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Monteiro TO, Alves PAADSDAN, Barradas Filho AO, Villa-Vélez HA, Cruz G. Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks. CHEMOSPHERE 2024; 352:141484. [PMID: 38368962 DOI: 10.1016/j.chemosphere.2024.141484] [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: 09/12/2023] [Revised: 01/18/2024] [Accepted: 02/15/2024] [Indexed: 02/20/2024]
Abstract
The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001-12.41% MAPE, 0.001-5.82 mg Nm-3 MAE, and 0.03-52.30 mg Nm-3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10-6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.
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Affiliation(s)
- Thalyssa Oliveira Monteiro
- Postgraduate Program in Mechanical Engineering (PPGMEC), Department of Mechanics and Materials, Federal Institute of Education, Science, and Technology of Maranhão (IFMA), São Luís, Maranhão, Brazil
| | | | - Alex Oliveira Barradas Filho
- Data Analysis and Artificial Intelligence Laboratory (DARTi), Department of Computational Engineering, Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil
| | | | - Glauber Cruz
- Postgraduate Program in Mechanical Engineering (PPGMEC), Department of Mechanics and Materials, Federal Institute of Education, Science, and Technology of Maranhão (IFMA), São Luís, Maranhão, Brazil; Processes and Thermal Systems Laboratory (LPSisTer), Department of Mechanical Engineering, Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil.
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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.
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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.
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Pandey AK, Park J, Ko J, Joo HH, Raj T, Singh LK, Singh N, Kim SH. Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications. BIORESOURCE TECHNOLOGY 2023; 370:128502. [PMID: 36535617 DOI: 10.1016/j.biortech.2022.128502] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/11/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.
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Affiliation(s)
- Ashutosh Kumar Pandey
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Jungsu Park
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Jeun Ko
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Hwan-Hong Joo
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Tirath Raj
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Lalit Kumar Singh
- Department of Biochemical Engineering, Harcourt Butler Technical University, Kanpur 208002, Uttar Pradesh (UP), India
| | - Noopur Singh
- Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh (UP), India
| | - Sang-Hyoun Kim
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
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Abdelkareem MA, Soudan B, Mahmoud MS, Sayed ET, AlMallahi MN, Inayat A, Radi MA, Olabi AG. Progress of artificial neural networks applications in hydrogen production. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.03.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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Mikheeva ER, Katraeva IV, Litti YV, Kovalev AA, Kovalev DA. Influence of confectionery wastewater pretreatment in vortex layer apparatus on its physical and chemical properties. BIO WEB OF CONFERENCES 2022. [DOI: 10.1051/bioconf/20224802011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The paper studies the effect of pretreatment of highly concentrated wastewater from confectionery production in a vortex layer apparatus (VLA) on its physical and chemical properties, with the aim of its further use as a substrate for dark fermentation with the production of biohydrogen. Pretreatment in VLA resulted in a 2.6-fold increase in the iron content and 6.5% increase in soluble chemical oxygen demand after 3 minutes of exposure. After pretreatment in VLA, an increase in the content of acetic acid and a decrease in the contents of propionic, butyric and caproic acids were observed. An increase in the content of mono- and disaccharides was registered, and the effect of the VLA exposure time of confectionery wastewater on its physicochemical properties was studied. An increase in the concentration of iron and simple sugars in wastewater makes the use of VLA promising for improving the process of its subsequent dark fermentation.
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Ni D, Xiao Z, Lim MK. Machine learning in recycling business: an investigation of its practicality, benefits and future trends. Soft comput 2021. [DOI: 10.1007/s00500-021-05579-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Ahmad A, Banat F, Taher H. Comparative study of lactic acid production from date pulp waste by batch and cyclic-mode dark fermentation. WASTE MANAGEMENT (NEW YORK, N.Y.) 2021; 120:585-593. [PMID: 33176940 DOI: 10.1016/j.wasman.2020.10.029] [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: 07/21/2020] [Revised: 10/20/2020] [Accepted: 10/20/2020] [Indexed: 06/11/2023]
Abstract
Biowaste valorization into lactic acid (LA) by treatment with indigenous microbiota has recently gained considerable attention. LA production from date pulp waste provides an opportunity for resource recovery, reduces environmental issues, and possibly turns biomass into wealth. This study aimed to compare the performance of batch and cyclic fermentation processes in LA production with and without enzymatic pretreatment. The fermentation studies were conducted in the absence of an external inoculum source (relying on indigenous microbiota) and without the addition of nutrients. The highest LA volumetric productivity (3.56 g/liter/day), yield (0.07 g/g-TS), and concentration (21.66 g/L) were attained with enzymatic pretreated date pulp in the cyclic-mode fermentation at the optimized conditions. The productivity rate of LA was enhanced in the cyclic-mode as compared to the batch process. Enzymatic pretreatment increased the digestibility of cellulose that led to higher LA yield. An Artificial Neural Network model was developed to optimize the process parameters and to predict the LA concentration from date pulp waste in both fermentation processes. The main advantage of the ANN approach is the ability to perform quick predictions without resource-consuming experiments. The model predicted optimal conditions well and demonstrated good agreement between experimental and predicted data.
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Affiliation(s)
- Ashfaq Ahmad
- Department of Chemical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
| | - Fawzi Banat
- Department of Chemical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
| | - Hanifa Taher
- Department of Chemical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
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