Control Structure Design for an Activated Sludge Process Michela Mulas1,2, Sigurd Skogestad2 1 Dipartimento di Ingegneria Chimica e Materiali Università degli Studi di Cagliari, Italy 2 Chemical Engineering Department NTNU, Trondheim, Norway

Outline Motivations Plant Description Process Model Control Structure Analysis Results Conclusions

More efficient procedures for WWTP management and control Motivations Wastewater treatment processes (WWTP) can be considered the largest industry in terms of volumes of raw material treated Outline Motivations 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

Objectives WWTP are generally operated with only elementary control systems 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 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?

Plant Description Nitrogen Removal The Control Structure Analysis is applied to a real plant, the TecnoCasic wastewater plant, located near Cagliari (Italy) Outline Motivations Objectives Plant Description The Activated Sludge Process (ASP) is the most widely used system for biological treatment of liquid waste ASP involves a biological reactor and a settler where from the biomass is recycled to the anoxic basin Nitrogen Removal

19 Stoichiometric and Kinetic Coefficients Process Model Bioreactor The Activated Sludge Model No.1 (Henze et al.,1987) is the state of art model when the biological phosphorus removal is not considered Outline Motivations Objectives Plant Description Process Model Bioreactor ASM No 1 Denitrification NO 3 - O 2 + N Nitrification NH 4 + 2 O NO 3 - H soluble 13 State Variables 13 State Variables particulate 8 Reaction Rates 8 Reaction Rates 19 Stoichiometric and Kinetic Coefficients 19 Stoichiometric and Kinetic Coefficients 19 Stoichiometric and Kinetic Coefficients Anoxic Zone Aerobic Zone Dissolved Oxygen (DO) Control

No biological reactions occur Process Model Secondary Settler Ref. Takács et al., 1997 Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Takács Layered Model Effluent Clarification Thickening WAS RAS 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 The settler is modelled as a stack of layers. The concentration within each layer is assumed to be constant No biological reactions occur Takács Model

Process Model Matlab/ Simulink 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

Test Motion TecnoCasic Plant Data Off-Line measurements: Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Off-Line measurements: Chemical Oxygen Demand (COD) Nitrogen Sludge Volume Index (SVI) available every two or three days On-Line measurements: Flow rates DO concentration in the basin Temperatures Simulink Exp Data

Control Structure Analysis Find candidate controlled variables with good self-optimizing properties Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis 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: Top-Down Design Bottom-Up Design Define operational objectives Identify degrees of freedom Identify primary controlled variables Determine where to set the production rate

“Top-Down” Analysis J = Q + Step 1 “Identify operational constraints and preferably a scalar cost function to be minimized” Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Cost Function Constraints Cost Function The energy consumption in terms of aeration power represents the major economic duty in our ASP J = Q air DeNitr + Nitr Constraints Effluent Constraints: defined by the legislation requirement for the effluent Operational Constraints: DO concentration Food-to-Microorganisms Ratio Sludge Retention Time 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

“Top-Down” Analysis Dynamic or Control DOF N = 7 N = 5 Step 2 “Identify dynamic and steady-state degrees of freedom (DOF)” Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Degrees of Freedom Dynamic or Control DOF N m = 7 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 = - active N opt , free = 1

“Top-Down” Analysis ( ) d J WAS L - = , Step 3 “Which (primary) variable should we control?” Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Controlled Variables We first need to control the variables directly related to ensuring optimal economical operation 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 ( ) d J WAS L opt - = , 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

“Top-Down” Analysis Closed Loop Open Loop Step 3 “Which (primary) variable should we control?” Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Controlled Variables To identify good candidate controlled variables, one should look for variables that satisfy all of the following requirements (Skogestad, 2000): 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). c1=SRT c2=F/M c3=TNp c4=WAS Closed Loop Open Loop

The cost function J goes down as the waste flowrate increases 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

c3 = TNDeNitr Closed Loop Results Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Results Positive Deviation Negative Deviation  c1 = SRT Closed Loop c3 = TNDeNitr Closed Loop d1=COD 38682 24800 38679 24816 d2=TKN 33756 27006 33765 26967 d3=TSS 34182 29607 30252 29591 c2 = F/M Closed Loop c4 = Open Loop 38628 24589 38650 24758 33648 26968 33749 26991 30255 29594 34171 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

Conclusions In this work we have considered alternative controlled variables for the TecnoCasic activated sludge process Outline Motivations Objectives Plant Description Process Model Bioreactor Secondary Settler Test Motion Top-Down Analysis Step 1 Step 2 Step 3 Results Conclusions 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 That is a good starting point to understand how this kind of system can be improve 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