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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
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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
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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
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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
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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
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Introduction Global (and faster) measurements temperature, conductivity, pH, redox turbidity light absorbance fixed wavelength spectra respirometry buffer capacity ... On-line In-line (sampling)
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Introduction Three methods under test Respirometry Absorbance spectra Buffer capacity Multivariate data analysis method Validation on simulation Experimental validation Conclusions
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Respirometry test: experimental set-up
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Respirometry test DO probe sludge + substrate Typical response curves
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Characteristic parameters OUR curve 4 parameters Maximal value of Oxygen Uptake Rate Oxygen volume (VO2) (5 or 15min) Peak width Initial slope
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Experimental results + CuSO 4
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Experimental results + dye
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2 respirometers in parallel toxics added in one respirometer CuSO 4 NaOH HCl White Spirit javel Gasoil Experimental results
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UV-visible spectrometry
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210 nm 220 nm 254 nm 270 nm UV-visible spectrometry Anthropogenic substances
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UV-visible spectrometry 210 nm 220 nm 254 nm 270 nm Detergents
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UV-visible spectrometry Dyes
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UV-visible spectrometry Norm. Abs Abs. Abs
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Buffer capacity Normally measured Wastewater pH Alkalinity Here Acidification (pH 3) Titration to pH 11 Buffer capacity versus pH
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Buffer capacity
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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
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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)
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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
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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......
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Effect of slow change in plant state PCA on 24 previous samples (1 sample/hr), estimation of actual sample
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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
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Plant layout Incoming water to be tested Secondary settler External recycle Aeration tank Biomass sample Primary settler Wastage flow River Biomass sample
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Concentration of toxic Release profile Concentration Detection
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Toxic release time Detection Release time Release profile
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Toxic release time Detection = 1.49 (0.07)Detection = 2.77 (0.17)
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Normal situation Normal 24hr cycle: dry weather normal activity
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Normal situation 5 initial variables : OURend, OURmax/A 254, VO 2 /A 254, width et A 254
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Critical situation: heavy metals HgSO 4 6 mg/l 30 mg/l K 2 Cr 2 O 7 6 mg/l
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Critical situation: diesel oil Addition of various amounts of diesel oil
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Critical situation: white spirit Addition of various amounts of white spirit very strong inhibition
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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
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Buffer capacity 5-6 Nov.2001, 14h : Wastewater + citrate
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UV-visible spectrophotometry
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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
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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
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