Hierarchical predictive control of integrated wastewater treatment systems

The paper proposes an approach to designing the control structure and algorithms for optimising control of integrated wastewater treatment plant-sewer systems (IWWTS) under a full range of disturbance inputs. The optimised control of IWWTS allows for significant cost savings, fulfilling the effluent discharge limits over a long period and maintaining the system in sustainable operation. Due to the specific features of a wastewater system a hierarchical control structure is applied. The functional decomposition leads to three control layers: supervisory, optimising and follow-up. A temporal decomposition that is applied in order to efficiently accommodate the system's multiple time scales leads to further decomposition of the optimising control layer into three control sublayers: slow, medium, and fast. An extended Kalman Filter is used to carry out an estimation of needed but not measured plant states in real time. The robustly feasible model predictive controller produces manipulated variable trajectories based on a dedicated grey box (GB) model of the biological processes and drawing its physical reality from the well known ASM2d model. The GB model parameters are dependant on the plant operating point and therefore are continuously estimated. As it is impossible to efficiently control the plant under all influent conditions that may occur by using one universal control strategy, different control strategies are designed. Recently developed mechanisms for soft switching between the MPC control strategies are applied in order to smooth the state and control transient processes during the switching. The methodologies and algorithms proposed in the paper are validated by simulation based on real data records from a wastewater system located in Kartuzy, northern Poland. The control system was implemented at the case-study site to generate in real time the control actions that were assessed by the plant operators and verified by simulation based on a calibrated plant model.

[1]  Alessandro Macchelli,et al.  16th IFAC World Congress, Prague , 2005 .

[2]  Marek Makowski,et al.  Model-Based Decision Support Methodology with Environmental Applications , 2000 .

[3]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[4]  K. Konarczak,et al.  Weichted Least Squares Parameter Estimation for Model Predictive Control of Integrated Wastewater Systems at Medium Time Scale , 2004 .

[5]  Mogens Henze,et al.  Activated sludge models ASM1, ASM2, ASM2d and ASM3 , 2015 .

[6]  Mietek A. Brdys,et al.  ROBUST MODEL PREDICTIVE CONTROL UNDER OUTPUT CONSTRAINTS , 2002 .

[7]  Robert Piotrowski,et al.  Dissolved Oxygen Tracking and Control of Blowers at Fast Time Scale , 2004 .

[8]  M. A. Brdys,et al.  Operational Control of Water Systems: Structures, Algorithms, and Applications , 1994 .

[9]  Isabelle Queinnec,et al.  State observation for fedbatch control of phenol degradation by Ralstonia eutropha , 1999, 1999 European Control Conference (ECC).

[10]  Piotr Tatjewski,et al.  Iterative Algorithms For Multilayer Optimizing Control , 2005 .

[11]  R. B. Newell,et al.  A systematic approach for reducing complex biological wastewater treatment models , 1997 .

[12]  S. Weijers Modelling, identification and control of activated sludge plants for nitrogen removal , 2000 .

[13]  Mietek A. Brdys,et al.  Invariant set-based robust softly switched model predictive control , 2005 .

[14]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[15]  Michał Grochowski,et al.  ANALYSIS AND DESIGN OF SOFTLY SWITCHED MODEL PREDICTIVE CONTROL , 2005 .

[16]  Jean-François Béteau,et al.  Multicriteria control strategy for cost/quality compromise in Wastewater Treatment Plants , 2004 .

[17]  Eduardo F. Camacho,et al.  Constrained Model Predictive Control , 2007 .

[18]  Robert Piotrowski,et al.  LOWER – LEVEL CONTROLLER FOR HIERARCHICAL CONTROL OF DISSOLVED OXYGEN CONCENTRATION IN ACTIVATED SLUDGE PROCESSES , 2005 .

[19]  K. Gernaey,et al.  Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes , 2004 .

[20]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[21]  Paul Lant,et al.  Multivariable control of nutrient-removing activated sludge systems , 1999 .

[22]  Mietek A. Brdys,et al.  Dissolved oxygen control for activated sludge processes , 2005, Int. J. Syst. Sci..

[23]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[24]  Michał Grochowski,et al.  Softly Switched Model Predictive Control for Control of Integrated Wastewater Treatment Systems at Medium Time Scale , 2004 .

[25]  Mietek A. Brdys,et al.  Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems , 2005 .

[26]  Thomas J. McAvoy,et al.  Control of an alternating aerobic–anoxic activated sludge system — Part 1: development of a linearization-based modeling approach , 2000 .

[27]  Eric Duviella,et al.  Supervision and hybrid control accommodation for water asset management , 2007 .

[28]  Mietek A. Brdys,et al.  Robust estimation of variables and parameters in dynamic networks , 1999 .