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The challenges of utilising bottom ash from waste to energy plant

- Venkatraman, Kartik, Wilcox, Steve, Verheyen, Vincent, Panther, Barbara

  • Authors: Venkatraman, Kartik , Wilcox, Steve , Verheyen, Vincent , Panther, Barbara
  • Date: 2021
  • Type: Text , Journal article
  • Relation: International Journal of Energy, Environment and Economics Vol. 28, no. 4 (2021), p. 305-333
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  • Description: Bottom ash generated from Waste to Energy (WtE) processes is complex to manage and dispose of with minimal environmental impacts. Most European (EU) nations and many Asian countries have tested and developed alternative end-uses for bottom ash. This has ensured relatively safe containment of contaminants which otherwise would be released into the environment posing major risk to human, flora and fauna health. Although bottom ash has been physically and chemically incorporated into aggregates, cement and road construction, for example, the lifecycle and impact on the environment of these products are not yet quantified. The lifecycle of these materials and products that use bottom ash as one of their constituents needs to be completely understood, through these currently unanswered questions: What will be the fate of the contaminants? Is there a limit on the number of years before the contained contaminants will be released into the environment? Are we giving rise to a new problem with long term implications? This paper will focus on the thermal treatment of municipal solid waste and the resulting utilisation of bottom ash. © Nova Science Publishers, Inc.

Potential use of treated bottom ash as a capillary barrier in phytocaps

- Venkatraman, Kartik, Wilcox, Steve, Verheyen, Vincent, Panther, Barbara

  • Authors: Venkatraman, Kartik , Wilcox, Steve , Verheyen, Vincent , Panther, Barbara
  • Date: 2021
  • Type: Text , Journal article
  • Relation: International Journal of Energy, Environment and Economics Vol. 28, no. 4 (2021), p. 271-304
  • Full Text: false
  • Reviewed:
  • Description: Waste to Energy (WtE) technology has been implemented in European and many Asia countries with tremendous success and social acceptance. Bottom Ash (BA) generated from this process has also been effectively used as raw material or by-product in many applications. This technology, however, is still in its infancy in Australia. Additionally, the market and appetite for enduse of BA is still to be explored. One such option for its enduse is in landfill capping as a capillary barrier to reduce water infiltration into buried waste. Bottom ash from WtE plants could be used as a capillary barrier in phytocaps-an alternative landfill capping techniques which uses tree and soil to reduce water infiltration into waste. Literature suggests that with proper treatment (stabilisation) or proper mixing and adequate curing, the hydraulic conductivity of BA can be increased (Circa 10-7 cm/s) by increasing its density and significantly reducing the threshold limits of heavy metals and other contaminants that may otherwise pose a risk to the environment. © Nova Science Publishers, Inc.

Assessing transformer oil quality using deep convolutional networks

- Alam, Mohammad, Karmakar, Gour, Islam, Syed, Kamruzzaman, Joarder, Chetty, Madhu, Lim, Suryani, Appuhamillage, Gayan, Chattopadhyay, Gopinath, Wilcox, Steve, Verheyen, Vincent

  • Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopinath , Wilcox, Steve , Verheyen, Vincent
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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  • Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
  • Description: E1
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