1
|
Mullai P, Vishali S, Sambavi SM, Dharmalingam K, Yogeswari MK, Vadivel Raja VC, Bharathiraja B, Bayar B, Abubackar HN, Al Noman MA, Rene ER. Energy generation from bioelectrochemical techniques: Concepts, reactor configurations and modeling approaches. CHEMOSPHERE 2023; 342:139950. [PMID: 37648163 DOI: 10.1016/j.chemosphere.2023.139950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023]
Abstract
The process industries play a significant role in boosting the economy of any nation. However, poor management in several industries has been posing worrisome threats to an environment that was previously immaculate. As a result, the untreated waste and wastewater discarded by many industries contain abundant organic matter and other toxic chemicals. It is more likely that they disrupt the proper functioning of the water bodies by perturbing the sustenance of many species of flora and fauna occupying the different trophic levels. The simultaneous threats to human health and the environment, as well as the global energy problem, have encouraged a number of nations to work on the development of renewable energy sources. Hence, bioelectrochemical systems (BESs) have attracted the attention of several stakeholders throughout the world on many counts. The bioelectricity generated from BESs has been recognized as a clean fuel. Besides, this technology has advantages such as the direct conversion of substrate to electricity, and efficient operation at ambient and even low temperatures. An overview of the BESs, its important operating parameters, bioremediation of industrial waste and wastewaters, biodegradation kinetics, and artificial neural network (ANN) modeling to describe substrate removal/elimination and energy production of the BESs are discussed. When considering the potential for use in the industrial sector, certain technical issues of BES design and the principal microorganisms/biocatalysts involved in the degradation of waste are also highlighted in this review.
Collapse
Affiliation(s)
- P Mullai
- Department of Chemical Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, 608 002, Tamil Nadu, India.
| | - S Vishali
- Department of Chemical Engineering, SRM Institute of Science and Engineering, Kattankulathur, 603 203, Tamil Nadu, India.
| | - S M Sambavi
- Department of Chemical and Biological Engineering, Energy Engineering with Industrial Management, University of Sheffield, Sheffield, United Kingdom.
| | - K Dharmalingam
- Department of Biotechnology, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India.
| | - M K Yogeswari
- Department of Chemical Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, 608 002, Tamil Nadu, India.
| | - V C Vadivel Raja
- Department of Chemical Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, 608 002, Tamil Nadu, India.
| | - B Bharathiraja
- Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College, Chennai, 600062, Tamil Nadu, India.
| | - Büşra Bayar
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República (EAN), 2780-157 Oeiras, Portugal.
| | - Haris Nalakath Abubackar
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República (EAN), 2780-157 Oeiras, Portugal.
| | - Md Abdullah Al Noman
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX, Delft, the Netherlands.
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX, Delft, the Netherlands.
| |
Collapse
|
2
|
Kumar T, Naik S, Jujjavarappu SE. A critical review on early-warning electrochemical system on microbial fuel cell-based biosensor for on-site water quality monitoring. CHEMOSPHERE 2022; 291:133098. [PMID: 34848233 DOI: 10.1016/j.chemosphere.2021.133098] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 11/21/2021] [Accepted: 11/25/2021] [Indexed: 05/15/2023]
Abstract
The microbial fuel cell (MFC) sensor is a very promising self-powered self-sustainable system for early warning water quality detection. These sensors are cost-effective, biodegradable, compact in design, and portable in nature are favorable for real-time in situ water quality monitoring. This review represents the mechanism action behind the toxicity detection, optimization strategies, process parameters, role of biofilm, the role of external resistance, hydrodynamic study, and mathematical modeling for improving the performance of the sensor. Additionally, the techno-economic prospect of this MFC-based sensor and its challenges, limitations are addressed to make it economically more favorable for commercial use. The future direction is also explored based on the sensor's disadvantages and limitations. Comprehensively, this review covered all the possible directions of MFC sensor fabrication, their application, recent advancement, prospects challenges, and their possible solutions.
Collapse
Affiliation(s)
- Tukendra Kumar
- Department of Biotechnology, National Institute of Technology, Raipur, Chhattisgarh, 492001, India
| | - Sweta Naik
- Department of Biotechnology, National Institute of Technology, Raipur, Chhattisgarh, 492001, India
| | | |
Collapse
|
3
|
Leveraging artificial intelligence in bioelectrochemical systems. Trends Biotechnol 2021; 40:535-538. [PMID: 34893375 DOI: 10.1016/j.tibtech.2021.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 11/23/2022]
Abstract
Bioelectrochemical systems (BESs) are highly evolved and sophisticated systems that produce bioenergy via exoelectrogenic microbes. Artificial intelligence (AI) helps to understand, relate, model, and predict both process parameters and microbial diversity, resulting in higher performance. This approach has revolutionized BESs through highly advanced computational algorithms that best suit the systems' architecture.
