Comparing new perspective of hybrid approach and conventional kinetic modelling techniques of a submerged biofilm reactor performance

Kordkandi, Salman Alizadeh; Berardi, Luigi

doi:10.1016/j.bej.2015.07.007

Cited by 19 publications

(16 citation statements)

References 32 publications

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“…Biofilm processes have received widespread attention in biological wastewater treatment technology. Currently, experimental research and studies on mathematical modeling of processes in bioreactors with biofilms are conducted all over the world …”

Section: Introductionmentioning

confidence: 99%

“…Currently, experimental research and studies on mathematical modeling of processes in bioreactors with biofilms are conducted all over the world. [2][3][4][5][6] Fluidized-bed bioreactors have been successfully used for several decades [7][8][9] both in laboratory and industrial scale. It arises from many advantages of these bioreactors, including high surface area of fine particles, on which the biofilm grows and the possibility of separating the residence time of the biomass from the residence time of the liquid.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling and simulation of a two‐phase fluidized‐bed bioreactor with an external aerator

Skoneczny

2018

Biotechnology Progress

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The article presents a mathematical model and steady-state characteristics of a continuous-flow two-phase fluidized-bed bioreactor with external aeration. The quantitative description includes biofilm growth on inert carriers, distributions of density, and diffusion coefficients in biofilm, decay of microorganisms, gas-liquid and liquid-biofilm mass transfer, biofilm detachment, biomass thickening and recirculation. The method for the determination of steady-states and the assessment of stability based on the orthogonal collocation method was presented. The effect of key parameters on steady-state properties of the bioreactor was analyzed. These parameters are: carbonaceous substrate concentration in the feed stream, mean residence time of the liquid and the liquid recirculation ratio. It was shown, that stream recirculation improves aeration of the liquid phase and the degree of conversion of a carbonaceous substrate. The effect of a growth kinetic model on the steady-state multiplicity was proved and discussed. The applicability of the bioreactor was assessed for aerobic processes characterized by a varied oxygen requirement, expressed by the growth yield coefficient w . © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 2018 © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1109-1119, 2018.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and simulation of a two‐phase fluidized‐bed bioreactor with an external aerator

Skoneczny

2018

Biotechnology Progress

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“…To overcome this challenge, in this study, a network structure was coupled with Cuckoo Optimization Algorithm (COA) as a model for function evaluation, while Artificial Neural Network (ANN) was applied to evaluate the effects of various operating parameters. The main function of ANN is to discover the relationships that link patterns of input data to associated output data as well as precise prediction of a process performance .…”

Section: Introductionmentioning

confidence: 99%

Optimization of peroxone reaction rate using metaheuristic approach in the dearomatization and discoloration process

Kordkandi

Motlagh

2017

Env Prog and Sustain Energy

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The main objective of this research focused on the optimization of apparent reaction rate constants (kapp) for degradation of a diazo compound, reactive yellow 42 (RY42) using peroxone reaction. Artificial Neural Network (ANN) was adopted to evaluate the effects of initial RY42 concentration, influent ozone mass flow rate, initial ozone (O3)/peroxide hydrogen ratio (H2O2), and pH on the discoloration rate constant. A proposed model by ANN was chosen after analysis of the topology based on R‐square (R2) and mean squared error (MSE) criteria followed by coupling the obtained best net with a novel evolutionary algorithm named Cuckoo Optimization Algorithm (COA). Although the relative importance of pH and O3/H2O2 on kapp was at the same level (about 18%), initial dye concentration (34%), and influent ozone mass flow rate (30%) were the most important factors. A systematic ANN‐COA approach indicated that the optimum values of pH and O3/H2O2 for 150 mg L−1 of RY42 were 7.5 and 1.44 L h−1, respectively, resulted in relatively different kapp for decolorization (0.18 min−1) and dearomatization (0.056 min−1). To confirm the validity of kinetic analysis, the predicted values were correlated with experimental values, which indicated the high degree of accuracy (R2= 0.98) for optimization of the peroxone process. This research with the experimental and modeling studies can be used as a guide for the design of full‐scale treatment processes for various types of industrial wastewater containing aromatic compounds with diazo groups. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 695–702, 2018

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“…Due to limitations such as technical difficulties, severe environments, unacceptable expense of analytical instruments, and time delay of measurements, it is not easy to measure all the process variables online. Over the past two decades, soft sensors have been developed and widely used for estimating and predicting quality or other important process variables . Among all the developed soft sensors, data‐based models are probably the most commonly used ones in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Various advanced modifications or extensions for linear methods have been developed . Particularly, artificial neural networks (ANN) are the commonly used methodologies for developing nonlinear soft‐sensor models . In principle, any complex nonlinear relationships can be approximated by an ANN with the desired degree of accuracy, thus, the nonlinear soft sensor model can be efficiently constructed.…”

Section: Introductionmentioning

confidence: 99%

Locality preserving based data regression and its application for soft sensor modelling

Miao

2016

Can J Chem Eng

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A new local‐based data regression technique named locality preserving regression (LPR) is developed and applied for soft sensor modelling in the present study. By taking the local variation obtained by locality preserving projections into consideration, the regression algorithm LPR is employed to construct a soft sensor model and applied to industrial case. Furthermore, to deal with the time‐varying behaviour of the process variables, just‐in‐time learning is also integrated to regularly update the soft sensor. Two case studies on a fermentation process for penicillin concentration prediction and the Tennessee Eastman process for output component prediction are provided to demonstrate the performance of the proposed method. Finally, the effectiveness and robustness of the proposed local‐based technique for soft sensor modelling are assessed and compared with the global‐based soft sensors based on the mean square error and the coefficient of determination. Experimental results showed that the novel soft sensor model could estimate the output with higher accuracy and generalization ability than the general soft sensor based on the global information.

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Comparing new perspective of hybrid approach and conventional kinetic modelling techniques of a submerged biofilm reactor performance

Cited by 19 publications

References 32 publications

Mathematical modeling and simulation of a two‐phase fluidized‐bed bioreactor with an external aerator

Mathematical modeling and simulation of a two‐phase fluidized‐bed bioreactor with an external aerator

Optimization of peroxone reaction rate using metaheuristic approach in the dearomatization and discoloration process

Locality preserving based data regression and its application for soft sensor modelling

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