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Control Structure Analysis for an Activated Sludge Process The Seventh Italian Conference on Chemical and Process Engineering Mulas, Skogestad Control.

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Presentation on theme: "Control Structure Analysis for an Activated Sludge Process The Seventh Italian Conference on Chemical and Process Engineering Mulas, Skogestad Control."— Presentation transcript:

1 Control Structure Analysis for an Activated Sludge Process The Seventh Italian Conference on Chemical and Process Engineering Mulas, Skogestad Control Structure Design for an Activated Sludge Process Michela Mulas 1,2, Sigurd Skogestad 2 1 Dipartimento di Ingegneria Chimica e Materiali Università degli Studi di Cagliari, Italy 2 Chemical Engineering Department NTNU, Trondheim, Norway

2 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Outline Motivations Plant Description Process Model Control Structure Analysis Results Conclusions

3 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Motivations Outline Motivations Wastewater treatment processes (WWTP) can be considered the largest industry in terms of volumes of raw material treated Industrial expansion and urban population growth have increased the amount and diversity of wastewater generated Because of the most recent guidelines and regulation which require the achievement of specific standards to the treated wastewater, a great effort has been devoted to the improvement of treatment processes The WWTP has become part of a production process, e.g. for fresh water reuse purpose More efficient procedures for WWTP management and control

4 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Objectives Outline Motivations Objectives The problems are: the inflow is variable, in both quantity and quality there are few and unreliable on-line analyzers most of the data related to the process are subjective and cannot be numerically quantified WWTP are generally operated with only elementary control systems With a proper control structure design we might implement the optimal operation policy for an ASP Which variables should be measured, which inputs should be manipulated and which link should be made between the two sets?

5 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Plant Description Outline Motivations Objectives Plant Description The Control Structure Analysis is applied to a real plant, the TecnoCasic wastewater plant, located near Cagliari (Italy) ASP involves a biological reactor and a settler where from the biomass is recycled to the anoxic basin The Activated Sludge Process (ASP) is the most widely used system for biological treatment of liquid waste Nitrogen Removal

6 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Process Model Outline Motivations Objectives Plant Description Process Model Bioreactor ASM No 1 The Activated Sludge Model No.1 (Henze et al.,1987) is the state of art model when the biological phosphorus removal is not considered 13 State Variables soluble particulate 8 Reaction Rates Aerobic Zone 13 State Variables 8 Reaction Rates Denitrification NO 3 3O 2 N 2 Nitrification NH 4 2O 2 NO 3 2H H 2 O Anoxic Zone Dissolved Oxygen (DO) Control Bioreactor 19 Stoichiometric and Kinetic Coefficients 19 Stoichiometric and Kinetic Coefficients 19 Stoichiometric and Kinetic Coefficients

7 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Process Model Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Takács Layered Model Secondary Settler WAS RAS Effluent Clarification Thickening Ref. Takács et al., 1997 When entering the settler, all the particulate components in the ASM1 model are lumped into a single variable X. The reverse process is performed as for the outlet No biological reactions occur The settler is modelled as a stack of layers. The concentration within each layer is assumed to be constant Takács Model

8 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Process Model Outline Motivations Objectives Plant Description Process Model A representation of the TecnoCasic plant can be implemented in several different ways, using different software and simulators Matlab/ Simulink

9 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Test Motion Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion On-Line measurements: Flow rates DO concentration in the basin Temperatures Off-Line measurements: Chemical Oxygen Demand (COD) Nitrogen Sludge Volume Index (SVI) TecnoCasic Plant Data Simulink Exp Data available every two or three days

10 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Control Structure Analysis Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Find candidate controlled variables with good self- optimizing properties Self-Optimizing Control is when acceptable operation can be achieved using constant setpoints for the controlled variables The procedure proposed by Skogestad (2004) is divided in two main part: Bottom-Up Design Define operational objectives Identify degrees of freedom Identify primary controlled variables Determine where to set the production rate Top-Down Design

11 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Top-Down Analysis Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Cost Function Constraints Step 1 Identify operational constraints and preferably a scalar cost function to be minimized The energy consumption in terms of aeration power represents the major economic duty in our ASP J Q air Q air DeNitr Q air Nitr Cost Function Constraints Operational Constraints: DO concentration Food-to-Microorganisms Ratio Sludge Retention Time Effluent Constraints: defined by the legislation requirement for the effluent Disturbances In the TecnoCasic plant an equalization tank is present at the top of the ASP The influent compositions are the disturbances which we cannot affect

12 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Top-Down Analysis Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Degrees of Freedom Step 2 Identify dynamic and steady-state degrees of freedom (DOF) N m 7 Dynamic or Control DOF N m 5 Optimization DOF N opt 3 The optimization is generally subject to constraints and at the optimum many of these are usually actives, e.g. in the ASP the DO concentrations in both anoxic and aerated zone N opt,free N opt N active N opt,free 1

13 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Top-Down Analysis Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Controlled Variables Step 3 Which (primary) variable should we control? We first need to control the variables directly related to ensuring optimal economical operation dJdWASJL optWAS, The optimisation of a system is selecting conditions to achieve the best possible result with some limits: we are interested in steady state optimization of the ASP in the TecnoCasic plant The magnitude of the loss will depend on the control strategy used to adjust the WAS flowrate during operation Open-Loop Strategies: we want to keep the WAS flowrate at its setpoint Closed-Loop Strategies: we adjust WAS in a feedback fashion in an attempt to keep the controlled variable at its setpoint

14 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Top-Down Analysis Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Controlled Variables Step 3 To identify good candidate controlled variables, one should look for variables that satisfy all of the following requirements (Skogestad, 2000): Which (primary) variable should we control? The optimal value of should be insensitive to disturbance The controlled variable should be easy to measure and control The controlled variable should be sensitive to changes in the manipulated variables (the steady degree of freedom). c 1 =SRTc 2 =F/Mc 3 =TN p c 4 =WAS Closed Loop Open Loop

15 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Results Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Results The cost function J goes down as the waste flowrate increases

16 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Results Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Results Positive DeviationNegative DeviationPositive DeviationNegative Deviation c 1 = SRT Closed Loopc 3 = TNDeNitr Closed Loop d 1 =COD38682248003867924816 d 2 =TKN33756270063376526967 d 3 =TSS34182296073025229591 c 2 = F/M Closed Loopc 4 = Open Loop d 1 =COD38628245893865024758 d 2 =TKN33648269683374926991 d 3 =TSS30255295943417129607 The anoxic zone behaviour can influence the overall cost function; even if the air flowrate in it is quite small compared with the aerobic part

17 ICheaP-7 16-18 May 2005 Control Structure Analysis for an Activated Sludge Process Mulas, Skogestad Dipartimento di Ingegneria Chimica e Materiali Università di Cagliari, Italy Chemical Engineering Department NTNU, Trondheim, Norway Conclusions Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Results Conclusions In this work we have considered alternative controlled variables for the TecnoCasic activated sludge process That is a good starting point to understand how this kind of system can be improve Following the plantwide control structure design procedure proposed by Skogestad (2004), we have found that a better response to influent disturbances can be obtained using as controlled variable the total Nitrogen in the anoxic zone, manipulating the WAS flowrate The optimization part has to be implemented and studied for systems with a different configuration For an activated sludge plant the only steady state occurs when the process is shut down (Olsson and Newell, 2001). For that reason it will be interesting to find a kind of dynamic steady state and apply the top-down analysis in this case


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