Membrane Bioreactor Process Modeling and Optimization: Ulu Pandan Water Reclamation Plant

AbstractThe operating energy efficiency of the Ulu Pandan membrane bioreactor plant is optimized by artificial neural network (ANN) and bioprocess models. The ANN model mines historical plant data to uncover optimal operating settings. Historical plant data indicate that adjusting the membrane scouring aeration cycle will lead to direct energy savings. The ANN model concurs and shows the same correlation. Changes to plant operations carry substantial risks to the stability of the plant and place limitations on the range of operational variations. A plant risk assessment is conducted to ascertain the risk proposition for the adjustment of operating parameters. The bioprocess model investigates the underlying biological treatment mechanisms to identify the impact of the solids retention time on the volatile suspended solids, soluble microbial products, and endogenous decay coefficient. Results of the modeling show qualitatively good agreement with measured operating data. The concentrations of volatile susp...

[1]  Simon Judd,et al.  Aerobic MBRs for domestic wastewater treatment: a review with cost considerations , 2000 .

[2]  C. Brepols,et al.  An aeration energy model for an immersed membrane bioreactor. , 2008, Water research.

[3]  A. Andreadakis,et al.  Fractionation of proteins and carbohydrates of extracellular polymeric substances in a membrane bioreactor system. , 2009, Bioresource technology.

[4]  R. Ely,et al.  Comparison of Artificial Neural Network, Genetic Programming, and Mechanistic Modeling of Complex Biological Processes , 2001 .

[5]  Giuseppe Laera,et al.  Effects of sludge retention time on the performance of a membrane bioreactor treating municipal sewage , 2008 .

[6]  Examination of the Activated Sludge Model No. 2 with an alternating process , 1995 .

[7]  Nazim Cicek,et al.  STATE-OF-THE-ART OF MEMBRANE BIOREACTORS: WORLDWIDE RESEARCH AND COMMERCIAL APPLICATIONS IN NORTH AMERICA , 2006 .

[8]  Zhou Jiti,et al.  Effect of Sludge Retention Time on Membrane Bio‐Fouling Intensity in a Submerged Membrane Bioreactor , 2006 .

[9]  K. Kawasaki,et al.  Stable Operational Condition of Submerged Membrane Activated Sludge Process without Sludge Withdrawal , 2003 .

[10]  S. Liang,et al.  Soluble microbial products in membrane bioreactor operation: Behaviors, characteristics, and fouling potential. , 2007, Water research.

[11]  P Cornel,et al.  Investigation of oxygen transfer rates in full scale membrane bioreactors. , 2003, Water science and technology : a journal of the International Association on Water Pollution Research.

[12]  Bruce E Rittmann,et al.  Non-steady state modeling of extracellular polymeric substances, soluble microbial products, and active and inert biomass. , 2002, Water research.

[13]  S. Judd The status of membrane bioreactor technology. , 2008, Trends in biotechnology.

[14]  Xia Huang,et al.  Effect of sludge retention time on microbial behaviour in a submerged membrane bioreactor , 2001 .

[15]  T Higuchi,et al.  A model for membrane bioreactor process based on the concept of formation and degradation of soluble microbial products. , 2001, Water research.

[16]  H. Ng,et al.  Effect of mean cell residence time on the performance and microbial diversity of pre-denitrification submerged membrane bioreactors. , 2008, Chemosphere.

[17]  A. Grasmick,et al.  Optimization of the operations conditions in membrane bioreactors through the use of ASM3 model simulations , 2009 .

[18]  K. Kawasaki,et al.  Study of Biological Activity and Process Stability in Submerged Membrane Bioreactors , 2006 .

[19]  J S Almeida,et al.  Two-dimensional fluorometry coupled with artificial neural networks: a novel method for on-line monitoring of complex biological processes. , 2001, Biotechnology and bioengineering.

[20]  C. Brepols,et al.  Modelling of a membrane bioreactor system for municipal wastewater treatment , 2003 .

[21]  João G Crespo,et al.  Non-mechanistic modelling of complex biofilm reactors and the role of process operation history. , 2005, Journal of biotechnology.

[22]  Bin Dong,et al.  Characteristics and behaviors of soluble microbial products in sequencing batch membrane bioreactors at various sludge retention times , 2009 .

[23]  J. J. Heijnen,et al.  A metabolic model for the biological phosphorus removal process , 1995 .

[24]  J Pinnekamp,et al.  Design and operating experiences of municipal MBRs in Europe. , 2008, Water science and technology : a journal of the International Association on Water Pollution Research.

[25]  Mogens Henze,et al.  Activated Sludge Model No.2d, ASM2D , 1999 .

[26]  V. Padmaja,et al.  Statistical optimization of process variables for the large-scale production of Metarhizium anisopliae conidiospores in solid-state fermentation. , 2008, Bioresource technology.

[27]  D. Chopp,et al.  Modeling how soluble microbial products (SMP) support heterotrophic bacteria in autotroph-based biofilms. , 2009, Journal of theoretical biology.

[28]  George Tchobanoglous,et al.  Decentralized wastewater management: challenges and opportunities for the twenty-first century , 2004 .

[29]  Ch Brepols,et al.  Considerations on the design and financial feasibility of full-scale membrane bioreactors for municipal applications. , 2010, Water science and technology : a journal of the International Association on Water Pollution Research.

[30]  Mogens Henze,et al.  Wastewater and biomass characterization for the Activated Sludge Model No. 2: biological phosphorus removal , 1995 .