Abstract:Aim: To study the effects of different sizes of groundnut shell on biogas and methane yields using batch reactor at mesophilic temperature.
Place and Duration of Study: The laboratory experiment was carried out at the Laboratory of the Department of Agricultural Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria, between August and October, 2018.
Methodology: Batch experiment was set up for a period of 35 days with substrate reduced to 2, 4 and 6 mm sizes. The digesters were s… Show more
“…Hydrolysis is perceived to be the rate-limiting stage in the process because of the substrate resistance to the enzymatic attack of the microorganisms due to their structural arrangements (Olatunji et al, 2021). This hydrolysis stage can be improved by applying pretreatment methods such as mechanical, chemical and biological pretreatments (Jekayinfa et al, 2020; Karuppiah and Ebenezer Azariah, 2019).…”
Optimising biogas yields from anaerobic digestion of organic wastes is significant to maximum energy recovery in the biodigestion process and has become an important topic of interest. Substrate particle size is an important process parameter in biogas production, and it precedes other pretreatments methods for the majority of the lignocellulose materials. Optimisation of biogas yield using Response Surface Methodology (RSM) was done, and temperature, hydraulic retention time and particle size were considered variables to develop the predictive models. Pretreatment of groundnut shells was investigated using particle size reduction of mechanical pretreatment methods. After pretreatment, 30 samples were digested in a batch digester at mesophilic temperature. The experimental results showed that the temperature, hydraulic retention time and particle size had significant effects of interaction ( p < 0.05). The optimum experimental and predicted yields are: 44.70 and 42.92 (lNkgoDM) organic dry matter biogas yield, 20.80 and 19.09 (lN/kgFM) fresh mass biogas yield, 24.00 and 22.68 (lNCH4oDM) organic dry methane yield and 12.30 and 15.59 (lNCH4FM) fresh mass methane yield, respectively. The R2 recorded for the four yield components were 0.6268, 0.5875, 0.6109 and 0.5547. These values seem to be lower and a sign of the average fit of the model. Biogas production from groundnut shells was significantly improved with statistical optimisation and the pretreatment method.
“…Hydrolysis is perceived to be the rate-limiting stage in the process because of the substrate resistance to the enzymatic attack of the microorganisms due to their structural arrangements (Olatunji et al, 2021). This hydrolysis stage can be improved by applying pretreatment methods such as mechanical, chemical and biological pretreatments (Jekayinfa et al, 2020; Karuppiah and Ebenezer Azariah, 2019).…”
Optimising biogas yields from anaerobic digestion of organic wastes is significant to maximum energy recovery in the biodigestion process and has become an important topic of interest. Substrate particle size is an important process parameter in biogas production, and it precedes other pretreatments methods for the majority of the lignocellulose materials. Optimisation of biogas yield using Response Surface Methodology (RSM) was done, and temperature, hydraulic retention time and particle size were considered variables to develop the predictive models. Pretreatment of groundnut shells was investigated using particle size reduction of mechanical pretreatment methods. After pretreatment, 30 samples were digested in a batch digester at mesophilic temperature. The experimental results showed that the temperature, hydraulic retention time and particle size had significant effects of interaction ( p < 0.05). The optimum experimental and predicted yields are: 44.70 and 42.92 (lNkgoDM) organic dry matter biogas yield, 20.80 and 19.09 (lN/kgFM) fresh mass biogas yield, 24.00 and 22.68 (lNCH4oDM) organic dry methane yield and 12.30 and 15.59 (lNCH4FM) fresh mass methane yield, respectively. The R2 recorded for the four yield components were 0.6268, 0.5875, 0.6109 and 0.5547. These values seem to be lower and a sign of the average fit of the model. Biogas production from groundnut shells was significantly improved with statistical optimisation and the pretreatment method.
“…In contrast, it does not for lignocellulose materials like rice straw and maize stalk (Menardo et al, 2012). In line with this assertion, groundnut shell has been established as a lignocellulose material that particle size reduction beyond a particular size does not favour its biogas and methane yields (Jekayinfa et al, 2020). This agrees with what was earlier noticed: if the substrate can be reduced to a point at which it is easy to degrade, overloading of digesters is possible with high organic loading, especially in the batch system.…”
Section: Discussionmentioning
confidence: 96%
“…Another similar research reported that the biogas and methane yields were enhanced until the particle size was 6 mm, but below 6 mm particle size, the yield started to reduce ( Herrmann et al, 2012 ). Jekayinfa et al (2020) reported 6 mm particle size as the optimum value for fresh biogas and methane yields of lignocellulose material. This result also supports what was reported by an earlier researcher when particle size was considered ( Olatunji et al, 2022b ).…”
Section: Discussionmentioning
confidence: 99%
“…The particle sizes selected for this research were 2, 4, 6 and 8 mm. These were done in modification to the earlier recommendation for the particle selection for anaerobic digestion of lignocellulose materials ( Jekayinfa et al, 2020 ; Menardo et al., 2012 ; Xiao et al, 2013 ). The digesters were loaded and labelled as shown in Table 3 .…”
Section: Methodsmentioning
confidence: 99%
“…Arachis Hpogea shell is one of the abundant feedstocks that have been investigated to have an excellent potential for biogas and methane generation ( Jekayinfa et al, 2020 ). The study shows that different particle sizes of Arachis hypogea significantly influence the biogas yield, and the expected yield determines the choice of particle size ( Olatunji et al, 2022b ).…”
A smart energy recovery process can achieve maximum energy recovery from organic wastes. Pretreatment of feedstock is essential to biogas and methane yields during the anaerobic digestion process. This work combined particle size reduction with Fe3O4 nanoparticles to investigate their influence on biogas and methane yields from anaerobic digestion of Arachis hypogea shells. Twenty milligrams per litre of Fe3O4 nanoparticles was implemented with 2, 4, 6 and 8 mm particle sizes and a single treatment of Fe3O4 for 35 days. The treatments were compared with each other and were discovered to significantly ( p < 0.05) enhance biogas yield by 37.40%, 50.10%, 54.40%, 51.40% and 35.50% compared with control, respectively. Specific biogas yield recorded was 966.2, 1406, 1552.7, 1317.4, 766.2 and 413 mL g−1 volatile solid. This study showed the combination of Fe3O4 with 6 mm particle size of Arachis hypogea shells produced the optimum biogas and methane yields. The addition of Fe3O4 to particle sizes below 6 mm resulted in over-accumulation of volatile fatty acids and lowered the gas yield. This can be applied on an industrial scale.
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.
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