Application of neural network prediction model to full-scale anaerobic sludge digestion

Güçlü, Dünyamin; Yilmaz, Nihat; Ozkan-Yucel, Umay Gokce

doi:10.1002/jctb.2569

Cited by 38 publications

(14 citation statements)

References 21 publications

(21 reference statements)

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“…For the two reasons mentioned above, a hybrid model, combined with the two ANN models and the mechanism model was developed for the sodium gluconate fermentation process. As one of the most popular and intelligent modeling methods, the ANN model has been successfully used for biological process modeling …”

Section: Hybrid Model Of Sodium Gluconate Fermentation Processmentioning

confidence: 99%

“…As one of the most popular and intelligent modeling methods, the ANN model has been successfully used for biological process modeling. 21,22 BPNN for the mycelium growth rate The mechanism model of Equations (1) to (3) indicates that the concentration of sodium gluconate and glucose is strongly associated with mycelium growth rate (r X = dX / dt ). Thus, the accurate predictive values of r X are the bases of the mechanism model.…”

Section: Hybrid Model Of Sodium Gluconate Fermentation Processmentioning

confidence: 99%

See 1 more Smart Citation

Development of a hybrid model for sodium gluconate fermentation byAspergillus niger

Dong

Fan

Yan

et al. 2013

J. Chem. Technol. Biotechnol.

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BACKGROUND A hybrid model for sodium gluconate fermentation by Aspergillus niger was proposed. First, because of the lack of part mechanism knowledge on mycelium growth in the mechanism model of the sodium gluconate fermentation process, a back propagation neural network (BPNN) was utilized to develop a model of mycelium growth rate. Second, a three‐layer feed‐forward network combined with the Alopex‐differential evolution (Alopex‐DE) algorithm was employed to develop a model of kinetic parameters given that mechanism model mismatch exists in different fermentation batches. A hybrid model based on these two artificial neural network (ANN) models and the mechanism model was developed for the fermentation process. RESULTS The BPNN and ANN models were capable of developing relationships between a certain input and the mycelium growth rate and kinetic parameters. The reliability of the proposed hybrid model was investigated based on 18 batches of experimental fermentation data. Satisfactory results were obtained. CONCLUSIONS The proposed hybrid model is a solution to the mechanism model problems of missing mechanism knowledge and model mismatch in different batches. The model exhibits better performance than pure mechanism and pure BPNN models. © 2013 Society of Chemical Industry

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Section: Hybrid Model Of Sodium Gluconate Fermentation Processmentioning

confidence: 99%

Section: Hybrid Model Of Sodium Gluconate Fermentation Processmentioning

confidence: 99%

Development of a hybrid model for sodium gluconate fermentation byAspergillus niger

Dong

Fan

Yan

et al. 2013

J. Chem. Technol. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…The results show that their model had a good prediction ability for COD removal efficiency. In another work, Güçlü and co-authors [14] implemented back-propagation ANN models for predicting effluent volatile solid (VS) concentration and methane yield. Effluent VS and methane yields were predicted with the ANN using pH, temperature, flowrate, VFA, alkalinity, dry matter and organic matter as model inputs.…”

Section: Introductionmentioning

confidence: 99%

“…Briefly, most of the studies in the past were focused on predicting VFA for a laboratory-scale anaerobic digester by using available input parameters without considering the difficulty of measuring them. Furthermore, most of the developed models were trained based on the very limited operational conditions, thus the generalization ability and performance of the models in different situations is ambiguous [14,15].…”

Section: Introductionmentioning

confidence: 99%

Robust Data-Driven Soft Sensors for Online Monitoring of Volatile Fatty Acids in Anaerobic Digestion Processes

et al. 2020

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The concentration of volatile fatty acids (VFAs) is one of the most important measurements for evaluating the performance of anaerobic digestion (AD) processes. In real-time applications, VFAs can be measured by dedicated sensors, which are still currently expensive and very sensitive to harsh environmental conditions. Moreover, sensors usually have a delay that is undesirable for real-time monitoring. Due to these problems, data-driven soft sensors are very attractive alternatives. This study proposes different data-driven methods for estimating reliable VFA values. We evaluated random forest (RF), artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and genetic programming (GP) based on synthetic data obtained from the international water association (IWA) Benchmark Simulation Model No. 2 (BSM2). The organic load to the AD in BSM2 was modified to simulate the behavior of an anaerobic co-digestion process. The prediction and generalization performances of the different models were also compared. This comparison showed that the GP soft sensor is more precise than the other soft sensors. In addition, the model robustness was assessed to determine the performance of each model under different process states. It is also shown that, in addition to their robustness, GP soft sensors are easy to implement and provide useful insights into the process by providing explicit equations.

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“…ANN's capability to simplify and learn new data is advantageous, as well as its ability to process nonlinear data and being highly tolerant to failures. Not only that, ANN can also be implemented with more than one layer for prediction purposes, for example the implementation of a three-layered ANN using pH and residence time as input for prediction of pH, acetic acid and propionic acid concentration in an acidogeni Apart from its broad applicability,c reactor [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

Yatim,

Hamid,

Ismail

et al. 2019

IJEAT

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This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.

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Application of neural network prediction model to full-scale anaerobic sludge digestion

Cited by 38 publications

References 21 publications

Development of a hybrid model for sodium gluconate fermentation byAspergillus niger

Development of a hybrid model for sodium gluconate fermentation byAspergillus niger

Robust Data-Driven Soft Sensors for Online Monitoring of Volatile Fatty Acids in Anaerobic Digestion Processes

Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

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