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一篇关于厌氧消化的综述,重点介绍生物质共消化、沼气生产和增强的建模和优化作用。

A review on anaerobic digestion with focus on the role of biomass co-digestion, modelling and optimisation on biogas production and enhancement.

机构信息

Center of New Energy Systems, Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa; Department of Fuels and Energy, Chinhoyi University of Technology, Zimbabwe.

Center of New Energy Systems, Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa.

出版信息

Bioresour Technol. 2022 Jan;344(Pt B):126311. doi: 10.1016/j.biortech.2021.126311. Epub 2021 Nov 12.

Abstract

The status, recent trends and future perspectives in modelling and optimisation of anaerobic co-digestion is investigated. Areas that can be focused on and those which need further research towards enhancing biogas production are pointed out. Co-digestion, modelling and optimisation of anaerobic digestion as well as techno-economic aspects are reviewed in this paper. It was noted that co-digestion requires more research into a variety of bio-resources and their specific blend proportions. Modelling and optimisation of co-digestion with substrate seasonal fluctuations has not been addressed in previous studies. Controlling key process factors including temperature, pH, and carbon to nitrogen ratio is critical in improving biogas yield. Biogas hybridisation is yet to be explored in depth. The majority of researches are focused on mono-digestion, feedstock co-digestion, modelling, and optimisation of anaerobic digestion needs significant further investigations. A multi-objective approach taking all technical and economic parameters in the modelling and optimization is essential.

摘要

本研究调查了厌氧共消化的建模和优化的现状、近期趋势和未来展望。指出了可以关注的领域和需要进一步研究以提高沼气产量的领域。本文综述了厌氧消化的共消化、建模和优化以及技术经济方面。研究指出,共消化需要对各种生物资源及其特定混合比例进行更多的研究。以前的研究没有涉及到针对底物季节性波动的共消化建模和优化。控制包括温度、pH 值和碳氮比在内的关键工艺因素对于提高沼气产量至关重要。沼气混合仍有待深入探索。大多数研究都集中在单相消化、原料共消化、建模和优化上,因此厌氧消化的建模和优化需要进一步的研究。在建模和优化中采用考虑所有技术和经济参数的多目标方法是必要的。

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