , it compares the outcome of reducing them to 30 cm 2. The best classification accuracies obtained range from 76.6% to 77.5% indicating it is possible to overcome problems such as limited available materials, time, or storage capabilities, by using a setup with 5 cameras, 5 lights and applying simple software image manipulation techniques. The same method can be used to create deep neural network training databases to recognise a wider range of materials typically found in solid waste streams, in real-time. Furthermore, it offers flexibility as the classification cameras could be deployed at different stages within solid waste processing plants, providing feedback for process control, with the potential of increasing plant efficiency and reducing costs.

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Bioenergy, material, and nutrients recovery from household waste: Advanced material, substance, energy, and cost flow analysis of a waste refinery process

Cited by 28 publications

References 42 publications

Refuse recovered biomass fuel from municipal solid waste. A life cycle assessment

Refuse recovered biomass fuel from municipal solid waste. A life cycle assessment

Flow analysis of the carbon metabolic processes in Beijing using carbon imbalance and external dependence indices

Deep learning in material recovery: Development of method to create training database

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