Collapse
|
4
|
Hyun Chung T, Ranjan Dhar B. A multi-perspective review on microbial electrochemical technologies for food waste valorization. BIORESOURCE TECHNOLOGY 2021; 342:125950. [PMID: 34852436 DOI: 10.1016/j.biortech.2021.125950] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/08/2021] [Accepted: 09/12/2021] [Indexed: 06/13/2023]
Abstract
The worldwide generation of food waste (FW) has been increasing enormously due to the growing food industry and population. However, FW contains a large amount of biodegradable organics that can be converted to clean energy, which can potentially minimize the utilization of fossil fuels. Conventional biowaste valorization technologies, such as anaerobic digestion and composting, have been adopted for FW management for recovering useful biogas and compost. However, they are often limited by high capital and operation costs, low recovery efficiency, slow process kinetics, and system instability. On the other hand, microbial electrochemical technologies (METs) have been highly promising for efficiently harvesting bioenergy and high value-added products from FW. Hence, this article critically reviews up-to-date studies on applying various METs regarding their value-added products recovery efficiencies from FW. Moreover, this review lists existing challenges, ways to optimize the system performance and provides perspectives on future research needs.
Collapse
Affiliation(s)
- Tae Hyun Chung
- Department of Civil and Environmental Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB T6G 1H9, Canada
| | - Bipro Ranjan Dhar
- Department of Civil and Environmental Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB T6G 1H9, Canada.
| |
Collapse
|
5
|
Meena M, Shubham S, Paritosh K, Pareek N, Vivekanand V. Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling. BIORESOURCE TECHNOLOGY 2021; 340:125642. [PMID: 34315128 DOI: 10.1016/j.biortech.2021.125642] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
Bioenergy may be a major replacement of fossil fuels which can make the path easier for sustainable development and decrease the dependency on conventional sources of energy. The main concern with the bioenergy is the availability of feedstock, dealing with its economics as well as its demand and supply chain management. This review deals with the finding of distinct potential of different Artificial Intelligence technologies focusing the challenges in bioenergy production system and its overall improvement in application. The study also highlights the contribution of Artificial Intelligence techniques for the prediction of energy from biomass and evaluates the computing-reasoning techniques for managing bioenergy production, biomass supply chain and optimization of process parameters for efficient bioconversion technologies.
Collapse
Affiliation(s)
- Manish Meena
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India
| | - Shubham Shubham
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India
| | - Kunwar Paritosh
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India
| | - Nidhi Pareek
- Department of Microbiology, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan 305801, India
| | - Vivekanand Vivekanand
- Centre for Energy and Environment, Malviya National Institute of Technology, JLN Marg, Jaipur, Rajasthan 302017 India.
| |
Collapse
|
6
|
Tsompanas MA, You J, Philamore H, Rossiter J, Ieropoulos I. Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications. Front Robot AI 2021; 8:633414. [PMID: 33748191 PMCID: PMC7969642 DOI: 10.3389/frobt.2021.633414] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 01/28/2021] [Indexed: 12/14/2022] Open
Abstract
The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.
Collapse
Affiliation(s)
- Michail-Antisthenis Tsompanas
- Bristol BioEnergy Centre, Bristol Robotics Laboratory, Frenchay Campus, University of the West of England, Bristol, United Kingdom
| | - Jiseon You
- Bristol BioEnergy Centre, Bristol Robotics Laboratory, Frenchay Campus, University of the West of England, Bristol, United Kingdom
| | - Hemma Philamore
- SoftLab, Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Jonathan Rossiter
- SoftLab, Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Ioannis Ieropoulos
- Bristol BioEnergy Centre, Bristol Robotics Laboratory, Frenchay Campus, University of the West of England, Bristol, United Kingdom
| |
Collapse
|
7
|
de Ramón-Fernández A, Salar-García M, Ruiz Fernández D, Greenman J, Ieropoulos I. Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells. ENERGY (OXFORD, ENGLAND) 2020; 213:118806. [PMID: 33335352 PMCID: PMC7695679 DOI: 10.1016/j.energy.2020.118806] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/20/2020] [Accepted: 09/06/2020] [Indexed: 05/27/2023]
Abstract
Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology.
