Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network

daiem, Mahmoud M. Abdel; Hatata, Ahmed Y.; Galal, Osama H.; Said, Noha; Ahmed, Dalia A.

doi:10.1016/j.renene.2021.06.050

Cited by 34 publications

(16 citation statements)

References 53 publications

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“…The optimization helps to support decision-making regarding sustainable development strategies that utilize eco-friendly technologies for efficient power generation. In this case, it usually predicts the model behaviour from biomass residues (Hatata et al, 2021), requiring many experimental datasets (Şenol, 2021). The optimization of process parameters in an anaerobic digestion process can accelerate the hydraulic retention time and assist in optimizing the specific parameters that are considered predictors of the desired response (Cremona et al, 2022).…”

Section: Why Optimisation Technique (Methodology Of the Study)mentioning

confidence: 99%

“…As mentioned earlier, finding the most suitable optimization tool for the performance of biogas production has been a challenge. Previously, the ant colony algorithm was used as an optimization technique by Beltramo et al (2016); however, Hatata et al (2021) study proposed the moth flame optimization (MFO) technique to identify the optimal structure of multilayer feedforward network (MFFNN) to predict the biogas production. The study used four novel two-dimensional mathematical models (TDMMs) and ANN to stimulate and predict biogas production via an anaerobic co-digestion process using waste-activated sludge and wheat straw.…”

Section: Research On Mathematical Modeling For Improving Biogas Yieldmentioning

confidence: 99%

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Improvement of biogas yields in an anaerobic digestion process via optimization technique

Obileke,

Makaka,

Tangwe

et al. 2024

Environ Dev Sustain

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Anaerobic digestion for biogas production was first used in 1895 for electricity generation and treating municipal solid waste in 1939. Since then, overcoming substrate recalcitrance and methane production has been one way to assess the quality of biogas production in a sustainable manner. These are achieved through pre-treatment methods and mathematical modeling predictions. However, previous studies have shown that optimisation techniques (pre-treatment and mathematical modeling) improve biogas yield efficiently and effectively. The good news about these techniques is that they address the challenges of low efficiency, cost, energy, and long retention time usually encountered during anaerobic digestion. Therefore, this paper aims to comprehensively review different promising pre-treatment technologies and mathematical models and discuss their latest advanced research and development, thereby highlighting their contribution towards improving the biogas yield. The comparison, application, and significance of findings from both techniques, which are still unclear and lacking in the literature, are also presented. With over 90 articles reviewed from academic databases (Springer, ScienceDirect, SCOPUS, Web of Science, and Google Scholar), it is evident that artificial neural network (ANN) predicts and improves biogas yield efficiently and accurately. On the other hand, all the pre-treatment techniques are unique in their mode of application in enhancing biogas yield. Hence, this depends on the type of substrate used, composition, location, and conversion process. Interestingly, the study reveals research findings from authors concerning the enhancement of biogas yield to arrive at a conclusion of the best optimization technique, thereby making the right selection technique. Graphical Abstract

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Section: Why Optimisation Technique (Methodology Of the Study)mentioning

confidence: 99%

Section: Research On Mathematical Modeling For Improving Biogas Yieldmentioning

confidence: 99%

Improvement of biogas yields in an anaerobic digestion process via optimization technique

Obileke,

Makaka,

Tangwe

et al. 2024

Environ Dev Sustain

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“…This was attributed to variations in the pH caused by biological conversion during the anaerobic digestion process, when high volumes of organic acids were produced by the acidogenic bacteria. Acid accumulation occurred and disrupted this process, while under normal conditions, the pH is controlled by the bicarbonate produced by methanogens and the ammonia formed in the reaction medium (Dobre et al 2014;Abdel daiem et al 2021b).…”

Section: Fig 4 Cumulative Methane Production From Different Reactorsmentioning

confidence: 99%

“…Beltramo et al (2019) conducted FFNN modeling of the biogas production rate from digestion of maize and grass silages together with pig and cattle manure. Biogas production from anaerobic codigestion of waste activated sludge and wheat straw was modeled and predicted with FFNN modeling (Abdel daiem et al 2021b).…”

Section: Introductionmentioning

confidence: 99%

Effect of alkaline pretreatment on the characteristics of barley straw and modeling of methane production via codigestion of pretreated straw with sewage sludge

Alrowais,

Abdel daiem,

Helmi

et al. 2024

BioRes

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Straw pretreatment enhances the cellulose accessibility and increases the methane yield from anaerobic digestion. This study investigated the effects of alkali pretreatments with different chemical agents (NaOH, KOH, and Na2CO3) on the physicochemical and thermal characteristics of barley straw, as well as methane production from codigestion with sewage sludge. Artificial neural network modeling with a feedforward neural network (FFNN) and slime mold optimization (SMO) techniques were used to predict methane production. NaOH pretreatment was shown to be the best pretreatment for removing hemicellulose and lignin and for increasing the cellulose accessibility. Moreover, there was a 2.57-fold higher level of methane production compared to that from codigestion with untreated straw. The removal ratios for the total solids, volatile solids, and chemical oxygen demand reached 59.3, 67.2, and 73.4%, respectively. The modeling results showed that the FFNN-SMO method can be an effective tool for simulating the methane generation process, since training, validating, and testing produced very high correlation coefficients. The FFNN-SMO accurately predicted the amount of methane produced, with an R2 of 0.998 and a 3.1×10-5 root mean square error (RMSE).

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“…Kinetic parameters in the Modified ADM1 Model are adjusted to reflect the specific behavior of microorganisms in the co-digestion process, such as determining the rate at which different substrates are degraded and converted into biogas [25]. The model also accounts for inhibition effects, such as heavy metals or toxic compounds, if present in the sewage sludge or co-substrates [26]. The Modified ADM1 Model can be used to predict the performance of the anaerobic co-digestion process, estimating the amount of biogas (methane) produced, the rate of digestion, and the dynamics of microbial populations over time.…”

Section: Introductionmentioning

confidence: 99%

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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Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network

Cited by 34 publications

References 53 publications

Improvement of biogas yields in an anaerobic digestion process via optimization technique

Improvement of biogas yields in an anaerobic digestion process via optimization technique

Effect of alkaline pretreatment on the characteristics of barley straw and modeling of methane production via codigestion of pretreated straw with sewage sludge

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

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