Portia Opoku Boadi

Estimation of Biogas and Methane Yields in an UASB Treating Potato Starch Processing Wastewater with Backpropagation Artificial Neural Network

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R2) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model.

Functional bacterial and archaeal diversity revealed by 16S rRNA gene pyrosequencing during potato starch processing wastewater treatment in an UASB

Microbial community structure of sludge sampled from an UASB treating potato starch processing wastewater (PSPW) was investigated. Operational taxonomic units revealed at 97% sequence identity tolerance was 2922, 2869 and 3919 for bottom, middle and top sections of the reactor, respectively. Overall abundant phylum observed within the UASB was low-G+C-Gram-positive bacteria affiliated to Firmicutes (26.01%) followed by Chloroflexi (16.70%), Proteobacteria (12.71%), Cloacimonetes (10.72%), Bacteroidetes (7.87%), Synergistetes (9.02%) and Euryarchaeota (8.82%). Whiles Firmicutes had dominated the bottom and top section by 34.01% and 28.64%, respectively, middle section was predominantly Euryarchaeota (24.32%) with major dominance in methanogens affiliated to genus Methanosaeta. The results demonstrated substantial stratification of the microbial community structure along the reactor height with various functional bacterial groups which subsequently allowed degradation of organics in PSPW in sequential mode. The findings herein would provide guidance for optimizing the anaerobic process and operation of the UASB.

Efficiency of an upflow anaerobic sludge blanket reactor treating potato starch processing wastewater and related process kinetics, functional microbial community and sludge morphology

Herein, an upflow anaerobic sludge blanket reactor was employed to treat potato starch processing wastewater and the efficacy, kinetics, microbial diversity and morphology of sludge granules were investigated. When organic loading rate (OLR) ranging from 2.70 to 13.27kgCOD/m3.d was implemented with various hydraulic retention times (72h, 48h and 36h), COD removal could reach 92.0-97.7%. Highest COD removal (97.7%) was noticed when OLR was 3.65kgCOD/m3.d, but had declined to 92.0% when OLR was elevated to 13.27kgCOD/m3.d. Methane and biogas production increased from 0.48 to 2.97L/L.d and 0.90 to 4.28L/L.d, respectively. Kinetics and predictions by modified-Gompertz model agreed better with experimental data as opposed to first-order kinetic model. Functional population with highest abundance was Chloroflexi (28.91%) followed by Euryarchaeota (22.13%), Firmicutes (16.7%), Proteobacteria (16.25%) and Bacteroidetes (7.73%). Compared with top sludge, tightly-bound extracellular polymeric substances was high within bottom and middle sludge. Morphology was predominantly Methanosaeta-like cells, Methanosarcina-like cells, rods and cocci colonies.

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater.

In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH4+, VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R2), the BPANN model demonstrated significant performance with R2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.

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

Herein, 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 (), 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, NH4+ and HRT, or COD, NH4+, 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 (R2) observed was 97.29%, suggesting the efficiency of the models in making predictions. The developed models efficiencies concluded that the models could be employed to control the dynamic anaerobic process within UASBs since prediction of biogas obtained in the UASB agreed with the experimental result.