Regression based state space adaptive model of two-phase anaerobic reactor

Sendjaja, Antonius Yudi; Tan, Youming; Pathak, Santosh; Zhou, Yan; Majid, Maszenan bin Abdul; Liu, Jianlin; Ng, Wun Jern

doi:10.1016/j.chemosphere.2014.11.027

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…As shown in Table 2 , maximum error was for alkalinity model with 1.84 %, followed by VFA (1.48 %), COD (0.74 %), ammonia nitrogen (0.69 %), methane volume (0.61 %), total nitrogen (0.27 %) and finally pH (0.26 %). These results are in agreement with the results presented by Sendjaja [ 32 ] where the error for UASB reactor for VFA was 7.09 %; COD was 2.33 %; pH was 0.59 % and methane volume was 7.26 %.…”

Section: Resultssupporting

confidence: 93%

“…For model ANFIS, one learning stage is required and another for verification model, whereby the number of required data is quite high. This is in agreement with Sendjaja [ 32 ] where it is mentioned that one of the weaknesses of the models based on historical data is the need to obtain a lot of data, as well as maintaining stability in the process.…”

Section: Resultssupporting

confidence: 89%

“…This is in agreement with Sendjaja [ 32 ] where it is mentioned that one of the weaknesses of the models based on historical data is the need to obtain a lot of data, as well as maintaining stability in the process.…”

Section: Resultssupporting

confidence: 89%

“…3 e–g, respectively. As Sendjaja [ 32 ] mentioned, even though profiles show good prediction of various parameters, a quantitative measure is necessary. For this reason the accuracy of prediction values was evaluated by comparing the area bound by curves from predicted and experimental values and X-axis.…”

Section: Resultsmentioning

confidence: 99%

“…However, these approaches may involve many constant parameters with values that are specific and need to be sought from experiments or through assumptions. In addition, these constants are assumed to be the same forever; this may not be true in the presence of ever changing internal and external conditions [ 32 ]. The main difference of the proposed model and conventional statistical or empirical base model is that the coefficients are updated at every time step, providing adaptive ability in the presence of changing conditions over real time.…”

Section: Introductionmentioning

confidence: 99%

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A fast linear predictive adaptive model of packed bed coupled with UASB reactor treating onion waste to produce biofuel

2016

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BackgroundAgro-industrial wastes are an energy source for different industries. However, its application has not reached small industries. Previous and current research activities performed on the acidogenic phase of two-phase anaerobic digestion processes deal particularly with process optimization of the acid-phase reactors operating with a wide variety of substrates, both soluble and complex in nature. Mathematical models for anaerobic digestion have been developed to understand and improve the efficient operation of the process. At present, lineal models with the advantages of requiring less data, predicting future behavior and updating when a new set of data becomes available have been developed. The aim of this research was to contribute to the reduction of organic solid waste, generate biogas and develop a simple but accurate mathematical model to predict the behavior of the UASB reactor.ResultsThe system was maintained separate for 14 days during which hydrolytic and acetogenic bacteria broke down onion waste, produced and accumulated volatile fatty acids. On this day, two reactors were coupled and the system continued for 16 days more. The biogas and methane yields and volatile solid reduction were 0.6 ± 0.05 m3 (kg VSremoved)−1, 0.43 ± 0.06 m3 (kg VSremoved)−1 and 83.5 ± 9.8 %, respectively. The model application showed a good prediction of all process parameters defined; maximum error between experimental and predicted value was 1.84 % for alkalinity profile.ConclusionsA linear predictive adaptive model for anaerobic digestion of onion waste in a two-stage process was determined under batch-fed condition. Organic load rate (OLR) was maintained constant for the entire operation, modifying effluent hydrolysis reactor feed to UASB reactor. This condition avoids intoxication of UASB reactor and also limits external buffer addition.

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Section: Resultssupporting

confidence: 93%

Section: Resultssupporting

confidence: 89%

Section: Resultssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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