M.N. Pons, S. Le Bonté, O. Potier Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, Nancy Adaptive Principal Component Analysis for toxic event detection

Introduction New regulations: treatment in adequate facilities of all incoming waters stricter limits on effluent quality, on sludge Crisis: rainstorm accidental release of toxic components some may be forecast (fire water) other not

A short selection of potential toxics  Heavy metals: Hg, Cr, Pb, Cd, Zn, Cu...  Solvents: white spirit,...  Pesticides  Herbicides  Motor fuels: diesel oil,...  Detergents  Dyes

Introduction  New regulations:  treatment in adequate facilities of all incoming waters  stricter limits on effluent quality  Crisis:  rainstorm  accidental release of toxic components  some may be forecast (fire water)  other not  Improvement of plant control strategy  New scenarios

Introduction Characterisation of wastewater composition COD, BOD 5, SS N T, NH 4 +, NO 3 - P T, PO 4 - K, Ca, Mg,... Heavy metals (Cu, Zn, Cd, Hg, Cr, …) Micropolluants  Some are time-consuming  Some are very specific

Introduction  Global (and faster) measurements  temperature, conductivity, pH, redox  turbidity  light absorbance  fixed wavelength  spectra  respirometry  buffer capacity ... On-line In-line (sampling)

Introduction Three methods under test Respirometry Absorbance spectra Buffer capacity Multivariate data analysis method Validation on simulation Experimental validation Conclusions

Respirometry test: experimental set-up

Respirometry test DO probe sludge + substrate Typical response curves

Characteristic parameters OUR curve 4 parameters Maximal value of Oxygen Uptake Rate Oxygen volume (VO2) (5 or 15min) Peak width Initial slope

Experimental results + CuSO 4

Experimental results + dye

2 respirometers in parallel toxics added in one respirometer CuSO 4 NaOH HCl White Spirit javel Gasoil Experimental results

UV-visible spectrometry

210 nm 220 nm 254 nm 270 nm UV-visible spectrometry Anthropogenic substances

UV-visible spectrometry 210 nm 220 nm 254 nm 270 nm Detergents

UV-visible spectrometry Dyes

UV-visible spectrometry Norm. Abs Abs. Abs

Buffer capacity Normally measured Wastewater pH Alkalinity Here Acidification (pH  3) Titration to pH  11 Buffer capacity versus pH

Buffer capacity

Fault detection background Univariate SPCMultivariateSPC Overload of data PLS Partial Least Squares Projection to Latent Structures PCA Principal Component Analysis  Continuous process (steady state)  Kresta et al. (1991): fluidized bed and extractive distillation column  Batch and Fedbatch  Lennox et al. (1999): Fermentation processes ? ? Wastewater treatment plant = continuous process but not at steady state

Adaptive PCA Diurnal cycle 1 sample / 30 min (48 samples / day) or / 1hr (24 samples / day) 4 Principal Variables (PV i ) : Our ex max, Ourex T, Slope, Width (  15 min) In the case of 1 sample / 1 hr, the samples j to j+23 are used and 2 PCs are considered: PC 1 =  1 PV 1 +  1 PV 2 +  1 PV 3 +  1 PV 4 PC 2 =  2 PV 1 +  2 PV 2 +  2 PV 3 +  2 PV 4 At sample j+24: prediction PC 1 (j+24) =  1 PV 1 (j) +  1 PV 2 (j) +  1 PV 3 (j) +  1 PV 4 (j) PC 2 (j+24) =  2 PV 1 (j) +  2 PV 2 (j) +  2 PV 3 (j) +  2 PV 4 (j) At sample j+24: actual PC ’ 1 (j+24) =  1 PV 1 (j+24) +  1 PV 2 (j+24) +  1 PV 3 (j+24) +  1 PV 4 (j+24) PC ’ 2 (j+24) =  2 PV 1 (j+24) +  2 PV 2 (j+24) +  2 PV 3 (j+24) +  2 PV 4 (j+24)

 Adaptive PCA  Prediction error = Detection (Q statistic)  SPE = [PC 1 (j+24) - PC ’ 1 (j+24)] 2 + [PC 2 (j+24) - PC ’ 2 (j+24)] 2  Update of  i,  i,  i, and  i using samples j+1 to j+24

Adaptive PCA CP 1 CP 2 σ 1, μ 1 h h+1 h+2 h+3 h+4. h+23 h+24 σ 2, μ 2 h+25 σ 3, μ 3 …etc......

Effect of slow change in plant state PCA on 24 previous samples (1 sample/hr), estimation of actual sample

Why simulating ? Unsteady state Many factors to examine: Location of sludge sampling Ratio sludge / raw water Quality of detection in function of the toxic conc. and nature, release time and type …. Experiments on the real plant should be carefully selected « Experiments » on a simulated plant

Plant layout Incoming water to be tested Secondary settler External recycle Aeration tank Biomass sample Primary settler Wastage flow River Biomass sample

Concentration of toxic Release profile Concentration Detection

Toxic release time Detection Release time Release profile

Toxic release time Detection = 1.49 (0.07)Detection = 2.77 (0.17)

Normal situation Normal 24hr cycle: dry weather normal activity

Normal situation 5 initial variables : OURend, OURmax/A 254, VO 2 /A 254, width et A 254

Critical situation: heavy metals HgSO 4 6 mg/l 30 mg/l K 2 Cr 2 O 7 6 mg/l

Critical situation: diesel oil Addition of various amounts of diesel oil

Critical situation: white spirit Addition of various amounts of white spirit very strong inhibition

Buffer capacity 4 initial variables : pH, β (pH=4,75),β (pH=7,21), β (pH=9,25) SPE = [PC 1 (h) - PC’ 1 (h+24)] 2 + [PC 2 (h) - PC’ 2 (h+24)] 2

Buffer capacity 5-6 Nov.2001, 14h : Wastewater + citrate

UV-visible spectrophotometry

 Conclusions  Global (and rapid) characterization of the composition of wastewaters  Absorbance spectra - Buffer capacity - Respirometry  + Classical measurements (T, pH, rH, …)  + flowrate + rainfall  Combined with statistical methods  Community activity (design, control, critical situation)  We wish to thank  the Grand Nancy Council for its help  GEMCEA, LCPC, NANCIE  the students and colleagues

 Plant model  2D models for the primary settler (Stokes) and the final clarifier (Takacs et al.)  Reactors in series with backmixing = f(flowrate, aeration rate)  Basic control on sludge wastage  IAWQ ASM 1 + inhibition :  growth rate of heterotrophs and autotrophs  death rate  degradation of toxic  Influent description  COST 624 Benchmark  Functions describing the Nancy WWTP effluent  Respirometer model  FORTRAN code on PC