Collapse
Affiliation(s)
| | - M.J. Salar-García
- Bristol BioEnergy Centre, Bristol Robotic Laboratory, Block T, University of the West of England, Bristol, Coldharbour Lane, Bristol, BS16 1QY, UK
| | - D. Ruiz Fernández
- Department of Computer Technology, University of Alicante, Alicante, E-03690, Spain
| | - J. Greenman
- Bristol BioEnergy Centre, Bristol Robotic Laboratory, Block T, University of the West of England, Bristol, Coldharbour Lane, Bristol, BS16 1QY, UK
| | - I.A. Ieropoulos
- Bristol BioEnergy Centre, Bristol Robotic Laboratory, Block T, University of the West of England, Bristol, Coldharbour Lane, Bristol, BS16 1QY, UK
| |
Collapse
|
8
|
Hosseinzadeh A, Zhou JL, Altaee A, Baziar M, Li D. Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system. BIORESOURCE TECHNOLOGY 2020; 316:123967. [PMID: 32777721 DOI: 10.1016/j.biortech.2020.123967] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/29/2020] [Accepted: 08/02/2020] [Indexed: 06/11/2023]
Abstract
This study aims to analyze and model cathodic H2 recovery (rcat), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H2 production rate and total energy recovery with inputs of rcat and CE in a microbial electrolysis cell using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) procedures. Both ANN and ANFIS models demonstrated great goodness of fit for rcat, CE, H2 production rate and total energy recovery prediction with high R2 values. The sum square error values for rcat (0.0017), CE (0.0163), H2 production rate (0.1062) and total energy recovery (0.0136) in ANN models were slightly higher than those in ANFIS models at 0.0005, 0.0091, 0.1247 and 0.0148 respectively. Sensitivity analysis by ANN models demonstrated that voltage, EC, rcat and rcat were the most effective factors for rcat, CE, H2 production rate and total energy recovery, respectively.
Collapse
Affiliation(s)
- Ahmad Hosseinzadeh
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - John L Zhou
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
| | - Ali Altaee
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Mansour Baziar
- Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran
| | - Donghao Li
- Department of Chemistry, MOE Key Laboratory of Biological Resources of Changbai Mountain & Functional Molecules, Yanbian University, Yanji 133002, Jilin Province, PR China
| |
Collapse
|
9
|
Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners. Symmetry (Basel) 2020. [DOI: 10.3390/sym12111798] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future.
Collapse
|
10
|
Lin CW, Chen J, Zhao J, Liu SH, Lin LC. Enhancement of power generation with concomitant removal of toluene from artificial groundwater using a mini microbial fuel cell with a packed-composite anode. JOURNAL OF HAZARDOUS MATERIALS 2020; 387:121717. [PMID: 31767505 DOI: 10.1016/j.jhazmat.2019.121717] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/31/2019] [Accepted: 11/18/2019] [Indexed: 06/10/2023]
Abstract
Composite beads are packed in the anode chamber of a microbial fuel cell (MFC), providing more area for microbial attachment and growth, increasing the efficiency of removal of toluene from toluene-contaminated groundwater. The composite beads were fabricated by integrating carbon coke (CC) with a relatively large specific surface area to which microorganisms easily adhere with conductive carbon black (CCB), which has low electrical resistance. Since the advantages of both are complementary, the power generation of MFC is improved. The single layer-packed anode MFC (SP-MFC) completely degraded 200 mg L-1 of toluene - 2.3 times faster than the non-packed anode MFC (NP-MFC). The high power density (44.9 mW m-3) and oxidation peak (1 mA), with low internal resistance (207 Ω) revealed that SP effectively improved the power generation efficiency. A composition ratio (CRCCB:CC) of composite beads of one to two yielded the best performance with a removal efficiency of 100 % - 76 % faster than CC. The closed circuit voltage of CR1:2 MFC reached 340 mV, which was 16 times that of CC; the power density and oxidation peak reached 103 mW m-3 and 1.38 mA, respectively. Therefore, CR1:2 effectively increased the overall removal efficiency and power generation of the MFC.
Collapse
Affiliation(s)
- Chi-Wen Lin
- Department of Safety, Health and Environmental Engineering, National Yunlin University of Science and Technology, 123 University Rd., Sec. 3, Douliu, Yunlin 64002, Taiwan, ROC; National Yunlin University of Science and Technology, Feng Tay Distinguished Professor, Taiwan, ROC
| | - Jianmeng Chen
- College of Environment, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Jingkai Zhao
- College of Environment, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Shu-Hui Liu
- Department of Safety, Health and Environmental Engineering, National Yunlin University of Science and Technology, 123 University Rd., Sec. 3, Douliu, Yunlin 64002, Taiwan, ROC.
| | - Li-Chen Lin
- Department of Safety, Health and Environmental Engineering, National Yunlin University of Science and Technology, 123 University Rd., Sec. 3, Douliu, Yunlin 64002, Taiwan, ROC
| |
Collapse
|