Publication detail

Digestate evaporation treatment in biogas plants: A techno-economic assessment by Monte Carlo, neural networks and decision trees

VONDRA, M. TOUŠ, M. TENG, S.

Original Title

Digestate evaporation treatment in biogas plants: A techno-economic assessment by Monte Carlo, neural networks and decision trees

Type

journal article in Web of Science

Language

English

Original Abstract

Biogas production is one of the most promising pathways toward fully utilizing green energy within a circular economy. The anaerobic digestion process is the industry standard technology for biogas production due to its lowered energy consumption and its reliance on microbiology. Even in such an environmental-friendly process, liquid digestate is still produced from the remains of digested bio-feedstock and will require treatment. With unsuitable treatment procedure for liquid digestate, the mass of bio-feedstock can potentially escape the circular supply chain within the economy. This paper recommends the implementation of evaporator systems to provide a sustainable liquid digestate treating mechanism within the economy. Studied evaporator systems are represented by vacuum evaporation in combination with ammonia scrubber, stripping and reverse osmosis. Nevertheless, complex multi-dimensional decisions should be made by stakeholders before implementing such systems. Our work utilizes a novel techno-economics model to study the techno-economics robustness in implementing recent state-of-art vacuum evaporation systems with exploitation of waste heat from combined heat and power (CHP) units in biogas plants (BGP). To take into the account the stochasticity of the real world and robustness of the analysis, we used the Monte-Carlo simulation technique to generate more than 20,000 of different possibilities for the implementation of the evaporation system. Favourable decision pathways are then selected using a novel methodology which utilizes the artificial neural network and a hyper-optimized decision tree classifier. Two pathways that give the highest probability of providing a fast payback period are identified. Descriptive statistics are also used to analyse the distributions of decision parameters that lead to success in implementing the evaporator system. The results highlighted that integration of evaporation system are favourable when transport costs and incentives for CHP units are large and while feed-in tariffs for electricity production and specific investment costs are low. The result of this work is expected to pave the way for BGP stakeholders and decision makers in implementing liquid digestate treating technologies within the currently existing infrastructure.

Keywords

Anaerobic digestion; Machine learning; Vacuum evaporation; Liquid digestate; Biogas plant; Energy consumption; Nutrient recovery; Circular economy; Ammonium sulphate solution

Authors

VONDRA, M.; TOUŠ, M.; TENG, S.

Released

20. 11. 2019

Publisher

Elsevier Ltd

ISBN

0959-6526

Periodical

Journal of Cleaner Production

Number

238

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

26

Pages count

26

URL

http://www.sciencedirect.com/science/article/pii/S0959652619327404

BibTex

@article{BUT158894,
  author="Marek {Vondra} and Michal {Touš} and Sin Yong {Teng}",
  title="Digestate evaporation treatment in biogas plants: A techno-economic assessment by Monte Carlo, neural networks and decision trees",
  journal="Journal of Cleaner Production",
  year="2019",
  number="238",
  pages="1--26",
  doi="10.1016/j.jclepro.2019.117870",
  issn="0959-6526",
  url="http://www.sciencedirect.com/science/article/pii/S0959652619327404"
}