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Processes
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Bioelectrochemical System for Wastewater Treatment and Energy Recovery
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Bioelectrochemical System for Wastewater Treatment and Energy Recovery

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 4090

Special Issue Editor

Prof. Dr. Shaoan Cheng

E-Mail Website
Guest Editor
State Key Laboratory of Clean Energy Utilization, Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Interests: bioelectrochemistry; microbial fuel cell; advanced oxidation process; wastewater treatment and energy recovery; electroactive biofilm

Special Issue Information

Dear Colleagues,

We invite you to make a submission to this Special Issue of Processes focused on “Bioelectrochemical Systems for Wastewater Treatment and Energy Recovery”. Bioelectrochemical systems have been hotspots in the microbiology, electrochemistry, chemical engineering, environment, technology, and material sciences in the past years. Great progress has been made in both fundamental and applied research, resulting in many new bioelectrochemical systems and potential applications. This Special Issue seeks novel research contributions in, but not limited to, the following areas:

  • Developing new materials used in bioelectrochemical systems;
  • The design, engineering, and optimization of bioelectrochemical systems for enhancing wastewater treatment and energy recovery;
  • Combination of bioelectrochemical systems with other wastewater treatment technologies
  • Formation and regulation of electrode biofilm;
  • The simulation, modeling, and design of bioelectrochemical systems;
  • Bioelectrochemical systems for pollutant removal, hydrogen production, and CO2 reduction

We welcome and look forward to your submissions.

Thank you,

Prof. Dr. Shaoan Cheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioelectrochemical system
  • electrode material
  • configuration design
  • biofilm formation and regulation
  • combination technology
  • wastewater treatment
  • energy recovery

Published Papers (2 papers)

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Research

11 pages, 1258 KiB  
Article
Nitrogen Removal from the High Nitrate Content Saline Denitration Solution of a Coal-Fired Power Plant by MFC
by Shaoan Cheng, Zhipeng Huang and Zhihua Wang
Processes 2022, 10(8), 1540; https://doi.org/10.3390/pr10081540 - 05 Aug 2022
Viewed by 1365
Abstract
Oxidation denitration is one of the most efficient ways to remove NOx from flue gas in a coal-fired power plant. However, this oxidation denitration produces saline solution containing a high concentration of nitrate, which needs to be well treated. In this paper, MFC [...] Read more.
Oxidation denitration is one of the most efficient ways to remove NOx from flue gas in a coal-fired power plant. However, this oxidation denitration produces saline solution containing a high concentration of nitrate, which needs to be well treated. In this paper, MFC was firstly used to treat the high nitrate content saline denitration solution from ozone oxidation denitration of a coal-fired power plant. The influences of chemical oxygen demand (COD) and initial nitrate concentration on the nitrate removal and electricity generation of MFC were investigated by sequencing batch mode. The results showed that using MFCs could efficiently remove nitrate from coal-fired power plant saline denitration solution with nitrate nitrogen (NO3−-N) concentration up to 1510 mg/L. The average nitrate nitrogen removal rate was as high as 248.3 mg/(L·h) at initial nitrate nitrogen concentration of 745 mg/L and COD concentration of 6.5 g/L, which was eight times as high as that of the conventional biological method. Furthermore, the MFC required an average COD consumption of 3.42 g/g-NO3−-N which was lower than most of the conventional biological methods. In addition, MFC could produce a maximum power density of 241.1 mW/m2 while treating this saline denitration solution. Full article
(This article belongs to the Special Issue Bioelectrochemical System for Wastewater Treatment and Energy Recovery)
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14 pages, 2789 KiB  
Article
Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion
by Ain Cheon, Jwakyung Sung, Hangbae Jun, Heewon Jang, Minji Kim and Jungyu Park
Processes 2022, 10(1), 158; https://doi.org/10.3390/pr10010158 - 14 Jan 2022
Cited by 15 | Viewed by 2074
Abstract
The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, [...] Read more.
The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models. Full article
(This article belongs to the Special Issue Bioelectrochemical System for Wastewater Treatment and Energy Recovery)
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