Development of Ann-Based Models to Predict Biogas and Methane Productions in Anaerobic Treatment of Molasses Wastewater

Yetilmezsoy, Kaan; Türkdoğan, Fatma; Temizel, Ilknur; Günay, Aslı

doi:10.1080/15435075.2012.727116

Cited by 67 publications

(26 citation statements)

References 73 publications

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“…Furthermore, the size of a representative granule is manually but carefully selected while applying a bioparticle model. Biogas and methane production rates [38] ANFIS, adaptive neuro-fuzzy inference system; OLR, volumetric organic loading rate; TCOD, volumetric total chemical oxygen demand; AMIMO, multiple inputs and multiple outputs; TSS, total suspended solids; VSS, volatile suspended solids; COD, chemical oxygen demand; VFA, volatile fatty acid; T, temperature; BOD, biological oxygen demand; TKN, total Kjeldahl nitrogen; HRT, hydraulic retention time; Q , reactor flow rate; and OLR, organic loading rate. Table 1.…”

Section: Reactor Modeling With a Bioparticle Modelmentioning

confidence: 99%

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Approaches for Modeling Anaerobic Granule-Based Reactors

Yang¹

2020

Bacterial Biofilms

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Anaerobic granule sludge is a self-forming biofilm. This biofilm can be developed without the presence of bio-carriers. Anaerobic granule sludge-based technologies are dominant technologies in the field of anaerobic wastewater treatment. Although they are successful technologies, many efforts are still needed for a better understanding of the granules and granule-based reactors because reactor failure can occur. Here, reactor modeling is highly helpful in understanding the performance of anaerobic bioreactors. A model that can accurately model bioprocesses in a sludge bed reactor and predict concentrations of effluent components is valuable. This is because the model can provide insights into the reactor and be useful in reactor control. Current models of granules are models on bioprocesses in a single granule sludge or on the hydrodynamics and biokinetics in a sludge bed reactor. Here, we review advances in reactor model and its applications as well as limitations and further improvements in the models.

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Section: Reactor Modeling With a Bioparticle Modelmentioning

confidence: 99%

“…However, while reactors are treating the same kind of wastewater, model inputs and output can vary significantly [29,30,32]. As a result, different ANN models can be applied to UASB reactors to treat the same kind of wastewater [36][37][38].…”

Section: Neural Network Modelingmentioning

confidence: 99%

Approaches for Modeling Anaerobic Granule-Based Reactors

Yang¹

2020

Bacterial Biofilms

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“…[19][20][21][22][23][24] In recent years, due to their flexibility, considerable progress has been made in the appli-ANN MODELING OF METHANE EMISSIONS 423 cation of ANNs for predicting CH 4 for the chosen area. Some examples of studies include modeling and prediction of ventilation methane emissions of U.S. longwall mines, 18 prediction of methane productions in anaerobic treatment of molasses wastewater, 25 and prediction of methane emissions from wetland ecosystems. 26 This paper presents the development and evaluation of an ANN model for the prediction of CH 4 emissions at national levels, using sustainability, economic and industrial indicators as inputs.…”

Section: Introductionmentioning

confidence: 99%

Modeling of methane emissions using artificial neural network approach

Lidija

Aleksandra

Pocajt³

2015

JSCS

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The aim of this study was to develop a model for forecasting CH 4 emissions at the national level, using artificial neural networks (ANN) with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a backpropagation neural network (BPNN) and a general regression neural network (GRNN). A conventional multiple linear regression (MLR) model was also developed in order to compare the model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH 4 emissions at the national level using the ANN model could be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique, which could be used to support the implementation of sustainable development strategies and environmental management policies.

