Digestate Evaporation Treatment in Biogas Plants: A Techno-economic Assessment by Monte Carlo, Neural Networks and Decision Trees

Vondra, Marek and Touš, Michal and Teng, Sin Yong (2019): Digestate Evaporation Treatment in Biogas Plants: A Techno-economic Assessment by Monte Carlo, Neural Networks and Decision Trees. Published in: Journal of Cleaner Production , Vol. 238, No. 117870 (20 November 2019)

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Anaerobic Digestion; Machine Learning; Vacuum Evaporation; Liquid Digestate; Biogas Plant; Energy Consumption; Nutrient Recovery; Circular economy; Ammonium sulphate solution
• Subjects: C - Mathematical and Quantitative Methods > C0 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs; E - Macroeconomics and Monetary Economics > E0 - General; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles; E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook; L - Industrial Organization > L1 - Market Structure, Firm Strategy, and Market Performance; L - Industrial Organization > L6 - Industry Studies: Manufacturing; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities

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