Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach
The anaerobic digestion (AD) of distillery by-products presents benefits such as greenhouse gas (GHG) emission savings and electricity savings, as well as drawbacks such as reduced animal feed and protein production and the potential import of animal feeds. This work balances these benefits and draw...
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Green Wave Publishing of Canada
2021
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oai:doaj.org-article:519834a42f8644ffa02403fe105fbc322021-11-30T13:24:36ZDistillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach2292-878210.18331/BRJ2021.8.3.2https://doaj.org/article/519834a42f8644ffa02403fe105fbc322021-09-01T00:00:00Zhttps://www.biofueljournal.com/article_135704_090d2fca664de576d130dc4c97c1bfbd.pdfhttps://doaj.org/toc/2292-8782The anaerobic digestion (AD) of distillery by-products presents benefits such as greenhouse gas (GHG) emission savings and electricity savings, as well as drawbacks such as reduced animal feed and protein production and the potential import of animal feeds. This work balances these benefits and drawbacks using compromise programming (CP). The best combination of by-products (from 9,261 scenarios) to use in AD was selected based on criteria chosen by management of a large distillery. The use of all by-products maximises benefits and drawbacks; the contrary also applies. When benefits and drawbacks are equally important, CP recommends using 50% of available draff, 50% of available thick stillage, and 55% of available thin stillage. The best combination when accounting for criteria weights chosen by distillery management is the use of 100% of available draff and 100% of available thick stillage. This could replace 48% of natural gas consumption at the distillery, reduce Scope 1 emissions by 45%, achieve a Scope 3 emissions savings of 22% of current Scope 1 emissions, and reduce electricity consumption in the feeds recovery plant of the distillery by 63%. Protein loss of 9,618 t could require the import of 19.59 kilo-tonne wet weight of material (ktwwt) of distillers grains and 9.15 ktwwt of soybean meal. If different criteria or criteria weights were used, a different result would be recommended. The methodology developed herein can aid in decarbonising the food and beverage industry by allowing decision-makers to balance the benefits and drawbacks of AD while accounting for subjective preferences.Richard O’SheaRichen LinDavid WallJames BrowneJerry MurphyGreen Wave Publishing of Canadaarticleindustrial decarbonisationrenewable heatcircular economybiogasmulti criteria decision analysisFuelTP315-360Energy industries. Energy policy. Fuel tradeHD9502-9502.5ENBiofuel Research Journal, Vol 8, Iss 3, Pp 1417-1432 (2021) |
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industrial decarbonisation renewable heat circular economy biogas multi criteria decision analysis Fuel TP315-360 Energy industries. Energy policy. Fuel trade HD9502-9502.5 |
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industrial decarbonisation renewable heat circular economy biogas multi criteria decision analysis Fuel TP315-360 Energy industries. Energy policy. Fuel trade HD9502-9502.5 Richard O’Shea Richen Lin David Wall James Browne Jerry Murphy Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach |
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The anaerobic digestion (AD) of distillery by-products presents benefits such as greenhouse gas (GHG) emission savings and electricity savings, as well as drawbacks such as reduced animal feed and protein production and the potential import of animal feeds. This work balances these benefits and drawbacks using compromise programming (CP). The best combination of by-products (from 9,261 scenarios) to use in AD was selected based on criteria chosen by management of a large distillery. The use of all by-products maximises benefits and drawbacks; the contrary also applies. When benefits and drawbacks are equally important, CP recommends using 50% of available draff, 50% of available thick stillage, and 55% of available thin stillage. The best combination when accounting for criteria weights chosen by distillery management is the use of 100% of available draff and 100% of available thick stillage. This could replace 48% of natural gas consumption at the distillery, reduce Scope 1 emissions by 45%, achieve a Scope 3 emissions savings of 22% of current Scope 1 emissions, and reduce electricity consumption in the feeds recovery plant of the distillery by 63%. Protein loss of 9,618 t could require the import of 19.59 kilo-tonne wet weight of material (ktwwt) of distillers grains and 9.15 ktwwt of soybean meal. If different criteria or criteria weights were used, a different result would be recommended. The methodology developed herein can aid in decarbonising the food and beverage industry by allowing decision-makers to balance the benefits and drawbacks of AD while accounting for subjective preferences. |
format |
article |
author |
Richard O’Shea Richen Lin David Wall James Browne Jerry Murphy |
author_facet |
Richard O’Shea Richen Lin David Wall James Browne Jerry Murphy |
author_sort |
Richard O’Shea |
title |
Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach |
title_short |
Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach |
title_full |
Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach |
title_fullStr |
Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach |
title_full_unstemmed |
Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach |
title_sort |
distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach |
publisher |
Green Wave Publishing of Canada |
publishDate |
2021 |
url |
https://doaj.org/article/519834a42f8644ffa02403fe105fbc32 |
work_keys_str_mv |
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