Control of Activated Sludge Processes

A large number of control schemes have been applied to activated sludge processes. The control schemes can be broadly classified into two categories: measurement-based control and model-based control. Pure measurement-based control schemes - such as proportional (P), proportionalintegral (PI), proportional-integral-derivative (PID), and fuzzy control strategies - do not require mathematical models. In contrast to measurement-based control, mathematical models are essential for model-based control, including dynamic matrix control (DMC), generic model control (GMC), linear quadratic (LQ) control, generic distributed parameter model control (GDPMC), and HM-control. The

Table 8 Problems encountered in sludge-water separation

Problem

Possible cause

Consequence

Deflocculation (dispersed growth)

Overaeration

Bacteria failing to aggregate

Turbid effluent

Slime or nonfilamentous bulking

Development of nonfilamentous microbes leading to deficiency in either nitrogen or phosphorous.

Poor solid settleability Solid carry-over from clarifier

Filamentous bulking

High numbers of filamentous microbes in the mixed liquor

High SVI

Solid overflow from clarifiers

Pin point floc

Breakage of small and weak flocs Smaller fragments fail to settle

Turbid effluent Low SVI

Blanket rising

Gas generation processes lift flocs to the surface of equipment.

Exacerbated by long retention of solids in classifiers.

Surface of clarifiers covered with buoyant layer of biomass

Foaming/scum formation

Excessive hydrophobic bacteria

Biomass floatation to surface by foams Solid carry-over from clarifiers

Table 9 Model classification

Model classes Representations Sub-classes

Empirical (black-box) Various formats for sub-classes Input-output functional models

Neural network models Qualitative models

Distributed parameter models (DPMs) Partial differential equations (PDEs) Mechanistic models

Gray-box models

Lumped parameter models (LPSs) Ordinary differential or differential algebraic Reduced from PDEs equations (ODEs or DAEs) CSTR in series

Linear models Linear difference equations or ODEs Linearization of nonlinear ODEs

Input-output model Reduced order models

Table 10 Control schemes applied to activated sludge processes

Control scheme

Control objectives

Manipulated variables

P, PI controller PI, PID controller PI, PID controller SISO fuzzy control Single-loop PID controller Nonlinear optimal control Adaptive control Operational scheduling GMC

PI and Kalman filter LQ and DMC DMC GDPMC Aerobic Anoxic Anoxic Anaerobic Multivariable, H^-control

Sludge concentration and solid inventory COD

Effluent suspended solids Sludge age and aerator concentration Effluent Snh4, Sno3, and Snox As above

Effluent Snh4 and Sno3 DO concentration DO with adjustable set point Effluent nitrogen Regulation phosphorous

Effluent SNH|4 Interior SNO

Outlet Sno3 3 Interior SPO4

Effluent Snh4, Sno3, and Spo4

Airflow rate WAS or RAS flow Additional sludge inventory RAS flow

RAS and WAS flows Cycle length of SBR External carbon source

Airflow rate Airflow rate

External carbon addition

Air flow rate Nitrogen recycle flow Nitrogen recycle flow RAS flow

RAS flow, external SA flow, and NOX recycle flow

Table 11 Model types used in model based control

Control scheme

Model type

GDPMC

Hœ-control

Optimal control

Linear black-box models Nonlinear LPMs with ODEs Nonlinear DPMs with PDEs Reduced order linear models Reduced order linear model Nonlinear LPMs

Characteristics

Simple to use, but lack of physical insights Simpler, but less accurate and flexible than GDPMC General, flexible, but complex Robust, well-developed, but requires linearity Mature, but requires linearity

Fulfillment of optimality, but difficult to address uncertainty main control schemes applied to activated sludge processes are listed in Table 10.

Model types used in various model-based control schemes are listed in Table 11 .

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