Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects

Khan, Muzammil; Chuenchart, Wachiranon; Surendra, K.C.; Khanal, Samir Kumar

doi:10.1016/j.biortech.2022.128501

Cited by 38 publications

(9 citation statements)

References 98 publications

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“…Te German standard, VDI 4630, recommends a substrate-inoculum ratio of less than 0.50 [33]. For example, Ibro et al [23] evaluated the efect of the inoculum-substrate ratio on biogas production performance, and the highest biodegradability and biogas production was attained at an ISR of 2. Despite their potential to initiate the start-up of the biochemical reaction, it is not easy to compare kinetic parameters due to the complex nature of each experimental setup (inoculum source and temperature) [53].…”

Section: Inoculum-substrate Ratiomentioning

confidence: 99%

“…Te mathematical models used to describe the kinetic parameters include the Monod model, frst-order kinetic, modifed Gompertz models [21], and transference function [22]. In addition, the models applied to forecast and simulate the mono AD process include artifcial intelligence (artifcial neural networks (ANNs), ant colony optimization (ACO), genetic algorithms (GAs), and particle swarm optimization (PSO)), and hybrids of these models [23,24]. Furthermore, a variety of diferent common software programs have been applied to simulate and optimize the AcoD process, such as Aspen Plus (AP), AQUASIM, ADM1, and sewage treatment operation analysis over time (STOAT) [20,25,26].…”

Section: Introductionmentioning

confidence: 99%

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Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Kelif Ibro,

Ramayya Ancha,

Beyene Lemma

2024

Journal of Chemistry

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Many operational parameters, either discretely or collectively, can influence the biodegradation performance towards enhancing biogas yield and quality. Among the operating parameters, organic loading rate (OLR), inoculum-substrate ratio, and carbon-nitrogen ratio (C/N) are the most critical parameters in the optimization and enhancement of biogas yield. Optimization of the biogas production processes depends on the ability of anaerobic microorganisms to respond to variations in operational parameters such as pH, redox potential, and intermediate products to enhance the biogas yield. This review article focuses on the role of process parameters, kinetic models, artificial intelligence, Aspen Plus (AP), and anaerobic digestion model no. 1 (ADM1) in optimizing biogas yield via an anaerobic codigestion (AcoD) process. The review showed that biomaterials codigestion upgraded biogas yield to the extent of 400%, and organic removal efficiency reached up to 90% compared to a single substrate. In addition, the current work has verified that the kinetic model is the most effective tool for signifying that the hydrolysis phase is the rate-limiting step, whereas AP is the most effective tool in the design and optimization of the AcoD process parameters. The reviewed kinetic and AI models show strong correlation values ranging from 0.931 to 0.9991 and 0.8700 to 0.9998, respectively. The AcoD system involves complex chemical reactions, but AP might have limitations in representing such complex chemical processes with nonideal behavior and complicated reaction mechanisms. The design and optimization of AcoD with reliable input parameters are highly limited or nonexistent. The AcoD process design with AP opens fresh research opportunities, including improved efficiency, finding appropriate retention time, and saving time, as well as finding the optimum biogas yield. This review article gives an insightful understanding of AcoD process parameter optimization and valuable strategies for policy development enhancing sustainability in the biogas sector.

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Section: Inoculum-substrate Ratiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Kelif Ibro,

Ramayya Ancha,

Beyene Lemma

2024

Journal of Chemistry

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“…ADM2 is a valuable tool for understanding and optimizing anaerobic digestion processes and accommodates a wider range of substrates. It incorporates more microbial groups and interactions and employs more detailed kinetic equations [14]. However, ADM2 is more complex than its predecessor ADM1, which means it requires a larger number of parameters to be estimated.…”

Section: Introductionmentioning

confidence: 99%

“…However, ADM2 is more complex than its predecessor ADM1, which means it requires a larger number of parameters to be estimated. Parameter estimation can be challenging and time-consuming, as obtaining accurate parameter values often requires extensive experimental data [14]. The increased complexity of ADM2 compared to ADM1 can make simulations computationally intensive, particularly for larger and more intricate systems.…”

Section: Introductionmentioning

confidence: 99%

“…The model represents microbial growth and metabolic kinetics using mathematical equations [15]. However, microbial activity can vary significantly due to factors such as pH, temperature, and changes in substrate composition, making it more complicated to modify to suit a specific application, such as co-digestion; hence, ADM1 was used for this study [14,16,17].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Methane Production through Anaerobic Co-Digestion of Sewage Sludge: A Modified ADM1 Model Approach

Mudzanani,

Phadi,

Iyuke

et al. 2023

Fermentation

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The International Water Association’s (IWA) established Anaerobic Digestion Model No. 1 (ADM1) was created to serve as a backup for experimental findings regarding the actual anaerobic digestion process. The previous model idea was adjusted and used to simulate an anaerobic digestion process in this study. Testing procedures, such as benchmark tests and balance checks, were performed in order to verify the accuracy of the implementation. These measures worked in tandem to ensure that the model was implemented flawlessly and without inconsistencies. The primary objective of this article is to construct a method that is based on the ADM1 for evaluating co-digestion and predicting the performance of the digestion process or methane yield based on the analyzed substrates’ physicochemical properties. Additional equations and simulations have been added to the standard model to create tools for evaluating the feasibility of anaerobic co-digestion. The study’s two most intriguing aspects are the optimal mixture and parameter dependence. The adjusted ADM1 is accurate in predicting the measured values of effluent COD, pH, methane, and produced biogas flows with a reasonable degree of accuracy, according to the validation results. This research shows how to use ADM1 in a wastewater treatment plant and other settings where anaerobic digestion is of interest.

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Machine learning: an advancement in biochemical engineering

Saha,

Chauhan,

Rastogi Verma

2024

Biotechnol Lett

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Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects

Cited by 38 publications

References 98 publications

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Enhancing Methane Production through Anaerobic Co-Digestion of Sewage Sludge: A Modified ADM1 Model Approach

Machine learning: an advancement in biochemical engineering

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