Simulation and Optimization of Pressurized Anaerobic Digestion and Biogas Upgrading Using Aspen Plus

作者
Davide Scamardella,Carmen De Crescenzo,Antonia Marzocchella,Antonio Molino,Simeone Chianese,Vincenzo Savastano,Raffaele Tralice,Despina Karatza,Dino Musmarra
出处
期刊:Chemical engineering transactions [AIDIC-Italian Association of Chemical Engineering]
被引量:13
标识
DOI:10.3303/cet1974010
摘要

Anaerobic digestion is a technology used to biologically convert organic substrates into biogas in the absence of oxygen. The resulting biogas is a renewable energy source mainly consisting of a mixture of methane (60÷70% v/v) and carbon dioxide (30÷40% v/v), with traces of some minor compounds, such as H2S and NH3. Anaerobic digestion takes place through a sequence of four biological phases - hydrolysis, acidogenesis, acetogenesis, and methanogenesis - performed by the action of particular species of bacteria. Operating parameters such as temperature, pH, pressure and organic substrates govern the process and affect the starting biomass transformation and the content of methane into the biogas. The biogas from anaerobic digestion can be upgraded to biomethane by removing CO2 and the minor compounds. The techniques commonly used for this purpose, like pressure swing adsorption and membrane separation, are energy-intensive as they require the compression of biogas. In this paper, an innovative energy-saving approach for biogas production and its upgrading to biomethane is proposed. The concept is based on anaerobic digestion carried out at a pressure higher than the atmospheric one, called pressured anaerobic digestion (PAD), in order to directly produce high pressure biogas that can be upgraded to high pure biomethane (CH4 = 95% v/v) avoiding the compression phase during the upgrading. The variation of the main operating parameters has been simulated in order to investigate their effect on biomethane production and composition and to define the best operating conditions. The simulation of the process has been carried out by using Aspen Plus®.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
从容书雁发布了新的文献求助10
刚刚
从容书雁发布了新的文献求助10
刚刚
从容书雁发布了新的文献求助30
1秒前
小童发布了新的文献求助10
1秒前
慕青应助英俊qiang采纳,获得10
2秒前
花花发布了新的文献求助20
2秒前
xxx发布了新的文献求助10
3秒前
小太阳发布了新的文献求助10
3秒前
Irislee完成签到,获得积分10
4秒前
从容书雁发布了新的文献求助10
4秒前
5秒前
斯文败类应助大壮采纳,获得10
5秒前
碧蓝的以彤完成签到 ,获得积分10
7秒前
7秒前
隐形曼青应助阔达之卉采纳,获得10
7秒前
雨雨完成签到,获得积分20
7秒前
ming2026应助花半里里采纳,获得10
7秒前
9秒前
科研通AI2S应助ly采纳,获得10
9秒前
cxm666完成签到,获得积分10
11秒前
大个应助ak24765采纳,获得10
11秒前
12秒前
12秒前
无辜的帽子完成签到,获得积分10
13秒前
研友_VZG7GZ应助xwm采纳,获得10
13秒前
13秒前
14秒前
大壮完成签到,获得积分10
15秒前
15秒前
nn完成签到,获得积分10
16秒前
Pami发布了新的文献求助10
16秒前
Redamancy发布了新的文献求助10
16秒前
wuang应助Kahanto采纳,获得10
17秒前
妮妮完成签到 ,获得积分10
18秒前
kvakqiang完成签到,获得积分10
18秒前
18秒前
18秒前
19秒前
19秒前
20秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Römisch-Germanische Forschungen 1000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
Green Fire Retardants for Polymeric Materials 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7616524
求助须知:如何正确求助?哪些是违规求助? 9191949
关于积分的说明 19698323
捐赠科研通 7189135
什么是DOI,文献DOI怎么找? 3271842
关于科研通互助平台的介绍 2434652
邀请新用户注册赠送积分活动 2266873