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“…Nine regression coefficients, i.e., β 0 , β 1 , …, β 8 , were considered and its respective values were estimated and used in the models. The optimum models were selected based on the following statistical performance criterion: standard error of the estimate (SEE) [36] (Eq.9), sum of squared residuals (SSR) (Eq.8), coefficient of multiple determination (R 2 ) (Eq.6), adjusted coefficient of multiple determination (Adj-R 2 ) [37] (Eq.7), VIF (Eq.10), DurbinWatson statistics (DWS) (Eq.11) and p-value [15] (Eq.12). …”

Section: Evaluation and Selection Of The Modelsmentioning

confidence: 99%

“…Other reports also confirmed that process modeling based on previously acquired data is one technical route to enhancing the performance of anaerobic processes. These process models are often developed [14,15]. Nonetheless, modeling of anaerobic digestion is quite challenging and tough because performance of anaerobic systems is complex and varies considerably with influent characteristics and operational conditions [16].…”

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confidence: 99%

Modelling Biogas Fermentation from Anaerobic Digestion: Potato Starch Processing Wastewater Treated Within an Up flow Anaerobic Sludge Blanket

Antwi¹,

Li²,

Shi³

et al. 2017

J Bioremediat Biodegrad

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IntroductionPotato is one of the most valuable food crop grown in many countries [1]. It has been reported that, a considerable proportion of the potato cultivated globally consumed through starch processing which subsequently generates tons of wastewater that goes to pollute water bodies [1][2][3]. Wastewater of raw potato processed into starch are classified as complex wastewater [4,5], and its concentration of chemical oxygen demand (COD), total suspended solid (TSS) and volatile suspended solid of (VSS) can yield concentrations of 50000, 9700 and 9500 mg/L, respectively [6]. Arhoun et al. argued that recovering valuable resource such as bioenergy (biogas) from such wastewater to supplement energy needs will be beneficial to humans and society at large [6,7]. Anaerobic digestion has severally been reported as a successful bioprocess treating various organic wastewaters and subsequently generating biogas [7][8][9][10][11][12]. However, the biological mechanism of anaerobic digestion is not well understood due to the complexity of the bacterial community structure and bioconversion [13]. Hu et al. asserted that process modeling is a good tool for predicting and describing the performance of biological processes [13]. Other reports also confirmed that process modeling based on previously acquired data is one technical route to enhancing the performance of anaerobic processes. These process models are often developed [14,15]. Nonetheless, modeling of anaerobic digestion is quite challenging and tough because performance of anaerobic systems is complex and varies considerably with influent characteristics and operational conditions [16].Some predictive models have been developed in the past decades for biogas estimation during anaerobic treatment processes. For instance, a regression analysis model for estimating biogas generated in a landfill leachate treatment process was developed by Akaya et al. AbstractHerein, a modeling approach to predict biogas yield within a mesophilic (35 ± 1°C) upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW) for pollutant removal was conducted. HRTs and seven anaerobic process-related parameters viz; chemical oxygen demand (COD), ammonium (NH 4 + ), alkalinity, total Kjeldahl Nitrogen, total phosphorus, volatile fatty acids (VFAs) and pH with average concentration of 4028.91, 110.09, 4944.67, 510.47, 45.20, 534.44 mg/L and 7.09, respectively, were used as input variables (x) to develop stochastic models for predicting biogas yield from the anaerobic digestion of PSPW. Based on the prediction accuracy of the models, it was established that, prediction of biogas yield from the UASB with the combination of COD, NH 4 + and HRT, or COD, NH 4 + , HRT and VFAs as input variables proved more efficient as opposed to HRT, alkalinity, total Kjeldahl Nitrogen, total phosphorus and pH. Highest coefficient of determination (R 2 ) observed was 97.29%, suggesting the efficiency of the models in making predictions. The developed models efficiencies co...

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Development of Ann-Based Models to Predict Biogas and Methane Productions in Anaerobic Treatment of Molasses Wastewater

Cited by 67 publications

References 73 publications

Approaches for Modeling Anaerobic Granule-Based Reactors

Approaches for Modeling Anaerobic Granule-Based Reactors

Modeling of methane emissions using artificial neural network approach

Modelling Biogas Fermentation from Anaerobic Digestion: Potato Starch Processing Wastewater Treated Within an Up flow Anaerobic Sludge Blanket

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