QSARs for trace organics removal by activated carbon and membrane filtration

Transcription

1 Transnational Action Program on Emerging Substances QSARs for trace organics removal by activated carbon and membrane filtration On the sense and non-sense of QSARs for these processes

2 QSAR-QSPR Quantitative structure activity/property relationships REMOVAL 2 approaches: - Structure properties removal - Structure removal What is the property in QSPR? Is it removal itself, or is it a property affecting removal?

3 Different QSAR models 1) Models directly connecting structure and removal: Multi-lineair regression (MLR) models Artificial neural networks (ANN), coupled with Principal Component Analysis (PCA) 2) Models based on mechanistic modelling (e.g., transport and affinity models) Both model types discussed in following sections

4 CASE STUDY 1: activated carbon

5 Adsorption MLR MLR: multivariable linear regression Endpoint = a p 1 + b p 2 + c p 3 + d q e K F, n F Normalisation factor Constants Solute properties Carbon properties BV till breakthrough Drawbacks of MLR and ANN models: Difficult to get real mechanistic insight in the process Difficult to improve the process in this manner (e.g., development of novel materials) Large datasets needed: training vs validation sets

6 Adsorption Solute properties Hydrophobicity H bond donor/acceptor groups Aromaticity Polarity Polarizability Molecular volume / surface area Molecular orbital energy (E HOMO, E LUMO ) Molecule shape (symmetry, compactness)

7 Adsorption Solute properties

8 Adsorption Solute properties

9 Adsorption Solute properties

10 Adsorption Thermodynamic approach Breaking bonds costs energy Creating bonds gives energy W pcw = W pc +W ww -W pw -W cw

11 Adsorption Thermodynamic approach LW LW WSL GSL 2 S L S L S L Liquid γ LW (mj/m 2 ) γ + (mj/m 2 ) γ - (mj/m 2 ) Water Cyclohexane Ethylene glycol Glycerol Diiodomethane

12 Adsorption Thermodynamic approach Immersion calorimetry Contact angle 1. Carbon-water 2. Carbon-pollutant 1. pollutant-water 2. Carbon-pollutant

13 Adsorption Thermodynamic approach

14 Adsorption Thermodynamic approach 6 Carbons & 17 pollutants

15 CASE STUDY 2: nanofiltration/reverse osmosis

16 Rejection (%) Example of trace organics removal by typical RO membrane NDMA = 74 g/mol TBA = 74 g/mol benzene = 78 g/mol 1,4-dioxane = 88 g/mol MTBE = 88 g/mol ETBE = 102 g/mol TAME = 102 g/mol ethylbenzene = 106 g/mol NMOR = 116 g/mol naphtalene = 128 g/mol diglyme = 134 g/mol 2-MIB = 168 g/mol glyphosate = 169 g/mol triglyme = 178 g/mol phenazon = 188 g/mol carbendazim = 191 g/mol Solute rejection by NF/RO membranes is not only dependent on size Rejection has to be related to some sort of affinity as well

17 Rejection of organic solutes by NF/RO: combination of size exclusion & solute-membrane affinity Membrane size exclusion

18 Rejection of organic solutes by NF/RO: combination of size exclusion & solute-membrane affinity G i <0 Membrane solute-membrane affinity: interaction energy Gi affinity/attraction (~ hydrophobic solutes) no affinity/repulsion (~ hydrophilic solutes) G i >0

19 Solute transport in NF/RO external mass transfer: concentration polarisation internal mass transfer: pore convection and diffusion C b c m at membrane surface: partitioning C m r s < r p and G < 0 c m r s < r p and G > 0 or r s > r p

20 Organics transport in osmotic membranes Extension of solution-diffusion model: combination of diffusive and convective transport (better to also describe NF) After integration over membrane thickness (x): R dc J s K d. D Kc. J v. c dx 1 1 K v c 1 K c exp Kd D c J K concentration polarisation x partition coefficient: ratio of solute concentrations inand outside of the membrane dependent on G i hindrance against convection hindrance against diffusion

21 Determination of solute-membrane affinity (Gi) XDLVO approach: interaction energy is dependent on van der Waals and Lewis acid-base interactions from contact angle measurements (Van Oss, Coll.& Surf. B, 54 (1) (2007) 2) G A i G S LW S ' L ' LS" 2 L L LW S ' S" L S ' S" S ' LW S" S" LW L L LW S ' LW S" S ' S" LW L G i <0: spontaneous transfer of solute to membrane phase (hydrophobic solutes) G i >0: no spontaneous transfer to membrane phase (hydrophilic solutes) G i =0: steric model

22 Rejection (%) Rejection prediction for pharmaceuticals Virgin NF Paracetamol (151) Caffeine (193) Bisphenol A (228) Carbamazepine (236) Trimethoprim (290)

23 Rejection (%) Rejection prediction for pharmaceuticals Virgin Virgin NF270 Virgin predicted Paracetamol (151) Caffeine (193) Bisphenol (228) Carbamazepine (236) Trimethoprim (290) Paracetamol (151) Caffeine (193) Bisphenol A (228) Carbamazepine (236) Trimethoprim (290) G caffeine > G bisphenol-a

24 Membrane-in-series model l c x r p,2 l 2 l 1 r p,1 K c,2 K c,1 K d,2 K d,1 2 1 Integration over layers Extra boundary condition: 1.c 1 (l c ) = 2.c 2 (l c ) Explains increases, as well as decreases in rejection

25 Membrane-in-series model l c x r p,2 l 2 l 1 r p,1 K c,2 K c,1 K d,2 K d,1 2 1 R 1 K K CECP 1 2 c,1 c,2 K c,1 K c,1 K c,1 K c,2 K c, 1 K c,2 2 Kc,2 1 exp Jv lc 1 Kc,1 1 exp Jv lc 1 Kc,1 2 Kc,2 exp Jv lc exp Jv x 1 Kc,1 exp K d,1 K d,1 K d,1 K Jv lc exp Jv x d,2 K d,1 K d,2

26 Rejection (%) Rejection prediction for pharmaceuticals Virgin 100 Virgin Virgin NF270 Virgin predicted Paracetamol (151) Caffeine (193) Bisphenol (228) Carbamazepine (236) Trimethoprim (290) Paracetamol (151) Caffeine (193) Bisphenol A (228) Carbamazepine (236) Trimethoprim (290)

27 Rejection (%) Rejection prediction for pharmaceuticals Alginate fouled Alginate fouled Paracetamol (151) Caffeine (193) Bisphenol A (228) Carbamazepine (236) Trimethoprim (290)

28 Rejection (%) Rejection prediction for pharmaceuticals Alginate fouled Alginate fouled Alginate fouled predicted Alginate fouled Paracetamol Paracetamol (151) (151) Caffeine Caffeine (193) (193) Bisphenol Bisphenol A (228) (228) Carbamazepine Carbamazepine (236) (236) Trimethoprim Trimethoprim (290) (290)

29 Conclusions Does it make sense to apply QSARs to NF/RO? - NO, because typical variations in membrane elements are already larger than the modelling error - NO, since experimental errors in determining rejection and model parameters are larger than predicted solute passages - YES, since insight in mechanistics will help improve material design and better design of the process Models are mechanistically correct, but Monte-Carlo simulations show impossibility to model very low solute passages. ANNs or MLRs could be of help here, yet they do not improve physical insight in the process

30 Partners